Thu, 21 Aug 2008 09:53:58 BST CiteULike: laughcry's library [28 articles] http://www.citeulike.org/user/laughcry CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/laughcry/article/2661783 <i>BMC Bioinformatics, Vol. 9 (11 April 2008), 192.</i> uShuffle: a useful tool for shuffling biological sequences while preserving the k-let counts Minghui Jiang James Anderson Joel Gillespie Martin Mayne doi:10.1186/1471-2105-9-192 BMC Bioinformatics, Vol. 9 (11 April 2008), 192. 2008-04-12T18:37:21-00:00 2008 BMC Bioinformatics 1471-2105 9 192 bioinfpdl http://www.citeulike.org/user/laughcry/article/2680719 <i>BMC Bioinformatics, Vol. 9 (16 April 2008), 200.</i> ParaKMeans: Implementation of a parallelized K-means algorithm suitable for general laboratory use. Piotr Kraj Ashok Sharma Nikhil Garge Robert Podolsky Richard Mcindoe doi:10.1186/1471-2105-9-200 BMC Bioinformatics, Vol. 9 (16 April 2008), 200. 2008-04-17T06:42:52-00:00 2008 BMC Bioinformatics 1471-2105 9 200 bioinfpdl kmean http://www.citeulike.org/user/laughcry/article/2776008 <i>BMC Bioinformatics, Vol. 9, No. Suppl 3. (2008)</i><br /><br />BACKGROUND:Growing interest in the application of natural language processing methods to biomedical text has led to an increasing number of corpora and methods targeting protein-protein interaction (PPI) extraction. However, there is no general consensus regarding PPI annotation and consequently resources are largely incompatible and methods are difficult to evaluate.RESULTS:We present the first comparative evaluation of the diverse PPI corpora, performing quantitative evaluation using two separate information extraction methods as well as detailed statistical and qualitative analyses of their properties. For the evaluation, we unify the corpus PPI annotations to a shared level of information, consisting of undirected, untyped binary interactions of non-static types with no identification of the words specifying the interaction, no negations, and no interaction certainty.We find that the F-score performance of a state-of-the-art PPI extraction method varies on average 19 percentage units and in some cases over 30 percentage units between the different evaluated corpora. The differences stemming from the choice of corpus can thus be substantially larger than differences between the performance of PPI extraction methods, which suggests definite limits on the ability to compare methods evaluated on different resources. We analyse a number of potential sources for these differences and identify factors explaining approximately half of the variance. We further suggest ways in which the difficulty of the PPI extraction tasks codified by different corpora can be determined to advance comparability. Our analysis also identifies points of agreement and disagreement in PPI corpus annotation that are rarely explicitly stated by the authors of the corpora.CONCLUSIONS:Our comparative analysis uncovers key similarities and differences between the diverse PPI corpora, thus taking an important step towards standardization. In the course of this study we have created a major practical contribution in converting the corpora into a shared format. The conversion software is freely available at http://mars.cs.utu.fi/PPICorpora. Comparative analysis of five protein-protein interaction corpora Sampo Pyysalo Antti Airola Juho Heimonen Jari Bjorne Filip Ginter Tapio Salakoski doi:10.1186/1471-2105-9-S3-S6 BMC Bioinformatics, Vol. 9, No. Suppl 3. (2008) 2008-05-09T14:34:10-00:00 2008 BMC Bioinformatics 9 Suppl 3 bioinfpdl ppi http://www.citeulike.org/user/laughcry/article/2397764 <i>Bioinformatics, Vol. 24, No. 4. (15 February 2008), pp. 591-593.</i><br /><br />Summary: We introduce DECOMP, a tool that computes the sum formula of all molecules whose mass equals the input mass. This problem arises frequently in biochemistry and mass spectrometry (MS), when we know the molecular mass of a protein, DNA or metabolite fragment but have no other information. A closely related problem is known as the Money Changing Problem (MCP), where all masses are positive integers. Recently, efficient algorithms have been developed for the MCP, in which DECOMP applies to real-valued MS data. The excellent performance of this method on proteomic and metabolomic MS data has recently been demonstrated. DECOMP has an easy-to-use graphical interface, which caters for both types of users: those interested in solving MCP instances and those submitting MS data. Availability: DECOMP is freely accessible at http://bibiserv.techfak.uni-bielefeld.de/decomp/ Contact: anton.pervukhin@minet.uni-jena.de 10.1093/bioinformatics/btm631 DECOMP--from interpreting Mass Spectrometry peaks to solving the Money Changing Problem Sebastian Bocker Zsuzsanna Liptak Marcel Martin Anton Pervukhin Henner Sudek doi:10.1093/bioinformatics/btm631 Bioinformatics, Vol. 24, No. 4. (15 February 2008), pp. 591-593. 