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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| Vydané v: | BMC bioinformatics Ročník 25; číslo 1; s. 267 - 14 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
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London
BioMed Central
19.08.2024
BioMed Central Ltd Springer Nature B.V BMC |
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| Abstract | 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. |
|---|---|
| AbstractList | 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.
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 .
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. 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. 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. 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. 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.BACKGROUNDThe 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.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 .RESULTSIn 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 .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.CONCLUSIONSExperimental 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. Abstract 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. BackgroundThe 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.ResultsIn 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.ConclusionsExperimental 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. 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 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. Keywords: Haplotype assembly, SNP phasing, Graph minimum-cut algorithm, Error correction 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. |
| ArticleNumber | 267 |
| Audience | Academic |
| Author | Zhai, Haixia Luo, Junwei Wang, Jiayi Wang, Junfeng |
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| Cites_doi | 10.1038/s41587-019-0217-9 10.1016/j.tig.2018.05.008 10.1038/nbt.4060 10.1186/s13059-021-02512-x 10.1089/cmb.2014.0157 10.1093/bioinformatics/btac058 10.1186/s12864-015-1408-5 10.1186/s12859-020-03584-5 10.1002/j.1538-7305.1970.tb01770.x 10.1038/s41467-019-13355-3 10.1111/ahg.12364 10.1038/nature15393 10.1038/nmeth.4184 10.1109/TCBB.2020.3034910 10.1093/bioinformatics/bty191 10.1186/s13059-021-02328-9 10.1371/journal.pcbi.1003502 10.1093/bioinformatics/btw537 10.1038/s41592-018-0236-3 10.1093/bioinformatics/btp352 10.1038/s41467-018-08148-z 10.1186/s12864-020-06935-x 10.1101/gr.097261.109 10.1186/s13059-020-02158-1 10.1038/sdata.2016.25 10.1186/1471-2105-13-185 10.1016/j.ygeno.2022.110369 10.1101/gr.213462.116 |
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| Keywords | SNP phasing Haplotype assembly Graph minimum-cut algorithm Error correction |
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| References | H Li (5901_CR27) 2018; 34 W Lan (5901_CR1) 2020; 19 JH Lin (5901_CR26) 2022; 38 J Wu (5901_CR18) 2014; 24 JT Simpson (5901_CR5) 2017; 14 R Li (5901_CR13) 2010; 20 BW Kernighan (5901_CR30) 1970; 49 A Sankararaman (5901_CR22) 2020; 21 MR Vollger (5901_CR8) 2019 P Edge (5901_CR23) 2017; 27 EL van Dijk (5901_CR6) 2018; 34 O Abou Saada (5901_CR14) 2022; 114 M Xie (5901_CR24) 2016; 32 S Garg (5901_CR4) 2021; 22 X Victoria Wang (5901_CR10) 2012; 13 E Berger (5901_CR17) 2014; 10 M Patterson (5901_CR20) 2015; 22 5901_CR29 5901_CR3 X Luo (5901_CR25) 2021; 22 M Jain (5901_CR11) 2018; 36 S Majidian (5901_CR21) 2020; 21 AM Wenger (5901_CR12) 2019; 37 D He (5901_CR16) 2018; 19 MR Vollger (5901_CR9) 2019; 16 S Das (5901_CR19) 2015; 16 JM Zook (5901_CR31) 2016; 3 MJP Chaisson (5901_CR2) 2019; 10 SD Schrinner (5901_CR15) 2020; 21 H Du (5901_CR7) 2019; 10 5901_CR28 |
