The Experts below are selected from a list of 108135 Experts worldwide ranked by ideXlab platform

Denis Milan - One of the best experts on this subject based on the ideXlab platform.

  • boosting em for radiation hybrid and Genetic Mapping
    Workshop on Algorithms in Bioinformatics, 2001
    Co-Authors: Thomas Schiex, Patrick Chabrier, Martin Bouchez, Denis Milan
    Abstract:

    Radiation hybrid (RH) Mapping is a somatic cell technique that is used for ordering markers along a chromosome and estimating physical distances between them. It nicely complements the Genetic Mapping technique, allowing for finer resolution. Like Genetic Mapping, RH Mapping consists in finding a marker ordering that maximizes a given criteria. Several software packages have been recently proposed to solve RH Mapping problems. Each package offers specific criteria and specific ordering techniques. The most general packages look for maximum likelihood maps and may cope with errors, unknowns and polyploid hybrids at the cost of limited computational efficiency. More efficient packages look for minimum breaks or two-points approximated maximum likelihood maps but ignore errors, unknowns and polyploid hybrids. In this paper, we present a simple improvement of the EM algorithm [5] that makes maximum likelihood estimation much more efficient (in practice and to some extent in theory too). The boosted EM algorithm can deal with unknowns in both error-free haploid data and error-free backcross data. Unknowns are usually quite limited in RH Mapping but cannot be ignored when one deals with Genetic data or multiple populations/panels consensus Mapping (markers being not necessarily typed in all panels/populations). These improved EM algorithms have been implemented in the CARTHAGENE software. We conclude with a comparison with similar packages (RHMAP and MapMaker) using simulated data sets and present preliminary results on mixed simultaneous RH/Genetic Mapping on pig data.

  • WABI - Boosting EM for Radiation Hybrid and Genetic Mapping
    Lecture Notes in Computer Science, 2001
    Co-Authors: Thomas Schiex, Patrick Chabrier, Martin Bouchez, Denis Milan
    Abstract:

    Radiation hybrid (RH) Mapping is a somatic cell technique that is used for ordering markers along a chromosome and estimating physical distances between them. It nicely complements the Genetic Mapping technique, allowing for finer resolution. Like Genetic Mapping, RH Mapping consists in finding a marker ordering that maximizes a given criteria. Several software packages have been recently proposed to solve RH Mapping problems. Each package offers specific criteria and specific ordering techniques. The most general packages look for maximum likelihood maps and may cope with errors, unknowns and polyploid hybrids at the cost of limited computational efficiency. More efficient packages look for minimum breaks or two-points approximated maximum likelihood maps but ignore errors, unknowns and polyploid hybrids. In this paper, we present a simple improvement of the EM algorithm [5] that makes maximum likelihood estimation much more efficient (in practice and to some extent in theory too). The boosted EM algorithm can deal with unknowns in both error-free haploid data and error-free backcross data. Unknowns are usually quite limited in RH Mapping but cannot be ignored when one deals with Genetic data or multiple populations/panels consensus Mapping (markers being not necessarily typed in all panels/populations). These improved EM algorithms have been implemented in the CARTHAGENE software. We conclude with a comparison with similar packages (RHMAP and MapMaker) using simulated data sets and present preliminary results on mixed simultaneous RH/Genetic Mapping on pig data.

Haibao Tang - One of the best experts on this subject based on the ideXlab platform.

  • Genotype-Corrector: improved genotype calls for Genetic Mapping in F2 and RIL populations.
    Scientific Reports, 2018
    Co-Authors: Chenyong Miao, Jingping Fang, D. F. Li, Pingping Liang, Xingtan Zhang, Jinliang Yang, James C. Schnable, Haibao Tang
    Abstract:

    F2 and recombinant inbred lines (RILs) populations are very commonly used in plant Genetic Mapping studies. Although genome-wide Genetic markers like single nucleotide polymorphisms (SNPs) can be readily identified by a wide array of methods, accurate genotype calling remains challenging, especially for heterozygous loci and missing data due to low sequencing coverage per individual. Therefore, we developed Genotype-Corrector, a program that corrects genotype calls and imputes missing data to improve the accuracy of Genetic Mapping. Genotype-Corrector can be applied in a wide variety of Genetic Mapping studies that are based on low coverage whole genome sequencing (WGS) or Genotyping-by-Sequencing (GBS) related techniques. Our results show that Genotype-Corrector achieves high accuracy when applied to both synthetic and real genotype data. Compared with using raw or only imputed genotype calls, the linkage groups built by corrected genotype data show much less noise and significant distortions can be corrected. Additionally, Genotype-Corrector compares favorably to the popular imputation software LinkImpute and Beagle in both F2 and RIL populations. Genotype-Corrector is publicly available on GitHub at https://github.com/freemao/Genotype-Corrector .

