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 58,342 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Reconstruction of phylogenetic trees for very large datasets is a known example of a computationally hard problem. In this paper, we present a parallel computing model for the widely used Multiple Instruction Multiple Data (MIMD) architecture. Following the idea of divide-and-conquer, our model adapts the recursive-DCM3 decomposition method [Roshan, U., Moret, B.M.E., Williams, T.L., Warnow, T, 2004a. Performance of suptertree methods on various dataset decompositions. In: Binida-Emonds, O.R.P. (Eds.), Phylogenetic Supertrees: Combining Information to Reveal the Tree of Life, vol. 3 of Computational Biology, Kluwer Academics, pp. 301-328; Roshan, U., Moret, B.M.E., Williams, T.L., Warnow, T., 2004b. Rec-I-DCM3: A Fast Algorithmic Technique for reconstructing large phylogenetic trees, Proceedings of the IEEE Computational Systems Bioinformatics Conference (ICSB)] to divide datasets into smaller subproblems. It distributes computation load over multiple processors so that each processor constructs subtrees on each subproblem within a batch in parallel. It finally collects the resulting trees and merges them into a supertree. The proposed model is flexible as far as methods for dividing and merging datasets are concerned. We show that our method greatly reduces the computational time of the sequential version of the program. As a case study, our parallel approach only takes 22.1h on four processors to outperform the best score to date (Found at 123.7h by the Rec-I-DCM3 program [Roshan, U., Moret, B.M.E., Williams, T.L., Warnow, T, 2004a. Performance of suptertree methods on various dataset decompositions. In: Binida-Emonds, O.R.P. (Eds.), Phylogenetic Supertrees: Combining Information to Reveal the Tree of Life, vol. 3 of Computational Biology, Kluwer Academics, pp. 301-328; Roshan, U., Moret, B.M.E., Williams, T.L., Warnow, T., 2004b. Rec-I-DCM3: A Fast Algorithmic Technique for reconstructing large phylogenetic trees, Proceedings of the IEEE Computational Systems Bioinformatics Conference (ICSB)] on one dataset. Developed with the standard message-passing library, MPI, the program can be recompiled and run on any MIMD systems.
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PMID:Reconstruction of large phylogenetic trees: a parallel approach. 1604 Feb 77

A microsatellite or simple sequence repeat (SSR) consensus map of barley was constructed by joining six independent genetic maps based on the mapping populations 'Igri x Franka', 'Steptoe x Morex', 'OWB(Rec) x OWB(Dom)', 'Lina x Canada Park', 'L94 x Vada' and 'SusPtrit x Vada'. Segregation data for microsatellite markers from different research groups including SCRI (Bmac, Bmag, EBmac, EBmag, HVGeneName, scsssr), IPK (GBM, GBMS), WUR (GBM), Virginia Polytechnic Institute (HVM), and MPI for Plant Breeding (HVGeneName), generated in above mapping populations, were used in the computer program RECORD to order the markers of the individual linkage data sets. Subsequently, a framework map was constructed for each chromosome by integrating the 496 "bridge markers" common to two or more individual maps with the help of the computer programme JoinMap 3.0. The final map was calculated by following a "neighbours" map approach. The integrated map contained 775 unique microsatellite loci, from 688 primer pairs, ranging from 93 (6H) to 132 (2H) and with an average of 111 markers per linkage group. The genomic DNA-derived SSR marker loci had a higher polymorphism information content value (average 0.61) as compared to the EST/gene-derived SSR loci (average 0.48). The consensus map spans 1,068 cM providing an average density of one SSR marker every 1.38 cM. Such a high-density consensus SSR map provides barley molecular breeding programmes with a better choice regarding the quality of markers and a higher probability of polymorphic markers in an important chromosomal interval. This map also offers the possibilities of thorough alignment for the (future) physical map and implementation in haplotype diversity studies of barley.
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PMID:A high density barley microsatellite consensus map with 775 SSR loci. 1734 60