TAPAS

TAPAS

TAPAS is an R-based software that detects polyadenylation sites within a gene from RNA-Seq data and identifies differentially polyadenylated sites between two samples.

The paper is titled TAPAS: tool for alternative polyadenylation site analysis
The application is free to download, and the README documentation was used as a reference to create the nextflow pipeline of this module.

This workflow qualifies for the APAeval identification and relative quantification challenges.

Running TAPAS workflow

Input & pre-processing

An example sample sheet is available at samplesheet.csv. samplesheet.csv can contain multiple entries, where each row in the samplesheet has two columns:

• sample: name of the sample for logs (e.g control_replicate1)
• bam: BAM input file for the sample
• read_length: read length of the sample

Docker and Singularity containers

This workflow uses docker containers. To run, make sure that docker is installed and running (e.g. by running the command docker --help and seeing a help message printed).

• If running with Docker, please include the -profile docker in the command, which enables Docker.
• If running with Singularity, please include the -profile singularity in the command, which enables Singularity.

Parameters

Parameters used to run the TAPAS are specified in the nextflow.config file. Parameters relevant to the workflow itself are:

• input - samplesheet.csv
• identification_bed_suffix
• relative_quantification_bed_suffix

Running the TAPAS method workflow

• Download the test data as follows. The current dataset is in a genomic region where there are enough reads to test TAPAS's PAS quantification functionality.

  • Download the '500 genes' test BAM files from the APAeval GDrive & extract the tarball
  • Subset the *_chr.bam BAMs to chr11 and re-index with samtools e.g. samtools view -bh siSrsf3_R1_500genes_chr.bam chr11 > chr11_siSrsf3_R1_500genes_chr.bam && samtools index chr11_siSrsf3_R1_500genes_chr.bam
  • (Optional but recommended to save time) subset the Gencode vM18 GTF to chr11

    wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_mouse/release_M18/gencode.vM18.annotation.gtf.gz
    gzip -d gencode.vM18.annotation.gtf.gz
    awk -F"\t" '{if($1=="chr11") {print $0}}' gencode.vM18.annotation.gtf > chr11.gencode.vM18.annotation.gtf
    

  • Generate PolyAsite BED with 'chr' prefixed chromosome names as nicely detailed by @ninsch3000 in PAQR's README :)
  • Update samples.tsv with paths to the subsetted BAMs, config with paths to to full/subsetted GTF & modified PolyASite BED

• Update the samplesheet.csv with the full path to the downloaded bam file(s).

sample,bam,read_length
sample,[path_to]/SRR6795721.bam,[int(read_length)]

• Run the pipeline with the samplesheet.csv with the input paths updated using either docker or singularity containers

nextflow main.nf --input samplesheet.csv --gtf [/path/to/gtf] -profile <docker/singularity>

• Note: the test data in tests/test_data will not produce any output

Output & post-processing

TAPAS outputs a file containing TAPAS identified entries, which are formatted into a bed file with the following columns:

chrom,chromStart,chromEnd,name,score,strand

Please do note that the column names are extracted from the transcript_id attribute in the last column of the gtf file.

Author contact

If you have any question or comment about TAPAS, please post on TAPAS GitHub Issues (https://github.com/arefeen/TAPAS/issues) or the author, Dr. Ashraful Arefeen.