lchrodinger / rmats-turbo

rMATS turbo v4.1.0

About

rMATS turbo is the C/Cython version of rMATS (refer to http://rnaseq-mats.sourceforge.net). The major difference between rMATS turbo and rMATS is speed and space usage. rMATS turbo is 100 times faster and the output file is 1000 times smaller than rMATS. These advantages make analysis and storage of a large scale dataset easy and convenient.

	Counting part	Statistical part
Speed (C/Cython version vs Python version)	20~100 times faster (one thread)	300 times faster (6 threads)
Storage usage (C/Cython version vs Python version)	1000 times smaller	-

Table of contents

• Dependencies
• Build
• Test
• Usage
  • Examples
    • Starting with FASTQ files
    • Starting with BAM files
    • Running prep and post separately
    • Using the paired stats model
  • Tips
  • All arguments
• Output

Dependencies

Tested with

• Python (either 2.7 or 3.6)
  • Cython (0.29.14)
  • numpy (1.16.5)
• BLAS, LAPACK
• GNU Scientific Library (GSL 2.5)
• GCC (5.4.0)
• gfortran (Fortran 77)
• CMake (3.15.4)
• PAIRADISE (optional)
• Samtools (optional)
• STAR (optional)

Build

If the required dependencies are already installed, then rMATS can be built with:

./build_rmats

And then run with:

python rmats.py {arguments}

The build_rmats script usage is:

./build_rmats [--conda] [--no-paired-model]

--conda: create a conda environment for Python and R dependencies
--no-paired-model: do not install dependencies for the paired model

With --conda build_rmats installs a conda environment that satisfies the required Python dependencies and also the R dependencies needed to use the paired model (PAIRADISE). The Python dependencies are listed in python_requirements.txt and the R dependencies are handled using install_r_deps.R after cloning the PAIRADISE git repo.

run_rmats is a wrapper to call rmats.py with the conda environment used by build_rmats. It also sources setup_environment.sh which can be modified to handle other setup that might be needed before running rmats (such as Environment Modules).

If rMATS was built with ./build_rmats --conda then it should be run with:

./run_rmats {arguments}

Test

test_rmats creates a conda environment and uses run_rmats to run the automated tests in tests/

Usage

Examples

Starting with FASTQ files

Suppose there are 2 sample groups with 2 sets of paired read (R1, R2) FASTQ files per group. (fastq.gz files can also be used)

• group 1 FASTQs
  • /path/to/1_1.R1.fastq
  • /path/to/1_1.R2.fastq
  • /path/to/1_2.R1.fastq
  • /path/to/1_2.R2.fastq
• group 2 FASTQs
  • /path/to/2_1.R1.fastq
  • /path/to/2_1.R2.fastq
  • /path/to/2_2.R1.fastq
  • /path/to/2_2.R2.fastq

Create txt files that will be used to pass this grouping of inputs to rMATS. The expected format is : to separate paired reads and , to separate replicates.

• /path/to/s1.txt

/path/to/1_1.R1.fastq:/path/to/1_1.R2.fastq,/path/to/1_2.R1.fastq:/path/to/1_2.R2.fastq

• /path/to/s2.txt

/path/to/2_1.R1.fastq:/path/to/2_1.R2.fastq,/path/to/2_2.R1.fastq:/path/to/2_2.R2.fastq

Details about the remaining arguments are discussed in All arguments

run rMATS on this input with:

python rmats.py --s1 /path/to/s1.txt --s2 /path/to/s2.txt --gtf /path/to/the.gtf --bi /path/to/STAR_binary_index -t paired --readLength 50 --nthread 4 --od /path/to/output --tmp /path/to/tmp_output

rMATS will first process the FASTQ input into BAM files stored in the --tmp directory. Then the splicing analysis will be performed.

Starting with BAM files

Reads can be mapped independently of rMATS with any aligner and then the resulting BAM files can be used as input to rMATS.

Suppose there are 2 sample groups with 2 BAM files per group.

• group 1 BAMs
  • /path/to/1_1.bam
  • /path/to/1_2.bam
• group 2 BAMs
  • /path/to/2_1.bam
  • /path/to/2_2.bam

Create txt files that will be used to pass this grouping of inputs to rMATS. The expected format is , to separate replicates.