| References_xml | – volume: 37 start-page: 1155 issue: 10 year: 2019 ident: 5901_CR12 publication-title: Nat Biotechnol doi: 10.1038/s41587-019-0217-9 – volume: 34 start-page: 666 year: 2018 ident: 5901_CR6 publication-title: Trends Genet doi: 10.1016/j.tig.2018.05.008 – volume: 36 start-page: 338 issue: 4 year: 2018 ident: 5901_CR11 publication-title: Nat Biotechnol doi: 10.1038/nbt.4060 – volume: 22 start-page: 1 issue: 1 year: 2021 ident: 5901_CR25 publication-title: Genome Biol doi: 10.1186/s13059-021-02512-x – volume: 22 start-page: 498 issue: 6 year: 2015 ident: 5901_CR20 publication-title: J Comput Biol doi: 10.1089/cmb.2014.0157 – volume: 38 start-page: 1816 issue: 7 year: 2022 ident: 5901_CR26 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btac058 – volume: 16 start-page: 260 year: 2015 ident: 5901_CR19 publication-title: BMC Genomics doi: 10.1186/s12864-015-1408-5 – volume: 21 start-page: 1 issue: 1 year: 2020 ident: 5901_CR21 publication-title: BMC Bioinf doi: 10.1186/s12859-020-03584-5 – volume: 49 start-page: 291 issue: 2 year: 1970 ident: 5901_CR30 publication-title: Bell Syst Tech J doi: 10.1002/j.1538-7305.1970.tb01770.x – volume: 10 start-page: 5360 year: 2019 ident: 5901_CR7 publication-title: Nat Commun doi: 10.1038/s41467-019-13355-3 – year: 2019 ident: 5901_CR8 publication-title: Ann Hum Genet doi: 10.1111/ahg.12364 – ident: 5901_CR3 doi: 10.1038/nature15393 – volume: 14 start-page: 407 issue: 4 year: 2017 ident: 5901_CR5 publication-title: Nat Methods doi: 10.1038/nmeth.4184 – ident: 5901_CR29 – volume: 19 start-page: 1715 issue: 3 year: 2020 ident: 5901_CR1 publication-title: IEEE/ACM Trans Comput Biol Bioinf doi: 10.1109/TCBB.2020.3034910 – volume: 34 start-page: 3094 issue: 18 year: 2018 ident: 5901_CR27 publication-title: Bioinformatics doi: 10.1093/bioinformatics/bty191 – volume: 22 start-page: 1 issue: 1 year: 2021 ident: 5901_CR4 publication-title: Genome Biol doi: 10.1186/s13059-021-02328-9 – volume: 10 start-page: e1003502 issue: 3 year: 2014 ident: 5901_CR17 publication-title: PLoS Comput Biol doi: 10.1371/journal.pcbi.1003502 – volume: 32 start-page: 3735 issue: 24 year: 2016 ident: 5901_CR24 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btw537 – volume: 16 start-page: 88 year: 2019 ident: 5901_CR9 publication-title: Nat Methods doi: 10.1038/s41592-018-0236-3 – ident: 5901_CR28 doi: 10.1093/bioinformatics/btp352 – volume: 19 start-page: 171 issue: 2 year: 2018 ident: 5901_CR16 publication-title: BMC Genomics – volume: 10 start-page: 1 issue: 1 year: 2019 ident: 5901_CR2 publication-title: Nat Commun doi: 10.1038/s41467-018-08148-z – volume: 21 start-page: 1 year: 2020 ident: 5901_CR22 publication-title: BMC Genomics doi: 10.1186/s12864-020-06935-x – volume: 20 start-page: 265 issue: 2 year: 2010 ident: 5901_CR13 publication-title: Genome Res doi: 10.1101/gr.097261.109 – volume: 21 start-page: 1 issue: 1 year: 2020 ident: 5901_CR15 publication-title: Genome Biol doi: 10.1186/s13059-020-02158-1 – volume: 3 start-page: 1 issue: 1 year: 2016 ident: 5901_CR31 publication-title: Sci. Data doi: 10.1038/sdata.2016.25 – volume: 13 start-page: 1 year: 2012 ident: 5901_CR10 publication-title: BMC Bioinf doi: 10.1186/1471-2105-13-185 – volume: 114 start-page: 110369 issue: 3 year: 2022 ident: 5901_CR14 publication-title: Genomics doi: 10.1016/j.ygeno.2022.110369 – volume: 27 start-page: 801 issue: 5 year: 2017 ident: 5901_CR23 publication-title: Genome Res doi: 10.1101/gr.213462.116 – volume: 24 start-page: 3753 year: 2014 ident: 5901_CR18 publication-title: Biomed Mater Eng |
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The utilization of long reads for single nucleotide polymorphism (SNP) phasing has become popular, providing substantial support for research on... The utilization of long reads for single nucleotide polymorphism (SNP) phasing has become popular, providing substantial support for research on human diseases... Background The utilization of long reads for single nucleotide polymorphism (SNP) phasing has become popular, providing substantial support for research on... BackgroundThe utilization of long reads for single nucleotide polymorphism (SNP) phasing has become popular, providing substantial support for research on... Abstract Background The utilization of long reads for single nucleotide polymorphism (SNP) phasing has become popular, providing substantial support for... |
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| SubjectTerms | 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 |
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| Title | GCphase: an SNP phasing method using a graph partition and error correction algorithm |
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