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AltAnalyze Information and
Instructions Version 2.0
Section 1 - Introduction
1.1 Program Descriptionhttp://code.google.com/p/altanalyze.
Alternative
exon analysis is currently compatible with the RNA-Seq aligned exon and junction data, Affymetrix Exon 1.0 ST, Affymetrix Gene 1.0 ST, Affymetrix HJAY, MJAY arrays and the custom exon-junction Affymetrix AltMouse A array, however, data from other platforms can be imported if supplied in BED format for over 50 species (Section 1.5). Analysis of these and conventional Affymetrix microarrays is supported for array normalization (RMA), calculation of array group statistics, dataset annotation and pathway over-representation analysis. For non-Affymetrix arrays, all these steps are supported with exception to array normalization.
1.2 UpdatesFor past and current updates see: http://code.google.com/p/altanalyze/wiki/News
http://code.google.com/p/altanalyze/wiki/UpdateHistory 1.3 Implementation AltAnalyze is provided as a
stand-alone application that can be run on Windows or Mac OS X operating
systems, without installation of any additional software. This software is
composed of a set of distinct modules written entirely in the programming language
Python and distributed as stand-alone programs and source-code. Python is a cross-platform compatible
language; therefore, AltAnalyze can be run on any operating system that has
Python and Tkinter for Python installed. On many operating systems, including Linux
and any Mac OS X operating systems the necessary python components are included
by default, however, on some operating systems, such as Ubuntu, Tkinter may
need to be installed when using the graphical user interface. AltAnalyze can be
run from either an intuitive graphic user interface or from the command-line.
To run AltAnalyze from source-code, rather than through the compiled executables,
see section 2.2) for more information.
1.4 RequirementsThe basic installation of AltAnalyze requires a minimum of 500MB of hard-drive space for all required databases and components. Species databases are downloaded separately by the user from within the program, for various database versions. Species gene databases, Affymetrix library and annotation files can all be automatically downloaded by AltAnalyze. A minimum of 1GB of RAM and Intel Pentium III processor speed are further required. At least an additional 1GB of free hard-drive space is recommended for building the required output files.Additional RAM (up to 8GB) and hard-drive space (up to 3GB free) is recommended for large exon or junction studies. 1.5
Pre-Processing, External Files and Applications
RNA
Sequence Alignment Data
Several options now exist for importing RNA-Seq data into AltAnalyze. We recommend reading our online instructions (http://code.google.com/p/altanalyze/wiki/ObtainingRNASeqInputs) to see which method best suits your data. When analyzing data from an RNA-Seq experiment, AltAnalyze can import data in the UCSC BED format (http://genome.ucsc.edu/FAQ/FAQformat.html#format1).or as output from the Applied Biosystems software BioScope. The junction BED file can be produced by various RNA-Seq exon-exon junction alignment applications, including TopHat (junction.bed), HMMSplicer (canonical.bed) and SpliceMap (junction_color.bed). These files provide genomic coordinates and corresponding aligned read counts for unique junctions. For AltAnalyze, all junction BED files must be given unique names and saved to a single folder for an experiment (minimum of two files belonging to two different experimental groups). Upon import, AltAnalyze will match the splice-site coordinates of each junction to Ensembl and UCSC mRNA annotated exon-junctions, individual exon splice sites, exons and introns. AltAnalyze will also identify trans-splicing events, where an aligned junction contains splice sites found with two distinct genes. If neither splice site of detected junction aligns to an Ensembl gene (between the 1st and last exons), the junction will be excluded from the analysis. Junctions and corresponding read counts will be saved to the folder “ExpressionInput” (user-defined output directory), with re-assigned standard AltAnalyze IDs (e.g., ENSMUSG00000033871:E13.1-E14.1). Reciprocal junctions will be analyzed for any known or novel junction predicted to alternatively regulate an associated exon (see section 3.2 – Reciprocal Junction Analysis). A corresponding exon expression BED file can be built for analyses where sequence alignment results are saved to a BAM format file (e.g., produced by TopHat). Exon expression values can be extracted from BAM files reasonably fast using the open-source application BEDTools (http://code.google.com/p/bedtools)in conjunction with a coordinate BED file produced by a cursory AltAnalyze junction analysis. For SOLiD sequencing, a viable alternative to these methods is the software BioScope, which produces both exon and junction expression estimates. The counttag (exon-level) and alternative-splicing (junction-level) files can be loaded once the extension Is changed from .txt to .tab. For further directions in obtaining exon and junction-level inputs, see: http://code.google.com/p/altanalyze/wiki/ObtainingRNASeqInputs. If
Affymetrix CEL files are processed directly by AltAnalyze (using APT and RMA),
two files will be produced; an expression file (containing probe set and
expression values for each array in your study) and a detection above
background (DABG) p-value file (containing corresponding DABG p-values for each
probe set). As mentioned, if FIRMA
is selected as the alternative exon analysis algorithm, APT will first perform
a separate RMA run to produce probe residuals for gene-level metaprobesets
(Ensembl associated AltAnalyze core, extended or full probe sets). The results
produced by AltAnalyze will be identical to those produced by APT or
ExpressionConsole(http://www.affymetrix.com/products/software/specific/expression_console_software.affx). For some exon arrays, users can choose to exclude
certain array probes based on genomic cross-hybridization (section 2.3).
For array summarization, all required components are either pre-installed or can be downloaded by AltAnalyze automatically (Affymetrix library and annotation files) for most array types. If the user is prompted for a species library file that cannot be downloaded, the user will be asked to download the appropriate file from the Affymetrix website. Offline analyses require the user to follow the instructions in Section 2.3.
Other Platforms
1.6 Help with AltAnalyzeAdditional documentation, tutorials, help, and user
discussions are available at the AltAnalyze website http://www.altanalyze.org or at our
support site http://code.google.com/p/altanalyze. Tutorial
1 applies to conventional microarray analysis, Tutorial
2 applies to exon array analysis and Tutorial 3 applies to RNA-Seq alternative exon analysis. Downloads, tutorials and
help for DomainGraph can be found at http://domaingraph.bioinf.mpi-inf.mpg.de.
Section 2 - Running AltAnalyze for Splicing Arrays2.1 Where to Save Input Expression Files?When
performing analyses in AltAnalyze, the user needs to store all of their
Affymetrix CEL files or sequence alignment count files (BED or TAB) in one directory. This directory can
be placed anywhere on your computer and will be later selected in AltAnalyze. Example files can be downloaded from:
Affymetrix Exon Array
Data BED and TAB Files
http://code.google.com/p/altanalyze/wiki/RNASeq_sample_data
Extract any downloaded TAR and/or GZIP compressed
files prior to analysis.
For Affymetrix array analyses, if the user has already
run normalization on their CEL files outside of AltAnalyze or have downloaded
already analyzed expression data from another source, you can save the
expression and DABG p-value file (optional) anywhere on your computer. These
files should be tab delimited text files that only consist of probe sets,
expression values and headers for each column. Example files can be downloaded http://AltAnalyze.org/normalized_hESC_differentiation.zip.
2.2 Running AltAnalyze from the Graphic User InterfaceWindows and
Mac Directions:
Once
you have saved your BED files, TAB files, CEL files or normalized expression values to
single directory on your computer, open the AltAnalyze application folder and
double-click on the executable file named “AltAnalyze.exe” (Windows) or
“AltAnalyze” (Mac). This will open
a set of user interface windows where you will be presented with a series of
program options (see following sections).
Unix/Linux
and Source Code Directions:
On
Linux, download the Linux executable and python source code archive version of
AltAnalyze. To run the compiled version, double-click the executable file named
“AltAnalyze” or open this file from command-line (./AltAnalyze).
If this file is not compatible with your configuration, you can start
AltAnalyze by opening a terminal window and going to the AltAnalyze main folder
(e.g. “cd AltAnalyze_v1release” from the program
parent directory). Once in this directory, typing “python AltAnalyze.py”
in the terminal window will begin to run AltAnalyze (you should see the
AltAnalyze main menu within a matter of seconds). Note: If there is a problem running the graphical user interface
version of AltAnalyze, you can report this to our help desk or use the command-line
options (Section 2.3), which do not require TK.
AltAnalyze
Graphical Interface Options:
There
are many options in AltAnalyze, which allow the user to customize their output,
the types of analyses they run and the stringency of those analyses. The
following sections show the sequential steps involved in running and navigating
AltAnalyze. Interactive tutorials for different analyses are provided from the
AltAnalyze website. Please note: if you will be using AltAnalyze on
a machine that does NOT have internet access, follow instructions 1-5 below on
an online machine and then copy the AltAnalyze program directory to an offline
machine.
1) Introduction Window - Upon opening
AltAnalyze, the user is presented with the AltAnalyze splash screen
and additional information. To directly open the AltAnalyze download
page, follow the hyperlink under "About AltAnalyze", otherwise select
"Begin Analysis".
2) Species Database Installation - The first time AltAnalyze is used, the user will be prompted to
download one or more species database (requires internet connection).
Independent of the data source (e.g., RNASeq or array
type) you are analyzing, select a species and continue. The user can select
from different versions of Ensembl. If your species is not present, select the
button “Add New Species”.
3) Select species and platform - Next, the user must select a species, array vendor or data type and
platform for analysis (Figure 2.1). Array vendors include Affymetrix, Illumina, Agilent and Codelink.
If multiple database versions have been downloaded, the user will also be able
to select a version pull-down menu. After selecting these options click
“Continue”. If the species of interest is unavailable select the button “Add
New Species Support”. Here you can add a two letter species code, full name and array vendor that will be used for all current and
future analyses.
4)
Select
analysis option–
In this window, the user must select the type data being analyzed. There are
four main types of data; A) BED, TAB or CEL files, B) Expression files (normalized
or read counts), C) AltAnalyze filtered files and D) results from 3rd party applications (External Results). Processing of CEL files will produce the
two file types (expression and DABG), while processing of BED/TAB files will
produce a file of exon and/or junction counts. Processing of Expression files, allows the
user to select tab-delimited text files where the data has already been
processed (e.g. RMA or read counts), which will also produce AltAnalyze
filtered files. AltAnalyze filtered files are written for any splicing array
analysis (not for gene expression only arrays). These later files allow the user to directly perform
splicing analyses, without performing the previous steps. The AltAnalyze
filtered files are stored to the folder “AltExpression”
under the appropriate array and species directories in the user output folder.
Since CEL file normalization and array filtering and summarization can take a
considerable amount of time (depending on the number of arrays), if
re-performing an alternative exon analysis with different parameters, it is
recommended that the user select the “Expression files” or “AltAnalyze filtered
files”, depending on which options the user wants to change. Users can also
import lists of regulated probe sets with statistics obtained from a 3rd party application (e.g., JETTA) other than AltAnalyze.
5) Processing Affymetrix CEL Files or Exon/Junction Files - If you
selected the first option from "Main Dataset Parameters", you
will be presented with a new window for selecting the location of your
CEL/BED/TAB files and desired output directory. Clicking the "select folder" icon will allow
you to browse your hard-drive to select the folder with these data files.
You can double-check the correct directory is selected by looking at
the adjacent text display. For Affymetrix CEL files, this window will be followed by an indicator
window that will automatically download the library and annotation files
for that array. If the array type is unrecognized
and you do not already have Affymetrix library files for your array
(e.g. PGF or CDF), you will need to download these files from the Affymetrix
website. To do so, select the link at the bottom left side of this window
named "Download Library Files". Select the array type being analyzed
from the web page and select the appropriate library files to download
and extract to your computer (requires an Affymetrix username and password)
(Figure 2.3 B). For RNA-Seq data, the user selects the folder containing their exon and junction input files in either BED (e.g., TopHat) or TAB (BioScope) files. If the user is analyzing junction alignment results but does not have exon-level results, the user can build an annotation file for the program BEDTools, to derive these results from an available BAM file (e.g., produced by TopHat). To do this, select the option "Build exon coordinate bed to file obtain BAM file exon counts". Instead of running the full AltAnalyze pipeline, AltAnalyze will immediately produce the exon annotation file for BEDTools to the BAMtoBED folder in the user output directory. Additional details can be found at: http://code.google.com/p/altanalyze/wiki/BAMtoBED.
6) Summarizing Gene Data and Filtering For Expression - After obtaining summary read counts or normalized CEL expression values, a number of options are available for summarizing gene level expression data and filtering out RNA-Seq reads and probe sets prior to alternative exon analysis. For Affymetrix splicing arrays, AltAnalyze calculates a “gene-expression” value based on the mean expression of all “core” (Affymetrix core probe sets and those aligning to known transcript exons) or “constitutive” (probe sets aligning to those exon regions most common among all transcripts) probe sets that have a mean DABG p-value less than and a mean expression value greater the user indicated thresholds for each gene. The same methods are used for RNA-Seq exon or junction counts, using the same Ensembl and UCSC combined constitutive evidence. Rather than observed counts, quantile normalized counts are selected by default, with counts and RPKM as alternative options (Section 3.2). These values are used to report predicted gene expression changes (independent of alternative splicing) for all user-defined comparisons (see following section). In addition, fold changes and ttest p-values are calculated for each of these group comparisons. These statistics along with several types of gene annotations exported to a file in the folder “ExpressionOutput” in the user-defined results directory. Along with this tab-delimited text file, a similar file with those values most appropriate for import into the pathway analysis program GenMAPP will also be produced (Figure 5.1). For splicing analyses, RNA-Seq reads or probe sets with user defined splicing cutoffs (expression and DABG p-values) will be retained for further analysis (see section 5.2 – ExpressionBuilder algorithm).
7) Select Alternative Exon Analysis Parameters - If using a junction (RNA-Seq or JAY array) or exon-sensitive array (e.g., Human Affymetrix Exon 1.0 or Gene 1.0 ST), the user will be presented with specific options for that platform (Figure 2.5). These options include alternative exon analysis methods, statistical thresholds, and options for additional analyses (e.g., MiDAS), however, the default options are typically recommended. Users can also choose whether to analyze biological groups as pairwise group comparisons, comparison of all groups to each other or both. These include combining values for exon-inclusion junctions and restricting an analysis to a conservative set of Affymetrix probe sets (e.g., “core”) and changing the threshold of splicing statistics. Note: that AltAnalyze’s “core” includes any probe set associated with a known exon. When complete, the user can select “Continue” in AltAnalyze to incorporate these statistics into the analysis.
8) Assigning Groups to Samples - When analyzing
a dataset for the first time, the user will need to establish which
samples correspond to which groups. Type in the name of the group adjacent
to each sample name from in your dataset (Figure 2.6 A). 9) Establishing Comparisons between Groups - Once sample-to-group relationships are added, the user can list which
comparisons they wish to be performed (Figure 2.6 B). For splicing and
non-splicing arrays, folds and p-values will be calculated for each comparison
for the gene expression summary file. For RNA-Seq reads or splicing
arrays, each comparison will be run in AltAnalyze to identify alternative
exons. Thus, the more pairwise comparisons the longer the analysis. If the user
designates to compare “all groups” and not designate a pairwise comparison,
this window will not be displayed
10) AltAnalyze Status Window - While the AltAnalyze program is running, several intermediate results files will be created, including probe set or RNA-Seq read, gene and dataset level summaries (Section 2.4). The results window (Figure 2.8) will indicate the progress of each analysis as it is running. When finished, AltAnalyze will prompt the user that the analysis is finished and a new “Continue” button will appear. A summary of results appears containing a basic summary of results from the analysis. This window contains buttons that will open the folder containing the results and suggestions for downstream interpretation and analysis. Selecting the button “Start DomainGraph in Cytoscape” will allow the user to directly open a bundled version of Cytoscape and DomainGraph (Section 8). In addition to viewing the program report, this information is written to a time-stamped log text file in the user-defined output directory.
11) If
the user selected “decide later” for the analysis option “Perform GO-Elite
Pathway Analysis”, the user will have the option to run GO-Elite after run
completion. A similar summary results window as above will also appear with the
GO-Elite pathway and Gene Ontology results.
2.3 Running AltAnalyze from Command-LineIn addition to using the
default AltAnalyze graphical user interface (GUI), AltAnalyze can be run by
command line options by calling the python source code in a terminal window
or through other remote services. This option can be used to run AltAnalyze
on a remote server, to batch script AltAnalyze services or avoid having to select
specific options in the GUI. To do this, the user or program passes specific flags to
AltAnalyze to direct it where files to analyze are, what options to use and
where to save results. Note: the algorithm options for junction analyses
differ from those for single exon analyses.