• /path/to/b1.txt

/path/to/1_1.bam,/path/to/1_2.bam

• /path/to/b2.txt

/path/to/2_1.bam,/path/to/2_2.bam

Details about the remaining arguments are discussed in All arguments

run rMATS on this input with:

python rmats.py --b1 /path/to/b1.txt --b2 /path/to/b2.txt --gtf /path/to/the.gtf -t paired --readLength 50 --nthread 4 --od /path/to/output --tmp /path/to/tmp_output

Running prep and post separately

rMATS analysis has two steps, prep and post. In the prep step, the input files are processed and a summary is saved to a .rmats file in the --tmp directory. That .rmats file tracks info from each BAM separately according to the full path of the BAM specified in the input .txt file. In the post step, one or more .rmats files are read and the final output files are created.

The --task argument allows the prep step of rMATS to be run independently for different subsets of input BAM files. Then the post step can be run on the independently generated .rmats files. This allows the computation to be run at different times and/or on different machines.

Suppose we have 8 BAMs and two machines that each have 4 CPU threads. Each machine can run the prep step on 4 BAMs concurrently. Then the post step can be run on one of the machines.

Split the BAMs into two groups. The assignment of BAMs to prep steps does not restrict the choice of --b1 and --b2 for a later post step.

• /path/to/prep1.txt

/path/to/1.bam,/path/to/2.bam,/path/to/3.bam,/path/to/4.bam

• /path/to/prep2.txt

/path/to/5.bam,/path/to/6.bam,/path/to/7.bam,/path/to/8.bam

On machine 1 run the prep step with prep1.txt:

python rmats.py --b1 /path/to/prep1.txt --gtf /path/to/the.gtf -t paired --readLength 50 --nthread 4 --od /path/to/output --tmp /path/to/tmp_output_prep_1 --task prep

On machine 2 run the prep step with prep2.txt:

python rmats.py --b1 /path/to/prep2.txt --gtf /path/to/the.gtf -t paired --readLength 50 --nthread 4 --od /path/to/output --tmp /path/to/tmp_output_prep_2 --task prep

Split the BAMs into two groups. This split is for statistically comparing the two groups and does not need to reflect the split used in the prep steps

• /path/to/post1.txt

/path/to/1.bam,/path/to/3.bam,/path/to/8.bam

• /path/to/post2.txt

/path/to/2.bam,/path/to/4.bam,/path/to/5.bam,/path/to/6.bam,/path/to/7.bam

Copy the .rmats files from the separate prep steps to a directory so that the post step can access all the prep data. The filenames have the format {datetime}.rmats and the filenames may conflict for prep steps run concurrently. The script cp_with_prefix.py is provided to disambiguate the .rmats filenames when copying to a shared directory:

python cp_with_prefix.py prep_1_ /path/to/tmp_output_post/ /path/to/tmp_output_prep_1/*.rmats
python cp_with_prefix.py prep_2_ /path/to/tmp_output_post/ /path/to/tmp_output_prep_2/*.rmats

On machine 1 run the post step:

python rmats.py --b1 /path/to/post1.txt --b2 /path/to/post2.txt --gtf /path/to/the.gtf -t paired --readLength 50 --nthread 4 --od /path/to/output --tmp /path/to/tmp_output_post --task post

Using the paired stats model

The default statistical model considers the samples to be unpaired. The --paired-stats flag can be used if each entry in --b1 is matched with its pair in --b2. As an example, if there are three replicates where each replicate has paired "a" and "b" data, then b1.txt and b2.txt should look like:

• /path/to/b1.txt

/path/to/pair_1_a.bam,/path/to/pair_2_a.bam,/path/to/pair_3_a.bam

• /path/to/b2.txt

/path/to/pair_1_b.bam,/path/to/pair_2_b.bam,/path/to/pair_3_b.bam

The --paired-stats flag can then be given so that the paired statistical model is used instead of the default unpaired model.