  • Genotype-Corrector: improved genotype calls for Genetic Mapping in F 2 and RIL populations
    Scientific reports, 2018
    Co-Authors: Chenyong Miao, Jingping Fang, Pingping Liang, Xingtan Zhang, Jinliang Yang, James C. Schnable, Haibao Tang
    Abstract:

    F2 and recombinant inbred lines (RILs) populations are very commonly used in plant Genetic Mapping studies. Although genome-wide Genetic markers like single nucleotide polymorphisms (SNPs) can be readily identified by a wide array of methods, accurate genotype calling remains challenging, especially for heterozygous loci and missing data due to low sequencing coverage per individual. Therefore, we developed Genotype-Corrector, a program that corrects genotype calls and imputes missing data to improve the accuracy of Genetic Mapping. Genotype-Corrector can be applied in a wide variety of Genetic Mapping studies that are based on low coverage whole genome sequencing (WGS) or Genotyping-by-Sequencing (GBS) related techniques. Our results show that Genotype-Corrector achieves high accuracy when applied to both synthetic and real genotype data. Compared with using raw or only imputed genotype calls, the linkage groups built by corrected genotype data show much less noise and significant distortions can be corrected. Additionally, Genotype-Corrector compares favorably to the popular imputation software LinkImpute and Beagle in both F2 and RIL populations. Genotype-Corrector is publicly available on GitHub at https://github.com/freemao/Genotype-Corrector .

Jeffrey R. Smith - One of the best experts on this subject based on the ideXlab platform.

  • A major zebrafish polymorphism resource for Genetic Mapping
    Genome biology, 2007
    Co-Authors: Kevin M. Bradley, J. Bradford Elmore, Joan P. Breyer, Brian L. Yaspan, Jason R. Jessen, Ela W. Knapik, Jeffrey R. Smith
    Abstract:

    We have identified 645,088 candidate polymorphisms in zebrafish and observe a single nucleotide polymorphism (SNP) validation rate of 71% to 86%, improving with polymorphism confidence score. Variant sites are non-random, with an excess of specific novel T- and A-rich motifs. We positioned half of the polymorphisms on zebrafish Genetic and physical maps as a resource for positional cloning. We further demonstrate bulked segregant analysis using the anchored SNPs as a method for high-throughput Genetic Mapping in zebrafish.

Thomas Schiex - One of the best experts on this subject based on the ideXlab platform.

  • boosting em for radiation hybrid and Genetic Mapping
    Workshop on Algorithms in Bioinformatics, 2001
    Co-Authors: Thomas Schiex, Patrick Chabrier, Martin Bouchez, Denis Milan
    Abstract:

    Radiation hybrid (RH) Mapping is a somatic cell technique that is used for ordering markers along a chromosome and estimating physical distances between them. It nicely complements the Genetic Mapping technique, allowing for finer resolution. Like Genetic Mapping, RH Mapping consists in finding a marker ordering that maximizes a given criteria. Several software packages have been recently proposed to solve RH Mapping problems. Each package offers specific criteria and specific ordering techniques. The most general packages look for maximum likelihood maps and may cope with errors, unknowns and polyploid hybrids at the cost of limited computational efficiency. More efficient packages look for minimum breaks or two-points approximated maximum likelihood maps but ignore errors, unknowns and polyploid hybrids. In this paper, we present a simple improvement of the EM algorithm [5] that makes maximum likelihood estimation much more efficient (in practice and to some extent in theory too). The boosted EM algorithm can deal with unknowns in both error-free haploid data and error-free backcross data. Unknowns are usually quite limited in RH Mapping but cannot be ignored when one deals with Genetic data or multiple populations/panels consensus Mapping (markers being not necessarily typed in all panels/populations). These improved EM algorithms have been implemented in the CARTHAGENE software. We conclude with a comparison with similar packages (RHMAP and MapMaker) using simulated data sets and present preliminary results on mixed simultaneous RH/Genetic Mapping on pig data.

