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10.1093/bioinformatics/btu143 http://scihub22266oqcxt.onion/10.1093/bioinformatics/btu143 C4071209!4071209!24618466     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Bioinformatics 2014 ; 30 (13): 1876-83 Nephropedia Template TP {{tp|p=24618466|t=2014. FISH: fast and accurate diploid genotype imputation via segmental hidden Markov model |pdf=|usr=}}{{24618466}} gab.com Text FISH: fast and accurate diploid genotype imputation via segmental hidden Markov model JO: Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=24618466 #FOAvip Motivation: Fast and accurate genotype imputation is necessary for facilitating gene-mapping studies, especially with the ever increasing numbers of both common and rare variants generated by high-throughput-sequencing experiments. However, most of the existing imputation approaches suffer from either inaccurate results or heavy computational demand.Results: In this article, aiming to perform fast and accurate genotype-imputation analysis, we propose a novel, fast and yet accurate method to impute diploid genotypes. Specifically, we extend a hidden Markov model that is widely used to describe haplotype structures. But we model hidden states onto single reference haplotypes rather than onto pairs of haplotypes. Consequently the computational complexity is linear to size of reference haplotypes. We further develop an algorithm ?merge-and-recover (MAR)? to speed up the calculation. Working on compact representation of segmental reference haplotypes, the MAR algorithm always calculates an exact form of transition probabilities regardless of partition of segments. Both simulation studies and real-data analyses demonstrated that our proposed method was comparable to most of the existing popular methods in terms of imputation accuracy, but was much more efficient in terms of computation. The MAR algorithm can further speed up the calculation by several folds without loss of accuracy. The proposed method will be useful in large-scale imputation studies with a large number of reference subjects.Availability: The implemented multi-threading software FISH is freely available for academic use at https://sites.google.com/site/lzhanghomepage/FISH.Contact:zlbio12@gmail.com; pyf0419@gmail.com; hdeng2@tulane.eduSupplementary information:Supplementary data are available at Bioinformatics online. Twit Text FOAVip FISH: fast and accurate diploid genotype imputation via segmental hidden Markov model ????Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=24618466 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=24618466 #FOAvip English Wikipedia <ref>{{Cite pmid|24618466}}</ref> FISH: fast and accurate diploid genotype imputation via segmental hidden Markov model #MMPMID24618466 Zhang L; Pei YF; Fu X; Lin Y; Wang YP; Deng HW Bioinformatics 2014[Jul]; 30 (13): 1876-83 PMID24618466show ga Motivation: Fast and accurate genotype imputation is necessary for facilitating gene-mapping studies, especially with the ever increasing numbers of both common and rare variants generated by high-throughput-sequencing experiments. However, most of the existing imputation approaches suffer from either inaccurate results or heavy computational demand.Results: In this article, aiming to perform fast and accurate genotype-imputation analysis, we propose a novel, fast and yet accurate method to impute diploid genotypes. Specifically, we extend a hidden Markov model that is widely used to describe haplotype structures. But we model hidden states onto single reference haplotypes rather than onto pairs of haplotypes. Consequently the computational complexity is linear to size of reference haplotypes. We further develop an algorithm ?merge-and-recover (MAR)? to speed up the calculation. Working on compact representation of segmental reference haplotypes, the MAR algorithm always calculates an exact form of transition probabilities regardless of partition of segments. Both simulation studies and real-data analyses demonstrated that our proposed method was comparable to most of the existing popular methods in terms of imputation accuracy, but was much more efficient in terms of computation. The MAR algorithm can further speed up the calculation by several folds without loss of accuracy. The proposed method will be useful in large-scale imputation studies with a large number of reference subjects.Availability: The implemented multi-threading software FISH is freely available for academic use at https://sites.google.com/site/lzhanghomepage/FISH.Contact:zlbio12@gmail.com; pyf0419@gmail.com; hdeng2@tulane.eduSupplementary information:Supplementary data are available at Bioinformatics online. äFISH: fast and accurate diploid genotype imputation via segmental hidd(epmc).pdf DeepDyve Pubget Overpricing