  Motivation: Most existing methods for DNA sequence analysis rely on accurate
sequences or genotypes. However, in applications of the next-generation
sequencing (NGS), accurate genotypes may not be easily obtained (e.g.
multi-sample low-coverage sequencing or somatic mutation discovery). These
applications press for the development of new methods for analyzing sequence
data with uncertainty.
  Results: We present a statistical framework for calling SNPs, discovering
somatic mutations, inferring population genetical parameters and performing
association tests directly based on sequencing data without explicit genotyping
or linkage-based imputation. On real data, we demonstrate that our method
achieves comparable accuracy to alternative methods for estimating site allele
count, for inferring allele frequency spectrum and for association mapping. We
also highlight the necessity of using symmetric datasets for finding somatic
mutations and confirm that for discovering rare events, mismapping is
frequently the leading source of errors.
  Availability: http://samtools.sourceforge.net.
  Contact: hengli@broadinstitute.org.

Genomics