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10.1371/journal.pcbi.1005727 http://scihub22266oqcxt.onion/10.1371/journal.pcbi.1005727 C5600398!5600398!28873405     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       PLoS+Comput+Biol 2017 ; 13 (9): ä Nephropedia Template TP {{tp|p=28873405|t=2017. kWIP: The k-mer weighted inner product, a de novo estimator of genetic similarity |pdf=|usr=}}{{28873405}} gab.com Text kWIP: The k-mer weighted inner product, a de novo estimator of genetic similarity JO: PLoS Comput Biol http://www.kidney.de/pget.php?&tw=1&pmid=28873405 #FOAvip Modern genomics techniques generate overwhelming quantities of data. Extracting population genetic variation demands computationally efficient methods to determine genetic relatedness between individuals (or ?samples?) in an unbiased manner, preferably de novo. Rapid estimation of genetic relatedness directly from sequencing data has the potential to overcome reference genome bias, and to verify that individuals belong to the correct genetic lineage before conclusions are drawn using mislabelled, or misidentified samples. We present the k-mer Weighted Inner Product (kWIP), an assembly-, and alignment-free estimator of genetic similarity. kWIP combines a probabilistic data structure with a novel metric, the weighted inner product (WIP), to efficiently calculate pairwise similarity between sequencing runs from their k-mer counts. It produces a distance matrix, which can then be further analysed and visualised. Our method does not require prior knowledge of the underlying genomes and applications include establishing sample identity and detecting mix-up, non-obvious genomic variation, and population structure. We show that kWIP can reconstruct the true relatedness between samples from simulated populations. By re-analysing several published datasets we show that our results are consistent with marker-based analyses. kWIP is written in C++, licensed under the GNU GPL, and is available from https://github.com/kdmurray91/kwip. Twit Text FOAVip kWIP: The k-mer weighted inner product, a de novo estimator of genetic similarity ????PLoS Comput Biol http://www.kidney.de/pget.php?&tw=1&pmid=28873405 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28873405 #FOAvip English Wikipedia <ref>{{Cite pmid|28873405}}</ref> kWIP: The k-mer weighted inner product, a de novo estimator of genetic similarity #MMPMID28873405 Murray KD; Webers C; Ong CS; Borevitz J; Warthmann N PLoS Comput Biol 2017[Sep]; 13 (9): ä PMID28873405show ga Modern genomics techniques generate overwhelming quantities of data. Extracting population genetic variation demands computationally efficient methods to determine genetic relatedness between individuals (or ?samples?) in an unbiased manner, preferably de novo. Rapid estimation of genetic relatedness directly from sequencing data has the potential to overcome reference genome bias, and to verify that individuals belong to the correct genetic lineage before conclusions are drawn using mislabelled, or misidentified samples. We present the k-mer Weighted Inner Product (kWIP), an assembly-, and alignment-free estimator of genetic similarity. kWIP combines a probabilistic data structure with a novel metric, the weighted inner product (WIP), to efficiently calculate pairwise similarity between sequencing runs from their k-mer counts. It produces a distance matrix, which can then be further analysed and visualised. Our method does not require prior knowledge of the underlying genomes and applications include establishing sample identity and detecting mix-up, non-obvious genomic variation, and population structure. We show that kWIP can reconstruct the true relatedness between samples from simulated populations. By re-analysing several published datasets we show that our results are consistent with marker-based analyses. kWIP is written in C++, licensed under the GNU GPL, and is available from https://github.com/kdmurray91/kwip. äkWIP: The k-mer weighted inner product, a de novo estimator of genetic(epmc).pdf DeepDyve Pubget Overpricing