http://precedings.nature.com/tags/algorithm Recently posted documents tagged with 'algorithm' Nature Publishing Group en Nature Precedings http://precedings.nature.com/images/header_logo.gif http://precedings.nature.com http://dx.doi.org/10.1038/npre.2009.3547.1 The anatomy of model species is described in ontologies, which are used to standardize the annotations of experimental data, such as gene expression patterns. To compare such data between species, we aim to establish homology relations between ontologies describing different species. We present a new algorithm, and its implementation in the software Homolonto, to create new relationships between anatomical ontologies, based on the homology concept. These relationships and the Homolonto software are available at http://bgee.unil.ch/. http://dx.doi.org/10.1038/npre.2009.3547.1 Tue, 04 Aug 2009 13:06:04 UTC Generating Homology Relationships by Alignment of Anatomical Ontologies doi:10.1038/npre.2009.3547.1 2009-08-04 Frederic B. Bastian Nature Precedings 2009-08-04T13:06:04Z Manuscript Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2008.2142.2 Background: Recognizing similarities and deriving relationships among protein molecules is a fundamentalrequirement in present-day biology. Similarities can be present at various levels which can be detected through comparison of protein sequences or their structural folds. In some cases similarities obscure at these levels could be present merely in the substructures at their binding sites. Inferring functional similarities between protein molecules by comparing their binding sites is still largely exploratory and not as yet a routine protocol. One ofthe main reasons for this is the limitation in the choice of appropriate analytical tools that can compare binding sites with high sensitivity. To benefit from the enormous amount of structural data that is being rapidly accumulated, it is essential to have high throughput tools that enable large scale binding site comparison.Results: Here we present a new algorithm PocketMatch for comparison of binding sites in a frame invariantmanner. Each binding site is represented by 90 lists of sorted distances capturing shape and chemical nature of the site. The sorted arrays are then aligned using an incremental alignment method and scored to obtain PMScores for pairs of sites. A comprehensive sensitivity analysis and an extensive validation of the algorithm have been carried out. Perturbation studies where the geometry of a given site was retained but the residue types were changed randomly, indicated that chance similarities were virtually non-existent. Our analysis also demonstrates that shape information alone is insufficient to discriminate between diverse binding sites, unlesscombined with chemical nature of amino acids.Conclusions: A new algorithm has been developed to compare binding sites in accurate, efficient andhigh-throughput manner. Though the representation used is conceptually simplistic, we demonstrate that alongwith the new alignment strategy used, it is sufficient to enable binding comparison with high sensitivity. Novel methodology has also been presented for validating the algorithm for accuracy and sensitivity with respect to geometry and chemical nature of the site. The method is also fast and takes about 1/250th second for one comparison on a single processor. A parallel version on BlueGene has also been implemented. http://dx.doi.org/10.1038/npre.2008.2142.2 Fri, 21 Nov 2008 18:51:29 UTC PocketMatch: A new algorithm to compare binding sites in protein structures doi:10.1038/npre.2008.2142.2 2008-11-21 Nagasuma Chandra Nature Precedings 2008-11-21T18:51:29Z Manuscript Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/2142/version/1 Background: Recognizing similarities and deriving relationships among protein molecules is a fundamentalrequirement in present-day biology. Similarities can be present at various levels which can be detected through comparison of protein sequences or their structural folds. In some cases similarities obscure at these levels could be present merely in the substructures at their binding sites. Inferring functional similarities between protein molecules by comparing their binding sites is still largely exploratory and not as yet a routine protocol. One ofthe main reasons for this is the limitation in the choice of appropriate analytical tools that can compare binding sites with high sensitivity. To benefit from the enormous amount of structural data that is being rapidly accumulated, it is essential to have high throughput tools that enable large scale binding site comparison.Results: Here we present a new algorithm PocketMatch for comparison of binding sites in a frame invariantmanner. Each binding site is represented by 90 lists of sorted distances capturing shape and chemical nature of the site. The sorted arrays are then aligned using an incremental alignment method and scored to obtain PMScores for pairs of sites. A comprehensive sensitivity analysis and an extensive validation of the algorithm have been carried out. Perturbation studies where the geometry of a given site was retained but the residue types were changed randomly, indicated that chance similarities were virtually non-existent. Our analysis also demonstrates that shape information alone is insufficient to discriminate between diverse binding sites, unlesscombined with chemical nature of amino acids.Conclusions: A new algorithm has been developed to compare binding sites in accurate, efficient andhigh-throughput manner. Though the representation used is conceptually simplistic, we demonstrate that alongwith the new alignment strategy used, it is sufficient to enable binding comparison with high sensitivity. Novel methodology has also been presented for validating the algorithm for accuracy and sensitivity with respect to geometry and chemical nature of the site. The method is also fast and takes about 1/250th second for one comparison on a single processor. A parallel version on BlueGene has also been implemented. http://precedings.nature.com/documents/2142/version/1 Fri, 01 Aug 2008 09:49:21 UTC PocketMatch: A new algorithm to compare binding sites in protein structures hdl:10101/npre.2008.2142.1 2008-08-01 Yeturu Kalidas Nature Precedings 2008-08-01T09:49:21Z Manuscript Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2007.1030.1 TreeMap is a computer program for analysing host-parasite cospeciation. We respond to Dowling’s (Cladistics, 18: 416-435) recent comparison of TreeMap and Brooks Parsimony Analysis (BPA) by showing that Dowling’s comparison suffers from several mistakes and flaws. We discuss the problems with both BPA and TreeMap, and show that BPA incorrectly counts the true number coevolutionary events more often than TreeMap 1. We also discuss the two main limitations of TreeMap 1 correctly identified by Dowling, namely its inability to handle widespread parasites, and its coarse optimality criterion (the number of cospeciation events). We suggest a simple fix for widespread parasites. The newly released TreeMap 2 uses a more sensitive optimality criterion than TreeMap 1, addressing Dowling’s second concern. http://dx.doi.org/10.1038/npre.2007.1030.1 Tue, 18 Sep 2007 10:49:19 UTC Treemap Versus BPA (Again): A Response to Dowling doi:10.1038/npre.2007.1030.1 2009-03-04 Roderic Page Nature Precedings 2007-09-18T10:49:19Z Manuscript Bioinformatics Evolutionary Biology http://creativecommons.org/licenses/by/2.5/ http://dx.doi.org/10.1038/npre.2007.278.1 Checksum algorithms are used in biological databases for integrity check and identification purposes. CRC64 is the only checksum algorithm already included in Biopython. This work proposes two new implementation of known algorithms (GCG Checksum and SEGUID). There is also an application based on SEGUID: Looking for redundancy between two FASTA files full of protein sequences based only in sequence information, by comparing the SEGUIDs of both files.The code is shown in the manuscript and may be available at Biopython.org. http://dx.doi.org/10.1038/npre.2007.278.1 Thu, 28 Jun 2007 04:48:13 UTC New checksum functions for Biopython doi:10.1038/npre.2007.278.1 2007-06-28 Sebastian Bassi Nature Precedings 2007-06-28T04:48:13Z Presentation Bioinformatics http://creativecommons.org/licenses/by/2.5/