2008-02-19T08:49:14-00:00 2008 Bioinformatics 24 4 591 593 bioinfpdl http://www.citeulike.org/user/laughcry/article/2278694 <i>Bioinformatics (2 January 2008)</i><br /><br />SUMMARY: The identification of protein complexes is a fundamental challenge in interpreting protein-protein interaction data. Cross-species analysis allows coping with the high levels of noise that are typical to these data. The NetworkBLAST web-server provides a platform for identifying protein complexes in protein-protein interaction networks. It can analyze a single network or two networks from different species. In the latter case, NetworkBLAST outputs a set of putative complexes that are evolutionarily conserved across the two networks. AVAILABILITY: NetworkBLAST is available as web-server at: www.cs.tau.ac.il/~roded/networkblast.htm. CONTACT: kalaevma@post.tau.ac.il, roded@post.tau.ac.il. NetworkBLAST: Comparative analysis of protein networks. Maxim Kalaev Mike Smoot Trey Ideker Roded Sharan doi:10.1093/bioinformatics/btm630 Bioinformatics (2 January 2008) 2008-01-23T03:05:29-00:00 2008 Bioinformatics 1460-2059 bioinfpdl http://www.citeulike.org/user/laughcry/article/2011926 <i>Bioinformatics (27 November 2007), btm576.</i><br /><br />Motivation: Protein-protein interactions have proved to be a valuable starting point for understanding the inner workings of the cell. Computational methodologies have been built which both predict interactions and use interaction datasets in order to predict other protein features. Such methods require gold standard positive (GSP) and negative (GSN) interaction sets. Here we examine and demonstrate the usefulness of homologous interactions in predicting good quality positive and negative interaction datasets. Results: We generate GSP interaction sets as subsets from experimental data using only interaction and sequence information. We can therefore produce sets for several species (many of which at present have no identified GSPs). Comprehensive error rate testing demonstrates the power of the method. We also show how the use of our datasets significantly improves the predictive power of algorithms for interaction prediction and function prediction. Furthermore we generate GSN interaction sets for yeast and examine the use of homology along with other protein properties such as localisation, expression and function. Using a novel method to assess the accuracy of a negative interaction set we find that the best single selector for negative interactions is a lack of co-function. However, an integrated method using all the characteristics shows significant improvement over any current method for identifying GSN interactions. The nature of homologous interactions is also examined and we demonstrate that interologs are found more commonly within species than across species. Conclusion: GSP sets built using our homologous verification method are demonstrably better than standard sets in terms of predictive ability. We can build such GSP sets for several species. When generating GSNs we show a combination of protein features and lack of homologous interactions gives the highest quality interaction sets. Availability: GSP and GSN datasets for all the studied species can be downloaded from (http://www.stats.ox.ac.uk/~deane/HPIV). Contact: deane@stats.ox.ac.uk 10.1093/bioinformatics/btm576 An assessment of the uses of homologous interactions Ramazan Saeed Charlotte Deane doi:10.1093/bioinformatics/btm576 Bioinformatics (27 November 2007), btm576. 2007-11-29T08:46:48-00:00 2007 Bioinformatics btm576 bioinfpdl http://www.citeulike.org/user/laughcry/article/2459951 <i>Bioinformatics, Vol. 24, No. 5. (1 March 2008), pp. 696-703.