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How to analyze the Zika RNA-Seq data with hisat2

This recipe performs an RNA Seq analysis of the Zika data using an alignment based method

1 result • updated 3.3 years ago by Bioinformatics Admin

This recipe performs an RNA Seq analysis of the Zika data using an alignment based method

This recipe reproduces the data analysis of published research.

Downloading the human reference files is not included in the recipe. These are a more substantial download and should be done only once then reused for different recipes.

To get the human genomic build and the human reference files use the [zika-references.sh][zika] helper script:

 # Download the helper script
 wget http://data.biostarhandbook.com/redo/zika/zika-references.sh

 # Run the helper script to get the human genomic builds.
 bash zika-references.sh

Then run this recipe.

For more information see the

Bioinformatics Data Analysis online course.
Biostar Handbook for system setup and other information.

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#
# The script assumes that reference data have been already installed with
#
# http://data.biostarhandbook.com/redo/zika/zika-references.sh
#

# Stop on errors.
set -uex

# Set the project name.
PROJECT=PRJNA313294

# Shortcut to annotations
GTF=~/refs/Homo_sapiens.GRCh38.96.chr.gtf

# Full run information.
RUNINFO=runinfo.all.csv

# Run info for single end reads.
SINGLE=runinfo.single.csv

# Run information for paired end reads.
PAIRED=runinfo.paired.csv

# How many reads per sample to unpack.
READNUM=1000000

# The HiSat2 index name.
# The index was installed with
# http://data.biostarhandbook.com/redo/zika/zika-references.sh
IDX=~/refs/grch38/genome

# Downloading the run information.
esearch -db sra -query $PROJECT | efetch -format runinfo > $RUNINFO

# Separate SRR numbers for the single end files.
cat $RUNINFO | grep  SRR.*SINGLE | cut -f 1 -d , > $SINGLE

# Separate SRR numbers for the paired end files.
cat $RUNINFO | grep  SRR.*PAIRED | cut -f 1 -d , > $PAIRED

# Download and unpack single end reads.
cat $SINGLE  | parallel fastq-dump -X $READNUM -O reads >> log.txt

# Download and unpack paired end reads
cat $PAIRED  | parallel fastq-dump -X $READNUM --split-files -O reads >> log.txt

# Store bam files here.
mkdir -p bam

# How many CPU cores on the system.
CPUS=4

# Run hisat2 on the paired data.
cat $PAIRED | parallel "hisat2 -p $CPUS -x $IDX -1 reads/{}_1.fastq -2 reads/{}_2.fastq 2>> log.txt | samtools sort > bam/{}.bam"

# Run hisat2 on the single end data.
cat $SINGLE | parallel  "hisat2 -p $CPUS -x $IDX -U reads/{}.fastq 2>> log.txt | samtools sort > bam/{}.bam"

# Run a samtools index on all BAM files
ls -1 bam/*.bam | parallel samtools index {}

# These are SRR number of the the control (MOCK) numbers
MOCK=(SRR3191542 SRR3191543 SRR3194428 SRR3194429)

# These are the SRR number of infected (ZIKV) samples.
ZIKV=(SRR3191544 SRR3191545 SRR3194430 SRR3194431)

# We do some extra work to generate bam file names
# such as bam/SRR3191542.bam grouped by condition

# Generate MOCK bam file names.
for SRR in ${MOCK[@]}; do
    MOCK_BAMS+="bam/${SRR}.bam "
done

# Generate ZIKV bam file names.
for SRR in ${ZIKV[@]}; do
    ZIKV_BAMS+="bam/${SRR}.bam "
done

# Count the features over each gene name.
featureCounts -g gene_name -a $GTF -o counts.txt ${MOCK_BAMS} ${ZIKV_BAMS} 2>> log.txt

# Simplify the counts.
cat counts.txt | cut -f 1,7-14 > simple_counts.txt

# Get the script that runs the deseq 1 method.
wget -q -nc  http://data.biostarhandbook.com/rnaseq/code/deseq1.r

# Perform the differential expression study.
cat simple_counts.txt | Rscript deseq1.r 4x4 > results.txt 2>> log.txt

# Download the script to draw the heatmaps (requires the glplots package).
wget -q -nc http://data.biostarhandbook.com/rnaseq/code/draw-heatmap.r

# Generate heatmap from the deseq1 normalized matrix.
cat norm-matrix-deseq1.txt | Rscript draw-heatmap.r > heatmap-norm-matrix-deseq1.pdf  2>> log.txt

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