Quantitation

Counting as a measure of Expression

Most RNASEQ techniques deal with count data.

• The reads are mapped to a reference and the number of reads mapped to each gene/transcript is counted
• Read counts are roughly proportional to gene-length and abundance
• The more reads the better
  • Artifacts occur because of:
    • Sequencing Bias
    • Positional bias along the length of the gene Gene annotations (overlapping genes) Alternate splicing
    • Non-unique genes
    • Mapping errors

The typical steps in quantitation of mapped reads is as follows:

• Count mapped reads
• Count each read once (deduplicate)
• Discard reads that:
  • have poor quality alignment scores
  • are not uniquely mapped
  • overlap several genes
  • Have paired reads do not map together
• Remember to document what was done

Count Normalization

There are three metrics commonly used to attempt to normalize for sequencing depth and gene length.

Counting as a measure of Expression

An example of a counts matrix for RNASEQ data.

Log Transformed Data

Because of its vast dynamic ranges RNASEQ data is typically log transformed in order to: provide better visualizations and to present analysis software with a more "normal distribution".

Common counting Programs

• Subread (featureCount)
• STAR (quantmode)
• HTseq (counts)
• RSEM (RNA-Seq by Expectation Maximization) Salmon, Kallisto - pseudoaligners
• Salmon (pseudo aligner and counter)