RNA-seq Analysis Pipeline

File naming convention

To standardlize the process, we strongly suggest to start with universal file naming format. In this pipeline, we will use filename convention as: [PD|AD|HD|HC]_[subjectID]_[cellType]_<b#>_<rep#>.[R1|R2].<fq|fastq>.gz where,

• Beginning with patient type, PD="Parkinson's disease", AD="Alzhimer's disease", HD="Huntington's disease" and HC="Healthy controls", in 2-letter abbreviation;
• Following with subjectID (NOT lane# or sequencing date), which is unique per subject;
• Following with cell type or tissue short name, e.g. SNDA="Substantial nigra dopamine neuron", MCPY="Motor cortex pyramidal neuron" etc.;
• b# and rep# are optional, in format of (if any);
• batch number: b1, b2 etc.
• replication number (capitalized one for biological rep), e.g. rep1 is for technique replicate 1, Rep1 for biological replicate 1
• Use R1 or R2 to tell the two mates of pair-end sequencing data;
• Use zipped fastq;

Folder structure

For each project (e.g. PDMap), it should have a folder named with project short name and followed by the type of data, such as PDMap_RNAseq, HDPredict_smallRNA etc.

• rawfiles
• raw fastq files from sequencing;
• use soft links for external location or unformated file name
• log file (readme.txt or Excel)
• filtered
• filtered files (e.g. adaptor removal/clip)
• run_output
• sub-folder per sample (e.g. sample1, sample2 etc.)
  • output of Tophat/Cufflinks/htseq-count runs
  • uniq subfolder for the runs for unique reads only
  • status indication file (.status*) for tracking the progress
• for_display
• files used for display on UCSC / IGV, such as *.bam, *.bam.bai, *.bw, *.gtf etc.
• use soft links to the output files
• results
• result files for integrative analysis, e.g. differential analysis by combining all samples.

Pipeline requirement

1. Install programs: tophat, cufflinks, bowtie, bedtools, samtools, fastq-mcf, fastqc, and htseq-count;
2. Install Jim Kent's executable programms: http://hgdownload.cse.ucsc.edu/admin/exe/;
3. Install R and bioconductor packages: DESeq2, MatrixEQTL, SPIA etc.
4. Add path of the executable programs to the $PATH;

Pipeline structure

![flowchart_rnaseq] (./src/flowchart_rnaseq.png "Flowchart of RNAseq pipeline")

Main script:

RNAseq.pipeline.sh

• Usage: RNAseq.pipeline.sh /data/neurogen/rnaseq_PD/rawfiles
• Function: Main script for submitting RNAseq data analysis jobs to high-properformance computing cluster in batch. For now, it's configured to support only HPC environment with LSF job scheduler.
• Input: absolute path of folder for the raw fastq files
• Output: Tophat/Cufflinks/htseq-count/callSNP etc. runs for each sample using both multiple and unique mappers

Modules:

_RNAseq.sh

• Usage: _RNAseq.sh HC_BN10-39_2.R1.fastq.gz HC_BN10-39_2.R2.fastq.gz
• Function: Routine steps for pair-end RNAseq data
• Input: a pair of fastq files from PE RNAsequencing
• Output: BAM/SAM (from alignment), BED/GTF (from assembly) etc.

_bam2vcf.sh

• Usage:
• Function:
• Input:
• Output:

_callSNP.sh

• Usage:
• Function:
• Input:
• Output:

_sam2variation.awk

• Usage:
• Function:
• Input:
• Output:

_clustComRNASeq.R

• Usage:
• Function: performs sample clustering analysis based on expression values
• Input: genes.fpkm_tracking and/or isoforms.fpkm_tracking from Cufflinks' output
• Output:

_cluster.sh

• Usage:
• Function:
• Input:
• Output:

_DE_cuffdiff.sh

• Usage:
• Function:
• Input:
• Output:

_DE_DEseq.R

• Usage:
• Function:
• Input:
• Output:

_eQTL.R

• Usage: Rscript $PIPELINE_PATH/_eQTL.R
• Function: Rscript to run eQTL analysis using Matrix eQTL
• Input:
• Output:

_factor_analysis.R

• Usage:
• Function:
• Input:
• Output: