GCphase: an SNP phasing method using a graph partition and error correction algorithm

Background The utilization of long reads for single nucleotide polymorphism (SNP) phasing has become popular, providing substantial support for research on human diseases and genetic studies in animals and plants. However, due to the complexity of the linkage relationships between SNP loci and seque...

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Published in:BMC bioinformatics Vol. 25; no. 1; pp. 267 - 14
Main Authors: Luo, Junwei, Wang, Jiayi, Zhai, Haixia, Wang, Junfeng
Format: Journal Article
Language:English
Published: London BioMed Central 19.08.2024
BioMed Central Ltd
Springer Nature B.V
BMC
Subjects:
Accuracy
Algorithms
Bioinformatics
Biomedical and Life Sciences
Chromosomes
Clustering
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
DNA sequencing
Error correction
Error correction & detection
Error reduction
Error-correcting codes
Gene sequencing
Genetic research
Genomes
Graph minimum-cut algorithm
Graphic methods
Haplotype assembly
Haplotypes
High-Throughput Nucleotide Sequencing - methods
Humans
Information processing
Life Sciences
Maximum likelihood method
Methods
Microarrays
Nucleotide sequencing
Nucleotides
Partitions (mathematics)
Plant diseases
Polymorphism
Polymorphism, Single Nucleotide - genetics
Sequence Analysis, DNA - methods
Single nucleotide polymorphisms
Single-nucleotide polymorphism
SNP phasing
Software
China
SNP phasing
Haplotype assembly
Graph minimum-cut algorithm
Error correction
ISSN:1471-2105, 1471-2105
Online Access:Get full text
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Summary:Background The utilization of long reads for single nucleotide polymorphism (SNP) phasing has become popular, providing substantial support for research on human diseases and genetic studies in animals and plants. However, due to the complexity of the linkage relationships between SNP loci and sequencing errors in the reads, the recent methods still cannot yield satisfactory results. Results In this study, we present a graph-based algorithm, GCphase, which utilizes the minimum cut algorithm to perform phasing. First, based on alignment between long reads and the reference genome, GCphase filters out ambiguous SNP sites and useless read information. Second, GCphase constructs a graph in which a vertex represents alleles of an SNP locus and each edge represents the presence of read support; moreover, GCphase adopts a graph minimum-cut algorithm to phase the SNPs. Next, GCpahse uses two error correction steps to refine the phasing results obtained from the previous step, effectively reducing the error rate. Finally, GCphase obtains the phase block. GCphase was compared to three other methods, WhatsHap, HapCUT2, and LongPhase, on the Nanopore and PacBio long-read datasets. The code is available from https://github.com/baimawjy/GCphase . Conclusions Experimental results show that GCphase under different sequencing depths of different data has the least number of switch errors and the highest accuracy compared with other methods.
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ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-024-05901-8