Fast and accurate imputation of genotypes from noisy low-coverage sequencing data in bi-parental populations
Genotyping of bi-parental populations can be performed with low-coverage next-generation sequencing (LC-NGS). This allows the creation of highly saturated genetic maps at reasonable cost, precisely localized recombination breakpoints (i.e., the crossovers), and minimized mapping intervals for quanti...
Saved in:
| Published in | PloS one Vol. 20; no. 1; p. e0314759 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Public Library of Science
30.01.2025
Public Library of Science (PLoS) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1932-6203 1932-6203 |
| DOI | 10.1371/journal.pone.0314759 |
Cover
| Abstract | Genotyping of bi-parental populations can be performed with low-coverage next-generation sequencing (LC-NGS). This allows the creation of highly saturated genetic maps at reasonable cost, precisely localized recombination breakpoints (i.e., the crossovers), and minimized mapping intervals for quantitative-trait locus analysis. The main issues with these low-coverage genotyping methods are (1) poor performance at heterozygous loci, (2) high percentage of missing data, (3) local errors due to erroneous mapping of sequencing reads and reference genome mistakes, and (4) global, technical errors inherent to NGS itself. Recent methods like Tassel-FSFHap or LB-Impute are excellent at addressing issues 1 and 2, but nonetheless perform poorly when issues 3 and 4 are persistent in a dataset (i.e., "noisy" data). Here, we present a new algorithm for imputation of LC-NGS data that eliminates the need of complex pre-filtering of noisy data, accurately types heterozygous chromosomal regions, precisely estimates crossover positions, corrects erroneous data, and imputes missing data. The imputation of genotypes and recombination breakpoints is based on maximum-likelihood estimation. We compare its performance with Tassel-FSFHap and LB-Impute using simulated data and two real datasets. NOISYmputer is consistently more efficient than the two other software tested and reaches average breakpoint precision of 99.9% and average recall of 99.6% on illumina simulated dataset. NOISYmputer consistently provides precise map size estimations when applied to real datasets while alternative tools may exhibit errors ranging from 3 to 1845 times the real size of the chromosomes in centimorgans. Furthermore, the algorithm is not only highly effective in terms of precision and recall but is also particularly economical in its use of RAM and computation time, being much faster than Hidden Markov Model methods.
NOISYmputer and its source code are available as a multiplatform (Linux, macOS, Windows) Java executable at the URL https://gitlab.cirad.fr/noisymputer/noisymputerstandalone/-/tree/1.0.0-RELEASE?reftype=tags. |
|---|---|
| AbstractList | MotivationGenotyping of bi-parental populations can be performed with low-coverage next-generation sequencing (LC-NGS). This allows the creation of highly saturated genetic maps at reasonable cost, precisely localized recombination breakpoints (i.e., the crossovers), and minimized mapping intervals for quantitative-trait locus analysis. The main issues with these low-coverage genotyping methods are (1) poor performance at heterozygous loci, (2) high percentage of missing data, (3) local errors due to erroneous mapping of sequencing reads and reference genome mistakes, and (4) global, technical errors inherent to NGS itself. Recent methods like Tassel-FSFHap or LB-Impute are excellent at addressing issues 1 and 2, but nonetheless perform poorly when issues 3 and 4 are persistent in a dataset (i.e., “noisy” data). Here, we present a new algorithm for imputation of LC-NGS data that eliminates the need of complex pre-filtering of noisy data, accurately types heterozygous chromosomal regions, precisely estimates crossover positions, corrects erroneous data, and imputes missing data. The imputation of genotypes and recombination breakpoints is based on maximum-likelihood estimation. We compare its performance with Tassel-FSFHap and LB-Impute using simulated data and two real datasets. NOISYmputer is consistently more efficient than the two other software tested and reaches average breakpoint precision of 99.9% and average recall of 99.6% on illumina simulated dataset. NOISYmputer consistently provides precise map size estimations when applied to real datasets while alternative tools may exhibit errors ranging from 3 to 1845 times the real size of the chromosomes in centimorgans. Furthermore, the algorithm is not only highly effective in terms of precision and recall but is also particularly economical in its use of RAM and computation time, being much faster than Hidden Markov Model methods.AvailabilityNOISYmputer and its source code are available as a multiplatform (Linux, macOS, Windows) Java executable at the URL https://gitlab.cirad.fr/noisymputer/noisymputerstandalone/-/tree/1.0.0-RELEASE?reftype=tags. Motivation Genotyping of bi-parental populations can be performed with low-coverage next-generation sequencing (LC-NGS). This allows the creation of highly saturated genetic maps at reasonable cost, precisely localized recombination