</i><br /><br />Motivation: In high-throughput projects aiming to identify rare positives using a binary assay, smart-pooling constitutes an appealing strategy liable of significantly reducing the number of tests while correcting for experimental noise. In order to perform simulations for choosing an appropriate set of pools, and later to interpret the experimental results, the pool outcomes must be decoded'. The intuitive aim is clearly to identify the positives that gave rise to an observation, whether real or simulated. However, this goal is not well-formalized and has been the focus of very few studies. Results: We first provide a clear combinatorial formalization of the decoding problem'. We then present interpool, an exact algorithm to solve this problem. An efficient implementation is freely available. Its usefulness is illustrated in the context of yeast-two-hybrid interactome mapping with the Shifted Transversal Design. Availability: The implementation, licensed under the GNU GPL, can be downloaded from http://www-timc.imag.fr/Nicolas.Thierry-Mieg/ Contact: nicolas.thierry-mieg@imag.fr Supplementary information: Supplementary data are available at Bioinformatics online. 10.1093/bioinformatics/btn001 Interpool: interpreting smart-pooling results Nicolas Thierry-Mieg Gilles Bailly doi:10.1093/bioinformatics/btn001 Bioinformatics, Vol. 24, No. 5. (1 March 2008), pp. 696-703. 2008-03-03T09:18:29-00:00 2008 Bioinformatics 24 5 696 703 bioinfpdl http://www.citeulike.org/user/laughcry/article/2213094 <i>BMC Bioinformatics, Vol. 9, No. 1. (2008)</i><br /><br />BACKGROUND:The use of novel algorithmic techniques is pivotal to many important problems in life science. For example the sequencing of the human genome would not have been possible without advanced assembly algorithms. However, owing to the high speed of technological progress and the urgent need for bioinformatics tools, there is a widening gap between state-of-the-art algorithmic techniques and the actual algorithmic components of tools that are in widespread use. RESULTS:To remedy this trend we propose the use of SeqAn, a library of efficient data types and algorithms for sequence analysis in computational biology. SeqAn comprises implementations of existing, practical state-of-the-art algorithmic components to provide a sound basis for algorithm testing and development. In this paper we describe the design and content of SeqAn and demonstrate its use by giving two examples. In the first example we show an application of SeqAn as an experimental platform by comparing different exact string matching algorithms. The second example is a simple version of the well-known MUMmer tool rewritten in SeqAn. Results indicate that our implementation is very efficient and versatile to use. CONCLUSIONS:We anticipate that SeqAn greatly simplifies the rapid development of new bioinformatics tools by providing a collection of readily usable, well-designed algorithmic components which are fundamental for the field of sequence analysis. This leverages not only the implementation of new algorithms, but also enables a sound analysis and comparison of existing algorithms. SeqAn - An efficient, generic C++ library for sequence analysis Andreas Doring David Weese Tobias Rausch Knut Reinert doi:10.1186/1471-2105-9-11 BMC Bioinformatics, Vol. 9, No. 1. (2008) 2008-01-10T05:18:22-00:00 2008 BMC Bioinformatics 9 1 bioinfpdl http://www.citeulike.org/user/laughcry/article/2176269 <i>BMC Bioinformatics, Vol. 8 (27 December 2007), 488.</i> Background frequencies for residue variability estimates: BLOSUM revisited I Mihalek I Res O Lichtarge doi:10.1186/1471-2105-8-488 BMC Bioinformatics, Vol. 8 (27 December 2007), 488. 2007-12-28T03:31:51-00:00 2007 BMC Bioinformatics 1471-2105 8 488 bioinfpdl http://www.citeulike.org/user/laughcry/article/2155919 <i>BMC Bioinformatics, Vol. 8, No. 1. (2007)</i><br /><br />BACKGROUND:The web has seen an explosion of chemistry and biology related resources in the last 15 years: thousands of scientific journals, databases, wikis, blogs and resources are available with a wide variety of types of information. There is a huge need to aggregate and organise this information. However, the sheer number of resources makes it unrealistic to link them all in a centralised manner. Instead, search engines to find information in those resources flourish, and formal languages like Resource Description Framework and Web Ontology Language are increasingly used to allow linking of resources. A recent development is the use of userscripts to change the appearance of web pages, by on-the-fly modification of the web