2013-05-26T01:38:23Zhttp://open-archive.highwire.org/handler

oai:open-archive.highwire.org:bioinfo:23/7/7852013-05-13HighWireOUPbioinfo:23:7

Large scale genotype phenotype correlation analysis based on phylogenetic trees Habib, Farhat Johnson, Andrew D. Bundschuh, Ralf Janies, Daniel PHYLOGENETICS We provide two methods for identifying changes in genotype that are correlated with changes in a phenotype implied by phylogenetic trees. The first method, VENN, works when the number of branches over which the change occurred are modest. VENN looks for genetic changes that are completely penetrant with phenotype changes on a tree. The second method, CCTSWEEP, allows for a partial matching between changes in phenotypes and genotypes and provides a score for each change using Maddison's concentrated changes test. The mutations that are highly correlated with phenotypic change can be ranked by score. We use these methods to find SNPs correlated with resistance to <it>Bacillus anthracis</it> in inbred mouse strains. Our findings are consistent with the current biological literature, and also suggest potential novel candidate genes. Contact: <inter-ref locator="farhat@pacific.mps.ohio-state.edu" locator-type="email">farhat@pacific.mps.ohio-state.edu</inter-ref> for software requests. Oxford University Press 2007-04-01 00:00:00.0 TEXT text/html http://bioinformatics.oxfordjournals.org/cgi/content/short/23/7/785 http://dx.doi.org/10.1093/bioinformatics/btm003 en Copyright (C) 2007, Oxford University Press

oai:open-archive.highwire.org:bioinfo:23/7/7892013-05-13HighWireOUPbioinfo:23:7

A simple shape characteristic of protein protein recognition Nicola, George Vakser, Ilya A. STRUCTURAL BIOINFORMATICS Motivation: Observation of co-crystallized protein–protein complexes and low-resolution protein–protein docking studies suggest the existence of a binding-related anisotropic shape characteristic of protein–protein complexes. Results: Our study systematically assessed the global shape of proteins in a non-redundant database of co-crystallized protein–protein complexes by measuring the distance of the surface residues to the protein's center of mass. The results show that on average the binding site residues are closer to the center of mass than the non-binding surface residues. Thus, the study directly detects an important and simple binding-related characteristic of protein shapes. The results provide an insight into one of the fundamental properties of protein structure and association. Contact: <inter-ref locator="vakser@ku.edu" locator-type="email">vakser@ku.edu</inter-ref> Oxford University Press 2007-04-01 00:00:00.0 TEXT text/html http://bioinformatics.oxfordjournals.org/cgi/content/short/23/7/789 http://dx.doi.org/10.1093/bioinformatics/btm018 en Copyright (C) 2007, Oxford University Press

oai:open-archive.highwire.org:bioinfo:23/7/7932013-05-13HighWireOUPbioinfo:23:7

PhyloGena a user-friendly system for automated phylogenetic annotation of unknown sequences Hanekamp, Kristian Bohnebeck, Uta Beszteri, Bánk Valentin, Klaus GENOME ANALYSIS Motivation: Phylogenomic approaches towards functional and evolutionary annotation of unknown sequences have been suggested to be superior to those based only on pairwise local alignments. User-friendly software tools making the advantages of phylogenetic annotation available for the ever widening range of bioinformatically uninitiated biologists involved in genome/EST annotation projects are, however, not available. We were particularly confronted with this issue in the annotation of sequences from different groups of complex algae originating from secondary endosymbioses, where the identification of the phylogenetic origin of genes is often more problematic than in taxa well represented in the databases (e.g. animals, plants or fungi). Results: We present a flexible pipeline with a user-friendly, interactive graphical user interface running on desktop computers that automatically performs a basic local alignment search tool (BLAST) search of query sequences, selects a representative subset of them, then creates a multiple alignment from the selected sequences, and finally computes a phylogenetic tree. The pipeline, named PhyloGena, uses public domain software for all standard bioinformatics tasks (similarity search, multiple alignment, and phylogenetic reconstruction). As the major technological innovation, selection of a meaningful subset of BLAST hits was implemented using logic programing, mimicing the selection procedure (BLAST tables, multiple alignments and phylogenetic trees) are displayed graphically, allowing the user to interact with the pipeline and deduce the function and phylogenetic origin of the query. PhyloGena thus makes phylogenomic annotation available also for those biologists without access to large computing facilities and with little informatics background. Although phylogenetic annotation is particularly useful when working with composite genomes (e.g. from complex algae), PhyloGena can be helpful in expressed sequence tag and genome annotation also in other organisms. Availability: PhyloGena (executables for LINUX and Windows 2000/XP as well as source code) is available by anonymous ftp from <inter-ref locator="http://www.awi.de/en/phylogena" locator-type="url">http://www.awi.de/en/phylogena</inter-ref> Contact: kvalentin@awi-bremerhaven.de Oxford University Press 2007-04-01 00:00:00.0 TEXT text/html http://bioinformatics.oxfordjournals.org/cgi/content/short/23/7/793 http://dx.doi.org/10.1093/bioinformatics/btm016 en Copyright (C) 2007, Oxford University Press