Tips

• The statistical comparison between the two input sample groups can be skipped with --statoff. It is also possible to use a single sample group (only --b1 or --s1) when using --statoff.
• The number of replicates can differ between sample 1 and sample 2 (example: 3 BAMs in --b1 and 1 BAM in --b2)
• fastq.gz files can be used with --s1, --s2. They will be automatically detected based on the .gz extension and decompressed
• A cluster environment can be utilized to run many prep steps concurrently and when the prep steps are finished a single post step can be run.
• When splitting the computation using --task {prep, post}, rMATS will consider all .rmats files in the --tmp directory when running the post step. The .rmats files from multiple prep steps can be copied to a shared location for running the post step. Replacing --task post in the command line that is going to be used for the post step with --task inte will perform an integrity check to verify that the BAM filenames in --b1 and --b2 match 1-to-1 with the BAM filenames recorded in the .rmats files in --tmp.
• The .rmats filenames from concurrently run prep steps may conflict. The script cp_with_prefix.py is provided to disambiguate the .rmats filenames when copying to a shared directory.
• The full path of the BAM files given in --b1 and --b2 for the prep step must match the full paths given in the post step. Otherwise the lookup into the .rmats file(s) will fail. As an example, if the full /path/to/1.bam is used in the prep step, a relative path of just 1.bam cannot be used in the post step.
• If analyzing a small data set, --task both can be used to perform the prep and post steps in a single run.
• --novelSS is an experimental feature that allows splicing events to be detected that involve an unannotated splice site.

All Arguments

python rmats.py -h

usage: rmats.py [options]

optional arguments:
  -h, --help            show this help message and exit
  --version             show program's version number and exit
  --gtf GTF             An annotation of genes and transcripts in GTF format
  --b1 B1               A text file containing a comma separated list of the
                        BAM files for sample_1. (Only if using BAM)
  --b2 B2               A text file containing a comma separated list of the
                        BAM files for sample_2. (Only if using BAM)
  --s1 S1               A text file containing a comma separated list of the
                        FASTQ files for sample_1. If using paired reads the
                        format is ":" to separate pairs and "," to separate
                        replicates. (Only if using fastq)
  --s2 S2               A text file containing a comma separated list of the
                        FASTQ files for sample_2. If using paired reads the
                        format is ":" to separate pairs and "," to separate
                        replicates. (Only if using fastq)
  --od OD               The directory for final output
  --tmp TMP             The directory for intermediate output such as ".rmats"
                        files from the prep step
  -t {paired,single}    Type of read used in the analysis: either "paired" for
                        paired-end data or "single" for single-end data.
                        Default: paired
  --libType {fr-unstranded,fr-firststrand,fr-secondstrand}
                        Library type. Use fr-firststrand or fr-secondstrand
                        for strand-specific data. Default: fr-unstranded
  --readLength READLENGTH
                        The length of each read
  --variable-read-length
                        Allow reads with lengths that differ from --readLength
                        to be processed. --readLength will still be used to
                        determine IncFormLen and SkipFormLen
  --anchorLength ANCHORLENGTH
                        The anchor length. Default is 1
  --tophatAnchor TOPHATANCHOR
                        The "anchor length" or "overhang length" used in the
                        aligner. At least "anchor length" NT must be mapped to
                        each end of a given junction. The default is 6. (Only
                        if using fastq)
  --bi BINDEX           The directory name of the STAR binary indices (name of
                        the directory that contains the SA file). (Only if
                        using fastq)
  --nthread NTHREAD     The number of threads. The optimal number of threads
                        should be equal to the number of CPU cores. Default: 1
  --tstat TSTAT         The number of threads for the statistical model.
                        Default: 1
  --cstat CSTAT         The cutoff splicing difference. The cutoff used in the
                        null hypothesis test for differential splicing. The
                        default is 0.0001 for 0.01% difference. Valid: 0 <=
                        cutoff < 1. Does not apply to the paired stats model
  --task {prep,post,both,inte}
                        Specify which step(s) of rMATS to run. Default: both.
                        prep: preprocess BAMs and generate a .rmats file.
                        post: load .rmats file(s) into memory, detect and
                        count alternative splicing events, and calculate P
                        value (if not --statoff). both: prep + post. inte
                        (integrity): check that the BAM filenames recorded by
                        the prep task(s) match the BAM filenames for the
                        current command line
  --statoff             Skip the statistical analysis
  --paired-stats        Use the paired stats model
  --novelSS             Enable detection of novel splice sites (unannotated
                        splice sites). Default is no detection of novel splice
                        sites
  --mil MIL             Minimum Intron Length. Only impacts --novelSS
                        behavior. Default: 50
  --mel MEL             Maximum Exon Length. Only impacts --novelSS behavior.
                        Default: 500

Output

Each alternative splicing event type has a corresponding set of output files. In the filename templates below [AS_Event] is replaced by one of [SE (skipped exon), MXE (mutually exclusive exons), A3SS (alternative 3' splice site), A5SS (alternative 5' splice site), RI (retained intron)] for the event specific filename.