Analyzing CEL files –
Affymetrix 3’ array using default options and GO-Elite
python AltAnalyze.py --species Mm --arraytype "3'array" --celdir "C:/CELFiles" --groupdir "C:/CELFiles/groups.CancerCompendium.txt"
--compdir "C:/CELFiles/comps.CancerCompendium.txt"
--output "C:/CELFiles" --expname "CancerCompendium"
Analyzing RNA-Seq (RNASeq) data – BED files using default options
python AltAnalyze.py --species Mm --platform RNASeq --bedDir "C:/BEDFiles" --groupdir "C:/BEDFiles/groups.CancerCompendium.txt" --compdir "C:/BEDFiles/comps.CancerCompendium.txt" --output "C:/BEDFiles" --expname "CancerCompendium"
Analyzing CEL files – Exon
1.0 array using default options
python AltAnalyze.py --species Mm --arraytype exon --celdir "C:/CELFiles" --groupdir "C:/CELFiles/groups.CancerCompendium.txt" --compdir "C:/CELFiles/comps.CancerCompendium.txt" --output "C:/CELFiles" --expname "CancerCompendium" python AltAnalyze.py --species Mm --platform RNASeq –-filterdir "C:/BEDFiles"
--altpermutep 1 --altp 1 --altpermute yes --additionalAlgorithm none --altmethod linearregres --altscore 2 --removeIntronOnlyJunctions yes
Analyzing CEL files – Exon 1.0 array
using custom options
python AltAnalyze.py --species Hs --arraytype exon --celdir "C:/CELFiles" --output "C:/CELFiles" --expname "CancerCompendium" --runGOElite no --dabgp 0.01 --rawexp 100 --avgallss yes ––noxhyb yes --analyzeAllGroups "all groups" ––GEcutoff 4 --probetype core --altp 0.001 --altmethod FIRMA
––altscore 8 --exportnormexp yes ––runMiDAS no --ASfilter yes --mirmethod "two or more" --calcNIp yes
Analyzing CEL files – HJAY
array using custom options
python AltAnalyze.py --species Hs --arraytype junction --celdir "C:/CELFiles"
--output "C:/CELFiles" --expname "CancerCompendium"
--runGOElite no --dabgp 0.01 --rawexp 100 --avgallss yes ––noxhyb yes --analyzeAllGroups "all groups" ––GEcutoff 4 --probetype core --altp 0.001 –altmethod "linearregres"
––altscore 8 --exportnormexp yes ––runMiDAS no --ASfilter yes --mirmethod "two or more" --calcNIp yes --additionalAlgorithm FIRMA ––additionalScore 8
Analyzing Expression file –
Gene 1.0 array using default options, without GO-Elite
python AltAnalyze.py --species Mm --arraytype gene --expdir "C:/CELFiles/ExpressionInput/exp.CancerCompendium.txt" --groupdir "C:/CELFiles/groups.CancerCompendium.txt" --compdir "C:/CELFiles/comps.CancerCompendium.txt" --statdir "C:/CELFiles/ExpressionInput/stats.CancerCompendium.txt" --output
"C:/CELFiles"
Analyzing Filtered Expression file
– Exon 1.0 array using default options
python AltAnalyze.py --species Hs --arraytype exon --filterdir "C:/CELFiles/Filtered/Hs_Exon_prostate_vs_lung.p5_average.txt"
--output "C:/CELFiles"
Annotate External Probe set
results – Exon 1.0 array using default options
python AltAnalyze.py --species Rn --arraytype exon
--annotatedir
"C:/JETTA_Results/Hs_tumor_progression.txt"
--output "C:/JETTA_Results" --runGOElite yes
Filter AltAnalyze results with
predefined IDs using default options
python AltAnalyze.py --species Mm --arraytype gene --celdir "C:/CELFiles" --output "C:/CELFiles" --expname "CancerCompendium" --returnAll yes
(Operating
System Example Folder Locations)
PC: "C:/CELFiles"
Mac
OSX: "/root/user/admin/CELFiles
Linux: "/hd3/home/admin/CELFiles
Primary Analysis Variables
No
default value for these variables is given and must be supplied by the user if
running an analysis. For example, if analyzing CEL files directly in
AltAnalyze, you must include the flags ––species ––arraytype ––celdir ––expname and ––output, with corresponding values.
Likewise, when analyzing an existing expression file you must include the flags ––species ––arraytype ––expdir and ––output.
Most of the variable values are file or folder locations. These variable values
will differ based on the directory path of your files and operating system (e.g, linux has a distinct path
structure than windows – see above examples). The variable name used in
the AltAnalyze source code for each flag is indicated below.
Universally Required Variables
--arraytype: (aka –-platform)
long variable name “array_type”. No default value for
this variable. Options are RNASeq, exon, gene, junction, AltMouse and “3’array”. This variable indicates the
general array type correspond to the input CEL files or expression file. An
example exon array is the Mouse Affymetrix Exon ST 1.0 array, an example gene
array is the Mouse Affymetrix Gene ST 1.0 array and example 3’array is the
Affymetrix Mouse 430 version 2.0 array. See Affymetrix
website for array classifications.
--species:
long variable name “cel_file_dir”. No default value
for this variable. Species codes are provided for this variable (e.g., Hs, Mm, Rn). Additional species can be added through the graphic
user interface.
--output:
long variable name “output_dir”. No default value for
this variable. Required for all analyses. This designates the directory which
results will be saved to.
Analysis Specific Required
Variables
--expname: long variable name “exp_name”. No default value for this variable. Required
when analyzing CEL files. This provides a name for your dataset. This name must
match any existing groups and comps files that already exist. The groups and
comps file indicate which arrays correspond to which biological groups and
which to compare. These files must exist in the designated output directory in
the folder “ExpressionInput” with the names “groups.*expname*.txt” and “comps.*expname*.txt” where expname is
the variable defined in this flag. Alternatively, the user can name their CEL
files such that AltAnalyze can directly determine which group they are (e.g.,
wildtype-1.CEL, cancer-1.CEL, cancer-2.CEL). See http://www.altanalyze.org/manual_groups_comps_creation.htm and http://www.altanalyze.org/automatic_groups_comps_creation.htm for more information
--celdir: (aka --bedDir)
long variable name “cel_file_dir”. No default value
for this variable. Required when analyzing CEL files. This provides the path of
the CEL files to analyze. These must all be in a single folder.
--expdir: long variable name “input_exp_file”. No default value for this variable.
Required when analyzing a processed expression file. This provides the path of
the expression file to analyze.
--statdir: long variable name “input_stats_file”. No default value for this variable. Optional when analyzing a processed expression file. This
provides the path of the DABG p-value file for the designated expression file
to analyze (see –expdir).
--filterdir: long variable name “input_filtered_dir”. No default value for this variable.
Required when aalyzing an AltAnalyze filtered
expression file. This provides the path of the AltAnalyze filtered expression
file to analyze.
--cdfdir: long variable name “input_cdf_file”. No default value for this variable.
Required when directly processing some CEL file types. This variable corresponds to the location of the Affymetrix CDF
or PGF annotation file for the analyzed array. If you are analyzing an exon,
gene, junction, AltMouse or 3’arrays, AltAnalyze has default internet locations
for which to download these files automatically, otherwise, you must download
the compressed CDF file from the Affymetrix website (support), decompress it
(e.g., WinZip) and reference it’s location on your hard-drive using this flag.
If you are unsure whether AltAnalyze can automatically download this file, you
can try to exclude this variable and see if annotations are included in your
gene expression results file.
--csvdir: long variable name “input_annotation_file”. No default value for this variable.
Required when analyzing some expression files or CEL file types. This variable corresponds to the location
of the Affymetrix CSV annotation file for the analyzed array. If you are
analyzing an exon, gene, junction, AltMouse or 3’arrays, AltAnalyze has default
internet locations for which to download these files automatically, otherwise,
you must download the compressed CSV file from the Affymetrix website
(support), decompress it (e.g., WinZip) and reference it’s location on your
hard-drive using this flag. If you are unsure whether AltAnalyze can
automatically download this file, you can try to exclude this variable and see if
annotations are included in your gene expression results file.
--annotatedir: long variable name “external_annotation_dir”. No default value for this
variable. Required when annotating a list regulated probe sets produced outside
of AltAnalyze. This variable corresponds to the location of the directory
containing one or more probe set files. These files can be in the standard
JETTA export format, or otherwise need to have probe set IDs in the first
column. Optionally, these files can have an associated fold change and p-value
(2nd and 3rd columns), which will be reported in the
results file.
--groupdir: long variable name “groups_file”. No default value for this variable. Location of an existing group file to be copied to the directory in
which the expression file is located or will be saved to.
--compdir: long variable name “comps_file”. No default value for this variable. Location
of an existing comps file to be copied to the directory in which the expression
file is located or will be saved to.
Optional Analysis Variables
These
variables are set as to default values when not selected. The default values
are provided in the configurations text file in the Config directory of AltAnalyze (default-**.txt) and can be changed by editing in a
spreadsheet program.
GO-Elite Analysis Variables
AltAnalyze
can optionally subject differentially or alternatively expressed genes
(AltAnalyze and user determined) to an over-representation analysis (ORA) along
Gene Ontology (GO) and pathways (WikiPathways) using the program GO-Elite.
GO-Elite is seamlessly integrated with AltAnalyze and thus can be run using
default parameters either the graphic user interface or command line. To run
GO-Elite using default parameters in command line mode, include the first flag
below with the option yes.
--runGOElite: long variable name “run_GOElite”, default value for this variable: no. Used to indicate whether to run
GO-Elite analysis following AltAnalyze. Indicating yes would prompt GO-Elite to run.
--mod:
long variable name “mod”, default value for this variable: Ensembl. Primary gene system for Gene Ontology (GO) and Pathway
analysis to link Affymetrix probe sets and other output IDs to. Alternative
values: EntrezGene.
--elitepermut: long variable name “goelite_permutations”, default value for this variable: 2000. Number of permutation used by
GO-Elite to calculate an over-representation p-value.
--method:
long variable name “filter_method”, default value for
this variable: z-score. Sorting
method used by GO-Elite to compare and select the top score of related GO
terms. Alternative values: “gene number”
combined
--zscore: long variable name “z_threshold”, default value for this variable: 1.96. Z-score threshold used following
over-representation analysis (ORA) for reported top scoring GO terms and
pathways.
--elitepval: long variable name “p_val_threshold”, default value for this variable: 0.05. Permutation p-value threshold
used ORA analysis for reported top scoring GO terms and pathways.
--dataToAnalyze: long variable name “resources_to_analyze”, default value for this variable: both. Indicates whether to perform ORA
analysis on pathways, Gene Ontology terms or both. Alternative values: Pathways or Gene Ontology
--num:
long variable name “change_threshold”, default value
for this variable: 3. The minimum
number of genes regulated in the input gene list for a GO term or pathway after
ORA, required for GO-Elite reporting.
--GEelitepval:
long variable name “ge_pvalue_cutoffs”, default value
for this variable: 0.05. The minimum
t-test p-value threshold for differentially expressed genes required for
analysis by GO-Elite.
--GEelitefold:
long variable name “ge_fold_cutoffs”, default value
for this variable: 2. The minimum
fold change threshold for differentially expressed genes required for analysis
by GO-Elite. Applied to any group comparisons designated by the user.
These
variables are used to determine the format of the expression data being read
into AltAnalyze, the output formats for the resulting gene expression data and
filtering thresholds for expression values prior to alternative exon analysis.
Since AltAnalyze can process both convention (3’array) as well as RNA-Seq data and splicing arrays (exon, gene, junction
or AltMouse), different options are available based on the specific platform.
Universal Array Analysis
Variables
--logexp: long variable name “expression_data_format”, default value for this variable: log for arrays and non-log for RNA-Seq. This is the format of
the input expression data. If analyzing CEL files in AltAnalyze or in running
RMA or GCRMA from another application, the output format of the expression data
is log 2 intensity values. If analyzing MAS5 expression data, this is non-log.
--inclraw: long variable name “include_raw_data”, default value for this variable: yes. When the value of this variable is no, all columns that contain the
expression intensities for individual arrays are excluded from the results
file. The remaining columns are calculated statistics (groups and comparison)
and annotations.
RNASeq, Exon, Gene, Junction or
AltMouse Platform Specific Variables
--dabgp: long variable name “dabg_p”, default value for this variable: 0.05. This p-value corresponds to the
detection above background (DABG) value reported in the “stats.” file from
AltAnalyze, generated along with RMA expression values. A mean p-value for each
probe set for each of the compared biological groups with a value less than
this threshold will be excluded, both biological groups don’t meet this
threshold for a non-constitutive probe set or if one biological group does not
meet this threshold for constitutive probe sets.
--rawexp: long variable name “expression_threshold”, default value for this variable: 70 for microarrays and 2 for RNASeq reads. For Affymetrix arrays, this value is the
non-log RMA average intensity threshold for a biological group required for
inclusion of a probe set. The same rules as the --dabgp apply to this threshold accept
that values below this threshold are excluded when the above rules are not met.
--avgallss: long variable name “avg_all_for_ss”, default value for this variable: no. For RNA-Seq analyses, default is yes. Indicating yes, will force AltAnalyze to use all exon aligning probe sets or
RNA-Seq reads rather than only features that align to predicted constitutive exons
for gene expression determination. This option applies to both the gene
expression export file and to the alternative exon analyses.
--runalt: long variable name “perform_alt_analysis”, default value for this variable: yes. Designating no for this variable will instruct AltAnalyze to only run the gene expression analysis portion of the program, but not the alternative exon analysis portion.
AltAnalyze Alternative Exon Analysis
Universal Array Analysis
Variables
--altmethod: long variable name “analysis_method”, default value for this variable: splicing-index (exon and gene) and ASPIRE (RNASeq,
junction and AltMouse). For exon, gene and junction arrays, the option FIRMA is also available and for RNASeq, junction and AltMouse platforms the option linearregres is available.
--altp: long variable name “p_threshold”, default value for this variable: 0.05. This variable is the p-value
threshold for reporting alternative exons. This variable applies to both the
MiDAS and splicing-index p-values.
--probetype: long variable name “filter_probe set_types”, default
value for this variable: core (exon
and gene) and all (junction and
AltMouse). This is the class of probe sets to be examined by the alternative
exon analysis. Other options include, extended and full (exon and gene) and “exons-only”, “junctions-only”, “combined-junctions” (RNASeq, junction
and AltMouse).
--altscore: long variable name “alt_exon_fold_variable”, default value for this variable: 2 (splicing-index) and 0.2 (ASPIRE).This is the corresponding
threshold for the default algorithms listed under --altmethod.
--GEcutoff:
long variable name “gene_expression_cutoff”, default
value for this variable: 3. This
value is the non-log gene expression threshold applied to the change in gene
expression (fold) between the two compared biological groups. If a fold change
for a gene is greater than this threshold it is not reported among the results,
since gene expression regulation may interfere with detection of alternative
splicing.
--analyzeAllGroups: long variable name “analyze_all_conditions”, default value for this variable: pairwise. This variable indicates
whether to only perform psiteifalternative exon analyses (between two groups) or to analyze all groups,
without specifying specific comparisons. Other options are “all groups” and both.
--altpermutep: long variable name “permute_p_threshold”, default value for this variable: 0.05. This is the permutation p-value
threshold applied to AltMouse array analyses when generating permutation based
alternative exon p-values. Alternative exon p-values can be applied to either
ASPIRE or linregress analyses.
--altpermute: long variable name “perform_permutation_analysis”, default value for this
variable: yes. This option directs
AltAnalyze to perform the alternative exon p-value analysis for the AltMouse
array (see –altpermutep).
--exportnormexp: long variable name “export_splice_index_values”, default value for this
variable: no. This option directs
AltAnalyze to export the normalized intensity expression values (feature
expression/constitutive expression) for all analyzed features (probe sets or
RNA-Seq reads) rather than perform the typical AltAnalyze analysis when its value
is yes. For junction-sensitive
platforms, rather than exporting the normalized intensities, the ratio of
normalized intensities for the two reciprocal-junctions are exported
(pNI1/pNI2). This step can be useful for analysis of exon array data outside of
AltAnalyze and comparison of alternative exon profiles for many biological
groups (e.g., expression clustering).
--runMiDAS: long variable name “run_MiDAS”, default value for this variable: yes. This option directs AltAnalyze to calculate
and filter alternative exon results based on the MiDAS p-value calculated using
the program Affymetrix Power Tools.
--calcNIp: long variable name “calculate_normIntensity_p”, default value for this
variable: yes. This option directs
AltAnalyze to filter alternative exon results based on the t-test p-value
obtained by comparing either the normalized intensities for the array groups
examined (e.g., control and experimental) (splicing-index) or a t-test p-value
obtained by comparing the FIRMA scores for the arrays in the two compared
groups.
--mirmethod: long variable name “microRNA_prediction_method”, default value for this
variable: one. This option directs
AltAnalyze to return any microRNA binding site predictions (default) or those
that are substantiated by multiple databases (two or more).
--ASfilter:
long variable name “filter_for_AS”, default value for
this variable: no. This option
directs AltAnalyze to only analyze probe sets or RNA-Seq reads for alternative expression that have
an alternative-splicing annotation (e.g., mutually-exclusive, trans-splicing, cassette-exon,
alt-5’, alt-3’, intron-retention), when set equal to yes.
--returnAll: long variable name “return_all”, default value for this variable is no. When set to yes, returns all un-filtered alternative
exon results by setting all associated filtering parameters to the lowest
stringency values. This is equivalent to providing the following flags: --dabgp 1 --rawexp 1 --altp 1 --probetype full --altscore 1 --GEcutoff 10000. Since this option will output all alternative exon scores for all
Ensembl annotated junctions or probe sets, the results file will be exceptionally large (>500,000
lines), unless the user has saved previously run alternative exon results
(e.g., MADS) to the directory “AltDatabase/filtering”
in the AltAnalyze program directory, with a name that matches the analyzed
comparison. For example, if the user has a list of 2,000 MADS regulated probe
sets for cortex versus cerebellum, then the MADS results should be saved to “AltDatabase/filtering” with the name “Cortex_vs_Cerebellum.txt”
and in AltAnalyze the CEL file groups should be named Cortex and Cerebellum and
the comparison should be Cortex versus Cerebellum. When the filename for a file
in the “filtering” directory is contained within the comparison filename
(ignoring “.txt”), only these AltAnalyze IDs or probe sets will be selected
when exporting the results. This analysis will produce a results file with all
AltAnalyze
statistics (default or custom) for
just the selected features, independent of the value of each statistic.
--additionalAlgorithm: long variable name
“additional_algorithms”, default value for this
variable: ‘splicing-index’. For Affymetrix arrays, setting this flag equal to FIRMA changes the individual probe set
analysis algorithm from splicing-index to FIRMA for junction arrays. This
method is applied to RNA-Seq data and junction arrays
following reciprocal-junction analysis (e.g., ASPIRE) in a second run. To
exclude this feature, set variable equal to none.