oai:open-archive.highwire.org:bioinfo:23/7/8022013-05-13HighWireOUPbioinfo:23:7

PROMALS: towards accurate multiple sequence alignments of distantly related proteins Pei, Jimin Grishin, Nick V. SEQUENCE ANALYSIS Motivation: Accurate multiple sequence alignments are essential in protein structure modeling, functional prediction and efficient planning of experiments. Although the alignment problem has attracted considerable attention, preparation of high-quality alignments for distantly related sequences remains a difficult task. Results: We developed PROMALS, a multiple alignment method that shows promising results for protein homologs with sequence identity below 10%, aligning close to half of the amino acid residues correctly on average. This is about three times more accurate than traditional pairwise sequence alignment methods. PROMALS algorithm derives its strength from several sources: (i) sequence database searches to retrieve additional homologs; (ii) accurate secondary structure prediction; (iii) a hidden Markov model that uses a novel combined scoring of amino acids and secondary structures; (iv) probabilistic consistency-based scoring applied to progressive alignment of profiles. Compared to the best alignment methods that do not use secondary structure prediction and database searches (e.g. MUMMALS, ProbCons and MAFFT), PROMALS is up to 30% more accurate, with improvement being most prominent for highly divergent homologs. Compared to SPEM and HHalign, which also employ database searches and secondary structure prediction, PROMALS shows an accuracy improvement of several percent. Availability: The PROMALS web server is available at: <inter-ref locator="http://prodata.swmed.edu/promals/" locator-type="url">http://prodata.swmed.edu/promals/</inter-ref> Contact: <inter-ref locator="jpei@chop.swmed.edu" locator-type="email">jpei@chop.swmed.edu</inter-ref> Supplementary information: Supplementary data are available at <it>Bioinformatics</it> online. Oxford University Press 2007-04-01 00:00:00.0 TEXT text/html http://bioinformatics.oxfordjournals.org/cgi/content/short/23/7/802 http://dx.doi.org/10.1093/bioinformatics/btm017 en Copyright (C) 2007, Oxford University Press

oai:open-archive.highwire.org:bioinfo:23/7/8092013-05-13HighWireOUPbioinfo:23:7

SCOOP: a simple method for identification of novel protein superfamily relationships Bateman, Alex Finn, Robert D. SEQUENCE ANALYSIS Motivation: Profile searches of sequence databases are a sensitive way to detect sequence relationships. Sophisticated profile–profile comparison algorithms that have been recently introduced increase search sensitivity even further. Results: In this article, a simpler approach than profile–profile comparison is presented that has a comparable performance to state-of-the-art tools such as COMPASS, HHsearch and PRC. This approach is called SCOOP (Simple Comparison Of Outputs Program), and is shown to find known relationships between families in the Pfam database as well as detect novel distant relationships between families. Several novel discoveries are presented including the discovery that a domain of unknown function (DUF283) found in Dicer proteins is related to double-stranded RNA-binding domains. Availability: SCOOP is freely available under a GNU GPL license from <inter-ref locator="http://www.sanger.ac.uk/Users/agb/SCOOP/" locator-type="url">http://www.sanger.ac.uk/Users/agb/SCOOP/</inter-ref> Contact: <inter-ref locator="agb@sanger.ac.uk" locator-type="email">agb@sanger.ac.uk</inter-ref> Supplementary information: Supplementary data are available at <it>Bioinformatics</it> online. Oxford University Press 2007-04-01 00:00:00.0 TEXT text/html http://bioinformatics.oxfordjournals.org/cgi/content/short/23/7/809 http://dx.doi.org/10.1093/bioinformatics/btm034 en Copyright (C) 2007, Oxford University Press

