ArticleBioinformatics (Oxford, England)2013
A dynamic Bayesian Markov model for phasing and characterizing haplotypes in next-generation sequencing.
Article in Bioinformatics (Oxford, England), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 17 citations in OpenAlex.
- Pairwise comparative analysis of six haplotype assembly methods based on users' experience.BMC genomic data · 2023Article
- Evaluating allopolyploid origins in strawberries (Fragaria) using haplotypes generated from target capture sequencing.BMC evolutionary biology · 2017Article
- Recombination in viruses: mechanisms, methods of study, and evolutionary consequences.Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases · 2015Review
- Continuous-time Markov chain-based flux analysis in metabolism.Journal of computational biology : a journal of computational molecular cell biology · 2014Article
- De novo inference of stratification and local admixture in sequencing studies.BMC bioinformatics · 2013Article
- Coval: improving alignment quality and variant calling accuracy for next-generation sequencing data.PloS one · 2013Article
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1 author at 1 institution in 1 country.
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Abstract
motivationNext-generation sequencing (NGS) technologies have enabled whole-genome discovery and analysis of genetic variants in many species of interest. Individuals are often sequenced at low coverage for detecting novel variants, phasing haplotypes and inferring population structures. Although several tools have been developed for SNP and genotype calling in NGS data, haplotype phasing is often done separately on the called genotypes.
resultsWe propose a dynamic Bayesian Markov model (DBM) for simultaneous genotype calling and haplotype phasing in low-coverage NGS data of unrelated individuals. Our method is fully probabilistic that produces consistent inference of genotypes, haplotypes and recombination probabilities. Using data from the 1000 Genomes Project, we demonstrate that DBM not only yields more accurate results than some popular methods, but also provides novel characterization of haplotype structures at the individual level for visualization, interpretation and comparison in downstream analysis. DBM is a powerful and flexible tool that can be applied to many sequencing studies. Its statistical framework can also be extended to accommodate broader scopes of data. AVAILABILITY AND IMPLEMENTATION: http://stat.psu.edu/∼yuzhang/software/dbm.tar. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.