Using RNA-Seq to quantify gene levels and assay for differential expression for transposable elements

Background

• Transposable elements make up between 20 to 80% of the genome sequence for many eukaryotes, yet are typically excluded from the analysis that follows transcriptomic profiling with RNA-seq. This exclusion is due to the repetitive nature of transposons and the ambiguity that accompanies assigning multi-mapping reads.

Step by step analysis

• Mapping
  • Use ​STAR or another spliced mapper to map short reads to the genome of choice.
  • See our ​mapping SOP for more details.

• Quantification of raw counts

• Is your sequencing library stranded or unstranded? This information is needed to help these tools accurately count features. Strandedness of some library prep methods:
  • TruSeq Stranded mRNA Kits ("TruSeqStrandedPolyA") reads are reverse stranded (stranded in the reverse direction relative to the transcript orientation).
  • SMART-Seq v4 Ultra Low Input RNA Kit ("SMARTerUltra-lowPOLYA-V4") reads are unstranded.
  • KAPA RNA HyperPrep Kits ("KAPAHyperPrepmRNA") reads are reverse stranded.
  • The Whitehead Genome Core has some more ​Library Prep Descriptions.
• See SAMBAMqc (and/or look at mapped reads in a genome browser) to determine or verify strandedness

# single-end reads (unstranded)
featureCounts -a gene_anotations.gtf -o MySample.featureCounts.txt MySample.bam
# single-end reads (forward stranded)
featureCounts -s 1 -a gene_anotations.gtf -o MySample.featureCounts.txt MySample.bam
# single-end reads (reverse stranded)
featureCounts -s 2 -a gene_anotations.gtf -o MySample.featureCounts.txt MySample.bam


# paired-end reads (unstranded)
featureCounts -p -a gene_anotations.gtf -o MySample.featureCounts.txt MySample.bam
# paired-end reads (forward stranded)
featureCounts -p -s 1 -a gene_annotations.gtf -o MySamples.featureCounts.txt *sortedByName.bam
# paired-end reads (reverse stranded)
featureCounts -p -s 2 -a gene_annotations.gtf -o MySamples.featureCounts.txt *sortedByName.bam

• Other
  • Review articles:
    • ​Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data. - Rapaport F, Khanin R, Liang Y, Pirun M, Krek A, Zumbo P, Mason CE, Socci ND, Betel D. Genome Biol. 2013 Sep 10;14(9):R95.
    • ​A survey of statistical software for analyzing RNA-seq data - Gao D, Kim J, Kim H, Phang TL, Selby H, Tan AC, Tong T. Hum Genomics. 2010 Oct;5(1):56-60.
    • ​From RNA-seq reads to differential expression results - Oshlack A, Robinson MD, Young MD. Genome Biol. 2010;11(12):220. Epub 2010 Dec 22.
  • For more practical information, see the third session of ​An introduction to R and Bioconductor: A BaRC Short Course and the ​BaRC Hot Topic (under "Short Read Sequencing", see "Practical RNA-Seq analysis")