--additionalScore: long variable name
“additional_score”, default value for this variable: 2. Setting this flag equal to another
numeric value (range 1 to infinity) changes the non-log fold change for the addition_algorithm.
AltAnalyze Database Updates
Universal Array Analysis
Variables
--update:
long variable name “update_method”, default value for
this variable: empty. Setting this
flag equal to Official, without
specifying a version, will download the most up-to-date database for that
species. Other options here are used internally by
AltAnalyze.org for building each new database. See the method “commandLineRun” in AltAnalyze.py for more details.
--version:
long variable name “ensembl_version”, default value
for this variable: current. Setting
this flag equal to a specific Ensembl version name (e.g. EnsMart49) supported by AltAnalyze will download that specific
version for the selected species, while setting this to current will download
the current version.
2.4 AltAnalyze Analysis OptionsThere are a number of analysis options
provided through the AltAnalyze interface. This section provides an overview of
these options for the different compatible analyses (gene expression arrays,
exon arrays, junction arrays and RNA-Seq data). For
new users, we recommend first running the program with the pre-set defaults and
then modifying the options as necessary.
Selecting the
Platform and Species
When beginning AltAnalyze, the user can
select from a variety of species and platform types. Only array manufacturers
and array types supported for each downloaded species will be displayed along
with support for RNA-Seq analysis. When multiple gene
database versions are installed, a drop-down box at the top of this screen will
appear that allows the user to select different gene database versions. These
gene databases include all resources necessary for gene annotation, alternative
exon analysis (where applicable) and Gene Ontology and pathway analysis.
Microarray file normalization and summarization options are only available for
Affymetrix arrays, while annotation and statistical analyses are supported for
all Affymetrix, Agilent, Illumina and CodeLink arrays supported by Ensembl. At the
bottom of this interface is a check-box that the user
can select to download updated species gene databases, which will bring-up the
database downloader window.
Selecting
the RNA-Seq Analysis Method
Similar to microarray analysis options (see below), users can choose to analyze; 1) BED/TAB files, 2) an already built RNA-Seq expression file or 3) an AltAnalyze filtered RNA-Seq expression file. Since options 2 and 3 produce files from option 1, users will want to begin by loading a directory of RNA-Seq counts (junction and/or exon) from their analysis junction alignment analysis. Various programs can be used to produce the junction .bed format
files that are used as AltAnalyze input. These include HMMSplicer (http://derisilab14.ucsf.edu/software/hmmsplicer/)
and TopHat (http://tophat.cbcb.umd.edu/).
Exon .bed files are produced using BAM files and an AltAnalyze produced input exon coordinate BED file with BEDTools (http://code.google.com/p/altanalyze/wiki/BAMtoBED). Future implementations of AltAnalyze will likely allow users to download these
applications from within AltAnalyze and automatically call these applications
through a python subprocess. The junction and exon BED files
consist of junction splice-site coordinates along with the number of reads from
a sequencing run that correspond to that junction.
BED and TAB File
Summarization
After loading junction BED files,
AltAnalyze will like link each junction to an Ensembl gene and known
splice-sites based on the provided genomic coordinates. When novel splice-sites
are encountered, AltAnalyze will create a novel junction annotation for the
splice-site (5’ or 3’). In some cases, the splice-sites may be present in two
different genes, indicating trans-splicing. The number of known-splice sites,
novel splice sites and trans-splicing junctions will be reported upon import
and in the log file. The resulting file, saved to the folder
ExpressionInput/exp.NameYouEnter.txt, will contain unique identifiers
indicating the Ensembl gene and associated exon-junction (e.g., E13.1-E14.1),
indicating the exon block (e.g., “E13”) and exon region (e.g., “.1”) that the splice site positions aligns to. When the
splice-site is novel it will be annotated as aligning to the corresponding
exon, intron or UTR region with the additional notation “_position”, where
position is the genomic splice-site coordinate, following the exon region
(e.g., E13.1_1000347, I13.1_1000532, U15.1_1001023). If exon .bed files are present in this same directory have the same prefix name (e.g., sample1__exon.bed and sample1__junction.bed), the exon coordinates will be matched to AltAnalyze annotated exon and intron regions (Ensembl/UCSC) and novel exon regions inferred from the junction BED locations. Both junction expression and exon expression will be written to the same file with the prefix “exp.”. This file can subsequently be loaded as option 2 ("Process Expression File”). The same process is applied to .tab files from BioScope, accept that exon and junction count files are produced simultaneously and output to different format files.
Selecting
the Microarray Analysis Method
After the user has selected the species
of interest, they must choose what type of data they will next be analyzing.
Data can consist of; 1) Affymetrix CEL files, 2) an already processed
expression text files, 3) properly formatted and filtered AltAnalyze expression
input text file or 4) restricted list of probe sets to be directly analyzed. If
beginning with Affymetrix CEL files all three of these file types are produced
in series (see following section) and automatically processed without any user
intervention. If all CEL files from your study already been previously in
AltAnalyze or in another program, the user can load this file be selecting the
option “expression file” and choosing this text file from your computer. This
file needs to contain data from arrays corresponding to at least two biological
groups. Users may wish to re-analyze these files to change their expression
filtering parameters to be more or less stringent. For the two or more
biological groups (see how to define in Figure 2.6), AltAnalyze will segregate
the raw data based on the user-defined pairwise group comparisons and filter
the containing probe sets based on whether they match the
user-defined thresholds for inclusion and are associated with Ensembl genes
(see the below section: Expression
Analysis Parameters). These files will be saved to the folder “AltExpression” in the user-defined output directory. These
files can be later selected by choosing the option “AltAnalyze filtered”, if
the user wishes to re-run or use different AltAnalyze alternative exon analysis
options (see below section: Alternative Exon Analysis Parameters).
CEL files are one of the file types
produced after scanning an Affymetrix microarray. The CEL
file is produced automatically from the DAT file (an image file, similar to a
JPEG), by the Affymetrix software by overlaying a grid over the microarray
florescent image and assigning a numeric value to each cell or probe.
From this file, expression values for each probe set can be calculated and
normalized for all arrays in the study using various algorithms.
When choosing to analyze
CEL files in AltAnalyze, the user will be prompted to identify the folder
containing the CEL files and the folder in which to save these other results
to. The user will also need to assign a name to the dataset. These CEL files
will be summarized using the RMA algorithm using the program Affymetrix Power
Tools (APT). The APT C++ module “apt-probeset-summarize” is directly
called by AltAnalyze when running AltAnalyze on a Mac, PC or Linux operating
system. Unlike some other applications, APT is packaged with AltAnalyze
and thus does not require separate installation. However, because it is a
separate application there may be unknown compatibility issues that exist,
depending on your specific system configuration and account privileges. For
human and mouse exon arrays, AltAnalyze also allows for the masking of probes
with cross-hybridization potential, prior to running RMA. This is performed
through an experimental APT function (--kill-list), masking probes that are
indicated in files produced for the MADS application (http://biogibbs.stanford.edu/~yxing/MADS/Annotation.html)
that cross-hybridize to an off-target transcript within 3bp mismatches and a
person correlation coefficient > 0.55, as per the MADS recommendations. The probes with cross-hybridiation potential are indicated in the AltAnalyze
directory “AltDatabase/Hs/exon/Hs_probes_to_remove.txt”.
APT requires the presence
of a library file(s) specific for that array. AltAnalyze will automatically
determine the array type and can install these files if the user wishes
(currently most human, rat and mouse arrays supported). If AltAnalyze does not
recognize the specific array type or the user chooses to download these files
themselves, they will need to select the appropriate files when prompted in
AltAnalyze. For exon, gene and junction arrays, a PGF, CLF and antigenomic BGP file are required. These files will be
automatically downloaded and installed if the user selects “Download” when
prompted. For the AltMouse and 3’arrays, the appropriate CDF file will be
downloaded. In addition to these library files, a NetAffx CSV annotation file will be downloaded that allows for addition of gene
annotations (non-exon arrays) and Gene Ontology pathway annotations (all
arrays). Once installed, AltAnalyze will recognize these files and
automatically use them for all future analyses. Once the user selects the
appropriate directories and files, the user will be prompted to select the
remaining options in AltAnalyze, before APT is run. Once run, a tab-delimited text expression file will be
produced for all probe sets on the array and a detection-above background
(DABG) p-value file (not applicable to AltMouse or 3’arrays).
Loading a
Processed Expression File
If performing an RNA-Seq analysis, this is the file produced immediately after loading and aligning the junctions to exons, introns and UTR regions. For Affymetrix arrays, if CEL files are processed outside of AltAnalyze, the user must save the resulting expression text file in tab-delimited format. It is all right if the first rows in the file have run information as long as they are preceded by a pound sign (#).
Expression Analysis Parameters The options presented in this interface
(Figure 2.4) allow the user to determine what fields are present in the gene
expression output file, what scale the data is in (e.g. logarithmic), which RNA-Seq reads or probe sets (aka features) to use when calculating gene expression and how to
filter features for subsequent analyses.
1)
Perform
an alternative exon analysis - Selecting the option “just expression” will halt the analysis
after the gene expression result file has been written, such that no splicing
analysis is performed. This option is only available for splicing-sensitive platforms.
2)
Expression
data format - Indicates
the format in which the normalized expression values or counts have been
written. When CEL files have been processed by AltAnalyze, ExpressionConsole,
APT, RMAExpress or through
R, the file format will be logarithmic base 2 (log). The default format of
RNA-Seq counts is non-log. If the user designates “non-log”, then
expression values will be log base 2 (log2) transformed prior to analysis.
3)
Determine
gene expression levels using - For splicing RNA-Seq and sensitive
microarrays, the user has the choice to alter the way in which gene expression
values are calculated and how to filter their feature-level expression files
prior to alternative exon analysis. When “core” features are selected for this
option, all core features (Affymetrix core annotated
and any exon aligning feature) linked to a unique gene will be used to
calculate a measure of gene expression by taking the mean expression of all
associated feature values. When the “constitutive” is selected, only those
features that have been annotated as constitutive or common to the most
isoforms will be used for gene expression calculation. In either case, only features with at
least one array possessing a DABG p-value less than the user threshold (Affymetrix only) will be retained (if a DABG p-value file
is present). In order to exclude this threshold, set the minimum DABG p-value
equal to 1.
4)
Include
replicate experimental values in the export - Instructs AltAnalyze whether to include the expression
values associated with each BED or CEL file in the output file. If not selected
only the mean expression value of all BED or CEL files for each biological
group will be written.
5)
Remove probesets with a DABG p-value above – When a DABG file has been
produced (default when summarizing CEL files with AltAnalyze for exon-arrays),
this option is applied. The default DABG p-value cutoff is p<0.05. This will
filter out any non-constitutive probe set that has a mean DABG p>0.05 for
both compared biological groups. For probe sets used in determining gene
expression levels, both biological groups must have a DABG p < user-value.
In order to exclude this option, you can remove the DABG file (contains the
prefix “stats.”), or set this value equal to 1.
6)
Remove probesets/reads expressed below (non-log) – This statistic is treated the
same as the DABG p-value cutoff except in that probe sets with a mean
expression value less than this cutoff will be excluded. The same rules apply
to this value as to the DABG value, where both variables must be true for feature
inclusion (e.g., p<0.05 and mean expression > 70 for Affymetrix arrays). To exclude this option, set the default value to 100,000 (greater than
the maximum intensity value of most expression summaries).
7)
Perform
GO/pathway analysis (longer run-time) – Choosing “decide later” will allow the user to view the
GO-Elite pathway and Gene Ontology over-representation analysis options after
the main gene expression and/or alternative exon analysis is run. This will
prompt a separate status window and results summary window displaying
over-representation statistics for pathway analysis. If the option “run
immediately” is selected, GO-Elite will run right away without a separate
window. Please note, GO-Elite analysis can take up to
an hour per criterion when using the default parameters (e.g., 2000
permutations and analysis of both Gene Ontology and Pathways). For more details
on this analysis see: http://www.genmapp.org/go_elite/help_main.htm
Alternative
Exon Analysis Parameters
The options presented in this menu
(Figure 2.5) instruct AltAnalyze what statistical methods to use when
determining alternative exon expression, which features to select for analysis,
what domain-level and miR-BS analyses to perform and
what additional values to export for analyses in other tools. Details on each
analysis algorithm are covered in detail in section 3.2.
1)
Select
the alternative exon algorithm – For exon and gene arrays, the splicing index and FIRMA
methods are available and for RNA-Seq and junction
arrays the ASPIRE and linear regression methods are available. RNA-Seq and junction analyses can also include single
junction analyses (e.g., Splicing-index, MiDAS),
following the reciprocal junction analysis. These methods are used to calculate
an alternative exon score, relative to gene expression levels. The default
value for splicing-index and FIRMA analyses is 2, indicating that an adjusted
expression difference greater than two fold (up- or down-regulated) is required
for the probe set to be reported. Based on the algorithm,
different values and scales will apply. For junction analyses, the ASPIRE
algorithm default cutoff is 0.2, whereas the linear-regression algorithm is 2.
For linear-regression (linearregres), a minimum value
of 2 will select any linear-regression fold greater than 2 (result folds are
reported in log 2 scale, however), up- or down-regulated,
whereas ASPIRE’s scores ranging from -1 to 1. See
algorithm descriptions for more details (section 3.2).
2)
Minimum
alternative exon score – This value will vary based on the alternative exon analysis method
chosen (see above options).
3)
Max
MiDAS/normalized intensity p-value – This is the p-value cutoff applied to MiDAS and splicing-index
or FIRMA ttest p-values for single exon/junction
analyses. Currently, the user cannot set different p-value thresholds for these
two statistics. More on MiDAS can be found below and in section 3.2.
4)
Select probesets to include – This option is used to increase
or decrease the stringency of the analysis. In particular, this option allows
the user to restrict what type of features are to be
used to calculate an alternative exon score. In the case of junction analyses,
this option includes the ability to merge the expression values of junctions/exons
that measure the same differential inclusion of an exon (combined-junctions).
For exon and gene arrays, there are three options, “core”, “extended” and
“full”. Although these are the same probe set class names used by Affymetrix to
group probe sets, AltAnalyze uses a modification of these annotations.
Specifically, probe sets with the core annotation include all Affymetrix core
probe sets that specifically overlap with a single Ensembl gene
(2)
(based on genomic position) along with
any probe set that overlaps with an Ensembl or UCSC exon
(3)
. Likewise, extended and full probe sets
are those remaining probe sets that also align to a single Ensembl gene, with
the Affymetrix extended or full annotation. For RNA-Seq and junction arrays, options include “all”, “exons-only”,
“junctions-only”, “combined-junctions”.
5)
Maximum
absolute gene-expression change – This value indicates maximum gene expression fold change
(non-log, up- or down-regulated) that is allowed for a gene to be reported as
alternatively regulated. The default is 3-fold, up or down-regulated. This
filter is used with assumption that alternative splicing is a less critical
factor when a gene is highly differentially expressed.
6)
Perform
permutation analysis - (Junction
Analyses Only) This analysis reports a p-value
that represents the likelihood of the observed alternative exon score occurring
by change, after randomizing the expression values of all samples.
7)
Maximum
reciprocal-junction permutatep– (Junction Analyses Only) This p-value cutoff applies to the permutation based
alternative exon score p-values when performing ASPIRE or linearregres (see section 3.2).
8)
Export
all normalized intensities – This option can be used to compare alternative exon scores prior to
filtering for biological multiple comparisons, outside of AltAnalyze. For
example, if comparing multiple tissues, the user may wish to export all
normalized intensities (feature non-log expression divided by gene-expression)
for all tissue comparisons. The results will be stored to the AltResults/RawSpliceData folder in the user-defined output-directory. For
junction analyses, the ratio of the normalized intensities (NI) is reported for
the two reciprocal-junctions (j1 and j2).
9)
Calculate MiDAS p-values – This statistic is analogous to the ttest p-value calculated during the splicing-index analysis
(see section 3.2 for more details). If not selected, then only the
splicing-index or FIRMA (depending on the user selection) fold and p-value will
be used to filter alternative exon results.
10)
Calculate normalized intensity p-values – Indicates whether to calculate
the splicing-index or FIRMA ttest p-values and filter
using the above threshold.
11)
Filter results for predicted AS – This option instructs AltAnalyze
to only include regulated exons in the output that have been assigned a valid
splicing annotation (e.g., alternative-cassette exon) provided by AltAnalyze.
These annotations exclude exons with no annotations or those with only an
alternative N-terminal exon or alternative promoter annotation.
12)
Align feature to protein domains using – This option is used to restrict
the annotation source for domain/motif over-representation analysis. If
“direct-alignment” is chosen, only those features that overlap with the genomic
coordinates of a protein domains/motif will be included in the
over-representation analysis, otherwise, the inferred method is used (see
section 3.2 for more details).
13)
Number of algorithms required for miRNA binding
site reporting –
This option is used to filter out miR-BS predictions
that only occur in one of the four miR-BS databases
examined. For more miR database information see
section 6.5.
14)
Type
of group comparison to perform –This option indicates whether to
only perform pairwise alternative exon analyses (between two groups) or to
analyze all groups, without specifying specific comparisons.
