Evidence map›Paper›PMID 23445519›Full record

ArticleBMC genomics2013

MixSIH: a mixture model for single individual haplotyping.

Hirotaka Matsumoto, Hisanori Kiryu

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Article in BMC genomics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

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9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Article
  2. Unzipping haplotypes in diploid and polyploid genomes.Computational and structural biotechnology journal · 2020
    Review
  3. [Reconstruction of tumor clonal haplotypes based on an improved spanning algorithm].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2019
    Article
  4. Article
  5. Article
  6. Review
  7. Probabilistic single-individual haplotyping.Bioinformatics (Oxford, England) · 2014
    Article
  8. Article
  9. Article
4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Hirotaka MatsumotoDepartment of Computational Biology, Faculty of Frontier Science, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8561, Japan. matsumoto@cb.k.u-tokyo.ac.jp
Hisanori Kiryu

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHaplotype information is useful for various genetic analyses, including genome-wide association studies. Determining haplotypes experimentally is difficult and there are several computational approaches that infer haplotypes from genomic data. Among such approaches, single individual haplotyping or haplotype assembly, which infers two haplotypes of an individual from aligned sequence fragments, has been attracting considerable attention. To avoid incorrect results in downstream analyses, it is important not only to assemble haplotypes as long as possible but also to provide means to extract highly reliable haplotype regions. Although there are several efficient algorithms for solving haplotype assembly, there are no efficient method that allow for extracting the regions assembled with high confidence.

resultsWe develop a probabilistic model, called MixSIH, for solving the haplotype assembly problem. The model has two mixture components representing two haplotypes. Based on the optimized model, a quality score is defined, which we call the 'minimum connectivity' (MC) score, for each segment in the haplotype assembly. Because existing accuracy measures for haplotype assembly are designed to compare the efficiency between the algorithms and are not suitable for evaluating the quality of the set of partially assembled haplotype segments, we develop an accuracy measure based on the pairwise consistency and evaluate the accuracy on the simulation and real data. By using the MC scores, our algorithm can extract highly accurate haplotype segments. We also show evidence that an existing experimental dataset contains chimeric read fragments derived from different haplotypes, which significantly degrade the quality of assembled haplotypes.

conclusionsWe develop a novel method for solving the haplotype assembly problem. We also define the quality score which is based on our model and indicates the accuracy of the haplotypes segments. In our evaluation, MixSIH has successfully extracted reliable haplotype segments. The C++ source code of MixSIH is available at https://sites.google.com/site/hmatsu1226/software/mixsih.

Indexed as

AlgorithmsHaplotypesModels, StatisticalComputational BiologyHumansSequence Analysis, DNA

Identifiers

PMID23445519
PMCPMC3582441

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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.