Haplotype-based variant detection from short-read sequencing

Abstract

The direct detection of haplotypes from short-read DNA sequencing data requires changes to existing small-variant detection methods. Here, we develop a Bayesian statistical framework which is capable of modeling multiallelic loci in sets of individuals with non-uniform copy number. We then describe our implementation of this framework in a haplotype-based variant detector, FreeBayes.

9 pages, partial draft

References in corpus (2)

• A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data
• Popular Ensemble Methods: An Empirical Study

Cited by in corpus (9)

• Towards Better Understanding of Artifacts in Variant Calling from High-Coverage Samples
• Integrating sequencing datasets to form highly confident SNP and indel genotype calls for a whole human genome
• MOSAIK: A hash-based algorithm for accurate next-generation sequencing read mapping
• Minimum error correction-based haplotype assembly: considerations for long read data
• Matrix Completion and Performance Guarantees for Single Individual Haplotyping
• Learning from Data-Rich Problems: A Case Study on Genetic Variant Calling
• Computer Architecture-Aware Optimisation of DNA Analysis Systems
• FermiKit: assembly-based variant calling for Illumina resequencing data
• PopIns: population-scale detection of novel sequence insertions