2.5 Overview of Analysis ResultsAltAnalyze
will output two main types of files: 1) Gene expression (GE) summary 2) Alternative exon summary Gene
Expression Summary Data
The
GE summary files are two files that contain all computed gene expression values
from your dataset, saved to the folder “ExpressionOutput”
in the user-defined output directory. The first is a file is a complete dataset summary file with the prefix
“DATASET” followed by the user-defined dataset name containing all array expression values (gene-level for RNA-Seq and tiling-arrays), calculated group statistics (mean expression, folds, raw and adjusted t-test and f-test p-values) and gene annotations (e.g., gene symbol, description, Gene Ontology, pathway and some custom groups). For RNA-Seq and tiling arrays, the gene expression values are derived from either RNA-Seq reads or probe sets most informative for transcription (known exons or constitutive) (see “Select expression analysis parameters”, in Section 2.3). For RNA-Seq, if only junctions are present, then constitutive junctions or known junctions will be used, however, if exon reads are present, these will be used over junction reads. Constitutive features are determined by finding discrete exon regions that are common to the most mRNA transcripts (Ensembl and UCSC) for all transcripts used in the AltAnalyze database build (section 6). For the HJAY and MJAY junction arrays, both constitutive exon aligning probe sets and constitutive junction aligning junction probe sets (most common junctions in mRNAs) are used, whereas for RNA-Seq, constitutive exons are used. When one or more gene expression reporting probe sets have
DABG p-values with at least one biological group with a mean value below the
user defined threshold, these probe sets will be used to calculate gene
expression, otherwise, all gene expression reporting probe sets will be used.
The
second file, called the GenMAPP input file, contains a subset of columns from
the dataset summary file for import into GenMAPP
(4)
or PathVisio
(5)
(http://www.pathvisio.org/PathVisio2).
This file has the prefix “GenMAPP” and excludes all gene annotations and
individual sample expression values.
Alternative
Exon Summary Data
These
results are produced from all features (RNA-Seq reads or probe sets) that may
suggest alternative splicing, alterative promoter regulation, or any other
variation relative to the gene expression for that gene (derived from
comparisons file). When the user chooses to either analyze all groups rather
than just pairwise comparisons or both, the same output files will be produced
but report MiDAS p-values comparing all conditions and the maximum possible
splicing index fold between all conditions (Section 3.2). Each set of results
corresponds to a single pairwise comparison (e.g., cancer vs. normal) and will
be named with the group names you assigned. Four sets of results files are
produced in the end:
1)
RNA-Seq or probe
set-level – Feature-level
statistics, exon annotations, AS/APS annotations, and functional predictions
(protein, domain and miRNA binding site).
2)
Gene-level – Gene-level summary of data in feature-level
file.
3)
Domain-level – Over-representation analysis of gene-level
domain changes due alternative exon regulation.
4)
miRNA binding sites - Over-representation analysis of gene-level,
predicted miRNA binding sites present in alternatively regulation exons.
5)
Summary
statistics file – Global
statistics, reporting the number of genes alternatively regulated, number
differentially expressed and summary protein association information (e.g, mean regulated protein length).
Each
file is a tab delimited text file that can be opened, sorted and filtered in a
spreadsheet program. These files
are saved to the user-defined output directory under “AltResults/AlternativeOutput”,
all with the same prefix (pairwise group comparisons). AltAnalyze will analyze
all pairwise comparisons in succession and combine the feature-level and
gene-level results into two additional separate files (named based on the
splicing algorithm chosen).
Feature-
and Gene-Level Alternative Exon Result Files
The feature-level file contains alternative exon data
for either one probe set (exon-array), exon/junction IDs or reciprocal junctions (RNA-Seq and junction arrays). This includes:
The gene-level file contains a summary of the data at
the gene level, with each row representing a unique gene. This file also includes:
·
Gene Ontology
and pathway information for each gene extracted from any Affymetrix CSV
annotation files for that species present in the directory “AltDatabase/Affymetrix/*species*”.
Protein Domain/Motif
and miRNA Binding Site Over Representation Files
Over-representation analyses, (files 3 and 4) have
the same structure:
Comparison Evidence File
A common question for biologists analyzing alternative exon profiles is which events are most likely true versus false positive predictions. RNA-Seq and junction microarrays allow for independent detection of alternative splicing events using only exon-junctions or only detected exons. The comparison evidence file examines results from the reciprocal junction analysis (ASPIRE or Linear Regression) and the single feature (exon or junction) analysis (splicing-index), to determine which events are predicted by both analyses or only be one. Those splicing-events predicted by both represented independently verified events that are most likely to represent valid known or novel alternative splicing events in the dataset. This file includes:
Section 3 - Algorithms
Multiple
algorithms are available in AltAnalyze to identify individual features (for exon-sensitive platforms (EP)) or reciprocal probe sets (exon-exon junction platforms (JP)) that are differentially regulated relative to gene expression changes. These include the splicing index method (EP & JP), FIRMA (EP & JP), MiDAS (EP & JP), ASPIRE (JP) and Linear Regression (JP). For junction sensitive platforms (RNA-Seq and junction arrays), single feature analyses (e.g., splicing-index) are performed immediately after reciprocal junction analyses (e.g., ASPIRE) on the same list of expressed features. This allows the user to examine alternative exons predicted by pairs of reciprocal junctions in addition to those predicted by a single regulated exons/junctions (JP). In addition to these statistical methods, several novel methods are used to
predict which alternative proteins correspond to a regulated exon, which
protein domain/features differ between these and which RNA regulatory sequences
differ between the associated transcripts (e.g., miR-BS).
3.1 Default Methods
The
default options are stored in external text files in the folder “Config” as “defaults-expr.txt”, “defaults-alt_exon.txt”,
and “defaults-funct.txt”.
These
options correspond to those found in the configuration file “options.txt”. The
user is welcome to modify the defaults and theoretically even the options in
the “options.txt” file, however, care is required to ensure that these options
are supported the by the program. Since AltAnalyze is an open-source program, it is feasible for the user
to add new species and array support or to do so with AltAnalyze support. The
default algorithms for the currently supported arrays are as follows:
3.2 Algorithm DescriptionsExpression Normalization
For Affymetrix array analyses, AltAnalyze calls the software Affymetrix PowerTools, distributed with the GPU license. For conventional 3’ expression arrays, the RMA methods is applied to all Affymetrix CEL files (all supported formats) in the input user directory. Expression values are computed based on corresponding probeset annotations in the downloaded CDF file (automatically recognized and downloaded by AltAnalyze or supplied by the user). For exon, gene and junction arrays, RMA is also applied but for exon/junction-level probesets and not transcript cluster probesets (gene-level). In addition, detection above background (DABG) p-values are calculated for these arrays to evaluate likelihood of exon expression for calculating gene expression estimates and assessing probeset-level alternative exon expression (Section 3.3-3.4) For RNA-Seq studies, data is imported as junction and exon counts per region (junction or defined exon region). These counts can be analyzed directly in AltAnalyze, since alternative exon and junction expression are evaluate on a sample-by-sample basis, which allows for normalization of these values within a biological group and between group comparisons. However, gene expression will still be non-normalized when counts are used directly, hence, gene expression estimates may be incorrect, impacting all AltAnalyze gene expression fold changes. To account for this, the user can choose to normalize using basic quantile normalization (http://en.wikipedia.org/wiki/Quantile_normalization) and RPKM (http://www.clcbio.com/manual/genomics/Definition_RPKM.html). Quantile normalization currently only employs simply averaging the ranked counts for either junctions or exons and DOES NOT fit these to any a priori distribution. Users can choose between these normalization methods or no normalization methods (direct analysis of counts). In preliminary, in house analyses, no normalization performed best when a similar total number of sequenced reads were used for each sample, with quantile performing typically as well as no normalization and RPKM reporting fewer internally validated predictions (independent bioinformatics confirmation between exon and reciprocal-junction results). Gene
Expression Analysis
For
the simple gene expression output files saved to the ExpressionOutput directory, several basic expression statistics are calculated. These statistics
are performed for any user specified pairwise comparisons (e.g., cancer versus
normal) and between all groups in the user dataset (e.g., time-points of
differentiation). Expression values are reported as log2 values for all microarray
analyses and as non-log values for RNA-Seq analyses. These
statistics are comprised of the following: (1) rawp,
(2) adjp, (3) log-fold, (4) fold change, (5) ANOVA rawp, (6) ANOVA adjp and (7) max
log-fold. The rawp is a one-way analysis of variance
(ANOVA) p-value calculated for each pairwise comparison (two groups only). The adjp is the Benjamini-Hochberg
(BH) 1995 adjusted value of the rawp. The log-fold is
the log2 fold calculated by geometric subtraction of the experimental from the
control groups for each pairwise comparison. The fold change is the non-log2
transformed fold value. The ANOVA rawp is the same as
the comparison rawp, but for all groups analyzed
(note, this is reduced to the rawp when only two
groups are analyzed). The ANOVA adj is the BH
adjusted of the ANOVA rawp. The max log-fold is the
log2 fold value between the lowest group mean and the highest group expression
mean for all conditions in the dataset. These statistics are intended for
further data filtering and prioritization in order to assess putative
transcription differences between genes. For RNA-Seq analyses, these values will be the average of exon or junction read counts (exons when both present) for known or constitutive annotated features.
Reciprocal
Junction Analysis
Alternative exon analyses differ for exon-based
analyses as compared to junction sensitive analyses. For an exon-level
analysis, alternative exon regulation is determined by comparing the normalized
expression of one exon in two or more conditions (pairwise versus all groups analysis).
Normalized expression is the expression of an individual exon (aka a probe set)
divided by the gene-expression value (collective expression of gene features
that best indicate constitutive versus alternative expression). In contrast,
junction sensitive analyses (e.g., RNA-Seq and junction arrays) assess the expression of junctions often in addition to exons. For alternative splicing and often alternative promoter selection, two junctions (or an inclusion exon and exclusion junction) are differentially expressed in opposite directions, leading to the inclusion or exclusion of exons, exon-regions or introns. Thus, these methods are more sensitive in detecting alternative exon expression and the precise splicing-event that is occurring, as opposed to identifying on the exon features that are regulated (Figure 3.1). Although this method is more accurate at detecting true alternative splicing events, single exon or junction analyses are still useful at detecting other forms of alternative exon-region expression (e.g., alternative promoter selection and alternative 3’ end-processing). While junction arrays contain exon features for assessing alternative 3’ end-processing, RNA-Seq junction-only data
does not, thus, details on alternative polyadenylation will likely be missing.
To identify alternative exons from
junction analyses, AltAnalyze first identifies reciprocal junctions or pairs of
exon-junctions, one measuring the inclusion of an exon and the other measuring
exclusion of the exon. If exon expression is also measured, an exclusion
junction can be directly compared to the expression of an included exon. For
both of the reciprocal junction algorithms used by AltAnalyze (ASPIRE and
Linear Regression), the expression of the exclusion junction is directly
compared to the expression of the inclusion junction or exon for each sample.
In this way, these algorithms don’t require normalization of expression, which
is useful for RNA-Seq analyses were read counts
are directly used. ASPIRE does use the gene expression estimate as an
additional factor, however, since these values are calculated on a per sample
basis, normalization is not an issue. Pairs of reciprocal junctions or
reciprocal junction/exon pairs are extracted from the AltAnalyze gene database
for known junctions (Ensembl and UCSC) by comparing the exon block and region
structure of transcripts (Section 6.2) in addition to at runtime for novel
junctions detected from an RNA-Seq experiment. For the
HJAY and MJAY junction arrays, novel reciprocal junctions are included in the
gene database from Affymetrix predicted junctions,
using the same exon block and region comparison strategy. For the AltMouse
array, reciprocal probe set pairs are determined using the ExonAnnotate_module.py module using the “identifyPutativeSpliceEvents”
function, by comparing AltMerge exon block and region
annotations from Affymetrix. These methods allow for
the identification of cassette exon inclusion, alternative 5’ and 3’
splice-site selection, mutually-exclusive cassette inclusion, alternative
promoter selection, intron-retention and trans-splicing (RNA-Seq only).
Splicing
Index Method
This
algorithm is described in detail in the following publications:
(6, 7)
. In brief, the expression value of each RNA-Seq feature or probe set for a condition is
converted to log space (if necessary). For each feature
examined, its expression (log2) is subtracted from the
mean gene expression of value to create a gene expression corrected log ratio
(subtract instead of divide when these values are in log space). This value is the normalized intensity.
This normalized intensity is calculated for each experimental sample, using
only data from that sample. To
derive the splicing-index value, the group-normalized intensity of the control
is subtracted from the experimental. This value is the change in exon-inclusion (delta I, dI, or splicing index fold
change).
An f-test p-value is calculated (two tailed, assuming unequal variance) by comparing the normalized intensity for all samples between the two experimental groups. A Benjamin-Hochberg adjusted p-value from this f-test p is also calculated. A negative SI score of -1 thus indicates a two-fold change in the expression of the RNA-Seq feature or probe set, relative to the mean gene expression, with expression being
higher in the experimental versus the control. When more than two-groups are
being compared in AltAnalyze (“all groups” or “both options" for “Type of
group comparisons to perform”), the splicing-index value is calculated between
the two biological groups with the lowest and highest normalized intensities.
The two groups being compared are indicated in the alternative exon results
file.
FIRMA
Analysis
FIRMA or Finding Isoforms using Robust
Multichip Analysis
(8)
is an alternative to the splicing-index approach, to
calculate alternative splicing statistics. Rather than using the probe set
expression values to determine differences in the relative expression of an
exon for two or more conditions, FIRMA uses the residual values produced by the
RMA algorithm for each probe, corresponding to a gene. The median of the
residuals for each probe set, for each array sample is compared to the median
absolute deviation for all residuals and samples for the gene.
Although the core FIRMA methods are the same as the
original implementation in R, AltAnalyze FIRMA differs in several important
ways:
1)
The standard AltAnalyze core, extended, or full probe set definitions
define the probe composition of each gene, rather than the Affymetrix
transcript cluster definitions. Thus, each probe must correspond to a single
Ensembl gene to be analyzed.
2)
While FIRMA scores for each sample and probe set are calculated, only
summary statistics are reported in the standard AltAnalyze output files. This
statistic is the average FIRMA score for all samples in the experimental group
minus the average of the FIRMA scores in the designated baseline group. If no
comparisons are specified, the two groups with the largest difference in scores
are reported.
3)
FIRMA scores are organized into groups for calculation of summary
statistics (FIRMA fold change and t-test p-values). However, scores for each
probe set and sample can be optionally exported.
Users can
define whether to use the AltAnalyze core, extended or full annotations in the
program interface or using the --probetype flag in the command-line
interface. An unique numerical ID corresponding to each Ensembl gene and all
associated gene probe sets for FIRMA analysis are stored in a metaprobeset file in the array annotation directory (e.g.,
AltDatabase/EnsMart54/Hs/exon/Hs_exon_core.mps). This file is used to define
gene level probe sets by the program APT.
Similar to splicing-index
analysis of exon-tiling data, expression and DAGB filtering of probe set
expression, FIRMA score p-value filtering and gene expression
reporting/filtering based on either constitutive or all exon aligning probe
sets. To export FIRMA scores for each probe set and sample, select the option
“Export all normalized intensities” from the “Alternative Exon Analysis
Parameters” window, or by using the flag --exportnormexp in the command-line
interface.
MiDAS
The
MiDAS statistic is described in detail in the white paper:
www.affymetrix.com/support/technical/whitepapers/exon_alt_transcript_analysis_whitepaper.pdf.
This analysis method is available from the computer program APT, mentioned
previously. APT uses a series of text files to examine the expression values of
each probe set compared to the expression of user supplied gene expression
reporting probe sets. This method
can also be used with RNA-Seq junctions when
performing single feature-level analyses. Since AltAnalyze uses only features
found to align to a single Ensembl gene (with the exception of RNA-Seq for trans-splicing events), AltAnalyze creates it’s own unique numerical gene identifiers
(different than the Affymetrix transcript clusters). When written, a conversion
file is also written that allows AltAnalyze to translate from this arbitrary
numerical ID back to an Ensembl gene ID. These relationships are stored in the
following files along with the feature expression values:
When
the user selects the option “Calculate MiDAS
p-values”, AltAnalyze first exports expression data for selected RNA-Seq feature (e.g., AltAnalyze “core” annotated – Figure 2.5) to these
files for all pairwise comparisons. Once exported, AltAnalyze will communicate
with the APT binary files packaged with AltAnalyze to run the analysis
remotely. MiDAS will create a folder with the pairwise comparison dataset name
and a file with MiDAS p-values that will be automatically read by AltAnalyze
and used for statistical filtering (stored in the AltAnalyze program directory
under “AltResults/MiDAS”). These statistics will be
clearly labeled in the results file for each feature and used for filtering
based on the user-defined p-value thresholds (Figure 2.5). When more than two-groups
are being compared in AltAnalyze (“all groups” or “both options for “Type of
group comparisons to perform”), the MiDAS p-value is reported for all groups
designated by the user in combined array files or expression dataset file.
Note:
Different versions of the APT MiDAS binary have been distributed. AltAnalyze is
distributed with two versions that report slightly different p-values. The
older version (1.4.0) tends to report larger p-values than the most recent
distributed (1.10.1). Previous
versions of AltAnalyze used version 1.4.0, while AltAnalyze version 1.1 uses
MiDAS 1.10.1 that produces p-values that are typically equivalent to those as
the AltAnalyze calculated splicing-index p-values (larger).
