Evidence map›Paper›PMID 35579550›Full record

ArticleGigaScience2022

HaploMaker: An improved algorithm for rapid haplotype assembly of genomic sequences.

Mario Fruzangohar, William A Timmins, Olena Kravchuk, Julian Taylor

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Article in GigaScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Mario FruzangoharThe Biometry Hub, School of Agriculture, Food and Wine & Waite Research Institute, University of Adelaide, Glen Osmond, South Australia, 5064, Australia.ORCID 0000-0003-3525-8338
William A TimminsThe Biometry Hub, School of Agriculture, Food and Wine & Waite Research Institute, University of Adelaide, Glen Osmond, South Australia, 5064, Australia.ORCID 0000-0001-5266-056X
Olena KravchukThe Biometry Hub, School of Agriculture, Food and Wine & Waite Research Institute, University of Adelaide, Glen Osmond, South Australia, 5064, Australia.ORCID 0000-0001-5291-3600
Julian TaylorThe Biometry Hub, School of Agriculture, Food and Wine & Waite Research Institute, University of Adelaide, Glen Osmond, South Australia, 5064, Australia.ORCID 0000-0002-9145-052X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn diploid organisms, whole-genome haplotype assembly relies on the accurate identification and assignment of heterozygous single-nucleotide polymorphism alleles to the correct homologous chromosomes. This appropriate phasing of these alleles ensures that combinations of single-nucleotide polymorphisms on any chromosome, called haplotypes, can then be used in downstream genetic analysis approaches including determining their potential association with important phenotypic traits. A number of statistical algorithms and complementary computational software tools have been developed for whole-genome haplotype construction from genomic sequence data. However, many algorithms lack the ability to phase long haplotype blocks and simultaneously achieve a competitive accuracy.

resultsIn this research we present HaploMaker, a novel reference-based haplotype assembly algorithm capable of accurately and efficiently phasing long haplotypes using paired-end short reads and longer Pacific Biosciences reads from diploid genomic sequences. To achieve this we frame the problem as a directed acyclic graph with edges weighted on read evidence and use efficient path traversal and minimization techniques to optimally phase haplotypes. We compared the HaploMaker algorithm with 3 other common reference-based haplotype assembly tools using public haplotype data of human individuals from the Platinum Genome project. With short-read sequences, the HaploMaker algorithm maintained a competitively low switch error rate across all haplotype lengths and was superior in phasing longer genomic regions. For longer Pacific Biosciences reads, the phasing accuracy of HaploMaker remained competitive for all block lengths and generated substantially longer block lengths than the competing algorithms.

conclusionsHaploMaker provides an improved haplotype assembly algorithm for diploid genomic sequences by accurately phasing longer haplotypes. The computationally efficient and portable nature of the Java implementation of the algorithm will ensure that it has maximal impact in reference-sequence-based haplotype assembly applications.

Indexed as

AlgorithmsGenomicsAllelesHaplotypesHumansPolymorphism, Single NucleotideSequence Analysis, DNACCSCLRhaplotypeheterozygousHiFiIlluminaINDELPacBiopaired-endSNP

Identifiers

PMID35579550
PMCPMC9112781

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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.