This RNA-Seq analysis pipeline uses an annotated genome to identify differentially expressed genes and it consists of the following steps:
1. Quality Assessment
Data quality assessed using industry standard tools and quality assessment evaluated prior to downstream analysis.
- 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 Quality assessment used to decide if any preprocessing of the raw data is required and if so, preprocessing is performed.
- Deliverables: Trimmed/filtered fastq files.
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3. Mapping
Mapping to genome reference performed 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.
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