ASPIRE
For
exon-exon sensitive junction platforms (RNA-Seq, HJAY,
MJAY, AltMouse), the algorithm analysis of splicing by isoform reciprocity or
ASPIRE was adapted from the original report
(9)
for inclusion into AltAnalyze. Similar to the
splicing-index method, for each reciprocal junction, a ratio is calculated for
expression of the junction (non-log) divided by the mean of all gene expression
reporting junctions and exons (non-log), for the baseline and experimental
groups. The ASPIRE dI is then
calculated for the inclusion (ratio1) and exclusion (ratio2) junctions, as
such:
Rin =
baseline_ratio1/experimental_ratio1
Rex =
baseline_ratio2/experimental_ratio2
I1=baseline_ratio1/(baseline_ratio1+baseline_ratio2)
I2=experimental_ratio1/(experimental_ratio1+experimental_ratio2)
in1= ((Rex-1.0)*Rin)/(Rex-Rin)
in2= (Rex-1.0)/(Rex-Rin)
dI = -1*((in2-in1)+(I2-I1))/2.0
If
(Rin>1 and Rex<1) or (Rin<1 and Rex>1) and the
absolute dI score is greater than the user supplied threshold
(default is 0.2), then the dI is retained for the next step in the analysis. By default, AltAnalyze will calculate a
one-way ANOVA p-value for the sample ASPIRE scores (relative to the compared
condition group means) and a false-discovery rate p-value of this one-way ANOVA
(Benjamin-Hochberg). In place of these statics, the user can choose to perform
a permutation analysis of the raw input data to determine the likelihood of
each ASPIRE score occurring by chance alone. This permutation p-value is
maximally informative when the number of experimental replicates is > 3.
This permutation p-value is calculated by first storing all possible
combinations of the two group comparisons. For example, if there are 4 samples
(A-D) corresponding to the control group and 5 (E-H) samples in the
experimental group, then all possible combinations of 4 and 5 samples would be
stored (e.g, [B, C, G, H] and [A, D, E, F]). For each
permutation set, ASPIRE scores were re-calculated and stored for all of these
combinations. The permutation p-value is the number of times that the absolute
value of a permutation ASPIRE score is greater than or equal to the absolute
value of the original ASPIRE score (value = x) divided by the number of
possible permutations that produced a valid ASPIRE score ((Rin>1
and Rex<1) or (Rin<1 and
Rex>1)). If this
p-value is less than user defined threshold, or x<2 (since some datasets
have a small number of samples and thus little power for this analysis), the
reciprocal junctions are reported.
Linear
Regression
When
working with the same type of reciprocal junction data as ASPIRE, a linear
regression based approach can be used with similar results. This method is
based on previously described approach
(11)
. This algorithm uses the same input ASPIRE (junction
comparisons, constitutive adjusted expression ratios). To derive the slope for
each of the two biological conditions (control and experimental), the
constitutive corrected expression of all samples for both reciprocal junctions
is plotted against each other to calculate a slope for each of the two
biological groups using the least squared method. In each case, the slope is
forced through the origin of the graph (model = y ~ x – 1 as opposed to y
~ x). The final linear regression
score is the log2 ratio of the slope of the baseline group divided
by the experimental group. This ratio is analogous to a fold change, where 1 is
equivalent to a 2-fold change. When establishing cut-offs, select 2 to
designate a minimum 2-fold change. The same permutation analysis used for
ASPIRE is also available for this algorithm.
Note: For previous published
analyses (
(11)
and Salomonis et al. in preparation), linear regression was
implemented using the algorithm rlm, which is apart of the R mass package from bioconductor. The Python R interpreter rpy, was used to run these analyses (which requires installation
of R). To use this option, select “linearregres-rlm”
under “select the alternative exon algorithm” (Figure
2.5). If a related warning appears while running, you may need to load AltAnalyze.py and associated source code directly (requires installation of
R version specific rpy – see Linux and Source Code AltAnalyze instructions).
External
Alternative Exon Analysis File Import
In addition to providing several
alternative exon analysis algorithms options in AltAnalyze (MiDAS, FIRMA,
splicing-index), users can import Affymetrix alternative exon results from other programs for downstream functional annotation
analyses. These analyses consist of alternative exon annotation (alternative
splicing and alternative promoter selection), protein isoform, protein domain
and microRNA binding site disruption analysis and pathway over-representation.
Two options for external probe set analysis are now available:
1)
Annotation of 3rd party alternative exon data in AltAnalyze
2)
Restricted
analysis of probe sets using a pre-defined list
Both
options are conceptually similar; provide AltAnalyze with a list of probe sets
and optionally statistics, and AltAnalyze will return alternative exon
statistics (option 2) and/or functional annotations for the input probe sets
(option 1 and 2).
Option 1
– Import and annotation of 3rd party probe set results
For this option, no raw data is
required (e.g., CEL files or expression values) just a list of probe sets of
interest. This option is simply available by selecting the main analysis option
“Annotate External Results” and selecting a tab-delimited probe set list. The probe sets supplied in this list can be produced by any
alternative exon analysis program the user prefers, as long as the output is
exon-level Affymetrix probe sets. Users can provide direct output files
from JETTA or provide results in a more generic format, consisting of regulated
probe sets and result statistics (optional).
The
generic file format for alternative exon results import is:
1)
(Column 1) Probe
set ID (required)
2)
(Column 2)
Alternative-exon fold (optional)
3)
(Column 3)
Alternative-exon p-value (optional)
4)
(Column 4-100)
Ignored data (optional)
In
addition to this generic format, results from the program JETTA can also be
directly imported. Since this method produces two p-values for the MADS
algorithm, the smallest of the two p-values is used. The results from this
analysis are typical AltAnalyze result files, including input for DomainGraph
and include any alternative exon statistics supplied with the probe set list.
Option 2
– Restricted probe set analysis (Advanced Feature)
This
option can be performed with any of the main AltAnalyze analysis options (e.g.,
Process CEL Files, Expression Files or AltAnalyze Filtered). It is triggered by
supplying a list of probe sets for each comparison of interest in the directory
“filtering” in the AltAnalyze program directory
(AltAnalyze_v1release/AltDatabase/filtering). The restricted filtering files
are tab-delimited text files containing the corresponding exon probe set ID
(Column 1) and optionally exon statistics and annotations (Columns 1-100).
While the statistics and annotations provided in the restricted filtering file
are not used for any computation, they are appended as extra columns in the
alternative exon results file. Multiple files, corresponding to specific
biological comparisons (e.g., Tumor_vs_wild-type) can
exist in this folder and only those files who’s name matches the comparison
name will be matched and used for restricted analysis. For example, if
biological sample groups A, B and C are being compared, to filter C_vs_A, the restricted probe set file must be named
C_vs_A.txt. Note: If the user enters any filters (e.g., maximum DABG p value
threshold, minimum alternative exon score), these will further restrict which
“significant” probe sets are reported. To report all probe sets found in the
AltAnalyze database, make sure to set all thresholds to the minimum or maximum
value (e.g., --dabgp 1 --rawexp 1 --altp 1 --probetype full --altscore 1 --GEcutoff 10000 – Section 2.3).
Alternatively, users working with the command-line interface can use the option --returnAll to
automatically enter this minimum values (see section
2.3).
Domain/miR-BS Over-Representation Analysis
A z-score is calculated to assess over-representation of specific protein sequence motifs (e.g., domains) and miR-BS’s found to overlap with a probe set or RNA-Seq (feature) that are alternatively regulated according to the AltAnalyze user analysis. This z-score is calculated by subtracting the expected number of genes in with a protein feature or miR-BS meeting the criterion (alternatively regulated with the user supplied thresholds) from the observed number of genes and dividing by the standard deviation of the observed number of genes. This z-score is a normal approximation to the hypergeometric distribution. This equation is expressed as:
n = All genes associated
with a given element r = Alternatively regulated
genes associated with a given element N = All genes examined R = All alternatively
regulated genes Once z-scores have been calculated for all protein
features and miR-BS linked to alternatively regulated probe sets, a
permutation analysis is performed to determine the likelihood of observing
these z-scores by chance. This is done by randomly selecting the same
number of regulated probe sets from all probe sets examined and recalculating
z-scores for all terms 2000 times. The likelihood of a z-score occurring
by chance is calculated as the number of times a permutation z-score
is greater than or equal to the original z-score divided by 2000. A
Benjamini-Hochberg
correction is used to transform this p-value to adjusted for multiple hypothesis testing. Gene
Ontology and Pathway Over-Representation Analysis
To perform advanced pathway and Gene Ontology (GO)
over-representation AltAnalyze includes core python modules from the program
GO-Elite (version 1.2) in AltAnalyze (http://www.genmapp.org/go_eite).
GO-Elite performs Gene Ontology (GO) and WikiPathway over-representation using
the same algorithms listed for domain/miR-BS
over-representation analysis (ORA) (e.g., z-score,
permutation p-value, BH p-value calculation). After ORA, all GO terms and
pathways are filtered using user-defined statistical cut-offs (z-score,
permutation p-value and number of genes changed), with GO terms further pruned
to identify a minimally redundant set of reported GO terms, based on
hierarchical relationships and ORA scores (see GO-Elite documentation). The gene
database used for GO-Elite is specific to the version of Ensembl in the
downloaded AltAnalyze gene database (Plus versions only with associations
directly from Affymetrix included). All result files
normally produced by GO-Elite will be produced through AltAnalyze.
AltAnalyze
generates two types of gene lists for automated analysis in GO-Elite; differentially expressed genes and alternatively regulated
genes. Criterion for differentially expressed genes are defined by the user in the GO-Elite parameters window (e.g., fold difference
> 2 and t-test p < 0.05). The user can choose to use the rawp (one-way ANOVA, two groups) or adjp (BH adjusted value of the rawp) in the software. All
genes associated with alternative exons are used for GO-Elite analysis. Genes
with alternative exons that also have alternative splicing annotations can be
further selected using the “Filter results for
predicted AS” option. Results are exported to the “GO-Elite” directory
in the user-defined output directory, while input gene lists can be found in
the folders “GO-Elite/input” and “GO-Elite/denominator”. The gene lists for
differentially expressed genes have the prefix “GE.” while the alternative exon
files have the prefix “AS.”. The appropriate
denominator gene files for each is selected by GO-Elite. Species and array
specific databases for GO-Elite analysis are downloaded automatically from
AltAnalyze when the species database is installed. Array types supported for
each species include any Affymetrix supported at the time the Ensembl database
was released along with any arrays supported by Ensembl.
3.3 Probe set and RNA-Seq FilteringPrior to an Affymetrix alternative exon analysis, AltAnalyze can be used to remove probe sets that are not deemed as sufficiently expressed. For the two conditions that AltAnalyze compares (e.g., cancer versus normal), a probe set will be removed if neither condition has a mean detection above background (DABG) p value less than the user threshold (e.g., 0.05). Likewise, if neither condition has a mean probe set intensity greater than the user threshold (e.g., 70), then the probe set will be excluded from the analysis. When comparing two conditions (pairwise comparison) for probe sets used to determine gene transcription (e.g., constitutive aligning), both conditions will be required to meet these expression thresholds in order to ensure that the genes are expressed in both conditions and thus reliable for detecting alternative exons as opposed to changes in transcription. When comparing all biological groups in the user dataset, however, these additional filters are not used. For RNA-Seq analyses, the read counts associated with each exon or junction are used as a surrogate for the expression. These same filters applied to constitutive annotated exons or junctions used for reciprocal junction analysis. 3.4 Constitutive and gene expression calculationIdentifying exons and junctions for gene expression calculations A reliable estimate of transcriptional activity for each gene is required to both report basic gene expression statistics and perform alternative exon analysis. Affymetrix exon and junction arrays probe most well annotated exon regions, many introns, untranslated regions and theoretically transcribed regions. Gene expression values can be calculated in one of two ways from AltAnalyze; (A) using features aligned to constitutive exons, (B) using features aligned to all known exons. Using constitutive exons are recommended for use for all analyses.
Constitutive exons in AltAnalyze are classified as exons that align to a pre-determined set of exon regions that are most common to all mRNA transcripts used when the AltAnalyze database is created. RNA transcript exon structures and genomic positions are extracted from analogous versions of Ensembl and the UCSC genome browser database. While AltAnalyze database versions EnsMart49 and EnsMart52 used mRNA annotations from the Affymetrix probe set annotation file, EnsMart54 and later exclusively derives constitutive exon annotation from UCSC and Ensembl exon structures (Figure 3.1). If only one exon region is considered constitutive then the grouped exon regions with the second highest ranking (based on number of associated transcripts) are included as constitutive (when there at least 3 ranks). For next generation Affymetrix junction arrays (HJAY and MJAY), constitutive exons are obtained as described above. Constitutive junctions from these arrays are currently determined by two methods; 1) determine if both of the exons in the junction are annotated as constitutive and 2) determine if the junction is annotated as a putative ‘alternative’ probe set according to the manufacturer annotation file (e.g., mjay.r2.annotation_map). If both lines of evidence indicate the probeset is constitutive, then the probeset is annotated accordingly. Probe set annotations are provided in “AltDatabase/EnsMart55/Hs/junction/Hs_Ensembl_probesets.txt” (modify for your species, Ensembl version and array type). For RNA-Seq analyses, junctions are considered constitutive if the 5’ and 3’ splice sites of the detected junction map to the corresponding splice sites of two exons that are; 1) annotated as constitutive and 2) not annotated as alternative from the transcript reciprocal junction comparison analysis. Constitutive RNA-Seq junctions are only evaluated when no exon reads are present. In addition to constitutive exons, users have the option to use all features that have an Affymetrix core probe set annotation or align to an AltAnalyze defined exon region. Affymetrix defines probe sets according to three criterion, "core", "extended" and "full". The Affymetrix core annotations are recommended for calculation of constitutive levels by Affymetrix, however, these are often a mix of known alternative exons and AltAnalyze annotated constitutive exons. In addition to these Affymetrix core exons, the AltAnalyze core set consists of any probe set that aligns to an exon region, but excludes probe sets aligning to any non-exon features, including introns and untranslated regions (Figure 3.1). In version 2.0 of AltAnalyze, users are no longer restricted to genes containing constitutive annotated exons/junctions for alternative exon analysis. Prior to version 2.0, AltAnalyze removed genes during the filtering process that had no exon or junctions with constitutive annotation prior to the alternative exon analysis. To address this issue, all exon or junction features that align to known mRNAs are used to assess gene expression when no constitutive features are deemed “expressed” according to the user’s filtering parameters.
Calculating gene expression values Gene expression values are calculated twice during an exon array analysis; (1) To report gene expression fold changes and summary statistics, (2) To calculate gene expression normalized intensities for alternative exons. While both analyses use the same set of starting junctions or probe sets (constitutive or mRNA aligning), they differ in how filtering of these features occurs. These differences are important when trying to eliminate false positive alternative exon predictions. For gene expression summary reporting, expression values from all constitutive or core features (user defined) are filtered and then averaged. For this analysis, the constitutive or core features are first identified from the AltAnalyze database (Ensembl_probeset or Ensembl_exon file) and corresponding expression and detection probabilities extracted from the APT RMA result files from the input array files. For RNA-Seq analyses only total number of counts associated with each exon/junction are considered. For each biological group (typically containing replicates), the mean expression and mean detection above background probabilities are calculated for each feature. If the mean expression and the mean detection above background probabilities for at least one group does not meet the user defined thresholds (by default p<0.05 and expression>70), then this feature will not be used to calculate the gene expression value for the corresponding gene. The average expression for all remaining features, for each sample will calculated to obtain a mean gene expression value for each feature for each replicate. Thus, any differences between features used to calculate gene expression will be averaged. For example, when all core features are used, the expression of any alternative exons will be averaged with the non-alternative exons. If no constitutive or core features for a gene meet these criterion (see below), then all will be used to calculate gene expression. Only one set of gene expression values is reported for each Ensembl gene.
3.5 Alternative Splicing Prediction
De Novo Splicing Prediction and Exon Annotation
In order to identify exons with alternative splicing or promoters, AltAnalyze compares all available gene transcripts from UCSC and Ensembl to look for shared and different exons. To achieve this, all mRNA transcripts from UCSC’s species-specific “mRNA.txt” file that have genomic coordinates aligning to a single Ensembl gene and all Ensembl transcripts from each Ensembl build are extracted. Only UCSC transcripts that have a distinct exon composition from Ensembl transcripts are used in this analysis, excluding those that have a distinct genomic start or stop position for the first and last exon respectively (typically differing 5’ and 3’ UTR agreement), but identical exon-structure.
When
assessing alternative splicing, cases of intron retention are identified
first. These regions consist of a single exon that spans an two adjacent
exons at least one another transcript for that same gene. These retained
introns are stored for later analysis, but eliminated as annotated exons.
Remaining exons are clustered based on whether their genomic positions
overlap (e.g., alternate 5' or 3' start sites). Each exon cluster is
considered an exon block with one or more regions, where each block
and region is assigned a numerical ID based on genomic rank(e.g., E1.1,
E1.2, E2.1, E3.1). For each exon in a transcript, the exon is annotated
as corresponding to an exon block and region number (Figure 3.1). All possible pair-wise transcript comparisons
for each gene are then performed to identify exon pairs that show evidence
of alternative exon-cassettes, alternative 3' or 5' splice sites or
alternative-N or -C terminal exons (Figure 3.1). All transcript exon
pairs are considered except for those adjacent to a retained intron.
This analysis is performed by comparing the exon block ID and region
IDs of an exon and it's neighboring exons to the exon blocks and regions
in the compared transcript. Ultimately, a custom heuristic assigns the
appropriate annotation based on these transcript comparisons.
Incorporating UCSC Splicing Predictions In addition to all de novo splicing annotations, additional splicing annotations are imported from the UCSC genome database and linked to existing exon blocks and regions based on genomic coordinate overlap.This comparison is performed by the alignToKnownAlt.py module of AltAnalyze (called from EnsemlbImport.py). De novo and UCSC splicing annotations are stored along with probe set Ensembl gene alignment data in the file <species>_Ensembl_probesets.txt file (RNA-Seq). These Affymetrix annotations
are used by AltAnalyze and DomainGraph for annotation and visualization. In the AltAnalyze result file, UCSC KnownAlt the major splicing annotation types are altFivePrime, altThreePrime, cassetteExon, altPromoter, bleedingExon and retainedIntron.