content. This pens possibilities to aggregate information and computational results from different web resources into the web page of one of those resources.RESULTS:Several userscripts are presented that enrich biology and chemistry related web resources by incorporating or linking to other computational or data sources on the web. The scripts make use of Greasemonkey-like plugins for web browsers and are written in JavaScript. Information from third-party resources are extracted using open Application Programming Interfaces, while common Universal Resource Locator schemes are used to make deep links to related information in that external resource. The userscripts presented here use a variety of techniques and resources, and show the potential of such scripts.CONCLUSIONS:This paper discusses a number of userscripts that aggregate information from two or more web resources. Examples are shown that enrich web pages with information from other resources, and show how information from web pages can be used to link to, search, and process information in other resources. Due to the nature of userscripts, scientists are able to select those scripts they find useful on a daily basis, as the scripts run directly in their own web browser rather than on the web server. This flexibility allows the scientists to tune the features of web resources to optimise their productivity. Userscripts for the Life Sciences Egon Willighagen Noel O'Boyle Harini Gopalakrishnan Dazhi Jiao Rajarshi Guha Christoph Steinbeck David Wild doi:10.1186/1471-2105-8-487 BMC Bioinformatics, Vol. 8, No. 1. (2007) 2007-12-21T15:56:01-00:00 2007 BMC Bioinformatics 8 1 bioinfpdl http://www.citeulike.org/user/laughcry/article/2088955 <i>BMC Bioinformatics, Vol. 8, No. 1. (2007)</i><br /><br />BACKGROUND:The recent availability of new, less expensive high-throughput DNA sequencing technologies has yielded a dramatic increase in the volume of sequence data that must be analyzed. These data are being generated for several purposes, including genotyping, genome resequencing, metagenomics, and de novo genome assembly projects. Sequence alignment programs such as MUMmer have proven essential for analysis of these data, but researchers will need ever faster, high-throughput alignment tools running on inexpensive hardware to keep up with new sequence technologies.RESULTS:This paper describes MUMmerGPU, an open-source high-throughput parallel pairwise local sequence alignment program that runs on commodity Graphics Processing Units (GPUs) in common workstations. MUMmerGPU uses the new Compute Unified Device Architecture (CUDA) from nVidia to align multiple query sequences against a single reference sequence stored as a suffix tree. By processing the queries in parallel on the highly parallel graphics card, MUMmerGPU achieves more than a 10-fold speedup over a serial CPU version of the sequence alignment kernel, and outperforms the exact alignment component of MUMmer on a high end CPU by 3.5-fold in total application time when aligning reads from recent sequencing projects using Solexa/Illumina, 454, and Sanger sequencing technologies.CONCLUSIONS:MUMmerGPU is a low cost, ultra-fast sequence alignment program designed to handle the increasing volume of data produced by new, high-throughput sequencing technologies. MUMmerGPU demonstrates that even memory-intensive applications can run significantly faster on the relatively low-cost GPU than on the CPU. High-throughput sequence alignment using Graphics Processing Units Michael Schatz Cole Trapnell Arthur Delcher Amitabh Varshney doi:10.1186/1471-2105-8-474 BMC Bioinformatics, Vol. 8, No. 1. (2007) 2007-12-11T09:59:29-00:00 2007 BMC Bioinformatics 8 1 bioinfpdl http://www.citeulike.org/user/laughcry/article/2034540 <i>BMC Bioinformatics, Vol. 8 (30 November 2007), 467.</i> Generating confidence intervals on biological networks Thomas Thorne Michael Stumpf doi:10.1186/1471-2105-8-467 BMC Bioinformatics, Vol. 8 (30 November 2007), 467. 2007-12-01T02:47:21-00:00 2007 BMC Bioinformatics 1471-2105 8 467 bioinfpdl http://www.citeulike.org/user/laughcry/article/1966647 <i>BMC Bioinformatics, Vol. 8 (22 November 2007), 460.</i> Dendroscope: An interactive viewer for large phylogenetic trees Daniel Huson Daniel Richter Christian Rausch Tobias Dezulian Markus Franz Regula Rupp doi:10.1186/1471-2105-8-460 BMC Bioinformatics, Vol. 8 (22 November 2007), 460. 