Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/18385
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | ZAMANZAD GHAVIDEL, Fatemeh | - |
dc.contributor.author | CLAESEN, Jurgen | - |
dc.contributor.author | BURZYKOWSKI, Tomasz | - |
dc.date.accessioned | 2015-03-06T11:40:42Z | - |
dc.date.available | 2015-03-06T11:40:42Z | - |
dc.date.issued | 2015 | - |
dc.identifier.citation | JOURNAL OF COMPUTATIONAL BIOLOGY, 22 (2), p. 178-188 | - |
dc.identifier.issn | 1066-5277 | - |
dc.identifier.uri | http://hdl.handle.net/1942/18385 | - |
dc.description.abstract | The analysis of polygenetic characteristics for mapping quantitative trait loci (QTL) remains an important challenge. QTL analysis requires two or more strains of organisms that differ substantially in the (poly-)genetic trait of interest, resulting in a heterozygous offspring. The offspring with the trait of interest is selected and subsequently screened for molecular markers such as single-nucleotide polymorphisms (SNPs) with next-generation sequencing. Gene mapping relies on the co-segregation between genes and/or markers. Genes and/or markers that are linked to a QTL influencing the trait will segregate more frequently with this locus. For each identified marker, observed mismatch frequencies between the reads of the offspring and the parental reference strains can be modeled by a multinomial distribution with the probabilities depending on the state of an underlying, unobserved Markov process. The states indicate whether the SNP is located in a (vicinity of a) QTL or not. Consequently, genomic loci associated with the QTL can be discovered by analyzing hidden states along the genome. The aforementioned hidden Markov model assumes that the identified SNPs are equally distributed along the chromosome and does not take the distance between neighboring SNPs into account. The distance between the neighboring SNPs could influence the chance of co-segregation between genes and markers. To address this issue, we propose a nonhomogeneous hidden Markov model with a transition matrix that depends on a set of distance-varying observed covariates. The application of the model is illustrated on the data from a study of ethanol tolerance in yeast. | - |
dc.description.sponsorship | The authors are grateful to Steve Swinnen, Thiago Pais, Maria R. Foulquie-Moreno, and Johan M. Thevelein of the Laboratory of Molecular Cell Biology, Institute of Botany and Microbiology, KU Leuven, and Department of Molecular Microbiology, VIB (Flemish Institute for Biotechnology), for providing the data. The authors gratefully acknowledge the support from the IAP Research Network P7/06 of the Belgian State (Belgian Science Policy). | - |
dc.language.iso | en | - |
dc.rights | © Mary Ann Liebert, Inc. | - |
dc.subject.other | next-generation sequencing; nonhomogeneous hidden Markov model; quantitative trait loci analysis; single-nucleotide polymorphisms | - |
dc.title | A Nonhomogeneous Hidden Markov Model for Gene Mapping Based on Next-Generation Sequencing Data | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 188 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | 178 | - |
dc.identifier.volume | 22 | - |
local.bibliographicCitation.jcat | A1 | - |
dc.description.notes | [Ghavidel, Fatemeh Zamanzad; Claesen, Juergen; Burzykowski, Tomasz] Hasselt Univ, Interuniv Inst Biostat & Stat Bioinformat, B-3590 Diepenbeek, Limburg, Belgium. | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
dc.identifier.doi | 10.1089/cmb.2014.0258 | - |
dc.identifier.isi | 000349322900007 | - |
item.fullcitation | ZAMANZAD GHAVIDEL, Fatemeh; CLAESEN, Jurgen & BURZYKOWSKI, Tomasz (2015) A Nonhomogeneous Hidden Markov Model for Gene Mapping Based on Next-Generation Sequencing Data. In: JOURNAL OF COMPUTATIONAL BIOLOGY, 22 (2), p. 178-188. | - |
item.contributor | ZAMANZAD GHAVIDEL, Fatemeh | - |
item.contributor | CLAESEN, Jurgen | - |
item.contributor | BURZYKOWSKI, Tomasz | - |
item.validation | ecoom 2016 | - |
item.accessRights | Restricted Access | - |
item.fulltext | With Fulltext | - |
crisitem.journal.issn | 1066-5277 | - |
crisitem.journal.eissn | 1557-8666 | - |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
NH-HMM for gene mapping based on NGS data.pdf Restricted Access | 613.13 kB | Adobe PDF | View/Open Request a copy |
SCOPUSTM
Citations
6
checked on Sep 2, 2020
WEB OF SCIENCETM
Citations
11
checked on Sep 20, 2024
Page view(s)
68
checked on Apr 17, 2023
Download(s)
54
checked on Apr 17, 2023
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.