Improved haplotype inference by exploiting long-range linking and allelic imbalance in RNA-seq datasets

Biology Biology
Computational Methods Computational Methods
Statistics Statistics
Alex K. Shalek Alex K. Shalek
Sarah Nyquist Sarah Nyquist

Berger et al.▾ Berger, E., Yorukoglu, D., Zhang, L., Nyquist, S.K., Shalek, A.K., Kellis, M., Numanagić I., Berger, B.

Nature Communications , Volume 11

September, 2020


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.