Haplotype phasing in single-cell DNA-sequencing data

Gryte Satas, Benjamin J. Raphael

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

Motivation: Current technologies for single-cell DNA sequencing require whole-genome amplification (WGA), as a single cell contains too little DNA for direct sequencing. Unfortunately, WGA introduces biases in the resulting sequencing data, including non-uniformity in genome coverage and high rates of allele dropout. These biases complicate many downstream analyses, including the detection of genomic variants. Results: We show that amplification biases have a potential upside: long-range correlations in rates of allele dropout provide a signal for phasing haplotypes at the lengths of amplicons from WGA, lengths which are generally longer than than individual sequence reads. We describe a statistical test to measure concurrent allele dropout between single-nucleotide polymorphisms (SNPs) across multiple sequenced single cells. We use results of this test to perform haplotype assembly across a collection of single cells. We demonstrate that the algorithm predicts phasing between pairs of SNPs with higher accuracy than phasing from reads alone. Using whole-genome sequencing data from only seven neural cells, we obtain haplotype blocks that are orders of magnitude longer than with sequence reads alone (median length 10.2 kb versus 312 bp), with error rates <2%. We demonstrate similar advantages on whole-exome data from 16 cells, where we obtain haplotype blocks with median length 9.2 kb?comparable to typical gene lengths?compared with median lengths of 41 bp with sequence reads alone, with error rates <4%. Our algorithm will be useful for haplotyping of rare alleles and studies of allele-specific somatic aberrations.

Original languageEnglish (US)
Pages (from-to)i211-i217
JournalBioinformatics
Volume34
Issue number13
DOIs
StatePublished - Jul 1 2018

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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