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Improved haplotype inference by exploiting long-range linking and allelic imbalance in RNA-seq datasets
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s41467-020-18320-z.pdf
Description
Published version
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870.68 KB
Format
Adobe PDF
Checksum (MD5)
e0403c422046f1177ba75dc6f426c457
Author(s)
Berger, E
Yorukoglu, D
Zhang, L
Nyquist, SK
Shalek, AK
Kellis, M
Numanagić, I
Berger, B
Journal
Nature Communications
Publisher
Springer Science and Business Media LLC
Version
Final published version
Abstract
© 2020, The Author(s). Haplotype reconstruction of distant genetic variants remains an unsolved problem due to the short-read length of common sequencing data. Here, we introduce HapTree-X, a probabilistic framework that utilizes latent long-range information to reconstruct unspecified haplotypes in diploid and polyploid organisms. It introduces the observation that differential allele-specific expression can link genetic variants from the same physical chromosome, thus even enabling using reads that cover only individual variants. We demonstrate HapTree-X’s feasibility on in-house sequenced Genome in a Bottle RNA-seq and various whole exome, genome, and 10X Genomics datasets. HapTree-X produces more complete phases (up to 25%), even in clinically important genes, and phases more variants than other methods while maintaining similar or higher accuracy and being up to 10× faster than other tools. The advantage of HapTree-X’s ability to use multiple lines of evidence, as well as to phase polyploid genomes in a single integrative framework, substantially grows as the amount of diverse data increases.
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Creative Commons Attribution 4.0 International license
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DOI of Published Version
10.1038/s41467-020-18320-z