oai:open-archive.highwire.org:bioinfo:23/7/8152013-05-13HighWireOUPbioinfo:23:7

Assessment of phylogenomic and orthology approaches for phylogenetic inference Dutilh, B. E. van Noort, V. van der Heijden, R. T. J. M. Boekhout, T. Snel, B. Huynen, M. A. PHYLOGENETICS Motivation: Phylogenomics integrates the vast amount of phylogenetic information contained in complete genome sequences, and is rapidly becoming the standard for reliably inferring species phylogenies. There are, however, fundamental differences between the ways in which phylogenomic approaches like gene content, superalignment, superdistance and supertree integrate the phylogenetic information from separate orthologous groups. Furthermore, they all depend on the method by which the orthologous groups are initially determined. Here, we systematically compare these four phylogenomic approaches, in parallel with three approaches for large-scale orthology determination: pairwise orthology, cluster orthology and tree-based orthology. Results: Including various phylogenetic methods, we apply a total of 54 fully automated phylogenomic procedures to the fungi, the eukaryotic clade with the largest number of sequenced genomes, for which we retrieved a golden standard phylogeny from the literature. Phylogenomic trees based on gene content show, relative to the other methods, a bias in the tree topology that parallels convergence in lifestyle among the species compared, indicating convergence in gene content. Conclusions: Complete genomes are no guarantee for good or even consistent phylogenies. However, the large amounts of data in genomes enable us to carefully select the data most suitable for phylogenomic inference. In terms of performance, the superalignment approach, combined with restrictive orthology, is the most successful in recovering a fungal phylogeny that agrees with current taxonomic views, and allows us to obtain a high-resolution phylogeny. We provide solid support for what has grown to be a common practice in phylogenomics during its advance in recent years. Contact: <inter-ref locator="dutilh@cmbi.ru.nl" locator-type="email">dutilh@cmbi.ru.nl</inter-ref> Supplementary information: Supplementary data are available at <it>Bioinformatics</it> online. Oxford University Press 2007-04-01 00:00:00.0 TEXT text/html http://bioinformatics.oxfordjournals.org/cgi/content/short/23/7/815 http://dx.doi.org/10.1093/bioinformatics/btm015 en Copyright (C) 2007, Oxford University Press

oai:open-archive.highwire.org:bioinfo:23/7/8252013-05-13HighWireOUPbioinfo:23:7

A simulation test bed for hypotheses of genome evolution Beiko, Robert G. Charlebois, Robert L. PHYLOGENETICS Motivation: Microbial genomes undergo evolutionary processes such as gene family expansion and contraction, variable rates and patterns of sequence substitution and lateral genetic transfer. Simulation tools are essential for both the generation of data under different evolutionary models and the validation of analytical methods on such data. However, meaningful investigation of phenomena such as lateral genetic transfer requires the simultaneous consideration of many underlying evolutionary processes. Results: We have developed EvolSimulator, a software package that combines non-stationary sequence and gene family evolution together with models of lateral genetic transfer, within a customizable birth–death model of speciation and extinction. Here, we examine simulated data sets generated with EvolSimulator using existing statistical techniques from the evolutionary literature, showing in detail each component of the simulation strategy. Availability: Source code, manual and other information are freely available at <inter-ref locator="www.bioinformatics.org.au/evolsim" locator-type="url">www.bioinformatics.org.au/evolsim</inter-ref> Contact: <inter-ref locator="beiko@cs.dal.ca" locator-type="email">beiko@cs.dal.ca</inter-ref> Supplementary information: Supplementary data are available at <it>Bioinformatics</it> online. Oxford University Press 2007-04-01 00:00:00.0 TEXT text/html http://bioinformatics.oxfordjournals.org/cgi/content/short/23/7/825 http://dx.doi.org/10.1093/bioinformatics/btm024 en Copyright (C) 2007, Oxford University Press

