RNA-seq is a powerful and popular technology for studying posttranscriptional regulation of gene expression, such as alternative splicing. The first step in analyzing RNA-seq data is to map the sequenced reads back to the genome. However, commonly used RNA-seq aligners use the consensus splice site dinucleotide motifs to map reads across splice junctions. This can be deceiving due to genomic variants that create novel splice site dinucleotides, leaving the personal splice junction reads un-mapped to the reference genome. UCLA researchers developed and evaluated a method called RNA Personal Genome Alignment Analyzer (rPGA) to identify “hidden” splicing variations in personal transcriptomes, by mapping personal RNA-seq data to personal genomes. This work demonstrates that the personal genome approach to RNA-seq read alignment enables the discovery of a large but previously unknown catalog of splicing variations in human populations.
Flowchart of the rPGA pipeline to identify hidden splice variants in personaltranscriptomes
Availability – The most recent version of rPGA is available at: https://github.com/Xinglab/rPGA