2007-11-23T14:30:06-00:00 2007 BMC Bioinformatics 1471-2105 8 460 bioinfpdl http://www.citeulike.org/user/laughcry/article/976216 <i>Algorithms for Molecular Biology, Vol. 1 (05 December 2006), 24.</i> A novel functional module detection algorithm for protein-protein interaction network Woochang Hwang Young-Rae Cho Aidong Zhang Murali Ramanathan doi:10.1186/1748-7188-1-24 Algorithms for Molecular Biology, Vol. 1 (05 December 2006), 24. 2006-12-06T12:32:01-00:00 2006 Algorithms for Molecular Biology 1748-7188 1 24 bioinfpdl http://www.citeulike.org/user/laughcry/article/2427988 <i>BMC Bioinformatics, Vol. 9 (25 February 2008), 115.</i> Novel methodology for construction and pruning of quasi-median networks Sarah Ayling Terence Brown doi:10.1186/1471-2105-9-115 BMC Bioinformatics, Vol. 9 (25 February 2008), 115. 2008-02-26T02:14:30-00:00 2008 BMC Bioinformatics 1471-2105 9 115 bioinfpdl http://www.citeulike.org/user/laughcry/article/2398819 <i>BMC Bioinformatics, Vol. 9, No. 1. (2008)</i><br /><br />BACKGROUND:Last years' mapping of diverse genomes has generated huge amounts of biological data which are currently dispersed through many databases. Integration of the information available in the various databases is required to unveil possible associations relating already known data. Biological data are often imprecise and noisy. Fuzzy set theory is specially suitable to model imprecise data while association rules are very appropriate to integrate heterogeneous data.RESULTS:In this work we propose a novel fuzzy methodology based on a fuzzy association rule mining method for biological knowledge extraction. We apply this methodology over a yeast genome dataset containing heterogeneous information regarding structural and functional genome features. A number of association rules have been found, many of them agreeing with previous research in the area. In addition, a comparison between crisp and fuzzy results proves the fuzzy associations to be more reliable than crisp ones.CONCLUSIONS:An integrative approach as the one carried out in this work can unveil significant knowledge which is currently hidden and dispersed through the existing biological databases. It is shown that fuzzy association rules can model this knowledge in an intuitive way by using linguistic labels and few easy-understandable parameters. Fuzzy association rules for biological data analysis: a case study on yeast Francisco Lopez Armando Blanco Fernando Garcia Carlos Cano Antonio Marin doi:10.1186/1471-2105-9-107 BMC Bioinformatics, Vol. 9, No. 1. (2008) 2008-02-19T13:11:46-00:00 2008 BMC Bioinformatics 9 1 bioinfpdl http://www.citeulike.org/user/laughcry/article/2334989 <i>BMC Bioinformatics, Vol. 9, No. 1. (2008)</i><br /><br />BACKGROUND:The performance of different programming languages has previously been benchmarked using abstract mathematical algorithms, but not using standard bioinformatics algorithms. We compared the memory usage and speed of execution for three standard bioinformatics methods, implemented in programs using one of six different programming languages. Programs for the Sellers algorithm, the Neighbor-Joining tree construction algorithm and an algorithm for parsing BLAST file outputs were implemented in C, C++, C#, Java, Perl and Python.RESULTS:Implementations in C and C++ were fastest and used the least memory. Programs in these languages generally contained more lines of code. Java and C# appeared to be a compromise between the flexibility of Perl and Python and the fast performance of C and C++. The relative performance of the tested languages did not change from Windows to Linux and no clear evidence of a faster operating system was found. Source code and additional information are available from http://www.bioinformatics.org/benchmark/CONCLUSIONS:This benchmark provides a comparison of six commonly used programming languages under two different operating systems. The overall comparison shows that a developer should choose an appropriate language carefully, taking into account the performance expected and the library availability for each language. A comparison of common programming languages used in bioinformatics Mathieu Fourment Michael Gillings doi:10.1186/1471-2105-9-82 BMC Bioinformatics, Vol. 9, No. 1. (2008) 2008-02-05T13:42:14-00:00 2008 BMC Bioinformatics 9 1 bioinfpdl http://www.citeulike.org/user/laughcry/article/2321557 <i>BMC Bioinformatics, Vol. 9 (31 January 2008), 75.</i> Rank-based edge reconstruction for scale-free genetic regulatory networks Guanrao Chen Peter Larsen Eyad Almasri Yang Dai doi:10.1186/1471-2105-9-75 BMC Bioinformatics, Vol. 9 (31 January 2008), 75. 2008-02-02T00:42:17-00:00 2008 BMC Bioinformatics 1471-2105 9 75 bioinfpdl http://www.citeulike.org/user/laughcry/article/2439952 <i>PLoS Comput Biol, Vol. 4, No. 2. (8 February 2008)</i><br /><br />Biological networks have evolved to be highly functional within uncertain environments while remaining extremely adaptable. One of the main contributors to the robustness and evolvability of biological networks is believed to be their modularity of function, with modules defined as sets of genes that are strongly interconnected but whose function is separable from those of other modules. Here, we investigate the in silico evolution of modularity and robustness in complex artificial metabolic networks that encode an increasing amount of information about their environment while acquiring ubiquitous features of biological, social, and engineering networks, such as scale-free edge distribution, small-world property, and fault-tolerance. These networks evolve in environments that differ in their predictability, and allow us to study modularity from topological, information-theoretic, and gene-epistatic points of view using new tools that do not depend on any preconceived notion of modularity. We find that for our evolved complex networks as well as for the yeast protein-protein interaction network, synthetic lethal gene pairs consist mostly of redundant genes that lie close to each other and therefore within modules, while knockdown suppressor gene pairs are farther apart and often straddle modules, suggesting that knockdown rescue is mediated by alternative pathways or modules. The combination of network modularity tools together with genetic interaction data constitutes a powerful approach to study and dissect the role of modularity in the evolution and function of biological networks. Evolution of Complex Modular Biological Networks. Arend Hintze Christoph Adami doi:10.1371/journal.pcbi.0040023 PLoS Comput Biol, Vol. 4, No. 2. (8 February 2008) 2008-02-28T05:39:56-00:00 2008 PLoS Comput Biol 1553-7358 4 2 bioinfpdl http://www.citeulike.org/user/laughcry/article/2382507 <i>Mol Syst Biol, Vol. 4 (12 February 2008)</i> Accurate prediction of protein-protein interactions from sequence alignments using a Bayesian method Lukas Burger Erik van Nimwegen doi:10.1038/msb4100203 Mol Syst Biol, Vol. 4 (12 February 2008) 2008-02-14T20:28:48-00:00 2008 Mol Syst Biol 4 EMBO and Nature Publishing Group bioinfpdl http://www.citeulike.org/user/laughcry/article/2310449 <i>BMC Bioinformatics, Vol. 9, No. 1. (2008)</i><br /><br />BACKGROUND:The recent explosion in biological and other real-world network data has created the need for improved tools for large network analyses. In addition to well established global network properties, several new mathematical techniques for analyzing local structural properties of large networks have been developed. Small over-represented subgraphs, called network motifs, have been introduced to identify simple building blocks of complex networks. Small induced subgraphs, called graphlets, have been used to develop "network signatures" that summarize network topologies. Based on these network signatures, two new highly sensitive measures of network local structural similarities were designed: the relative graphlet frequency distance (RGF-distance) and the graphlet degree distribution agreement (GDD-agreement). Finding adequate null-models for biological networks is important in many research domains. Network properties are used to assess the fit of network models to the data. Various network models have been proposed. To date, there does not exist a software tool that measures the above mentioned local network properties. Moreover, none of the existing tools compare real-world networks against a series of network models with respect to these local as well as a multitude of global network properties.RESULTS:Thus, we introduce GraphCrunch, a software tool that finds well-fitting network models by comparing large real-world networks against random graph models according to various network structural similarity measures. It has unique capabilities of finding computationally expensive RGF-distance