oai:open-archive.highwire.org:bioinfo:23/7/8322013-05-13HighWireOUPbioinfo:23:7

Assessment of the probabilities for evolutionary structural changes in protein folds Viksna, Juris Gilbert, David STRUCTURAL BIOINFORMATICS Motivation: The evolution of protein sequences can be described by a stepwise process, where each step involves changes of a few amino acids. In a similar manner, the evolution of protein folds can be at least partially described by an analogous process, where each step involves comparatively simple changes affecting few secondary structure elements. A number of such evolution steps, justified by biologically confirmed examples, have previously been proposed by other researchers. However, unlike the situation with sequences, as far as we know there have been no attempts to estimate the comparative probabilities for different kinds of such structural changes. Results: We have tried to assess the comparative probabilities for a number of known structural changes, and to relate the probabilities of such changes with the distance between protein sequences. We have formalized these structural changes using a topological representation of structures (TOPS), and have developed an algorithm for measuring structural distances that involve few evolutionary steps. The probabilities of structural changes then were estimated on the basis of all-against-all comparisons of the sequence and structure of protein domains from the CATH-95 representative set. The results obtained are reasonably consistent for a number of different data subsets and permit the identification of several ‘most popular’ types of evolutionary changes in protein structure. The results also suggest that alterations in protein structure are more likely to occur when the sequence similarity is >10% (the average similarity being ∼6% for the data sets employed in this study), and that the distribution of probabilities of structural changes is fairly uniform within the interval of 15–50% sequence similarity. Availability: The algorithms have been implemented on the Windows operating system in C++ and using the Borland Visual Component Library. The source code is available on request from the first author. The data sets used for this study (representative sets of protein domains, matrices of sequence similarities and structural distances) are available on <inter-ref locator="http://bioinf.mii.lu.lv/epsrc_project/struct_ev.html" locator-type="url">http://bioinf.mii.lu.lv/epsrc_project/struct_ev.html</inter-ref>. Contact: <inter-ref locator="juris.viksna@mii.lu.lv" locator-type="email">juris.viksna@mii.lu.lv</inter-ref> Oxford University Press 2007-04-01 00:00:00.0 TEXT text/html http://bioinformatics.oxfordjournals.org/cgi/content/short/23/7/832 http://dx.doi.org/10.1093/bioinformatics/btm022 en Copyright (C) 2007, Oxford University Press

oai:open-archive.highwire.org:bioinfo:23/7/8422013-05-13HighWireOUPbioinfo:23:7

A hidden Markov model-based approach for identifying timing differences in gene expression under different experimental factors Yoneya, Takashi Mamitsuka, Hiroshi GENE EXPRESSION Motivation: Time series experiments of cDNA microarrays have been commonly used in various biological studies and conducted under a lot of experimental factors. A popular approach of time series microarray analysis is to compare one gene with another in their expression profiles, and clustering expression sequences is a typical example. On the other hand, a practically important issue in gene expression is to identify the general timing difference that is caused by experimental factors. This type of difference can be extracted by comparing a set of time series expression profiles under a factor with those under another factor, and so it would be difficult to tackle this issue by using only a current approach for time series microarray analysis. Results: We have developed a systematic method to capture the timing difference in gene expression under different experimental factors, based on hidden Markov models. Our model outputs a real-valued vector at each state and has a unique state transition diagram. The parameters of our model are trained from a given set of pairwise (generally multiplewise) expression sequences. We evaluated our model using synthetic as well as real microarray datasets. The results of our experiment indicate that our method worked favourably to identify the timing ordering under different experimental factors, such as that gene expression under heat shock tended to start earlier than that under oxidative stress. Contact: <inter-ref locator="t-yoneya@kirin.co.jp" locator-type="email">t-yoneya@kirin.co.jp</inter-ref> Supplementary information: Supplementary data are available at <it>Bioinformatics</it> online. Oxford University Press 2007-04-01 00:00:00.0 TEXT text/html http://bioinformatics.oxfordjournals.org/cgi/content/short/23/7/842 http://dx.doi.org/10.1093/bioinformatics/btl667 en Copyright (C) 2007, Oxford University Press

