This RNA-Seq analysis pipeline uses an annotated genome to identify differentially expressed genes and it consists of the following steps:
1. Quality Assessment
- Deliverables: Reports generated by FastQC.
Tools Used:
- FastQC: (Andrews 2010) used to generate quality summaries of data:
- Per base sequence quality report: useful for deciding if trimming necessary.
- Sequence duplication levels: evaluation of library complexity. Higher levels of sequence duplication may be expected for high coverage RNAseq data.
- Overrepresented sequences: evaluation of adapter contamination.
2. Fastq Preprocessing
If required, preprocessing of fastq files is performed.
- Deliverables: Trimmed/filtered fastq files.
Tools Used:
- Fastx-toolkit: Used to preprocess fastq files.
- Fastq quality trimmer: Trimming reads based on quality.
- Fastq quality filter: Filtering reads based on quality.
- Cutadapt: Used to remove adaptor from reads.
3. Mapping
Mapping to genome reference using BWA-mem or Tophat.
- Deliverables: Mapping results, as bam files and mapping statistics.
Tools Used:
- BWA-mem: (Li 2013) primary aligner used to generate read alignments.
- Tophat: (Kim 2011) aligner used to generate read alignments in a splice-aware manner and identify novel junctions.
- Samtools: (Li 2009) used to generate mapping statistics.
4. Gene/Transcript Counting
Counting the number of reads mapping to annotated intervals to obtain abundance of genes/transcripts.
- Deliverables: Raw gene/transcript counts
Tools Used:
- HTSeq-count: (Anders 2014) used to count reads overlapping gene intervals.
5. DEG Identification
Normalization and statistical testing to identify differentially expressed genes.
- Deliverables: DEG Summary and master file containing fold changes and p values for every gene, MA Plots.
Tools Used:
- DESeq2: (Love 2014) used to perform normalization and test for differential expression using the negative binomial distribution.