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Using RNA-Seq to Discover Genetic Polymorphisms That Produce Hidden Splice Variants
Shayna Stein1, Emad Bahrami-Samani1, Yi Xing2
1Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, 650 Charles E. Young Drive South, Los Angeles, CA, 90095, USA.
Methods in Molecular Biology (Clifton, N.J.)
|August 3, 2017
Summary
RNA sequencing (RNA-seq) analysis can miss splicing variations due to genomic differences. Our new method, RNA Personal Genome Alignment Analyzer (rPGA), maps RNA-seq data to personal genomes to uncover these hidden splicing events.
Area of Science:
- Genomics
- Transcriptomics
- Bioinformatics
Background:
- RNA sequencing (RNA-seq) is crucial for studying gene expression and alternative splicing.
- Standard RNA-seq alignment methods rely on consensus splice sites, potentially missing variations.
- Genomic variants can create novel splice sites, leading to unmapped reads in personal transcriptomes.
Purpose of the Study:
- To develop and evaluate a method for identifying "hidden" splicing variations in personal transcriptomes.
- To address the limitations of standard RNA-seq aligners in the presence of personal genomic variants.
- To enable the discovery of novel splicing variations by aligning RNA-seq data to personal genomes.
Main Methods:
- Developed the RNA Personal Genome Alignment Analyzer (rPGA) tool.
- Applied rPGA to map personal RNA-seq data to corresponding personal genomes.
- Evaluated the method's performance in identifying splicing variations.
Main Results:
- The personal genome approach significantly increases the detection of splicing variations.
- rPGA successfully identifies splice junctions missed by standard aligners due to genomic variants.
- A substantial catalog of previously unknown splicing variations in human populations was discovered.
Conclusions:
- Mapping RNA-seq data to personal genomes is essential for comprehensive splicing analysis.
- The rPGA method enables the discovery of a large, previously uncharacterized set of splicing variations.
- This approach advances our understanding of posttranscriptional regulation and human genetic diversity.
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