Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/80080
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dc.contributor.authorGuo, Jingen
dc.contributor.authorJain, Ritikaen
dc.contributor.authorYang, Pengen
dc.contributor.authorFan, Ruien
dc.contributor.authorKwoh, Chee Keongen
dc.contributor.authorZheng, Jieen
dc.date.accessioned2013-11-15T04:31:57Zen
dc.date.accessioned2019-12-06T13:40:18Z-
dc.date.available2013-11-15T04:31:57Zen
dc.date.available2019-12-06T13:40:18Z-
dc.date.copyright2013en
dc.date.issued2013en
dc.identifier.citationGuo, J., Jain, R., Yang, P., Fan, R., Kwoh, C. K., & Zheng, J. (2013). Reliable and Fast Estimation of Recombination Rates by Convergence Diagnosis and Parallel Markov Chain Monte Carlo. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 99, 1.en
dc.identifier.issn1545-5963en
dc.identifier.urihttps://hdl.handle.net/10356/80080-
dc.description.abstractGenetic recombination is an essential event during the process of meiosis resulting in an exchange of segments between paired chromosomes. Estimating recombination rate is crucial for understanding evolution. Experimental methods are normally difficult and limited to small scale estimations. Thus statistical methods using population genetic data are important for large-scale analysis. LDhat is an extensively used statistical method using rjMCMC algorithm to predict recombination rates. Due to the complexity of rjMCMC scheme, LDhat may take a long time to generate results for large SNP data. In addition, rjMCMC parameters should be manually defined in the original program that directly impact results. To address these issues, we designed an improved algorithm based on LDhat implementing MCMC convergence diagnostic algorithms to automatically predict values of parameters and monitor the mixing process. Then parallel computation methods were employed to further accelerate the new program. The new algorithms have been tested on ten samples from HapMap phase 2 datasets. The results were compared with previous code and showed nearly identical outputs, however our new methods achieved significant acceleration proving that they are more efficient and reliable for the estimation of recombination rates. The stand-alone package is freely available for download at the link below.en
dc.description.sponsorshipMOE (Min. of Education, S’pore)en
dc.language.isoenen
dc.relation.ispartofseriesIEEE/ACM transactions on computational biology and bioinformaticsen
dc.rights© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Published version of this article is available at http://dx.doi.org/10.1109/TCBB.2013.133. CPLDhat is an open source Java program.en
dc.subjectDRNTU::Engineering::Computer science and engineering::Theory of computation::Analysis of algorithms and problem complexityen
dc.titleReliable and fast estimation of recombination rates by convergence diagnosis and parallel Markov Chain Monte Carloen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Engineeringen
dc.identifier.doi10.1109/TCBB.2013.133en
dc.description.versionAccepted versionen
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