In comparison, the major AltAnalyze determined splicing annotation types
alt-5’, alt-3’, cassette-exon, alt-N-term, intron-retention,
exon-region-exclusion and alt-C-term. These splicing annotations are determined
by comparing relative exon-junction positions for all analyzed transcripts for
each gene (see proceeding section). The annotation "exon-region-exclusion" is the
opposite of intron-retention (region most commonly described as exon rather
than intron), alt-N-term is similar to altPromoter (distinct 5’ distal-transcript exon with shared 3’ exons) and alt-C-term is the
opposite of alt-N-term. To better understand these annotations, look at
specific probe set examples through the NetAffx website (www.affymetrix.com/analysis/index.affx)
using the UCSC browser option.
Incorporating Alternative Poly-Adenylation Predictions
Poly-adenylation (polyA) sites are identified using the polyA database (http://polya.umdnj.edu/) version 2. This database provides polyA site genomic coordinates for human, mouse, rat, chicken and zebrafish, generated in 2006 from various genomic database builds. Of these, only human polyA sites are provided as an annotation track in the UCSC genome database. For human, the polyA sites for the appropriate human genome build are downloaded and parsed using the same methods for the knownAlt UCSC annotation track. If multiple polyA sites are observed in a given Ensembl gene, exon regions overlapping with these binding sites are reported as “alternative_polyA” along with the alternative splicing annotations. To report these same annotations for other species, annotations from the polyADB_2 flat-file were converted to UCSC BED format and batch converted to the latest genome build using http://www.genome.ucsc.edu/cgi-bin/hgLiftOver. In some cases, multiple lift-over translations were required. Intermediate files are available at http://www.altanalyze.org/archiveDBs/. Due to missing chromosome annotations for most predictions in zebrafish, these annotations were not included.
Filtering for Alternative Splicing As mentioned in
previous sections, AltAnalyze includes the option restrict alternative exon
results to only those probe sets or reciprocal junction-pairs predicted to
indicate alternative splicing. AltAnalyze considers alternative splicing as any alternative exon
annotation other than an alternative N-terminal exon or alternative promoter
annotation derived from de novo or
UCSC genome database annotations.
3.6 Protein/RNA Inference AnalysisIdentifying Alternative Proteins Protein Domains RNA-Seq
and probe set sequences are used to identify which proteins align to or are
missing from transcripts for that gene. To do this, all Ensembl and UCSC mRNA
transcripts are extracted for a gene that corresponds to a given feature (probe
set or junction). For each transcript, all exon genomic coordinates are stored.
For exon-level arrays, transcripts with exons that contain the probe set (or RNA-Seq exon region) genomic
coordinates are considered transcript aligning, while all others are considered
non-aligning. For junction analyses, if two reciprocal junctions (or junction
and an exon) align to distinct isoforms, these relationships are stored rather
than the aligning and non-aligning isoforms, however, if both isoforms do not
align to distinct transcripts, then the one with a aligning and non-aligning
set of transcripts is stored for further exon comparisons.
If
a set of aligning and non-aligning isoforms (competitive) is identified for a single
feature or reciprocal junction pair, all possible aligning and non-aligning pairwise
combinations are identified to find those pairwise comparisons with the
smallest difference in exon composition. This is accomplished by determining
the number of different and common exons each transcript pair contains (based
on genomic start and stop of the exon). When comparing the different transcript
pairs, the most optimal pair is selected by first considering the combined
number of distinct exons in both transcripts and second the number of common
transcripts. Thus, if one transcript pair has 4 exons in common and 2 exons not
in common, while a second pair has 5 exons in common and 3 exon not in common,
the prior will be selected as the optimal since it contains less overall
differences in exon composition (even though it has less common exons than the
other pair). A theoretical example is illustrated in Figure 3.2.
Once
a single optimal isoform pair has been identified, protein sequence is obtained
for each by identifying protein IDs that correspond to the mRNA (Ensembl or
NCBI) and if not available, a predicted protein sequence is derived based on in silico translation. Although such a
protein sequence may not be valid, given that translation of the protein may
not occur, these sequences provide AltAnalyze the basis for identifying
conservative changes predictions for a change in protein size, sequence and
domain composition. Domain/protein features are obtained directly from UniProt’s sequence annotation features or from Ensembl’s
InterPro sequence annotations (alignment e-value <1) (see section 5 data
extraction protocols). Any InterPro sequences with a description field or any
UniProt sequence annotation feature that is not of the type “CHAIN”, “CONFLICT”,
“VARIANT”, “VARSPLIC” and “VAR_SEQ” are examined by
AltAnalyze. To compare domain or motif sequence composition differences,
the protein sequence that corresponds to the amino-acid start and stop positions of a domain for each transcript is searched for in
each of the compared protein isoforms. If the length of a motif sequence is
less than 6 amino-acids, flanking sequence is
included. If a domain is present in one but not another isoform, that domain is
stored as differentially present. To identify differences in protein sequence (e.g.,
alternative-N-terminus, C-terminus, truncation coding sequence and protein
length), the two protein sequences are directly compared for shared sequence in
the first and last five residues and comparison of the entire sequences. If the
N-terminal sequence is common to both isoforms but there is a reduction in more
than 50% of the sequence length, the comparison is annotated as truncated. If an Ensembl transcript, non-coding and nonsense mediated decay annotations are included from the Ensembl translation annotations. All of these annotations are stored for each junction, exon or probe set for import into AltAnalyze, with each new database build.
Direct
Domain/Motif Genomic Alignment
The
above strategy allows AltAnalyze to identify predicted protein domains and
motifs that are found in one isoform but not the other (aligning to a feature
and not aligning). In addition to these “inferred” domain predictions, that
include protein domains/motifs that do not necessarily overlap with the
regulated feature, AltAnalyze includes a distinct set of annotations that only
corresponds to domain/motif protein sequence that directly overlaps with a feature.
Alignment of features to InterPro IDs is achieved by comparing feature genomic
coordinates to InterPro genomic coordinates. To obtain InterPro genomic start
and end positions are determined by first identifying the relative amino-acid
positions of an InterPro region in an Ensembl protein, finding which exon and
at what position the InterPro region begins and ends and finally stroing the genomic position of these relative exon
coordinates.
These
feature-InterPro overlaps can be of two types; 1) features whose sequence is
present in the domain coding RNA sequence and 2) features whose sequence is not
present in the domain coding RNA sequence. The second type of overlap typically
occurs in the gene introns. While these associations are typically meaningful,
false “indirect” associations are possible. To reduce the occurrences of these
false positives, any feature that aligns to the UTR of an Ensembl gene or that
occurs in the first or last exon of an mRNA transcript are excluded. These
heuristics were chosen after looking at specific examples that the authors
considered to be potential false positives. For junction analyses (RNA-Seq and junction array), the associated critical exon
genomic position rather than probe set is used.
Identifying
microRNA Binding Sites associated with Alternative Exons
MicroRNA
binding site (miR-BS) sequence is obtained and
compared from five different microRNA databases (see section 6.5), and compared
to identify miR-BSs in common to or distinct to
different databases. Probe set sequences are obtained from Affymetrix,
while critical exon sequences (corresponding to two reciprocal junctions) are
obtained from chromosome FASTA sequence files (see section 6.7). Each miR-BS sequence is searched for within probe set or critical
exon sequence to identify a match. These relationships are stored with each new
database build and are used by AltAnalyze and DomainGraph.
Exhaustive
Protein Domain/Motif Analysis
Very
similar to the competitive protein domain/motif analysis is the exhaustive
protein comparison analysis. This feature is currently not available by default
in AltAnalyze and requires replacement database files from AltAnalyze support.
The purpose of these files is to obtain the most conservative possible
domain-level prediction results from the competitive analysis. This done by
storing all pairwise aligning and non-aligning isoform comparisons (Figure 3.2) and then obtaining protein
sequence for each transcript, as described in earlier sections and storing all
domain/motifs differentially found between all possible competitive isoforms.
From these stored results, all possible competitive isoforms are themselves
compared to find isoforms that ideally show only differences in central regions
of the protein (no N-terminal or C-terminal differences), next for those that
contain as few possible domain-level predictions and finally for those with the
smallest overall differences in protein length. For detailed algorithm
information see the IdentifyAltIsoforms module and the function “compareProteinFeaturesForPairwiseComps”.
3.7 Gene Annotation AssignmentAltAnalyze extracts useful gene annotations for determining whether a gene (A) encodes for protein, (B) is likely “drug-able”, (C) is a transcriptional regulator, (D) has putative microRNA targets, (E) participates in known pathways, (F) localizes to specific cellular compartment and (G) is a housekeeping gene. To obtain these annotations, AltAnalyze extracts various annotations for several public genomic databases (Ensembl, UniProt, miRBase, TargetScan, miRanda, Pictar, miRNAMap, WikiPathways, Gene Ontology, Affymetrix). Extracted from Ensembl are annotations for protein coding potential in addition to pseudogenes, rRNA, miRNAs, snoRNAs, lincRNA and other gene types. From UniProt, annotations for kinase, GPCR and coupling pathway, single transmembrane receptors transcriptional regulator and cellular compartment. Annotations for Gene Ontology termas and WikiPathways are extracted on the fly from the downloaded Official GO-Elite annotation databases. Housekeeping gene predictions are extracted from this same database, by searching for Ensembl-Affymetrix associations for probesets containing the ‘AFFX’ notation. Any microRNA targeting prediction from miRBase, TargetScan, miRanda, Pictar, miRNAMap (Section 6.5) are reported along with the source of the annotation and overlapping predictions.
Section 4 - Using R with AltAnalyze
4.1 Configuring R Although installation of R is not
required for any of the AltAnalyze analyses, for users who wish to use more
advanced statistics, it will be necessary. Currently, the only statistic that
requires installation of R is the linear regression method rlm.
rlm
is a regression statistical method apart of the R package mss.
This method is preferred by some over the alternative linear regression
method provided by default in AltAnalyze. Both methods produce very similar
statistics, with only a few probe sets differing between threshold parameters
out of hundreds of results.However,
since the rlm
method was used in the published linear regression analyses, other users
wishing to replicate these results may wish to use this algorithm.
To run the option
"linearegress-rlm" from the "Alternative Exon Analysis Parameters" window, you will
need to install a compatible version R (only version 2.1 has been extensively
tested). Along with R, the user will need to install the R statistical
package mss. R is interpreted by Python using the Python
program Rpy, which is packaged with the compiled versions of AltAnalyze, but
not the source code (http://rpy.sourceforge.net/download.html).
With some compiled installations of AltAnalyze, you will receive a warning
when running "linearegress-rlm". This error occurs
due to an Rpy compiling error that is specific to the OS executable version
of AltAnalyze, but should work fine when Rpy is installed for the associated
version of R by the user (each version of Rpy corresponds to several versions
of R). Whether dealing with a compiled or
source version of AltAnalyze, if Python reports that it cannot find the
current version of R, the user may need to update the computers Environment
Variables setting Path (Windows only). This is accessed through by opening
Control Panels>System Properties>Advanced>Environment Variables and
selecting the Variable "Path" and entering the path location of R (for
example, ";C:\Program Files\R\rw2010;" - No
spaces before or after ;). Contact AltAnalyze support if problems
persist. Section 5 - Software Infrastructure5.1 Overview AltAnalyze
consists of more than 30 modules and over 10,000 lines of code. The core
modules for AltAnalyze consist of the programs ExpressionBuilder and
AltAnalyze, which can be used in tandem or separately through the AltAnalyze
GUI. The user will never likely need to deal directly with these modules names
when running AltAnalyze, but these distinct core modules are used for different
analysis functions (Figure 5.1).
The ExpressionBuilder component builds constitutive gene expression summary files as well as filters the exon, junction or probe set or probe set expression data prior to alternative-exon analysis. The AltAnalyze module performs all of the alternative-exon analysis and MiDAS p-value calculations. 5.2 ExpressionBuilder ModuleThe ExpressionBuilder program is principally designed
to perform the following tasks:
1)
Import user
expression data from tab-delimited files.
2)
(Alternative
Exon Analysis) For Affymetrix arrays, exclude probe sets where no samples have a DABG p<user-threshold
(only applicable when DABG p-values exist) or an expression value >
user-threshold. For RNA-Seq, this module filters out
exons or junctions where no samples have a read count > user-threshold (e.g., 2
reads).
3)
Organize your
data according to biological groups and comparisons (specified by the user from
custom text files).
4)
(Alternative
Exon Analysis) Calculate gene
transcription levels for all Ensembl genes.
5)
Export
calculated gene expression values along with folds, t-test and f-test p-values
and gene annotations for all genes and all user indicated comparisons.
6)
(Alternative
Exon Analysis) Export all gene-linked feature
expression and DABG data for all pairwise comparisons for further filtering
(next step).
7)
(Alternative
Exon Analysis) Filter the resulting feature data using
mean expression values and probabilities specific for each pairwise comparison
and user-defined thresholds (Figure 2.4).
The
above tasks are performed in order by the ExpressionBuilder module. Detection
probabilities are assessed at two steps (2 and 7). In step 2, import of DABG
p-values are for the purpose of calculating a transcription intensity value
only for those constitutive features (present in all or most transcripts) that
show detection above background, since some probe sets will have weaker
expression/hybridization profiles as others for Affymetrix analyses and some junction counts are too low to be considered biologically
significant for RNA-Seq. If no features have a DABG
p-value less than the default or user supplied threshold (for at least one
sample in your dataset), all selected features will be used to calculate
expression (constitutive aligning only if default is selected).
In
step 7, the probe set DABG p-values are examined to include or exclude features
for alternative splicing analysis. This step is important in minimizing false
positive splicing calls. False
positive splicing calls can occur when a feature is expressed below detectable
limits and results in a transcription-corrected expression value that
artificially appears to be alternatively regulated. For Affymetrix expression and DABG p-value files output from APT, probe set expression values
are initially filtered to remove any probe sets where the expression and dabg p-values are below the user defined threshold for all
biological groups examined and export all pairwise comparison group files
(expression and dabg) for further filtering. For rcond
or probe sets used to determine gene expression levels (constitutive or known exons), the module FilterDABG is used to remove features
that are expressed below user defined thresholds (expression and dabg) in the two comparison groups for all pairwise
comparisons. If a feature is not used to determine gene expression levels, then
for at least one of the two biological groups, the same criterion must be true
(mean DABG p-value and expression). These filters ensure that: 1) the gene is
“expressed” in both conditions and 2) the feature is “expressed” in at least
one condition. The feature and expression values passing these user defined
filters are exported to a new file that is ready to use for splicing analyses,
stored to the user output directory under “AltExpression/ExonArray/*species*”. This file can also be directly selected
in future AltAnalyze runs as input for analysis (“AltAnalyze filtered file”
– Figure 2.2).
Runtime
of ExpressionBuilder is dependent on the number of conditions and array type
being analyzed (10 minutes plus for Affymetrix Exon 1.0 ST arrays). When analyzing junction-only RNA-Seq data, runtime is relativity fast (1-5 minutes), since the sequence space (pre-aligned junctions) is less than exon and junction tiling arrays. However, analysis of combined exon and junction RNA-Seq data is typically longer and more memory intensive than Affymetrix junction arrays. If multiple comparisons are present in a
single expression file, input files for AltAnalyze will all be generated at
once and thus runtimes will take longer. Note
1:
While this pipeline is mainly for use with alternative exon/junction platforms,
it is also compatible with a standard 3’ Affymetrix microarray dataset to
calculate folds, t-test p-values, and assign annotations to this data. This is useful when you have many comparisons
in your dataset and you don’t wish to manually calculate these values.
Note
2:
You do not need to run ExpressionBuilder if you have an alternative way of
building AltAnalyze input files for Affymetrix arrays.
To do so, your file headers for each arrays must have the name “group:sample_name”, where your
group names are different for each group and the denominator group is listed
first and the numerator is listed second. Below the header line should only be
probe set IDs and log2 expression values.
5.3 AltAnalyze ModuleThe AltAnalyze module is the primary software used
for all alternative exon analyses. This software imports the filtered
expression data and performs all downstream statistical and functional
analyses. This program will
analyze any number of input comparison files that are in the “AltExpression” results directory for that array type. The
main analysis steps in this program are:
1)
Import exon or
junction annotations, to determine which exon, junctions or probe sets to analyze, which
are predicted constitutive and which correspond to known AS or APS events.
2)
Import the user
expression data for the pairwise comparison.
3)
Store feature
level data (e.g., junction or probe set) for all features corresponding to
either a constitutive exon (or junction) or for splicing event, while storing
the group membership for each value.
4)
Calculate a
constitutive expression value for each gene and each sample (used for the
splicing score later on). OPTIONAL: If the user selected a cut-off for
constitutive fold changes allowed to look at alternative exon regulation, then
remove genes from the analysis that have a gene-expression difference between
the two groups > cut-off (up or down).
5)
(Alternative
Exon Analysis Only) OPTIONAL:
exports input for the Affymetrix Power Tools (APT) program to calculate a MiDAS
p-value for each probe set or RNA-Seq feature.
6)
Calculate a
splicing score and t-test p-value from the junction or probe set and
constitutive expression values. This calculation requires that splicing ratios
are calculate for each sample (exon/constitutive expression) and then compared
between groups. For exon arrays, the splicing index method (SI) is calculated
for each probe set. For junction analyses, ASPIRE, Linear Regression are used
with the pre-determined reciprocal junctions or alternatively are calculated
for individual features using the SI method.