breakpoints (i.e., the crossovers), and minimized mapping intervals for quantitative-trait locus analysis. The main issues with these low-coverage genotyping methods are (1) poor performance at heterozygous loci, (2) high percentage of missing data, (3) local errors due to erroneous mapping of sequencing reads and reference genome mistakes, and (4) global, technical errors inherent to NGS itself. Recent methods like Tassel-FSFHap or LB-Impute are excellent at addressing issues 1 and 2, but nonetheless perform poorly when issues 3 and 4 are persistent in a dataset (i.e., "noisy" data). Here, we present a new algorithm for imputation of LC-NGS data that eliminates the need of complex pre-filtering of noisy data, accurately types heterozygous chromosomal regions, precisely estimates crossover positions, corrects erroneous data, and imputes missing data. The imputation of genotypes and recombination breakpoints is based on maximum-likelihood estimation. We compare its performance with Tassel-FSFHap and LB-Impute using simulated data and two real datasets. NOISYmputer is consistently more efficient than the two other software tested and reaches average breakpoint precision of 99.9% and average recall of 99.6% on illumina simulated dataset. NOISYmputer consistently provides precise map size estimations when applied to real datasets while alternative tools may exhibit errors ranging from 3 to 1845 times the real size of the chromosomes in centimorgans. Furthermore, the algorithm is not only highly effective in terms of precision and recall but is also particularly economical in its use of RAM and computation time, being much faster than Hidden Markov Model methods. Availability NOISYmputer and its source code are available as a multiplatform (Linux, macOS, Windows) Java executable at the URL Genotyping of bi-parental populations can be performed with low-coverage next-generation sequencing (LC-NGS). This allows the creation of highly saturated genetic maps at reasonable cost, precisely localized recombination breakpoints (i.e., the crossovers), and minimized mapping intervals for quantitative-trait locus analysis. The main issues with these low-coverage genotyping methods are (1) poor performance at heterozygous loci, (2) high percentage of missing data, (3) local errors due to erroneous mapping of sequencing reads and reference genome mistakes, and (4) global, technical errors inherent to NGS itself. Recent methods like Tassel-FSFHap or LB-Impute are excellent at addressing issues 1 and 2, but nonetheless perform poorly when issues 3 and 4 are persistent in a dataset (i.e., "noisy" data). Here, we present a new algorithm for imputation of LC-NGS data that eliminates the need of complex pre-filtering of noisy data, accurately types heterozygous chromosomal regions, precisely estimates crossover positions, corrects erroneous data, and imputes missing data. The imputation of genotypes and recombination breakpoints is based on maximum-likelihood estimation. We compare its performance with Tassel-FSFHap and LB-Impute using simulated data and two real datasets. NOISYmputer is consistently more efficient than the two other software tested and reaches average breakpoint precision of 99.9% and average recall of 99.6% on illumina simulated dataset. NOISYmputer consistently provides precise map size estimations when applied to real datasets while alternative tools may exhibit errors ranging from 3 to 1845 times the real size of the chromosomes in centimorgans. Furthermore, the algorithm is not only highly effective in terms of precision and recall but is also particularly economical in its use of RAM and computation time, being much faster than Hidden Markov Model methods. NOISYmputer and its source code are available as a multiplatform (Linux, macOS, Windows) Java executable at the URL https://gitlab.cirad.fr/noisymputer/noisymputerstandalone/-/tree/1.0.0-RELEASE?reftype=tags. Genotyping of bi-parental populations can be performed with low-coverage next-generation sequencing (LC-NGS). This allows the creation of highly saturated genetic maps at reasonable cost, precisely localized recombination breakpoints (i.e., the crossovers), and minimized mapping intervals for quantitative-trait locus analysis. The main issues with these low-coverage genotyping methods are (1) poor performance at heterozygous loci, (2) high percentage of missing data, (3) local errors due to erroneous mapping of sequencing reads and reference genome mistakes, and (4) global, technical errors inherent to NGS itself. Recent methods like Tassel-FSFHap or LB-Impute are excellent at addressing issues 1 and 2, but nonetheless perform poorly when issues 3 and 4 are persistent in a dataset (i.e., "noisy" data). Here, we present a new algorithm for imputation of LC-NGS data that eliminates the need of complex pre-filtering of noisy data, accurately types heterozygous chromosomal regions, precisely estimates crossover positions, corrects erroneous data, and imputes missing data. The imputation of genotypes and recombination breakpoints is based on maximum-likelihood estimation. We compare its performance with Tassel-FSFHap and LB-Impute using simulated data and two real datasets. NOISYmputer is consistently more