--od contains the final output files:

• [AS_Event].MATS.JC.txt: Final output including only reads that span junctions defined by rmats (Junction Counts)
• [AS_Event].MATS.JCEC.txt: Final output including both reads that span junctions defined by rmats (Junction Counts) and reads that do not cross an exon boundary (Exon Counts)
• fromGTF.[AS_Event].txt: All identified alternative splicing (AS) events derived from GTF and RNA
• fromGTF.novelJunction.[AS_Event].txt: Alternative splicing (AS) events which were identified only after considering the RNA (as opposed to analyzing the GTF in isolation). This does not include events with an unannotated splice site.
• fromGTF.novelSpliceSite.[AS_Event].txt: This file contains only those events which include an unannotated splice site. Only relevant if --novelSS is enabled.
• JC.raw.input.[AS_Event].txt: Event counts including only reads that span junctions defined by rmats (Junction Counts)
• JCEC.raw.input.[AS_Event].txt: Event counts including both reads that span junctions defined by rmats (Junction Counts) and reads that do not cross an exon boundary (Exon Counts)
• Shared columns:
  • ID: rMATS event id
  • GeneID: Gene id
  • geneSymbol: Gene name
  • chr: Chromosome
  • strand: Strand of the gene
  • IJC_SAMPLE_1: Inclusion counts for sample 1. Replicates are comma separated
  • SJC_SAMPLE_1: Skipping counts for sample 1. Replicates are comma separated
  • IJC_SAMPLE_2: Inclusion counts for sample 2. Replicates are comma separated
  • SJC_SAMPLE_2: Skipping counts for sample 2. Replicates are comma separated
  • IncFormLen: Length of inclusion form, used for normalization
  • SkipFormLen: Length of skipping form, used for normalization
  • PValue: Significance of splicing difference between the two sample groups. (Only available if the statistical model is on)
  • FDR: False Discovery Rate calculated from p-value. (Only available if statistical model is on)
  • IncLevel1: Inclusion level for sample 1. Replicates are comma separated. Calculated from normalized counts
  • IncLevel2: Inclusion level for sample 2. Replicates are comma separated. Calculated from normalized counts
  • IncLevelDifference: average(IncLevel1) - average(IncLevel2)
• Event specific columns (event coordinates):
  • SE: exonStart_0base exonEnd upstreamES upstreamEE downstreamES downstreamEE
    • The inclusion form includes the target exon (exonStart_0base, exonEnd)
  • MXE: 1stExonStart_0base 1stExonEnd 2ndExonStart_0base 2ndExonEnd upstreamES upstreamEE downstreamES downstreamEE
    • If the strand is + then the inclusion form includes the 1st exon (1stExonStart_0base, 1stExonEnd) and skips the 2nd exon
    • If the strand is - then the inclusion form includes the 2nd exon (2ndExonStart_0base, 2ndExonEnd) and skips the 1st exon
  • A3SS, A5SS: longExonStart_0base longExonEnd shortES shortEE flankingES flankingEE
    • The inclusion form includes the long exon (longExonStart_0base, longExonEnd) instead of the short exon (shortES shortEE)
  • RI: riExonStart_0base riExonEnd upstreamES upstreamEE downstreamES downstreamEE
    • The inclusion form includes (retains) the intron (upstreamEE, downstreamES)
• summary.txt: Brief summary of all AS event types. Includes the total event counts and significant event counts. By default, events are counted as significant if FDR <= 0.05. The summary can be regenerated with different criteria by running rMATS_P/summary.py

--tmp contains the intermediate files generated by the prep step:

• {datetime}.rmats: Summary generated from processing the BAM(s)
• bam{sample_num}_{replicate_num}/Aligned.sortedByCoord.out.bam: result of mapping input FASTQ files