and GDD-agreement measures. In addition, it computes several standard global network measures and thus supports the largest variety of network measures thus far. Also, it is the first software tool that compares real-world networks against a series of network models and that has built-in parallel computing capabilities allowing for a user specified list of machines on which to perform compute intensive searches for local network properties. Furthermore, GraphCrunch is easily extendible to include additional network measures and models.CONCLUSIONS:GraphCrunch is a software tool that implements the latest research on biological network models and properties: it compares real-world networks against a series of random graph models with respect to a multitude of local and global network properties. We present GraphCrunch as a comprehensive, parallelizable, and easily extendible software tool for analyzing and modeling large biological networks. The software is open-source and freely available at http://www.ics.uci.edu/~bio-nets/graphcrunch/. It runs under Linux, MacOS, and Windows Cygwin. In addition, it has an easy to use on-line web user interface that is available from the above web page. GraphCrunch: a tool for large network analyses Tijana Milenkovic Jason Lai Natasa Przulj doi:10.1186/1471-2105-9-70 BMC Bioinformatics, Vol. 9, No. 1. (2008) 2008-01-31T10:05:23-00:00 2008 BMC Bioinformatics 9 1 bioinfpdl http://www.citeulike.org/user/laughcry/article/2305805 <i>BMC Bioinformatics, Vol. 9, No. 1. (2008)</i><br /><br />BACKGROUND:The development and improvement of reliable computational methods designed to evaluate the quality of protein models is relevant in the context of protein structure refinement, which has been recently identified as one of the bottlenecks limiting the quality and usefulness of protein structure prediction.RESULTS:In this contribution, we present a computational method (Artificial Intelligence Decoys Evaluator: AIDE) which is able to consistently discriminate between correct and incorrect protein models. In particular, the method is based on neural networks that use as input 15 structural parameters, which include solvent accessible surface, hydrophobic contacts and secondary structure content. The results obtained with AIDE on a set of decoy structures were evaluated using statistical indicators such as Pearson correlation coefficients, Znat, fraction enrichment, as well as ROC plots. It turned out that AIDE performances are comparable and often complementary to available state-of-the-art learning-based methods.CONCLUSIONS:In light of the results obtained with AIDE, as well as its comparison with available learning-based methods, it can be concluded that AIDE can be successfully used to evaluate the quality of protein structures. The use of AIDE in combination with other evaluation tools is expected to further enhance protein refinement efforts. Validation of protein models by a neural network approach Paolo Mereghetti Maria Ganadu Elena Papaleo Piercarlo Fantucci Luca De Gioia doi:10.1186/1471-2105-9-66 BMC Bioinformatics, Vol. 9, No. 1. (2008) 2008-01-30T03:24:20-00:00 2008 BMC Bioinformatics 9 1 bioinfpdl http://www.citeulike.org/user/laughcry/article/2313443 <i>BMC Bioinformatics, Vol. 9 (29 January 2008), 64.</i> CASCADE: a novel quasi all paths-based network analysis algorithm for clustering biological interactions Woochang Hwang Young-Rae Cho Aidong Zhang Murali Ramanathan doi:10.1186/1471-2105-9-64 BMC Bioinformatics, Vol. 9 (29 January 2008), 64. 2008-01-31T12:38:54-00:00 2008 BMC Bioinformatics 1471-2105 9 64 clustering pin http://www.citeulike.org/user/laughcry/article/2304396 <i>BMC Bioinformatics, Vol. 9, No. 1. (2008)</i><br /><br />BACKGROUND:Prediction of bacterial virulent protein sequences has implications for identification and characterization of novel virulence-associated factors, finding novel drug/vaccine targets against proteins indispensable to pathogenicity of pathogens, and understanding the complex virulence mechanism in pathogen.RESULTS:In the present study we propose a bacterial virulent protein prediction method based on bi-layer cascade Support Vector Machine (SVM). The first layer SVM classifiers were trained and optimized with different individual protein sequence features like amino acid composition, dipeptide composition (occurrences of the possible pairs of ith and i+1th