oai:open-archive.highwire.org:bioinfo:23/7/8502013-05-13HighWireOUPbioinfo:23:7

Gene expression network analysis and applications to immunology Nacu, Serban Critchley-Thorne, Rebecca Lee, Peter Holmes, Susan GENE EXPRESSION We address the problem of using expression data and prior biological knowledge to identify differentially expressed pathways or groups of genes. Following an idea of Ideker <it>et al</it>. (<cross-ref type="bib" refid="B11">2002</cross-ref>), we construct a gene interaction network and search for high-scoring subnetworks. We make several improvements in terms of scoring functions and algorithms, resulting in higher speed and accuracy and easier biological interpretation. We also assign significance levels to our results, adjusted for multiple testing. Our methods are succesfully applied to three human microarray data sets, related to cancer and the immune system, retrieving several known and potential pathways. The method, denoted by the acronym GXNA (Gene eXpression Network Analysis) is implemented in software that is publicly available and can be used on virtually any microarray data set. Contact: <inter-ref locator="serban@stat.stanford.edu" locator-type="email">serban@stat.stanford.edu</inter-ref> Supplementary information: The source code and executable for the software, as well as certain supplemental materials, can be downloaded from <inter-ref locator="http://stat.stanford.edu/~serban/gxna" locator-type="url">http://stat.stanford.edu/~serban/gxna</inter-ref>. Oxford University Press 2007-04-01 00:00:00.0 TEXT text/html http://bioinformatics.oxfordjournals.org/cgi/content/short/23/7/850 http://dx.doi.org/10.1093/bioinformatics/btm019 en Copyright (C) 2007, Oxford University Press

oai:open-archive.highwire.org:bioinfo:23/7/8592013-05-13HighWireOUPbioinfo:23:7

Functional evaluation of domain domain interactions and human protein interaction networks Schlicker, Andreas Huthmacher, Carola Ramírez, Fidel Lengauer, Thomas Albrecht, Mario SYSTEMS BIOLOGY Motivation: Large amounts of protein and domain interaction data are being produced by experimental high-throughput techniques and computational approaches. To gain insight into the value of the provided data, we used our new similarity measure based on the Gene Ontology (GO) to evaluate the molecular functions and biological processes of interacting proteins or domains. The applied measure particularly addresses the frequent annotation of proteins or domains with multiple GO terms. Results: Using our similarity measure, we compare predicted domain–domain and human protein–protein interactions with experimentally derived interactions. The results show that our similarity measure is of significant benefit in quality assessment and confidence ranking of domain and protein networks. We also derive useful confidence score thresholds for dividing domain interaction predictions into subsets of low and high confidence. Contact: <inter-ref locator="mario.albrecht@mpi-inf.mpg.de" locator-type="email">mario.albrecht@mpi-inf.mpg.de</inter-ref> Supplementary information: Supplementary data are available at <it>Bioinformatics</it> online. Oxford University Press 2007-04-01 00:00:00.0 TEXT text/html http://bioinformatics.oxfordjournals.org/cgi/content/short/23/7/859 http://dx.doi.org/10.1093/bioinformatics/btm012 en Copyright (C) 2007, Oxford University Press

