Statistical Binning for Barcoded Reads Improves Downstream Analyses.

ABSTRACT: Sequencing technologies are capturing longer-range genomic information at lower error rates, enabling alignment to genomic regions that are inaccessible with short reads. However, many methods are unable to align reads to much of the genome, recognized as important in disease, and thus report erroneous results in downstream analyses. We introduce EMA, a novel two-tiered statistical binning model for barcoded read alignment, that first probabilistically maps reads to potentially multiple "read clouds" and then within clouds by newly exploiting the non-uniform read densities characteristic of barcoded read sequencing. EMA substantially improves downstream accuracy over existing methods, including phasing and genotyping on 10x data, with fewer false variant calls in nearly half the time. EMA

SUBMITTER: Shajii A 

PROVIDER: S-EPMC6214366 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature