AIM: Given a reference sequence and a set of short reads, align each read to the reference sequence finding the most likely origin of the read sequence.

Aligners: STAR, HISAT2

• Traditional alignment is (relatively) slow and computationally intensive

• Traditional alignment is (relatively) slow and computationally intensive

• Traditional alignment is (relatively) slow and computationally intensive

Switch to quasi-mapping (Salmon) or pseudo-alignment (Kallisto)

• Unlike alignment based methods, pseudo-alignment methods focus on transcriptome (~2% of genome in human)
• Use exact kmer matching rather than aligning whole reads with mismatches and indels

• 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

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

If we had mapped our reads to the genome (rather than the transcript sequences), our mapping would look like this:

We also know the locations of exons of genes on the genome, from an annotation file (e.g. GFF or GTF)

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

However, Salmon does not work this way. We have mapped to the transcript sequences, not the genome. Quantification is performed as part of the quasi-mapping process.

Salmon also takes account of biases:

• Multimapping: Reads which map equally well to multiple locations

• 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

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

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