title: Computational Haplotype Inference from Pooled Samples.
abstract: Computationally inferring the identities and their relative frequencies from pooled samples that are whole-genome or segmentally genotyped or sequenced (e.g., using next-generation sequencing) in a pool is useful for population genetics analysis. To carry out such analysis, one needs to understand basics of how to use high-performance computing (HPC) facilities and the specifics of corresponding computational tools. Here, we describe the basic knowledge and step-by-step usage of a number of tools for haplotype inference on genotyping or next-generation sequencing data.
authors.affiliation: Departments of Biochemistry & Molecular Biology and Medical Genetics, Alberta Children's Hospital Research Institute and O'Brien Institute for Public Health, University of Calgary, Calgary, AB, Canada, T2N 4N1. quan.long@ucalgary.ca.
authors.forename: Quan
authors.initials: Q
authors.lastname: Long
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D005838:Genotype; D006239:Haplotypes / Q000235:genetics; D006801:Humans; D020641:Polymorphism, Single Nucleotide / Q000235:genetics
keywords: Bioinformatics; Computing cluster; Genotyping; Haplotype; Next-generation sequencing