7)
(Junction
Analysis Only) OPTIONAL:
performs a permutation analysis of the sample ASPIRE input values or Linear
Regression values to calculate a likelihood p-value for all possible sample
combinations.
8)
Retain only features
meeting the scoring thresholds for these statistics (splicing score, splicing
t-test p, permutation p, MiDAS p – see Section 3.2).
9)
Import feature-protein,
feature-domain and feature-miRNA associations pre-built local from flat files
(see Section 6).
10)
For the remaining features, import all protein domain
and miR-BS to all pre-built feature associations (see
Section 3.3 for details). Import all feature-domain and –miR-BS associations for all genes to calculate an
over-representation z-score for all domains and miR-BS’s along with a non-adjusted and adjusted p-value.
Export the resulting statistics and annotations to tab-delimited files in the “AltResults/AlternativeOutput” folder in the user-defined
output directory.
11)
(Junction Analysis Only) Import splicing and exon annotations for regulated
exons corresponding to each set of reciprocal junctions (e.g. for E1-E3
compared to E1-E2, E2 is the regulated exon). These annotation files are the
same as those for exon arrays, except that the probe set is replaced by the
exon predicted to be regulated by the reciprocal
junction-pair (see Section 6.2).
12)
For protein domain and miR-BS
annotations, reformat the direction/inclusion status of the annotation. For example, if a kinase domain is only
found in a protein that aligns to a exon, junction or probe set, but was
down-regulated, then the annotation is listed as (-) kinase domain, but if
up-regulated is listed as (+) kinase domain.
13)
Export the results from this analysis to the “AltResults/AlternativeOutput” folder in the user-defined
output directory.
14)
Summarize the probe set or reciprocal junction data
at the level of genes and export these results (along with Gene
Ontology/Pathway annotations).
15)
Export overall statistics from this run (e.g. number
of genes regulated, splicing events).
16)
(Junction Analysis Only) Combine and export the exported feature and gene
files for each comparison analyzed, to compare and contrast differences.
Result File
Types
When finished AltAnalyze will have generated five
files.
1)
name-scoringmethod-exon-inclusion-GENE-results.txt
2)
name-scoringmethod-exon-inclusion-results.txt
3)
name-scoringmethod-ft-domain-zscores.txt
4)
name-scoringmethod-miRNA-zscores.txt
5)
name-scoringmethod-DomainGraph.txt
Here,
“name” indicates the comparison file name from ExpressionBuilder, composed of
the species + platform_name + comparison_name (e.g. Hs_Exon_H9-CS-d40_vs_H9-ESC-d0), “scoringmethod”
is the type of algorithm used (e.g. SI) and the suffix indicates the type of
file.
The
annotation files used by AltAnalyze are pre-built using other modules with this
application (see Section 6). Although the user should not need to re-build these files on their own,
advanced users may wish to modify these tables manually or with programs provided
(see Section 6.7).
For
protein-level functional annotations (e.g., domain changes), this software
assumes that if an exon is up-regulated in a certain
condition, that the protein domain is also up-regulated and indicates it as
such. For example, for exon array data, if a probe set is up-regulated
(relative to gene constitutive expression) in an experimental group and this
domain is found in the protein aligning to this probe set, in the results file
this will be annotated as (+) domain. If the probe set were down-regulated (and aligns as indicated), this would be annotated as (-) domain.
Section 6 - Building AltAnalyze Annotation
Files
6.1 Splicing Annotations and Protein AssociationsA number of annotation files are built prior to
running AltAnalyze that are necessary for:
1)
Organizing exons
and introns from discrete transcripts into consistently ordered sequence blocks
(UCSCImport.py and EnsemblImport.py).
2)
Identifying
which exons and introns align to alternative annotations (alignToKnownAlt.py and EnsemblImport.py).
3)
Identifying
exons and junctions with likely constitutive annotations (EnsemblImport.py).
4)
Identifying
which probe sets align to which exons and introns (ExonArrayEnsemblRules.py).
5)
Extracting out
protein sequences with functional annotations (ExtractUniProtFunctAnnot.py and EnsemblSQL.py).
6)
Identifying features
that overlap with microRNA binding sites (MatchMiRTargetPredictions.py and ExonSeqModule.py).
7)
Matching feature
genomic coordinates to cDNA exon coordinates and identify the optimal matching and non-matching mRNA/protein for each feature (IdentifyAltIsoforms.py).
8)
Identifying
features that overlap with protein domains and UniProt motifs (ExonAnalyze_module.py, FeatureAlignment.py)
These annotation files are necessary for all exon and
junction analyses. Junction
analyses further require:
9)
Matching
reciprocal junctions to annotated exons or introns (JunctionArray.py, EnsemblImport.py and JunctionArrayEnsemblRules.py), creating a file analogous to (4) above.
10)
Matching reciprocal junction sequence to microRNA
binding sites (JunctionSeqSearch.py), creating a file analogous to (6) above.
11)
Matching junction sequence to cDNA sequences (mRNASeqAlign.py), prior to identification of optimal matching and
non-matching mRNA/proteins (IdentifyAltIsoforms.py).
With
the creation/update of these files, the user is ready to perform alternative
exon analyses for the selected species and platform. Since many of these analyses utilize genomic coordinate
alignment as opposed to direct sequence comparison, it is import to ensure that
all files were derived from the same genomic assembly.
Note: Although all necessary
files are available with the AltAnalyze program at installation and some files
can be updated automatically from the AltAnalyze server, users can use these
programs to adjust the content of these files, use the output for alternative
analyses, or create custom databases for currently unsupported species.
6.2 Building Ensembl-Exon Associations
Exon and Gene Arrays
The Affymetrix Exon 1.0 ST and Gene 1.0 ST arrays are
provided with probe set sequence, transcript cluster and probe set genomic
location from Affymetrix. Each of
these annotations is used by AltAnalyze to provide gene, transcript and exon
associations. Although transcript clusters represent putative genes, the
AltAnalyze pipeline derives new gene associations to Ensembl genes, so that
each probe set aligns to a single gene from a single gene database. This
annotation schema further allows AltAnalyze to determine which probe sets align
to defined exons regions (with external exon annotations), introns, and untranslated regions (UTR).
To
begin this process, Ensembl exons (each with a unique ID) and their genomic
location and transcript associations are downloaded for the most recent genomic
assembly using the AltAnalyze EnsemblSQL.py module, which parses various files on the Ensembl FTP
SQL database server to assemble the required fields. This file is saved to the directory “AltDatabase/ensembl/*species*/”
with the filename “*species*_Ensembl_transcript-annotations.txt". Since Ensembl transcript associations
are typically conservative, transcript associations are further augmented with
exon-transcript structure data from the UCSC genome database, from the file
“all_mrna.txt” (Downloads/*species*/Annotation database/all_mrna.txt.gz).
This file encodes genomic coordinates for exons in each transcript similar to
Ensembl. Transcript genomic
coordinates and genomic strand data from UCSC is matched to Ensembl gene
coordinates to identify genes that specifically overlap with Ensembl genes with
the Python program UCSCImport.py. Unique transcripts, with distinct exon structures
from Ensembl, are exported to the folder “AltDatabase/ucsc/*species*
“*species*_UCSC_transcript_structure_filtered_mrna.txt”, with the same
structure as the Ensembl_transcript-annotations file.
Once
both transcript-structure files have been saved to the appropriate directory, ExonArrayAffyRules.py calls the program EnsemblImport.py to perform the following steps:
1)
Imports these
two files, stores exon-transcript associations identify exon regions to exclude
from further annotations. These are exons that signify intron-retention
(overlapping with two adjacent spliced exons) and thus are excluded as valid
exon IDs. These regions are also
flagged as intron-retention regions for later probe set annotations.
2)
Assembles exons
from all transcripts for a gene into discrete exon clusters. If an exon cluster contains multiple
exons with distinct boundaries, the exon cluster is divided into regions that
represent putative alternative splice sites (Text S1 Figure 1). These splice
sites are explicitly annotated downstream. Each exon cluster is ordered and
number from the first to the last exon cluster (e.g., E1, E2, E3, E4, E5),
composed of one or more regions. These exon cluster/region coordinates and
annotations are stored in memory for downstream probe set alignment in the
module ExonArrayAffyRules.py.
3)
Identifies
alternative splicing events (cassette-exon inclusion, alternative 3’ or 5’
splice sites, alternative N-terminal and C-terminal exons, and combinations
there in) for all Ensembl and UCSC transcripts by comparing exon cluster and
region numbers for all pairs of exons in each transcript (see proceeding
sections for more information). Alternative exons/exon-regions and
corresponding exon-junctions are stored in memory for later probe set
annotation and exported to summary files for creation of databases for the
Cytoscape exon structure viewer, SubgeneViewer (currently in development).
4)
Constitutive
exon regions are defined by counting the number of unique Ensembl and UCSC mRNA
transcripts (based on structure) associated with each unique exon region. If
multiple exon regions have the same number of transcript associations, then
these are grouped. The grouped exon regions that contained the most transcripts
for the gene are defined as constitutive exon regions. If only one exon region
is considered constitutive then the grouped exon regions with the second
highest ranking (based on number of associated transcripts) are included as
constitutive (when there at least 3 ranks) (Figure 3.1).
Upon completion, ExonArrayAffyRules.py:
1)
Imports
Affymetrix Exon 1.0 ST probe sets genomic locations
and transcript cluster annotations from the Affymetrix probeset.csv annotation file (e.g., HuEx-1_0-st-v2.na23.hg18.probeset.csv). Note: prior to AltAnalzye 1.14, mRNA alignment count numbers were also
downloaded from this file to deduce constitutive probe sets. Although
transcript clusters will be disregarded at the end of the analysis, these are
used initially to group probe sets.
2)
Transcript
cluster genomic locations are matched to Ensembl genes genomic locations (gene start and stop) to
identify single transcript clusters that align to only one Ensembl gene for the
respective genomic strand. For transcript clusters aligning to more than one
Ensembl gene, coordinates for each individual probe set are matched to aligning
Ensembl genes, to identify unique matches. If multiple transcript clusters
align to a single Ensembl gene, only probe sets with an Affymetrix annotated
annotation corresponding to that Ensembl gene from the probeset.csv file, are stored as proper relationships. This ensures that if other genes, not
annotated by Ensembl exist in the same genomic interval, that they will not be
inaccurately combined with a nearby Ensembl gene. If multiple associations or other inconsistencies are found,
probe set coordinates are matched directly to the exon cluster locations
derived in EnsemblImport.py.
3)
Each probe sets is then aligned to exon clusters, regions, retained
introns, constitutive exon regions and splicing annotations for that gene. In
addition to splicing annotations from EnsemblImport.py, splicing annotations
from the UCSC genome annotation file “knownAlt.txt” (found in the same server
directory at UCSC as “all_mRNA.txt”) using the program alignToKnownAlt.py, are aligned to these
probe sets. If a probe set does not align to an Ensemblmport.py defined exon or intron
and is upstream of the first exon or downstream of the last exon, the probe set
is assigned a UTR annotation (e.g., U1.1). All aligning probe sets are
annotated based on the exon cluster number and the relative position of that
probe set in the exon cluster, based on relative 5’ genomic start (e.g., E2.1).
This can mean that probe set E2.1 actually aligns to the second exon cluster in
that gene in any of the exon regions (not necessarily the first exon region),
if it is the most 5’ aligning.
4)
These probe set annotations are exported to the directory “AltDatabase/*species*/exon” with the filename “*species* _Ensembl_probesets.txt”.
Exon block and region annotations for each probe set are designated in the
AltAnalyze result file.
For the
exon-junction AltMouse array, the same process is applied to the highlighted critical
exon(s) from all pre-determined reciprocal junction-pairs, exported by the
program ExonAnnotate_module.py. A highlighted critical exon is an exon
that is predicted to be regulated as the result of two
alternative junctions. For
example, if examining the exon-junctions E1-E2 and E1-E3, E2 would be the
highlighted critical exon. Alternatively, for the mutually-exclusive splicing event E2-E4 and E1-E3, E2 and E3 would be considered to be the
highlighted critical exons. To obtain the genomic locations of these exons,
sequences for each are obtained from a static build of the mouse AltMerge program (March 2002) (ExonAnalyze_module.py) and searched for in FASTA
formatted sequence obtained from BioMart for all
Ensembl genes with an additional 2 kb upstream and downstream sequence (JunctionArray.py and EnsemblImport.py). For both the AltMouse
and junction array, gene to Ensembl ID associations are obtained by comparing
gene symbol names and assigned accession numbers (GenBank for AltMouse and Ensembl for JAY) in common, as opposed to coordinate
comparisons.
For the
later generation HJAY and MJAY junction arrays, a similar strategy is employed
to the AltMouse to obtain genomic coordinates. First, probe set sequences are extracted
(exons and junctions) and searched for in the same FASTA formatted sequence
file downloaded for AltMouse probe set mapping. The same methods are employed
for both AltMouse and JAY array to obtain probeset genomic coordinates. This
allows for the export of an exon-coordinate file analogous to the exon probeset.csv file. As with the Affymetrix exon array
coordinate file, these genomic positions are aligned to AltAnalyze annotated
exon regions and annotations. For junction probe sets, the two exons that
compose the junction are individually mapped to exon regions (Figure 3.1). For
exon-junction alignments, if the 5’ junction exon aligns perfectly to the last
few base pairs of the exon and the 3’ junction exon aligns perfectly to the
first few bases, then the match is considered to be to the predicted junction.
If the match is not perfect, then new exon IDs are assigned to the
corresponding exons, reflecting the mismatching alignment genomic position.
Reciprocal probe
sets for the HJAY and MJAY arrays are determine using two approaches; 1)
AltAnalyze determined junction comparisons for all Ensembl and UCSC mRNA
isoforms (Figure 3.1), 2) comparison of Affymetrix assigned junctions exon cluster
ID annotations to find junction ends with common exon cluster IDs. Approach 1
is computed using the function “getJunctionComparisonsFromExport"
from JunctionArrayEnsemblRules.py and approach 2 is computed using the function “identifyJunctionComps" from JunctionArray.py. The resulting relationships are stored in “AltDatabase/EnsMart55/Mm/junction/Mm_junction_comps_updated.txt”
(or relevant species and database version). This approach allows us to capture
known alternative splicing events, based on mRNA evidence (approach 1) and
putative alternative splicing events predicted by Affymetrix (approach 2). This
does not currently capture all alternative splicing events and alternative
exons, since some junctions do not share an exon cluster ID, but do reflect
alternative modes of transcript regulation. To further detect such events, all
exon and junction data is also stored for later individual probeset analysis
using algorithms previously reserved for the exon and gene arrays (e.g.,
splicing-index, MiDAS, FIRMA).
Constitutive exon-junction probe sets
are for the HJAY and MJAY arrays are currently determined by two methods; 1)
determine if both of the exons in the junction are annotated as constitutive
and 2) determine if the junction is annotated as a putative ‘alternative’ probe
set according to the manufacturer annotation file (e.g., mjay.r2.annotation_map).
If both lines of evidence indicate the probeset is constitutive, then the
probeset is annotated accordingly. Probe set annotations are provided in
“AltDatabase/EnsMart55/Hs/junction/Hs_Ensembl_probesets.txt” (modify based on
species and Ensembl version). This functionality is executed in the JunctionArray.py function “identifyJunctionComps”
and the JunctionArrayEnsemblRules.py function “importAndReformatEnsemblJunctionAnnotations”.
The highlighted critical exons associated
with reciprocal junctions are used for annotation of the associated reciprocal
exon(s). In fact, these critical exons are the ones examined for assigning an
alternative exon annotation to reciprocal junctions (e.g,
alternative cassette-exon), Ensembl genomic domain overlap and microRNA binding
site targets. For Ensembl genomic domain overlap (FeatureAlignment.py), the exon genomic coordinates are used as opposed
to the junction coordinates. When aligning identified miRNA target sequences,
all Ensembl and UCSC identified exon sequences, identified during the database
build procedure discussed in previous sections, are extracted from Ensembl
chromosome FASTA files using the function getFullGeneSequences() in the module EnsemblSQL.py, stored in a temporary file and used as surrogates
for exon array probe set sequences in JunctionSeqSearch.py.
RNA-Seq Analyses
The AltAnalyze gene database for RNASeq database is built using many of the same modules as those used for junction
arrays. Instead of initially aligning known features to exons and junctions, as
with the junction microarrays, all known Ensembl/UCSC extracted exons,
junctions, reciprocal junctions, alternative exons, constitutive exons are exported by EnsemblImport.py. and stored in the format
as probe set aligned annotations. For example, instead of the file
Hs_Ensembl_probesets.txt, the files Hs_Ensembl_exons.txt and
Hs_Ensembl_junctions.txt are exported to the RNASeq database directory. Rather than unique probe set identifiers, unique IDs for
each exon and junction are stored in the format Ensembl-gene:ExonID and Ensembl-gene:5primeExonID-3primeExonID,
respectively. The exon IDs are the exon region IDs in
the format E1.3-E2.1. Associated Ensembl/UCSC exon annotations and genomic
coordinates are stored with each exon and junction ID. These identifiers are
treated the same as junction array probe set IDs and analyzed using the same
modules outlined above for mRNA isoform alignment (mRNASeqAlign.py), optimal reciprocal protein isoform identification
(IdentifyAltIsoforms.py), alternative domain (ExonAnalyze_module.py, FeatureAlignment.py) and miRNA binding site identification (MatchMiRTargetPredictions.py, JunctionSeqSearch.py).
