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.

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.

e.g. featureCounts or HTSeq

• Genes have multiple transcripts, alternative splicing introduces ambiguity
• Traditional alignment is (relatively) slow and computationally intensive
• Read sampling is not uniform, there are biases

• Genes have multiple transcripts, alternative splicing introduces ambiguity
• Traditional alignment is (relatively) slow and computationally intensive
• Read sampling is not uniform, there are biases

More sophisticated approaches:

• CuffLinks - Trapnell et al. (2010) Nature Biotechnology doi:10.1038/nbt.1621
• RSEM - Li and Dewey (2011) BMC Bioinformatics doi:10.1186/1471-2105-12-323
• Sailfish - Patro et al. (2014) Nature Biotechnology doi:10.1038/nbt.2862
• Kallisto - Bary et al. (2016) Nature Biotechnology doi:10.1038/nbt.3519
• Salmon - Patro et al. (2017) Nature Methods doi:10.1038/nmeth.4197

• Genes have multiple transcripts, alternative splicing introduces ambiguity

Count against the transcriptome instead.

Summarise to gene level for differential gene expression analysis.

• 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

• Read sampling is not uniform, there are biases

Include modelling for GC bias, positional bias and sequence bias in the quantification algorithm

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