• Quantification: How many reads have come from a genomic feature?
  • genomic feature can be gene or transcript or exon, but usually gene

We now have the locations of our reads on the genome.

We also know the locations of exons of genes on the genome.

So the simplest approach is to count how many reads overlap each gene.

• Broadly classified into two types …
  • Alignment based:
    • Takes bam file as input,therefore reads must be mapped prior to quantification
    • quantifies using simple counting procedure
    • Pros: Intuitive
    • Cons: Slow and can not correct biases in RNAseq data
    • Tools: HTseq, SubRead etc.
  • Alignment-free:
    • Also called quasi-mapping or pseudoalignment
    • Starts from fastq files and base-to-base alignment of the reads is avoided
    • Pros: Very fast and removes biases
    • Cons: Not intuitive
    • Tools: Kallisto, Sailfish, Salmon etc

• GC bias: Higher GC content sequences are less likely to be observed as PCR is not efficient with high GC content sequences.

• Positional bias: for most sequencing methods, the 3 prime end of transcripts are more likely to be observed.

• Complexity bias: some sequences are easier to be bound and amplified than others.

• Sequence-based bias: Bias in read start positions arising from the differential binding efficiency of random hexamer primers

• Fragment length bias: Induced by size selection

• Above biases are sample specific

• Methods like Salmon attempt to mitigate the effect of technical biases by estimating sample-specific bias parameters.

• Unlike alignment based methods, pseudo-alignment methods focus on transcriptome (~2% of genome)
• No base to base alignment required in pseudo-alignment methods

• Traditional alignment is (relatively) slow and computationally intensive

Switch to quasi-mapping or pseudo-alignment to transcriptome

• Traditional alignment is (relatively) slow and computationally intensive

Switch to quasi-mapping or pseudo-alignment

• Traditional alignment is (relatively) slow and computationally intensive

Switch to quasi-mapping or pseudo-alignment

1. Create and index to the transcriptome with Salmon
2. Quantify transcript expression using Salmon