amino acid residues), higher order dipeptide composition (pairs of ith and i+2nd residues) and Position Specific Iterated BLAST (PSI-BLAST) generated Position Specific Scoring Matrices (PSSM) using dataset of virulent proteins as BLAST database. A five-fold cross-validation technique was used for the evaluation of various prediction strategies in this study. The results from the first layer (SVM scores and PSI-BLAST result) were cascaded to the second layer SVM classifier to train and generate the final classifier. The cascade SVM classifier was able to accomplish an accuracy of 81.8%, covering 86% area in the Receiver Operator Characteristic (ROC) plot, better than that of either of the layer one SVM classifiers based either on single or multiple sequence features.CONCLUSION:VirulentPred is a SVM based method to predict bacterial virulent proteins sequences, which can be used to screen virulent proteins in proteomes. Together with experimentally verified virulent proteins, several putative, non annotated and hypothetical protein sequences have been predicted to be high scoring virulent proteins by the prediction method. VirulentPred is available as a freely accessible World Wide Web server - VirulentPred, at http://bioinfo.icgeb.res.in/virulent/. VirulentPred: a SVM based prediction method for virulent proteins in bacterial pathogens Aarti Garg Dinesh Gupta doi:10.1186/1471-2105-9-62 BMC Bioinformatics, Vol. 9, No. 1. (2008) 2008-01-29T15:57:12-00:00 2008 BMC Bioinformatics 9 1 prediction svm http://www.citeulike.org/user/laughcry/article/2429163 <i>BMC Bioinformatics, Vol. 9, No. 1. (2008)</i><br /><br />BACKGROUND:Amino acids responsible for structure, core function or specificity may be inferred from multiple protein sequence alignments where a limited set of residue types are tolerated. The rise in available protein sequences continues to increase the power of techniques based on this principle.RESULTS:A new algorithm, SMERFS, for predicting protein functional sites from multiple sequences alignments was compared to 14 conservation measures and to the MINER algorithm. Validation was performed on an automatically generated dataset of 1457 families derived from the protein interactions database SNAPPI-DB, and a smaller manually curated set of 148 families. The best performing measure overall was Williamson property entropy, with ROC_0.1 scores of 0.0087 and 0.0114 for domain and small molecule contact prediction, respectively. The Lancet method performed worse than random on protein-protein interaction site prediction (ROC_0.1 score of 0.0008). The SMERFS algorithm gave similar accuracy to the phylogenetic tree-based MINER algorithm but was superior to Williamson in prediction of non-catalytic transient complex interfaces. SMERFS predicts sites that are significantly more solvent accessible compared to Williamson. CONCLUSIONS:Williamson property entropy is the the best performing of 14 conservation measures examined. The difference in performance of SMERFS relative to Williamson in manually defined complexes was dependent on complex type. The best choice of analysis method is therefore dependent on the system of interest. Additional computation employed by Miner in calculation of phylogenetic trees did not produce improved results over SMERFS. SMERFS performance was improved by use of windows over alignment columns, illustrating the necessity of considering the local environment of positions when assessing their functional significance. The contrasting properties of conservation and correlated phylogeny in protein functional residue prediction Jonathan Manning Emily Jefferson Geoffrey Barton doi:10.1186/1471-2105-9-51 BMC Bioinformatics, Vol. 9, No. 1. (2008) 2008-02-26T11:58:51-00:00 2008 BMC Bioinformatics 9 1 function-prediction http://www.citeulike.org/user/laughcry/article/2401103 <i>BMC Bioinformatics, Vol. 9 (19 February 2008), 106.</i> K-OPLS package: Kernel-based orthogonal projections to latent structures for prediction and interpretation in feature space Max Bylesjo Mattias Rantalainen Jeremy Nicholson Elaine Holmes Johan Trygg doi:10.1186/1471-2105-9-106 BMC Bioinformatics, Vol. 9 (19 February 2008), 106. 2008-02-20T02:00:30-00:00 2008 BMC Bioinformatics 1471-2105 9 106 kernel-method http://www.citeulike.org/user/laughcry/article/227101 <i>Nature, Vol. 403, No. 6770. 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