oai:open-archive.highwire.org:bioinfo:23/7/8662013-05-13HighWireOUPbioinfo:23:7

Boolean dynamics of genetic regulatory networks inferred from microarray time series data Martin, Shawn Zhang, Zhaoduo Martino, Anthony Faulon, Jean-Loup SYSTEMS BIOLOGY Motivation: Methods available for the inference of genetic regulatory networks strive to produce a single network, usually by optimizing some quantity to fit the experimental observations. In this article we investigate the possibility that multiple networks can be inferred, all resulting in similar dynamics. This idea is motivated by theoretical work which suggests that biological networks are robust and adaptable to change, and that the overall behavior of a genetic regulatory network might be captured in terms of dynamical basins of attraction. Results: We have developed and implemented a method for inferring genetic regulatory networks for time series microarray data. Our method first clusters and discretizes the gene expression data using <it>k</it>-means and support vector regression. We then enumerate Boolean activation–inhibition networks to match the discretized data. Finally, the dynamics of the Boolean networks are examined. We have tested our method on two immunology microarray datasets: an IL-2-stimulated T cell response dataset and a LPS-stimulated macrophage response dataset. In both cases, we discovered that many networks matched the data, and that most of these networks had similar dynamics. Contact: <inter-ref locator="jfaulon@sandia.gov" locator-type="email">jfaulon@sandia.gov</inter-ref> Supplementary information: Supplementary data are available at <it>Bioinformatics</it> online. Oxford University Press 2007-04-01 00:00:00.0 TEXT text/html http://bioinformatics.oxfordjournals.org/cgi/content/short/23/7/866 http://dx.doi.org/10.1093/bioinformatics/btm021 en Copyright (C) 2007, Oxford University Press

oai:open-archive.highwire.org:bioinfo:23/7/8752013-05-13HighWireOUPbioinfo:23:7

Robustness analysis of the E.coli chemosensory system to perturbations in chemoattractant concentrations Patnaik, Pratap R. SYSTEMS BIOLOGY Motivation: Cells of <it>Escherichia coli</it> sense and move toward chemical attractants. This is done through an intricate sensory system that eventually directs the movements of flagellae which regulate the ‘runs’ and ‘tumbles’ of the cells. Under realistic conditions, chemical stimuli often fluctuate due to noise from the environment. The effect of noise on the chemosensory system has been investigated here through the sensitivity coefficients of the concentrations of four key proteins—the phosphorylated forms of CheA, CheB and CheY, and the FliM-CheY∼P complex—that govern chemotactic motility. The letter P denotes phosphorylation. Results: All sensitivities increased with time and then stabilized. However, the four sets of sensitivities differed in their magnitudes and the durations of their transient phases before stabilization. CheA∼P was the least sensitive and CheY∼P the most sensitive. Moreover, while the sensitivities of CheA∼P, CheB∼P and CheY∼P increased with chemoattractant concentration, that of the FliM complex decreased. These differences have been interpreted in terms of the mechanism of the chemosensory system and they have important implications for practical applications of chemotaxis. Contact: <inter-ref locator="pratap@imtech.res.in" locator-type="email">pratap@imtech.res.in</inter-ref> Oxford University Press 2007-04-01 00:00:00.0 TEXT text/html http://bioinformatics.oxfordjournals.org/cgi/content/short/23/7/875 http://dx.doi.org/10.1093/bioinformatics/btm028 en Copyright (C) 2007, Oxford University Press

