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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.

 

 

 

 

 

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