  • WABI - Boosting EM for Radiation Hybrid and Genetic Mapping
    Lecture Notes in Computer Science, 2001
    Co-Authors: Thomas Schiex, Patrick Chabrier, Martin Bouchez, Denis Milan
    Abstract:

    Radiation hybrid (RH) Mapping is a somatic cell technique that is used for ordering markers along a chromosome and estimating physical distances between them. It nicely complements the Genetic Mapping technique, allowing for finer resolution. Like Genetic Mapping, RH Mapping consists in finding a marker ordering that maximizes a given criteria. Several software packages have been recently proposed to solve RH Mapping problems. Each package offers specific criteria and specific ordering techniques. The most general packages look for maximum likelihood maps and may cope with errors, unknowns and polyploid hybrids at the cost of limited computational efficiency. More efficient packages look for minimum breaks or two-points approximated maximum likelihood maps but ignore errors, unknowns and polyploid hybrids. In this paper, we present a simple improvement of the EM algorithm [5] that makes maximum likelihood estimation much more efficient (in practice and to some extent in theory too). The boosted EM algorithm can deal with unknowns in both error-free haploid data and error-free backcross data. Unknowns are usually quite limited in RH Mapping but cannot be ignored when one deals with Genetic data or multiple populations/panels consensus Mapping (markers being not necessarily typed in all panels/populations). These improved EM algorithms have been implemented in the CARTHAGENE software. We conclude with a comparison with similar packages (RHMAP and MapMaker) using simulated data sets and present preliminary results on mixed simultaneous RH/Genetic Mapping on pig data.

Chenyong Miao - One of the best experts on this subject based on the ideXlab platform.

  • Genotype-Corrector: improved genotype calls for Genetic Mapping in F2 and RIL populations.
    Scientific Reports, 2018
    Co-Authors: Chenyong Miao, Jingping Fang, D. F. Li, Pingping Liang, Xingtan Zhang, Jinliang Yang, James C. Schnable, Haibao Tang
    Abstract:

    F2 and recombinant inbred lines (RILs) populations are very commonly used in plant Genetic Mapping studies. Although genome-wide Genetic markers like single nucleotide polymorphisms (SNPs) can be readily identified by a wide array of methods, accurate genotype calling remains challenging, especially for heterozygous loci and missing data due to low sequencing coverage per individual. Therefore, we developed Genotype-Corrector, a program that corrects genotype calls and imputes missing data to improve the accuracy of Genetic Mapping. Genotype-Corrector can be applied in a wide variety of Genetic Mapping studies that are based on low coverage whole genome sequencing (WGS) or Genotyping-by-Sequencing (GBS) related techniques. Our results show that Genotype-Corrector achieves high accuracy when applied to both synthetic and real genotype data. Compared with using raw or only imputed genotype calls, the linkage groups built by corrected genotype data show much less noise and significant distortions can be corrected. Additionally, Genotype-Corrector compares favorably to the popular imputation software LinkImpute and Beagle in both F2 and RIL populations. Genotype-Corrector is publicly available on GitHub at https://github.com/freemao/Genotype-Corrector .

  • Genotype-Corrector: improved genotype calls for Genetic Mapping in F 2 and RIL populations
    Scientific reports, 2018
    Co-Authors: Chenyong Miao, Jingping Fang, Pingping Liang, Xingtan Zhang, Jinliang Yang, James C. Schnable, Haibao Tang
    Abstract:

    F2 and recombinant inbred lines (RILs) populations are very commonly used in plant Genetic Mapping studies. Although genome-wide Genetic markers like single nucleotide polymorphisms (SNPs) can be readily identified by a wide array of methods, accurate genotype calling remains challenging, especially for heterozygous loci and missing data due to low sequencing coverage per individual. Therefore, we developed Genotype-Corrector, a program that corrects genotype calls and imputes missing data to improve the accuracy of Genetic Mapping. Genotype-Corrector can be applied in a wide variety of Genetic Mapping studies that are based on low coverage whole genome sequencing (WGS) or Genotyping-by-Sequencing (GBS) related techniques. Our results show that Genotype-Corrector achieves high accuracy when applied to both synthetic and real genotype data. Compared with using raw or only imputed genotype calls, the linkage groups built by corrected genotype data show much less noise and significant distortions can be corrected. Additionally, Genotype-Corrector compares favorably to the popular imputation software LinkImpute and Beagle in both F2 and RIL populations. Genotype-Corrector is publicly available on GitHub at https://github.com/freemao/Genotype-Corrector .