Alternative Splicing Analysis on SRA and User-Provided Data

Nuno Saraiva-Agostinho

2019-05-02


psichomics is an interactive R package for integrative analyses of alternative splicing and gene expression based on The Cancer Genome Atlas (TCGA) (containing molecular data associated with 34 tumour types), the Genotype-Tissue Expression (GTEx) project (containing data for multiple normal human tissues), Sequence Read Archive and user-provided data.

The following tutorial goes through the steps required to load custom RNA-seq data:

  1. Retrieve FASTQ files and sample-associated information from the Sequence Read Archive (SRA) (optional if you already have your own FASTQ samples);
  2. Map RNA-seq reads from the FASTQ files against a genome of reference using STAR, a splice-aware aligner;
  3. Merge and prepare the output of such aligners to be correctly interpreted by psichomics;
  4. Load data into psichomics.

Download data from SRA (optional)

SRA is a repository of biological sequence data that stores data from many published articles. SRA data may be useful to answer pressing biological questions using publicly available data.

The latest versions of psichomics support automatic downloading of SRA data from recount2, a resource of pre-processed data for thousands of SRA projects (including gene read counts, splice junction quantification and sample metadata). Check first if the project of your interest is not available through this resource, thus making it easier to analyse gene expression and alternative splicing for your samples of interest.

Data from SRA can be downloaded using the fastq-dump command from sra-tools. For instance, to retrieve samples from the SRP126561 project, we could do the following:

fastq-dump --gzip --split-3 SRR6368612
fastq-dump --gzip --split-3 SRR6368613
fastq-dump --gzip --split-3 SRR6368614
fastq-dump --gzip --split-3 SRR6368615
fastq-dump --gzip --split-3 SRR6368616
fastq-dump --gzip --split-3 SRR6368617

Arguments used in the previous command:

Sample-associated data is also available from the Run Selector page. Click RunInfo Table to download the whole metadata table for all samples.

Align RNA-seq data to quantify splice junctions

The quantification of each alternative splicing event is based on the proportion of junction reads that support the inclusion isoform, known as percent spliced-in or PSI (Wang et al., 2008).

To estimate this value for each splicing event, both alternative splicing annotation and quantification of RNA-Seq reads aligning to splice junctions (junction quantification) are required. While alternative splicing annotation is provided by the package, junction quantification will need to be prepared from user-provided data by aligning the RNA-seq reads from FASTQ files to a genome of reference. As junction reads are required to quantify alternative splicing, a splice-aware aligner will be used. psichomics currently supports STAR output.

STAR

Before aligning FASTQ samples against a genome of reference, a genome index needs to be prepared.

Start by downloading a FASTA file of the whole genome and a GTF file with annotated transcript. To run the following example, download the human FASTA and GTF files (hg19 assembly).

mkdir hg19_STAR
STAR --runThreadN 4 \
     --runMode genomeGenerate \
     --genomeDir hg19_STAR \
     --genomeFastaFiles /path/to/hg19.fa \
     --sjdbGTFfile /path/to/hg19.gtf

Arguments used in the previous command:


After the genome index is generated, align the FASTQ files using the following commands to align read counts to both genes and splice junctions. These commands will allow STAR to output both gene and junction read counts.

STAR --runThreadN 4 \
     --genomeDir hg19_STAR \
     --quantMode GeneCounts \
     --readFilesCommand zcat \
     --outFileNamePrefix SRR6368612 \
     --readFilesIn SRR6368612_1.fastq.gz SRR6368612_2.fastq.gz
     
STAR --runThreadN 4 \
     --genomeDir hg19_STAR \
     --quantMode GeneCounts \
     --readFilesCommand zcat \
     --outFileNamePrefix SRR6368613 \
     --readFilesIn SRR6368613_1.fastq.gz SRR6368613_2.fastq.gz
     
STAR --runThreadN 4 \
     --genomeDir hg19_STAR \
     --quantMode GeneCounts \
     --readFilesCommand zcat \
     --outFileNamePrefix SRR6368614 \
     --readFilesIn SRR6368614_1.fastq.gz SRR6368614_2.fastq.gz
     
STAR --runThreadN 4 \
     --genomeDir hg19_STAR \
     --quantMode GeneCounts \
     --readFilesCommand zcat \
     --outFileNamePrefix SRR6368615 \
     --readFilesIn SRR6368615_1.fastq.gz SRR6368615_2.fastq.gz
     
STAR --runThreadN 4 \
     --genomeDir hg19_STAR \
     --quantMode GeneCounts \
     --readFilesCommand zcat \
     --outFileNamePrefix SRR6368616 \
     --readFilesIn SRR6368616_1.fastq.gz SRR6368616_2.fastq.gz
     
STAR --runThreadN 4 \
     --genomeDir hg19_STAR \
     --quantMode GeneCounts \
     --readFilesCommand zcat \
     --outFileNamePrefix SRR6368617 \
     --readFilesIn SRR6368617_1.fastq.gz SRR6368617_2.fastq.gz

Arguments used in the previous command:

Prepare output for psichomics

To process the resulting data files, open an R console or RStudio and type:

# Change working directory to where the STAR output is
setwd("/path/to/aligned/output/")

library(psichomics)
prepareGeneQuant(
    "SRR6368612ReadsPerGene.out.tab", "SRR6368613ReadsPerGene.out.tab",
    "SRR6368614ReadsPerGene.out.tab", "SRR6368615ReadsPerGene.out.tab",
    "SRR6368616ReadsPerGene.out.tab", "SRR6368617ReadsPerGene.out.tab")
prepareJunctionQuant("SRR6368612SJ.out.tab", "SRR6368613SJ.out.tab", 
                     "SRR6368614SJ.out.tab", "SRR6368615SJ.out.tab",
                     "SRR6368616SJ.out.tab", "SRR6368617SJ.out.tab")
prepareSRAmetadata("SraRunTable.txt")

Load data in visual interface

Start psichomics with the following commands in an R console or RStudio:

Then, click Load user files. Click the Folder input tab and select the appropriate folder where the psichomics-prepared data is stored. Finally, click Load files to scan the folder for data that may be loaded by psichomics.

Load data in command-line interface (CLI)

Feedback

All feedback on the program, documentation and associated material (including this tutorial) is welcome. Please send any comments and questions to:

Nuno Saraiva-Agostinho (nunoagostinho@medicina.ulisboa.pt)

Disease Transcriptomics Lab, Instituto de Medicina Molecular (Portugal)

References

Wang,E.T. et al. (2008) Alternative isoform regulation in human tissue transcriptomes. Nature, 456, 470–476.