Just as with junction arrays, AltAnalyze exon region sequence and genomic
locations, for the critical exons associated with reciprocal junctions, are
used to identify overlapping miRNA targets and genomic overlapping InterPro
domains. For single-junction analyses (e.g., splicing-index and MiDAS), these annotations are built separately using the
same pipeline for individual junctions rather than reciprocal junctions, in a
similar manner to junction arrays. These single junction annotations are stored
to the sub-folder “junction” in the RNASeq directory.
When
a users begins an AltAnalyze analysis on their experimental RNA-Seq input files,
some of the above analyses are repeated to build an augmented, experiment
specific set of AltAnalyze databases that include de novo identified junctions, exons, reciprocal junction-pairs and
alternative exon annotations. Several files normally found in the AltAnalyze
program directory, AltDatabase/EnsMart<version>/<species>/RNASeq folder are stored to this
same path, in the user designated output directory. Thus, analyses can be
easily repeated at any step using these compiled dataset specific annotations.
Only identified exon and junction annotations are included in these databases
to maximize performance. These analyses are directed by the
module RNASeq.py.
In this module, the functionalignExonsAndJunctionsToEnsembl() extracts genomic locations of the identified junction splice sites (5’ and 3’) and exons, aligns these to Ensembl junctions genes, exons and introns and known splice sites. If both junction
splice-sites are found in the AltAnalyze database, those junction and
associated exon entries are migrated to the user database. If the junctions or
splice-sites are novel, new identifiers are created indicating which
exon/intron region the splice-site is found in with the splice-site genomic
location indicated in the exon/junction name (e.g.,
ENSMUSG00000019715:I2.1_29554605). If the two junction splice-sites align to
two different genes, the junction is annotated as trans-splicing. For any novel
identified exon junctions, reciprocal junction analysis is re-performed using
the function compareJunctions() in EnsemblImport.py to identify novel reciprocal junctions to be used during ASPIRE or Linear
Regression analyses. The number of novel junctions and trans-splicing events is
reported during BED file import. When an exon coordinates and read counts are present, these coordinates are aligned to Ensembl genes, exon regions and introns. Novel exons that overlap with an Ensembl gene are included.
6.3 Extracting UniProt Protein Domain Annotations Overview The UniProt protein database is a
highly curated protein database that provides annotations for whole proteins
as well as protein segments (protein features or domains). These protein
feature annotations correspond to specific amino acid (AA) sequences that are
annotated using a common vocabulary, including a class (feature key) and
detailed description field.An example
is the TCF7L1 protein (http://www.uniprot.org/uniprot/Q9HCS4),
which has five annotated feature regions, ranging in size from 7 to 210 AA.
One of these regions has the feature key annotation "DNA
binding" and the description "HMG box".To utilize
these annotations in AltAnalyze, these functional tags are extracted along
with full protein sequence, and external
annotations for each protein (e.g., Ensembl gene) from the "uniprot_sprot_*taxonomy*.dat"
file using the ExtractUniProtFunctAnnot.py program. FTP file locations for the UniProt database file
can be found in the file "Config/Default-file.csv"
for each supported species. To improve Ensembl-UniProt annotations, these
relationships are also downloaded from BioMart and stored in the folder
"AltDatabase/uniprot/*species*" as "*species*_Ensembl-UniProt.txt", which are
gathered at ExtractUniProtFunctAnnot.py runtime to include in the UniProt sequence annotation
file. These files are saved to "AltDatabase/uniprot/*species*" as "uniprot_feature_file.txt" and "uniprot_sequence.txt". 6.4 Extracting Ensembl Protein Domain Annotations Overview In
addition to protein domains/features extracted from UniProt, protein features
associated with specific Ensembl transcripts are extracted from the Ensembl
database. One advantage of these annotations over UniProt, is that alternative
exon changes that alter the sequence of a feature but not its inclusion will be
reported as a gain and loss of the same feature, as opposed to just one with
UniProt. This is because protein
feature annotations in UniProt only typically exist for one isoform of a gene
and thus, alteration of this feature in any way will result in this feature
being called regulated. Although an Ensembl annotated feature with a reported
gain and loss can be considered not changed at all, functional differences can
exist do to a minor feature sequence change that would not be predicted if the
gain and loss of the feature were not reported.
Three
separate annotation files are built to provide feature sequences and
descriptions, “Ensembl_Protein”, “Protein”, and “ProteinFeatures” files (“AltDatabase/ensembl/*species*”). The “ProteinFeatures”
contains relative AA positions for protein features for all Ensembl protein
IDs, genomic start and end locations along with InterPro annotations and IDs. Only those InterPro domains/features with an
alignment e-score < 1 are stored for alignment to regulated exons. The “Protein”
file contains AA sequences for each Ensembl protein. The “Ensembl_Protein "
provides Ensembl gene, transcript and protein ID associations. Data for these
files are downloaded and extracted using the previously mentioned EnsemblSQL.py module. The feature annotation source in these
files is InterPro, which provides a description similar to UniProt. As an
example, see: http://ensembl.genomics.org.cn/Homo_sapiens/protview?db=core;peptide=ENSP00000282111,
which has similar feature descriptions to UniProt for the same gene, TCF7L1.
6.5 Extracting microRNA Binding Annotations Overview
To
examine the potential gain or loss of microRNA bindings sites as the direct
result of exon-inclusion or exclusion, AltAnalyze requires putative microRNA
sequences from multiple prediction algorithms. These binding site annotations
are extracted from the following flat files:
Ensembl
gene to microRNA name and sequence are stored for all prediction algorithm flat
files and directly compared to find genes with one or more lines of microRNA
binding site evidence using the program MatchMiRTargetPredictions.py. The flat
file produced from this program (“combined_gene-target-sequences.txt”) was used
by the program ExonSeqSearch.py to search for
these putative microRNA binding site sequences among all probe sets from the
“*species*_Ensembl_probeset.txt” file built by ExonArrayEnsemblRules.py and probe set
sequence from the Affymetrix 1.0 ST probe set FASTA sequence file (Affymetrix)
or the reciprocal junction critical exon sequence file (see section 6.2). For
gene arrays, NetAffx currently only provides probe
sequences, thus, probe set sequences from the exon array are used for probe set
found to overlap in genomic space. Two resulting files, one with any binding
site predictions and another required to have evidence from at least two
algorithms, are saved to “AltDatabase/*species*/*array_type*/” as “*species*_probe set_microRNAs_any.txt”
and “*species*_probe set_microRNAs_multiple.txt”, respectively.
6.6 Inferring Protein-Exon Associations Overview To
obtain associations between specific exons, junctions or probe sets to proteins, the
programs IdentifyAltIsoforms.py (exon and
junction platforms) and ExonSeqModule.py (junction platforms
only) were written. The program IdentifyAltIsoforms.py grabs all
gene mRNA transcripts and associated exon genomic coordinates from Ensembl and
UCSC and compares these to probe set coordinates (or critical exon for
junction-sensitive platforms) to find pairs of transcripts (one containing the
probe set or critical exon and the other not) that have the least number of
differing exons (see Section 3.4). These transcript pairs are thus most likely
to be similar with exception to the region containing the probe set or critical
exon. Once these best matches are
identified, corresponding protein sequences for each mRNA are downloaded from
Ensembl using EnsemblSQL.py or are
downloaded using NCBI webservices via BioPython’sEntrez function.
If
protein sequences are unavailable for an mRNA accession, the mRNA sequence for
that identifier is downloaded (using the above mentioned services) and
translated using the custom IdentifyAltIsoforms.py function “BuildInSilicoTranslations”, which uses functions from
the BioPython module to translate an mRNA based on
all possible start and stop sites to identify the longest putative translation
that also shares either the first or last 5 AA of its sequence with the
N-terminus or C-terminus (respectfully) of a UniProt protein.
While
this same protocol is used for junction-sensitive platforms, prior to this
analysis reciprocal junctions are mapped to all possible aligning and
non-aligning transcripts through direct junction sequence comparison via mRNASeqAlign.py. This module takes the junction sequence
for all reciprocal junction-pairs and searches for a match among mRNA
transcript FASTA formatted sequences from Ensembl and UCSC mRNAs that
correspond to that gene. Only a 100% sequence match is allowed, (matches may
not occur do to polymorphisms between sequence sources and genomic assemblies). These associations are then used by IdentifyAltIsoforms.py, as described
above.
At
this point, only two mRNAs are matched toeach exon, junction, probe set or junction-pair, matching mRNAs. Next, differences in protein feature composition
between these two proteins, alternative N or C-terminal sequences, coding
sequence and protein length are assessed using ExonAnalyze_module.py and exported to text
files (associations and protein sequences) for import when AltAnalyze is run.
These two files have the suffix “exoncomp.txt”. Two analogous files with the
suffix “seqcomp.txt”, are derived by identifying the best matching and
non-matching mRNAs based on comparison of protein sequence as compared exon
composition (e.g. least differences in protein domain composition), but are
currently used only for internal analyses (contact the authors for the seqcomp files to replace exoncomp). The genomic locations of critical
exons associated with these reciprocal junctions are used to identify direct
and indirect overlapping InterPro domains from Ensembl in the function FeatureAlignment.py.
6.7 Required Files for Manual Update For
advanced users and developers that wish to build databases outside of
AltAnalyze’s normal release cycle or build custom database installations,
several built-in, automated tools are available to build AltAnalyze databases.
These functions are accessible from command-line flags in AltAnalyze. In
AltAnalyze 2.0, we have tried to increase ease and consistency in which these databases
are built, by building new methods to automatically download and extract
necessary external databases from resource FTP and HTTP servers, where
accessible. Although several external databases are required for the above
build strategies, the nearly all of these can be automatically downloaded by
AltAnalyze during the automated build processes. The exception to this rule is Affymetrix annotation files, which cannot be downloaded
without logging into the Affymetrix support site and
downloading these manually. These files include the Affymetrix probeset.csv annotaiton files and probeset FASTA sequence files. Current and
details on updating and customizing the AltAnalyze databases can be found at:
http://code.google.com/p/altanalyze/wiki/BuildingDatabases Section 7 - Evaluation of AltAnalyze Predictions To
assess AltAnalyze exon array analysis performance relative to other published
approaches, we analyzed published experimental confirmation results for a dataset
of splicing factor knockdown (mouse polypyrimidine tract binding protein
(PTB) short-hairpin RNA (shRNA)). From two independent analyses [11,12], alternative splicing (AS) for 109 probe sets was
assessed in the mouse PTB shRNA dataset by RT-PCR (Supplemental Tables 1,2
and 4 from the referenced study) [12]. Among these, 25 were false positives, one was a
true negative, one undetermined and 81 were true positives. Summary
of Published MADS Results In
the analysis by Xing et al., alternative exons discovered by multiple
splicing array platforms and RT-PCR were examined using a new algorithm named
microarray analysis of differential splicing or MADS. MADS implements a
modification of the splicing index method on gene expression values obtained
using multiple scripts from GeneBase and filtering of probes predicted to
hybridize to multiple genomic targets. Using this algorithm, the authors were
able to verify AS detected by RT-PCR of mouse Affymetrix Exon 1.0 array data
as well as predict and validate 27 novel splicing events by RT-PCR. Using the
microarray CEL files posted by Xing and colleagues, we ran AltAnalyze using
default options (same as used in the corresponding primary report), which
includes quantile normalization via RMA-sketch using AltAnalyze's interface
to Affymetrix Power Tools. AltAnalyze
Results
Of
the 78 analyzed probe sets, 26 were called by AltAnalyze to be alternatively
regulated (using default parameters), out of 194 probe sets called by
AltAnalyze as alternatively regulated. All 26 probe sets were annotated as true
positives according to the published RT-PCR data. Although 17 probe sets were
RT-PCR false positives among the 78 probe sets with RT-PCR data, only one false
positive was considered to be alternative regulated by AltAnalyze with any of
the AltAnalyze filters alone (splicing-index p<0.05 alone without additional
default options). The MADS algorithm was able to validate AS for 27 novel
splice events corresponding to 41 probe sets. Of these 41 probe sets, 33 were examined by AltAnalyze and 23 were considered
alternatively regulated.
Conclusions This analysis suggests that
AltAnalyze analysis using default parameters produces conservative results
with high specificity (100% true positives in this analysis) with reasonable
sensitivity(~42% that of MADS). For
smaller datasets, such as the PTB knock-down comparison, the decreased
sensitivity results will have a significant impact on the number of true
splicing events detected, however, for larger datasets with thousands of
regulated probe sets, AltAnalyze is likely to reduce the number of false
positives and reduction in overall noise. It is important to note however,
for the MADS analysis only a p-value threshold was used and for AltAnalyze,
both a MiDAS and splicing-index p-value in addition to splicing-index fold
change thresholds were used. For these analyses, we have not filtered probe
sets based on association with annotated splicing events (see Materials and
Methods for description), which should further decrease false positives. If
probe sets with annotated splicing events are filtered out, 20 versus 26 true
positives will remain. Although a false positive rate of
up-to 50% has been reported with the conventional splicing-index
implementation (e.g., using the Affymetrix ExACT software) [12],
AltAnalyze's analysis differs in several ways. First, in this analysis RMA-sketch was used
as the method for quantile normalization. After obtaining expression values
for probe sets (no low level filtering currently implemented), probe sets for
each of the two biological groups are filtered based on two main parameters:
DABG p-value and mean expression filters. Any probe set with a DABG p-value
> 0.05 in both biological groups or mean expression < 70 are excluded. Additional AltAnalyze specific
parameters relate to how probe set to gene associations are obtained, which
probe sets are selected for analysis and how probe sets are selected for
calculation of gene expression. Unlike ExACT, probe set to gene association
are via genomic coordinate alignment to Ensmebl/UCSC mRNA transcripts for
unique Ensembl genes rather than to Affymetrix transcript clusters. This
process ensures that a probe set only aligns to one Ensembl gene. Probe sets
can align to an exon, intron or UTR of a gene. Any probe set aligning to an
analyzed mRNA is used for analysis in the AltAnalyze "core" set along with any
Affymetrix annotated "core" probe set. To determine gene expression from the
exon-level a two-step method is employed. First, probe sets that are most
over-represented among mRNAs or mRNAs and ESTs (associations from Affymetrix
probe set annotation file) are selected as constitutive. Next if constitutive
probe set is "expressed" in both biological groups (using the DABG and mean
expression filters listed above), the probe set is retained for constitutive
expression calculation. If more than one "expressed" constitutive probe set
is present, the mean expression of all constitutive probe sets for each array
is calculated.As a final step, probe
sets with an associated gene expression difference between the two array
groups greater than 3 are not reported. These analysis steps result in a
unique splicing-index, splicing-index t
test p-value and MiDAS p-value calculation from other analysis methods. Since this validation set provides
a limited test case for analysis of type I (false positive) and type II (false
negative) errors, the AltAnalyze algorithms will continue to be assessed as
additional validation data is made available. Future implementations will
likely include "low-level" analyses that reduce the occurrence of type I
errors (e.g., elimination of expression data for specific probes that
introduce additional noise). However, this data supports the concept that
AltAnalyze produces conservative results with a level of confidence.
Section
8 - Analysis of AltAnalyze Results DomainGraph
Once alternative probe
sets have been identified from an AltAnalyze exon array analysis, you can
easily load this data in the Cytoscape plugin DomainGraph (http://domaingraph.bioinf.mpi-inf.mpg.de/) to:
1)
View prioritized AltAnalyze highlighted alternative exons and
annotations.
2)
Assess which probe sets overlap with a set of loaded genes and which
specific protein domains at a high-level (protein/domain/miRNA binding stie network/pathway) and low-level (domain/exon/probe set
view).
3)
View alternative
probe set data in the context of WikiPathways and Reactome gene networks.
Installing DomainGraph with AltAnalyze
Running DomainGraph
Detailed instructions on
running DomainGraph can be found here:
http://www.altanalyze.org/domaingraph.htm
and at:
http://domaingraph.bioinf.mpi-inf.mpg.de/
or in the AltAnalyze
application folder:
AltAnalyze_v1release/Documentation/domain_graph.pdf
Platform Compatibility
The DomainGraph database
is designed to specifically work with Affymetrix Exon 1.0 arrays, however,
AltAnalyze exports files for the Affymetrix Gene 1.0,
HJAY and MJAY arrays as well as human, mouse and rat RNA-Seq results that allow for visualization of these results as well. In order to
accomplish this, non-exon array probe set IDs are converted to exon array probe
set IDs. This translation is performed by matching the optimally aligning exons, junctions or probe sets using the AltAnalyze exon block/region annotations and probe set genomic positions. For reciprocal
probeset junction array analyses (ASPIRE or Linear Regression), matching is to
the predicted alternative exon rather than the reciprocal junctions.
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MS, et al. (2007) Integration of
biological networks and gene expression data using Cytoscape. Nat Protoc 2(10):2366-2382.
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TJ, et al. (2007) Ensembl 2007. Nucleic Acids Res 35(Database
issue):D610-617.
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D, et al. (2008) The UCSC Genome
Browser Database: 2008 update. Nucleic
Acids Res 36(Database issue):D773-779.
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N, et al. (2007) GenMAPP 2: new
features and resources for pathway analysis. BMC Bioinformatics 8:217.
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and exploring biological pathways with PathVisio. BMC Bioinformatics 9:399.
6. Srinivasan
K, et al. (2005) Detection and
measurement of alternative splicing using splicing-sensitive microarrays. Methods 37(4):345-359.
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PJ, et al. (2006) Alternative
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CW, et al. (2006) Unusual intron
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PL, et al. (2007) A
post-transcriptional regulatory switch in polypyrimidine tract-binding proteins
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Y, et al. (2008) MADS: a new and improved
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