efficient than the two other software tested and reaches average breakpoint precision of 99.9% and average recall of 99.6% on illumina simulated dataset. NOISYmputer consistently provides precise map size estimations when applied to real datasets while alternative tools may exhibit errors ranging from 3 to 1845 times the real size of the chromosomes in centimorgans. Furthermore, the algorithm is not only highly effective in terms of precision and recall but is also particularly economical in its use of RAM and computation time, being much faster than Hidden Markov Model methods.MOTIVATIONGenotyping of bi-parental populations can be performed with low-coverage next-generation sequencing (LC-NGS). This allows the creation of highly saturated genetic maps at reasonable cost, precisely localized recombination breakpoints (i.e., the crossovers), and minimized mapping intervals for quantitative-trait locus analysis. The main issues with these low-coverage genotyping methods are (1) poor performance at heterozygous loci, (2) high percentage of missing data, (3) local errors due to erroneous mapping of sequencing reads and reference genome mistakes, and (4) global, technical errors inherent to NGS itself. Recent methods like Tassel-FSFHap or LB-Impute are excellent at addressing issues 1 and 2, but nonetheless perform poorly when issues 3 and 4 are persistent in a dataset (i.e., "noisy" data). Here, we present a new algorithm for imputation of LC-NGS data that eliminates the need of complex pre-filtering of noisy data, accurately types heterozygous chromosomal regions, precisely estimates crossover positions, corrects erroneous data, and imputes missing data. The imputation of genotypes and recombination breakpoints is based on maximum-likelihood estimation. We compare its performance with Tassel-FSFHap and LB-Impute using simulated data and two real datasets. NOISYmputer is consistently more efficient than the two other software tested and reaches average breakpoint precision of 99.9% and average recall of 99.6% on illumina simulated dataset. NOISYmputer consistently provides precise map size estimations when applied to real datasets while alternative tools may exhibit errors ranging from 3 to 1845 times the real size of the chromosomes in centimorgans. Furthermore, the algorithm is not only highly effective in terms of precision and recall but is also particularly economical in its use of RAM and computation time, being much faster than Hidden Markov Model methods.NOISYmputer and its source code are available as a multiplatform (Linux, macOS, Windows) Java executable at the URL https://gitlab.cirad.fr/noisymputer/noisymputerstandalone/-/tree/1.0.0-RELEASE?reftype=tags.AVAILABILITYNOISYmputer and its source code are available as a multiplatform (Linux, macOS, Windows) Java executable at the URL https://gitlab.cirad.fr/noisymputer/noisymputerstandalone/-/tree/1.0.0-RELEASE?reftype=tags. Motivation: Genotyping of bi-parental populations can be performed with low-coverage next-generation sequencing (LC-NGS). This allows the creation of highly saturated genetic maps at reasonable cost, precisely localized recombination breakpoints (i.e., the crossovers), and minimized mapping intervals for quantitative-trait locus analysis. The main issues with these low-coverage genotyping methods are (1) poor performance at heterozygous loci, (2) high percentage of missing data, (3) local errors due to erroneous mapping of sequencing reads and reference genome mistakes, and (4) global, technical errors inherent to NGS itself. Recent methods like Tassel-FSFHap or LB-Impute are excellent at addressing issues 1 and 2, but nonetheless perform poorly when issues 3 and 4 are persistent in a dataset (i.e., "noisy" data). Here, we present a new algorithm for imputation of LC-NGS data that eliminates the need of complex pre-filtering of noisy data, accurately types heterozygous chromosomal regions, precisely estimates crossover positions, corrects erroneous data, and imputes missing data. The imputation of genotypes and recombination breakpoints is based on maximum-likelihood estimation. We compare its performance with Tassel-FSFHap and LB-Impute using simulated data and two real datasets. NOISYmputer is consistently more efficient than the two other software tested and reaches average breakpoint precision of 99.9% and average recall of 99.6% on illumina simulated dataset. NOISYmputer consistently provides precise map size estimations when applied to real datasets while alternative tools may exhibit errors ranging from 3 to 1845 times the real size of the chromosomes in centimorgans. Furthermore, the algorithm is not only highly effective in terms of precision and recall but is also particularly economical in its use of RAM and computation time, being much faster than Hidden Markov Model methods.Availability: NOISYmputer and its source code are available as a multiplatform (Linux, macOS, Windows) Java executable at the URL https://gitlab.cirad.fr/noisymputer/noisymputerstandalone/-/tree/1.0.0-RELEASE Motivation Genotyping of bi-parental populations can be performed with low-coverage next-generation sequencing (LC-NGS). This allows the creation of highly saturated genetic maps at reasonable cost, precisely localized recombination breakpoints (i.e., the crossovers), and minimized mapping intervals for quantitative-trait locus analysis. The main issues with these low-coverage genotyping methods are (1) poor performance at heterozygous loci, (2) high percentage of missing data, (3) local errors due to erroneous mapping of sequencing reads and reference genome mistakes, and (4) global, technical errors inherent to NGS itself. Recent methods like Tassel-FSFHap or LB-Impute are excellent at addressing issues 1 and 2, but nonetheless perform poorly when issues 3 and 4 are persistent in a dataset (i.e., “noisy” data). Here, we present a new algorithm for imputation of LC-NGS data that eliminates the need of complex pre-filtering of noisy data, accurately types heterozygous chromosomal regions, precisely estimates crossover positions, corrects erroneous data, and imputes missing data. The imputation of genotypes and recombination breakpoints is based on maximum-likelihood estimation. We compare its performance with Tassel-FSFHap and LB-Impute using simulated data and two real datasets. NOISYmputer is consistently more efficient than the two other software tested and reaches average breakpoint precision of 99.9% and average recall of 99.6% on illumina simulated dataset. NOISYmputer consistently provides precise map size estimations when applied to real datasets while alternative tools may exhibit errors ranging from 3 to 1845 times the real size of the chromosomes in centimorgans. Furthermore, the algorithm is not only highly effective in terms of precision and recall but is also particularly economical in its use of RAM and computation time, being much faster than Hidden Markov Model methods. Availability NOISYmputer and its source code are available as a multiplatform (Linux, macOS, Windows) Java executable at the URL https://gitlab.cirad.fr/noisymputer/noisymputerstandalone/-/tree/1.0.0-RELEASE?reftype=tags. |
| Audience | Academic |
| Author | Boizet, Alice Triay, Cécile Fragoso, Christopher Lorieux, Mathias Rami, Jean-François Gkanogiannis, Anestis |
| AuthorAffiliation | Bocconi University: Universita Bocconi, ITALY 4 Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, Connecticut, CT, United States of America 1 DIADE, IRD, Cirad, University of Montpellier, Montpellier, France 3 Verinomics, Inc., New Haven, CT, United States of America 5 Agrobiotechnology Unit, Alliance Bioversity-CIAT, International Center for Tropical Agriculture, Cali, Colombia 2 AGAP, Cirad, INRAE, Montpellier SupAgro, University of Montpellier, Montpellier, France |
| AuthorAffiliation_xml | – name: Bocconi University: Universita Bocconi, ITALY – name: 2 AGAP, Cirad, INRAE, Montpellier SupAgro, University of Montpellier, Montpellier, France – name: 5 Agrobiotechnology Unit, Alliance Bioversity-CIAT, International Center for Tropical Agriculture, Cali, Colombia – name: 1 DIADE, IRD, Cirad, University of Montpellier, Montpellier, France – name: 3 Verinomics, Inc., New Haven, CT, United States of America – name: 4 Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, Connecticut, CT, United States of America |
| Author_xml | – sequence: 1 givenname: Cécile orcidid: 0000-0002-7339-0762 surname: Triay fullname: Triay, Cécile – sequence: 2 givenname: Alice orcidid: 0000-0003-4096-6689 surname: Boizet fullname: Boizet, Alice – sequence: 3 givenname: Christopher orcidid: 0000-0001-5330-2588 surname: Fragoso fullname: Fragoso, Christopher – sequence: 4 givenname: Anestis orcidid: 0000-0002-6441-0688 surname: Gkanogiannis fullname: Gkanogiannis, Anestis – sequence: 5 givenname: Jean-François orcidid: 0000-0002-5679-3877 surname: Rami fullname: Rami, Jean-François – sequence: 6 givenname: Mathias orcidid: 0000-0001-9864-3933 surname: Lorieux fullname: Lorieux, Mathias |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39883620$$D View this record in MEDLINE/PubMed https://hal.inrae.fr/hal-04942849$$DView record in HAL |
| BookMark | eNqNk9tq3DAQhk1JaQ7tG5RWUCjNxW4ly7akqxJC0wQCgZ5uxViWvApeyZHktHn7ag8J2ZCL4guZ0Te_NL9mDos9550uircEzwll5PO1n4KDYT7m8BxTUrFavCgOiKDlrCkx3Xv0v18cxniNcU1507wq9qngnOaNg2I4g5gQuA6BUlOApJFdjlOCZL1D3qBeO5_uRh2RCX6JnLfxDg3-z0z5Wx2g1yjqm0k7ZV2POkiArEOtnY0QtEswoNGP07CWi6-LlwaGqN9s16Pi19nXn6fns8urbxenJ5cz1bAmzVhNoDKiJYwoBaqEloFgHAvTcaFKjJkmLa-xqJTQwEmLBWuN6Qyvy6oVjB4V7ze64-Cj3BoVJSUNYTVuGM3ExYboPFzLMdglhDvpwcp1wIdeQkhWDVoaBbgVusYGVMWACAUdbWtOFdWtakTW-rI9bWqXulO57ADDjujujrML2ftbSQjjhGGeFY43Cosneecnl3IVw5WoSl6JW5LZT9vTgs--xySXNio9DOC0nzZFClKJtQ0fnqDPW7GlesjVWmd8vqRaicoTXtZVw-pqpTV_hspfp5dW5RY0Nsd3Eo53EjKT9N_UwxSjvPjx_f_Zq9-77MdH7ELDkBbRD9O6wXbBd4-f5cHX-97PQLUBVPAxBm0eEILlasTu7ZKrEZPbEaP_AAt3GSY |
| Cites_doi | 10.1534/genetics.115.182071 10.1093/bfgp/elq031 10.1186/1471-2164-15-979 10.1101/gr.089516.108 10.1016/j.ajhg.2021.08.005 10.3835/plantgenome2014.05.0023 10.1007/s11032-017-0622-z 10.1038/ng.2354 10.1093/genetics/iyad055 10.1086/521987 10.1371/journal.pone.0019379 10.1111/j.2517-6161.1977.tb01600.x |
| ContentType | Journal Article |
| Copyright | Copyright: © 2025 Triay et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. COPYRIGHT 2025 Public Library of Science 2025 Triay et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Attribution 2025 Triay et al 2025 Triay et al 2025 Triay et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: Copyright: © 2025 Triay et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. – notice: COPYRIGHT 2025 Public Library of Science – notice: 2025 Triay et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: Attribution – notice: 2025 Triay et al 2025 Triay et al – notice: 2025 Triay et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM IOV ISR 3V. 7QG 7QL 7QO 7RV 7SN 7SS 7T5 7TG 7TM 7U9 7X2 7X7 7XB 88E 8AO 8C1 8FD 8FE 8FG 8FH 8FI 8FJ 8FK ABJCF ABUWG AEUYN AFKRA ARAPS ATCPS AZQEC BBNVY BENPR BGLVJ BHPHI C1K CCPQU D1I DWQXO FR3 FYUFA GHDGH GNUQQ H94 HCIFZ K9. KB. KB0 KL. L6V LK8 M0K M0S M1P M7N M7P M7S NAPCQ P5Z P62 P64 PATMY PDBOC PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS PTHSS PYCSY RC3 7X8 1XC BXJBU IHQJB VOOES 5PM DOA |
| DOI | 10.1371/journal.pone.0314759 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Gale In Context: Opposing Viewpoints Gale In Context: Science ProQuest Central (Corporate) Animal Behavior Abstracts Bacteriology Abstracts (Microbiology B) Biotechnology Research Abstracts Nursing & Allied Health Database Ecology Abstracts Entomology Abstracts (Full archive) Immunology Abstracts Meteorological & Geoastrophysical Abstracts Nucleic Acids Abstracts Virology and AIDS Abstracts Agricultural Science Collection Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) ProQuest Pharma Collection Public Health Database Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest One Sustainability ProQuest Central UK/Ireland Advanced Technologies & Aerospace Collection Agricultural & Environmental Science Collection ProQuest Central Essentials - QC Biological Science Collection ProQuest Central Technology Collection Natural Science Collection Environmental Sciences and Pollution Management ProQuest One ProQuest Materials Science Collection ProQuest Central Engineering Research Database Proquest Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student AIDS and Cancer Research Abstracts ProQuest SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) Materials Science Database Nursing & Allied Health Database (Alumni Edition) Meteorological & Geoastrophysical Abstracts - Academic ProQuest Engineering Collection ProQuest Biological Science Collection Agricultural Science Database ProQuest Health & Medical Collection Medical Database Algology Mycology and Protozoology Abstracts (Microbiology C) Biological Science Database Engineering Database Nursing & Allied Health Premium Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts Environmental Science Database Materials Science Collection ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China Engineering Collection Environmental Science Collection Genetics Abstracts MEDLINE - Academic Hyper Article en Ligne (HAL) HAL-SHS: Archive ouverte en Sciences de l'Homme et de la Société HAL-SHS: Archive ouverte en Sciences de l'Homme et de la Société (Open Access) Hyper Article en Ligne (HAL) (Open Access) PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Agricultural Science Database Publicly Available Content Database ProQuest Central Student ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials Nucleic Acids Abstracts SciTech Premium Collection ProQuest Central China Environmental Sciences and Pollution Management ProQuest One Applied & Life Sciences ProQuest One Sustainability Health Research Premium Collection Meteorological & Geoastrophysical Abstracts Natural Science Collection Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) Engineering Collection Advanced Technologies & Aerospace Collection Engineering Database Virology and AIDS Abstracts ProQuest Biological Science Collection ProQuest One Academic Eastern Edition Agricultural Science Collection ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Biological Science Database Ecology Abstracts ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts Environmental Science Collection Entomology Abstracts Nursing & Allied Health Premium ProQuest Health & Medical Complete ProQuest One Academic UKI Edition Environmental Science Database ProQuest Nursing & Allied Health Source (Alumni) Engineering Research Database ProQuest One Academic Meteorological & Geoastrophysical Abstracts - Academic ProQuest One Academic (New) Technology Collection Technology Research Database ProQuest One Academic Middle East (New) Materials Science Collection ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Pharma Collection ProQuest Central ProQuest Health & Medical Research Collection Genetics Abstracts ProQuest Engineering Collection Biotechnology Research Abstracts Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Bacteriology Abstracts (Microbiology B) Algology Mycology and Protozoology Abstracts (Microbiology C) Agricultural & Environmental Science Collection AIDS and Cancer Research Abstracts Materials Science Database ProQuest Materials Science Collection ProQuest Public Health ProQuest Nursing & Allied Health Source ProQuest SciTech Collection Advanced Technologies & Aerospace Database ProQuest Medical Library Animal Behavior Abstracts Materials Science & Engineering Collection Immunology Abstracts ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | Agricultural Science Database MEDLINE MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 4 dbid: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Sciences (General) Computer Science |
| DocumentTitleAlternate | Accurate imputation of genotypes in bi-parental populations |
| EISSN | 1932-6203 |
| ExternalDocumentID | 3161750673 oai_doaj_org_article_fca0b9e50fac47a19cad3b583c3ebc69 PMC11781708 oai_HAL_hal_04942849v1 A825467547 39883620 10_1371_journal_pone_0314759 |
| Genre | Journal Article |
| GeographicLocations | France |
| GeographicLocations_xml | – name: France |
| GrantInformation_xml | – fundername: ; – fundername: ; grantid: ANR-21-CE20-0012-03 – fundername: ; grantid: International RIce 485 Genome INitiative “IRIGIN” project |
| GroupedDBID | --- 123 29O 2WC 53G 5VS 7RV 7X2 7X7 7XC 88E 8AO 8C1 8CJ 8FE 8FG 8FH 8FI 8FJ A8Z AAFWJ AAUCC AAWOE AAYXX ABDBF ABIVO ABJCF ABUWG ACGFO ACIHN ACIWK ACPRK ACUHS ADBBV AEAQA AENEX AEUYN AFKRA AFPKN AFRAH AHMBA ALIPV ALMA_UNASSIGNED_HOLDINGS AOIJS APEBS ARAPS ATCPS BAWUL BBNVY BCNDV BENPR BGLVJ BHPHI BKEYQ BPHCQ BVXVI BWKFM CCPQU CITATION CS3 D1I D1J D1K DIK DU5 E3Z EAP EAS EBD EMOBN ESX EX3 F5P FPL FYUFA GROUPED_DOAJ GX1 HCIFZ HH5 HMCUK HYE IAO IEA IGS IHR IHW INH INR IOV IPY ISE ISR ITC K6- KB. KQ8 L6V LK5 LK8 M0K M1P M48 M7P M7R M7S M~E NAPCQ O5R O5S OK1 OVT P2P P62 PATMY PDBOC PHGZM PHGZT PIMPY PQQKQ PROAC PSQYO PTHSS PV9 PYCSY RNS RPM RZL SV3 TR2 UKHRP WOQ WOW ~02 ~KM ADRAZ BBORY CGR CUY CVF ECM EIF IPNFZ NPM RIG PMFND 3V. 7QG 7QL 7QO 7SN 7SS 7T5 7TG 7TM 7U9 7XB 8FD 8FK AZQEC C1K DWQXO FR3 GNUQQ H94 K9. KL. M7N P64 PJZUB PKEHL PPXIY PQEST PQGLB PQUKI PRINS RC3 7X8 PUEGO 1XC BXJBU ESTFP IHQJB VOOES 5PM |
| ID | FETCH-LOGICAL-c676t-751a4f9b171ccac2ab7a97809fd89c2007e1b85094c9ea81b097bffdf8524b973 |
| IEDL.DBID | M48 |
| ISSN | 1932-6203 |
| IngestDate | Wed Aug 13 01:19:12 EDT 2025 Wed Aug 27 01:21:45 EDT 2025 Thu Aug 21 18:38:11 EDT 2025 Fri Sep 12 12:36:48 EDT 2025 Fri Sep 05 10:10:51 EDT 2025 Fri Jul 25 11:23:00 EDT 2025 Tue Jun 17 21:59:27 EDT 2025 Tue Jun 10 21:01:18 EDT 2025 Fri Jun 27 05:17:20 EDT 2025 Fri Jun 27 05:16:30 EDT 2025 Thu May 22 21:25:22 EDT 2025 Tue May 06 01:31:43 EDT 2025 Tue Jul 01 02:22:46 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Language | English |
| License | Copyright: © 2025 Triay et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Attribution: http://creativecommons.org/licenses/by This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Creative Commons Attribution License |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c676t-751a4f9b171ccac2ab7a97809fd89c2007e1b85094c9ea81b097bffdf8524b973 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Competing Interests: The authors have declared that no competing interests exist. |
| ORCID | 0000-0003-4096-6689 0000-0001-9864-3933 0000-0002-5679-3877 0000-0002-7339-0762 0000-0002-6441-0688 0000-0001-5330-2588 |
| OpenAccessLink | http://journals.scholarsportal.info/openUrl.xqy?doi=10.1371/journal.pone.0314759 |
| PMID | 39883620 |
| PQID | 3161750673 |
| PQPubID | 1436336 |
| PageCount | e0314759 |
| ParticipantIDs | plos_journals_3161750673 doaj_primary_oai_doaj_org_article_fca0b9e50fac47a19cad3b583c3ebc69 pubmedcentral_primary_oai_pubmedcentral_nih_gov_11781708 hal_primary_oai_HAL_hal_04942849v1 proquest_miscellaneous_3161914997 proquest_journals_3161750673 gale_infotracmisc_A825467547 gale_infotracacademiconefile_A825467547 gale_incontextgauss_ISR_A825467547 gale_incontextgauss_IOV_A825467547 gale_healthsolutions_A825467547 pubmed_primary_39883620 crossref_primary_10_1371_journal_pone_0314759 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2025-01-30 |
| PublicationDateYYYYMMDD | 2025-01-30 |
| PublicationDate_xml | – month: 01 year: 2025 text: 2025-01-30 day: 30 |
| PublicationDecade | 2020 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States – name: San Francisco – name: San Francisco, CA USA |
| PublicationTitle | PloS one |
| PublicationTitleAlternate | PLoS One |
| PublicationYear | 2025 |
| Publisher | Public Library of Science Public Library of Science (PLoS) |
| Publisher_xml | – name: Public Library of Science – name: Public Library of Science (PLoS) |
| References | AP Dempster (pone.0314759.ref012) 1977; 39 B Howie (pone.0314759.ref008) 2012; 44 CA Fragoso (pone.0314759.ref010) 2016; 202 DD Kosambi (pone.0314759.ref011) 1944 K Swarts (pone.0314759.ref009) 2014; 7 X Huang (pone.0314759.ref003) 2009; 19 C Xu (pone.0314759.ref004) 2017; 37 H Cramér (pone.0314759.ref013) 1999 RJ Elshire (pone.0314759.ref002) 2011; 6 BL Browning (pone.0314759.ref007) 2021; 108 C Heffelfinger (pone.0314759.ref005) 2014; 15 T Furuta (pone.0314759.ref014) 2023; 224 SR Browning (pone.0314759.ref006) 2007; 81 JW Davey (pone.0314759.ref001) 2010; 9 |
| References_xml | – volume: 202 start-page: 487 issue: 2 year: 2016 ident: pone.0314759.ref010 article-title: Imputing Genotypes in Biallelic Populations from Low-Coverage Sequence Data publication-title: Genetics doi: 10.1534/genetics.115.182071 – volume: 9 start-page: 416 issue: 5-6 year: 2010 ident: pone.0314759.ref001 article-title: RADSeq: next-generation population genetics publication-title: Briefings in Functional Genomics doi: 10.1093/bfgp/elq031 – volume: 15 start-page: 979 issue: 1 year: 2014 ident: pone.0314759.ref005 article-title: Flexible and scalable genotyping-by-sequencing strategies for population studies publication-title: BMC Genomics doi: 10.1186/1471-2164-15-979 – volume: 19 start-page: 1068 issue: 6 year: 2009 ident: pone.0314759.ref003 article-title: High-throughput genotyping by whole-genome resequencing publication-title: Genome Research doi: 10.1101/gr.089516.108 – volume-title: Mathematical Methods of Statistics year: 1999 ident: pone.0314759.ref013 – volume: 108 start-page: 1880 issue: 10 year: 2021 ident: pone.0314759.ref007 article-title: Fast two-stage phasing of large-scale sequence data publication-title: The American Journal of Human Genetics doi: 10.1016/j.ajhg.2021.08.005 – volume: 7 issue: 3 year: 2014 ident: pone.0314759.ref009 article-title: Novel Methods to Optimize Genotypic Imputation for Low-Coverage, Next-Generation Sequence Data in Crop Plants publication-title: The Plant Genome doi: 10.3835/plantgenome2014.05.0023 – volume: 37 start-page: 20 issue: 3 year: 2017 ident: pone.0314759.ref004 article-title: Development of a maize 55 K SNP array with improved genome coverage for molecular breeding publication-title: Molecular Breeding doi: 10.1007/s11032-017-0622-z – start-page: 125 volume-title: D.D. Kosambi: Selected Works in Mathematics and Statistics year: 1944 ident: pone.0314759.ref011 – volume: 44 start-page: 955 issue: 8 year: 2012 ident: pone.0314759.ref008 article-title: Fast and accurate genotype imputation in genome-wide association studies through pre-phasing publication-title: Nature Genetics doi: 10.1038/ng.2354 – volume: 224 start-page: iyad055 issue: 2 year: 2023 ident: pone.0314759.ref014 article-title: GBScleanR: robust genotyping error correction using a hidden Markov model with error pattern recognition publication-title: Genetics doi: 10.1093/genetics/iyad055 – volume: 81 start-page: 1084 issue: 5 year: 2007 ident: pone.0314759.ref006 article-title: Rapid and Accurate Haplotype Phasing and Missing-Data Inference for Whole-Genome Association Studies By Use of Localized Haplotype Clustering publication-title: The American Journal of Human Genetics doi: 10.1086/521987 – volume: 6 start-page: e19379 issue: 5 year: 2011 ident: pone.0314759.ref002 article-title: A Robust, Simple Genotyping-by-Sequencing (GBS) Approach for High Diversity Species publication-title: PLoS ONE doi: 10.1371/journal.pone.0019379 – volume: 39 start-page: 1 issue: 1 year: 1977 ident: pone.0314759.ref012 article-title: Maximum Likelihood from Incomplete Data via the EM Algorithm publication-title: Journal of the Royal Statistical Society Series B (Methodological) doi: 10.1111/j.2517-6161.1977.tb01600.x |
| SSID | ssj0053866 |
| Score | 2.4737535 |
| Snippet | Genotyping of bi-parental populations can be performed with low-coverage next-generation sequencing (LC-NGS). This allows the creation of highly saturated... Motivation Genotyping of bi-parental populations can be performed with low-coverage next-generation sequencing (LC-NGS). This allows the creation of highly... MotivationGenotyping of bi-parental populations can be performed with low-coverage next-generation sequencing (LC-NGS). This allows the creation of highly... Motivation: Genotyping of bi-parental populations can be performed with low-coverage next-generation sequencing (LC-NGS). This allows the creation of highly... Motivation Genotyping of bi-parental populations can be performed with low-coverage next-generation sequencing (LC-NGS). This allows the creation of highly... |
| SourceID | plos doaj pubmedcentral hal proquest gale pubmed crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database |
| StartPage | e0314759 |
| SubjectTerms | Algorithms Analysis Availability Biochemistry, Molecular Biology Biology and Life Sciences Breakpoints Chromosomes Computer and Information Sciences Computer Science Crossovers Data Structures and Algorithms Datasets DNA sequencing Engineering and Technology Errors Gene mapping Gene sequencing Genomes Genomics Genotype Genotype & phenotype Genotypes Genotyping Haplotypes High-Throughput Nucleotide Sequencing - methods Humanities and Social Sciences Humans Life Sciences Mapping Markov chains Markov processes Mathematical Software Maximum likelihood estimation Methods Methods and statistics Missing data Modeling and Simulation Next-generation sequencing Nucleotide sequencing Populations Quantitative Trait Loci Recall Recombination Recombination, Genetic Research and Analysis Methods Software Source code |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3dj9QgECe6T74Yz6-rnorGRH3grrSlwONq3KzGj0Q9c28EKLhNNu3G7mr872VKt9maS_TB1zJpYX4DDHTmNwg99ax0lS0csVQ4UuhCEOErTYTO0lK6rNQ5ZCO__1Auz4u3F-zioNQXxIRFeuCouDNvdWqkY6nXtuCaSqur3DCR29wZW_ape6lM94epuAaHWVyWQ6JczunZgMvppm3cKRC2c-AmPdiIer7-cVW-uoKgyNlm3XaXOZ5_xk8ebEiLG-j64EnieRzBEbrimpvoaJirHX4-EEq_uIXWC91tsW4qrK3dATcErqGWQw8Kbj0Gola4i-0wZJvgpq27X3jd_iQWAjzDioOHiOuwz2GIKcV1g01NIHodsinxZiwD1t1G54vXX14tyVBlgdiSl1vCGdWFl4ZyGtC0mTZcAy2R9JWQFq4yHTUCePasdDp4uankxvvKC5YVRvL8Dpo1Qa_HCBspOHc-tRp4BAPuRghHna1oxYz1LEFkr3K1iWQaqv-jxsMhJGpNAURqgChBLwGXURaosPsHwUDUYCDqbwaSoEeAqop5peOEVvO-FABnBU_Qk14C6DAaiLf5pnddp958_PoPQp8_TYSeDUK-DZYR1BlzHMKYgGZrInkykQyT2k6_tgJlHAx8OX-n4BkQ-gSfQv6gCToGE92rrlM5HFYZlB4Kr9-b7eXNj8dm-DIE2jWu3UUZGU7NMnThbrTysRe5FCL4O2mCxMT-J92ctjT1qqcsp5QDE6S49z8QvY-uZVCFOYWb0xM0237fuQfBNdyah_0q8Bt05WXy priority: 102 providerName: Directory of Open Access Journals – databaseName: Health & Medical Collection dbid: 7X7 link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1Lj9MwELbYcuGCWF4bWMAgJODg3bh52D6hgqgK4iEBi3qzbMfeRqqSsGlB_Hs8iRsatEJc41HszMMeT2a-Qeipy3JbmNQSQ7klqUo54a5QhKtpnAs7zVUC1cgfPuaLs_TdMluGgFsb0ip3e2K3URe1gRj5aQKOeAZtVV423wl0jYK_q6GFxgG62kGXeX1my-HC5W05z0O5XMLoaZDOSVNX9gRg2xkglO4dRx1q_7A3H6wgNXLSrOv2Mvfz7yzKvWNpfgNdD_4knvUKcIiu2OomOgwW2-LnAVb6xS20nqt2g1VVYGXMFhAicAkdHTrR4NphgGuFiGyLoeYEV3XZ_sLr-icxkObp9x0c8q79aYchsxSXFdYlgRx2qKnEzdAMrL2NzuZvvr5ekNBrgZic5RvCMqpSJzRl1MvUTJVmCsCJhCu4MBDQtFRzQNszwirv68aCaecKx7NpqgVL7qBJ5fl6hLAWnDHrYqMATdBLX3NuqTUFLTJtXBYhsmO5bHpIDdn9V2P-KtJzTYKIZBBRhF6BXAZaAMTuHtQX5zLYl3RGxVrYLHbK-EmpMKpIdMYTk1htcv-SRyBV2VeXDmYtZ11DAJalLEJPOgoAxagg6-ZcbdtWvv307T-IvnweET0LRK72muHZ2Vc6-G8CsK0R5fGI0pu2Gc-2Ambsffhi9l7CM4D18Z6F-EEjdAQqumNdK_9Yin_9Tm0vH348DMPMkG5X2Xrb0wh_dxZ-CXd7LR9WkQjOvdcTR4iP9H-0zPFIVa464HJKGeBB8nv_Xtd9dG0KXZZjiIweo8nmYmsfeNdvox929v0bT9Bbgg priority: 102 providerName: ProQuest |
| Title | Fast and accurate imputation of genotypes from noisy low-coverage sequencing data in bi-parental populations |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/39883620 https://www.proquest.com/docview/3161750673 https://www.proquest.com/docview/3161914997 https://hal.inrae.fr/hal-04942849 https://pubmed.ncbi.nlm.nih.gov/PMC11781708 https://doaj.org/article/fca0b9e50fac47a19cad3b583c3ebc69 http://dx.doi.org/10.1371/journal.pone.0314759 |
| Volume | 20 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3db9MwELfW7oUXxPhaYRSDkICHVHHzYfsBoW5aKYgNNCjaW2Q79lqpSkrTAvvvuUvSaEEF8ZKH5OKPu7N9tu9-R8gLF8U2NaH1DBPWC1UoPOFS5Qk19GNph7EKMBr57DyeTMMPl9HlHtnmbK0ZWOzc2mE-qelqMfj1_fotDPg3ZdYGzrY_DZZ5ZgcIx84j2SH7sDbFuB07C5t7BRjd5e0lWi1ePPSDOpjub6W0FqsS07-ZuTszdJzsLhd5scs4_dPH8saiNb5DbtfWJh1V6nFA9mx2lxzU47mgr2rQ6df3yGKsijVVWUqVMRvEj6BzzPdQCo7mjiKYK57XFhQjUmiWz4trush_egadQGFWorVXNqyFFP1O6Tyjeu6hhztGXNJlkyqsuE-m49OvJxOvzsTgmZjHa49HTIVOasYZSNwMleYKoYukS4U0eNxpmRaIxWekVWAJ-5Jr51InomGoJQ8ekG4GfD0kVEvBuXW-UYg1CLqhhbDMmpSlkTYu6hFvy_JkWQFuJOWtG4eNSsW1BEWU1CLqkWOUS0OLcNnli3x1ldSjL3FG-VrayHfKQKVMGpUGOhKBCaw2MRTyFKWaVLGnzaBPRmW6AB6FvEeelxQImZGhT86V2hRF8v7Tt_8g-nLRInpZE7kcNAPYWcVBQJ8QiqtFedSihIFv2rXNkBk3Oj4ZfUzwHYL-gN0hf7AeOUQV3bKuSALc0EaYngiK36rt7s_Pms9YMzrjZTbfVDQSdtYSmvCw0vKmFYEUAmwiv0dES_9bzWx_yeazEtacMY5okeLRv7v9mNwaYg5mH89Nj0h3vdrYJ2AYrnWfdPglh6c4Yfgcv-uT_ePT888X_fKopV_OBb8BZ3poJQ |
| linkProvider | Scholars Portal |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1LbxMxELbacIALorwaKNQgEHDYdr0v2weEQqFKaFokaFFvxvbaTaRoN3QTqv4pfiOefZFFFeLS63pke2fG47E98w1CL2ycmFRHxtOEGS-SEfOYTaXHZOAn3ASJDCEb-fAoGZ5En07j0zX0q8mFgbDKxiaWhjrNNdyR74bgiMdQVuXd_IcHVaPgdbUpoVGpxYG5vHBHtuLt6IOT78sg2P94vDf06qoCnk5osvBoTGRkuSKUuNnrQCoqAYaH25RxDVd3higGuHKaG-m8Op9TZW1qWRxEitPQ9buObkRhmMAqYnttSImzHUlSp-eFlOzW2rAzzzOzAzDxFBBRV7a_skpAuxesTyAUszef5cVV7u7fUZsr2-D-HXS79l_xoFK4DbRmsrtoo7YQBX5dw1i_uYdm-7JYYJmlWGq9BEQKPIUKEqUq4NxigIeFG-ACQ44LzvJpcYln-YWnIazU2Tlcx3m73RVDJCueZlhNPYiZhxxOPG-LjxX30cm1SOEB6mWOr5sIK84oNdbXEtALnbYpxgwxOiVprLSN-8hrWC7mFYSHKN_xqDv6VFwTICJRi6iP3oNcWloA4C4_5Odnol7PwmrpK25i30rtBiVcyzRUMQt1aJROXCfbIFVRZbO2ZkQMygIENI5oHz0vKQCEI4MonzO5LAox-vztP4i-fukQvaqJbO40w7Gzyqxw_wTgXh3KrQ6lMyW6O9oEmLHy48PBWMA3gBFyngz_SfpoE1S0YV0h_qxM132jtlc3P2ubYWQI78tMvqxouDurczeFh5WWt7MIOWPOy_L7iHX0vzPNbks2nZRA6YRQwJ9kj_49r210c3h8OBbj0dHBY3QrgArPPtzKbqHe4nxpnji3c6Gelmsdo-_XbVx-A6lUmI4 |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3dTxQxEG_gSIwvRvziFKUajfqw3H63fSAGhAsIIkEwvK1tt4VLyO7J3kn4F_2rnNntnqwhxhdet5N-zEyn0-7Mbwh5bZPU5Do2ng648WIZc4_bXHpchn4qTJjKCLORP--n28fxp5PkZI78anNhMKyytYm1oc5LjW_kgwgd8QTLqgysC4s42Bx-GP_wsIIU_mlty2lIV2YhX6vhxlySx665uoTrXLW2swmyfxOGw62jj9ueqzjg6ZSlE48lgYytUAELYGU6lIpJhOgRNudC47OeCRRHzDktjASPzxdMWZtbnoSxEiyCfufJAoNTMu6RhY2t_YPD9lwAy5KmLnkvYsHA6crquCzMKoLIM8RLvXY41jUEZifF_BkGavbG52V1kzP8d0zntUNyeJ_cc94tXW_UcZHMmeIBWXT2o6LvHMj1-4fkfCirCZVFToGRU8SroCOsL1ErCi0tRfBYfB-uKGbA0KIcVVf0vLz0NAadghWkLgoczl6Kca50VFA18jCiHjM86XhWmqx6RI5vRQ6PSa8Avi4RqgRnzFhfS8Q2BF1UnJvA6DzIE6Vt0idey_Js3AB8ZPVfPgYXo4ZrGYoocyLqkw2Uy4wW4bnrD-XFaeZ2e2a19JUwiW-lhkEDoWUeqYRHOjJKp9DJCko1a3JdZ0YmW6_LE7AkZn3yqqZAiI4Clf1UTqsq2_ny7T-Ivh52iN46IluCZgA7m7wLWBNCf3UolzuUYGh0d7QzZMa1hW-v72X4DUGGwM8RP4M-WUIVbVlXZX_2LXTfqu3NzS9nzTgyBv8Vppw2NAJu8gKm8KTR8tksIsE5-GB-n_CO_nem2W0pRmc1jHoQMESn5E__Pa8VcgcMTba3s7_7jNwNsfyzj0-2y6Q3uZia5-CTTtQLt9kp-X7b9uU3rAKjhg |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Fast+and+accurate+imputation+of+genotypes+from+noisy+low-coverage+sequencing+data+in+bi-parental+populations&rft.jtitle=PloS+one&rft.au=Triay%2C+C%C3%A9cile&rft.au=Boizet%2C+Alice&rft.au=Fragoso%2C+Christopher&rft.au=Gkanogiannis%2C+Anestis&rft.date=2025-01-30&rft.pub=Public+Library+of+Science&rft.issn=1932-6203&rft.eissn=1932-6203&rft.volume=20&rft.issue=1&rft.spage=e0314759&rft_id=info:doi/10.1371%2Fjournal.pone.0314759&rft.externalDocID=A825467547 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1932-6203&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1932-6203&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1932-6203&client=summon |