The Experts below are selected from a list of 280650 Experts worldwide ranked by ideXlab platform

Hannah Rempel - One of the best experts on this subject based on the ideXlab platform.

André Eggen - One of the best experts on this subject based on the ideXlab platform.

  • A review on SNP and other types of molecular markers and their use in Animal Genetics
    Genetics selection evolution : GSE, 2002
    Co-Authors: Alain Vignal, Magali Sancristobal, Denis Milan, André Eggen
    Abstract:

    During the last ten years, the use of molecular markers, revealing polymorphism at the DNA level, has been playing an increasing part in Animal Genetics studies. Amongst others, the microsatellite DNA marker has been the most widely used, due to its easy use by simple PCR, followed by a denaturing gel electrophoresis for allele size determination, and to the high degree of information provided by its large number of alleles per locus. Despite this, a new marker type, named SNP, for Single Nucleotide Polymorphism, is now on the scene and has gained high popularity, even though it is only a bi-allelic type of marker. In this review, we will discuss the reasons for this apparent step backwards, and the pertinence of the use of SNPs in Animal Genetics, in comparison with other marker types.

Seungwoo Hwang - One of the best experts on this subject based on the ideXlab platform.

Martin Larsen - One of the best experts on this subject based on the ideXlab platform.

  • a parallel solver for Animal Genetics
    Parallel Computing, 1998
    Co-Authors: Per Madsen, Martin Larsen
    Abstract:

    The use of linear multivariate mixed models in Animal Genetics, leads to very large, sparse linear systems of equations. The sparse, symmetric coefficient matrix is too large to be constructed explicitly. We describe a parallel, iterative linear equation solver for large sparse systems, developed by DIAS and UNI-C. The solver takes advantage of the structure of the multivariate mixed model equations, and is based on Gauss-Seidel and second order Jacobi iteration. It is parallelized for distributed memory architectures.

  • PARA - A Parallel Solver for Animal Genetics
    Lecture Notes in Computer Science, 1998
    Co-Authors: Per Madsen, Martin Larsen
    Abstract:

    The use of linear multivariate mixed models in Animal Genetics, leads to very large, sparse linear systems of equations. The sparse, symmetric coefficient matrix is too large to be constructed explicitly. We describe a parallel, iterative linear equation solver for large sparse systems, developed by DIAS and UNI-C. The solver takes advantage of the structure of the multivariate mixed model equations, and is based on Gauss-Seidel and second order Jacobi iteration. It is parallelized for distributed memory architectures.

Luca Ferretti - One of the best experts on this subject based on the ideXlab platform.

  • Massive parallel sequencing in Animal Genetics: wherefroms and wheretos
    Animal genetics, 2010
    Co-Authors: Miguel Pérez-enciso, Luca Ferretti
    Abstract:

    Summary Next generation sequencing (NGS) has revolutionized genomics research, making it difficult to overstate its impact on studies of Biology. NGS will immediately allow researchers working in non-mainstream species to obtain complete genomes together with a comprehensive catalogue of variants. In addition, RNA-seq will be a decisive way to annotate genes that cannot be predicted purely by computational or comparative approaches. Future applications include whole genome sequence association studies, as opposed to classical SNP-based association, and implementing this new source of information into breeding programmes. For these purposes, one of the main advantages of sequencing vs. genotyping is the possibility of identifying copy number variants. Currently, experimental design is a topic of utmost interest, and here we discuss some of the options available, including pools and reduced representation libraries. Although bioinformatics is still an important bottleneck, this limitation is only transient and should not deter Animal geneticists from embracing these technologies.