oai:open-archive.highwire.org:bioinfo:23/7/8822013-05-13HighWireOUPbioinfo:23:7

A new versatile database created for geneticists and breeders to link molecular and phenotypic data in perennial crops: the AppleBreed DataBase Antofie, A. Lateur, M. Oger, R. Patocchi, A. Durel, C. E. Van de Weg, W. E. DATABASES AND ONTOLOGIES Objective: <it>AppleBreed DataBase</it> (DB) aims to store genotypic and phenotypic data from multiple pedigree verified plant populations (crosses, breeding selections and commercial cultivars) so that they are easily accessible for geneticists and breeders. It will help in elucidating the genetics of economically important traits, in identifying molecular markers associated with agronomic traits, in allele mining and in choosing the best parental cultivars for breeding. It also provides high traceability of data over generations, years and localities. <it>AppleBreed DB</it> could serve as a generic database design for other perennial crops with long economic lifespans, long juvenile periods and clonal propagation. Results: <it>AppleBreed DB</it> is organized as a relational database. The core element is the GENOTYPE entity, which has two sub-classes at the physical level: TREE and DNA-SAMPLE. This approach facilitates all links between plant material, phenotypic and molecular data. The entities TREE, DNA-SAMPLE, PHENOTYPE and MOLECULAR DATA allow multi-annual observations to be stored as individual samples of individual trees, even if the nature of these observations differs greatly (e.g. molecular data on parts of the apple genome, physico-chemical measurements of fruit quality traits, and evaluation of disease resistance). <it>AppleBreed DB</it> also includes synonyms for cultivars and pedigrees. Finally, it can be loaded and explored through the web, and comes with tools to present basic statistical overviews and with validation procedures for phenotypic and marker data to certify data quality. <it>AppleBreed DB</it> was developed initially as a tool for scientists involved in apple genetics within the framework of the European project, ‘High-quality Disease Resistance in Apples for Sustainable Agriculture’ (HiDRAS), but it is also applicable to many other perennial crops. Contact: <inter-ref locator="oger@cra.wallonie.be" locator-type="email">oger@cra.wallonie.be</inter-ref> Oxford University Press 2007-04-01 00:00:00.0 TEXT text/html http://bioinformatics.oxfordjournals.org/cgi/content/short/23/7/882 http://dx.doi.org/10.1093/bioinformatics/btm013 en Copyright (C) 2007, Oxford University Press

oai:open-archive.highwire.org:bioinfo:23/7/8922013-05-13HighWireOUPbioinfo:23:7

CGHcall: calling aberrations for array CGH tumor profiles van de Wiel, Mark A. Kim, Kyung In Vosse, Sjoerd J. van Wieringen, Wessel N. Wilting, Saskia M. Ylstra, Bauke GENOME ANALYSIS Summary: CGHcall achieves high calling accuracy for array CGH data by effective use of breakpoint information from segmentation and by inclusion of several biological concepts that are ignored by existing algorithms. The algorithm is validated for simulated and verified real array CGH data. By incorporating more than three classes, CGHcall improves detection of single copy gains and amplifications. Moreover, it allows effective inclusion of chromosome arm information. Availability: An R-package (GUI), a manual and an example data set are available at <inter-ref locator="http://www.few.vu.nl/~mavdwiel/CGHcall.html" locator-type="url">http://www.few.vu.nl/~mavdwiel/CGHcall.html</inter-ref>. Contact: <inter-ref locator="mark.vdwiel@vumc.nl" locator-type="email">mark.vdwiel@vumc.nl</inter-ref> Supplementary information: Supplementary data are available at <it>Bioinformatics</it> online. Oxford University Press 2007-04-01 00:00:00.0 TEXT text/html http://bioinformatics.oxfordjournals.org/cgi/content/short/23/7/892 http://dx.doi.org/10.1093/bioinformatics/btm030 en Copyright (C) 2007, Oxford University Press

oai:open-archive.highwire.org:bioinfo:23/7/8952013-05-13HighWireOUPbioinfo:23:7

NetPhosYeast: prediction of protein phosphorylation sites in yeast Ingrell, Christian R. Miller, Martin L. Jensen, Ole N. Blom, Nikolaj SEQUENCE ANALYSIS Summary: We here present a neural network-based method for the prediction of protein phosphorylation sites in yeast—an important model organism for basic research. Existing protein phosphorylation site predictors are primarily based on mammalian data and show reduced sensitivity on yeast phosphorylation sites compared to those in humans, suggesting the need for an yeast-specific phosphorylation site predictor. NetPhosYeast achieves a correlation coefficient close to 0.75 with a sensitivity of 0.84 and specificity of 0.90 and outperforms existing predictors in the identification of phosphorylation sites in yeast. Availability: The NetPhosYeast prediction service is available as a public web server at <inter-ref locator="http://www.cbs.dtu.dk/services/NetPhosYeast/" locator-type="url">http://www.cbs.dtu.dk/services/NetPhosYeast/</inter-ref> Contact: <inter-ref locator="nikob@cbs.dtu.dk" locator-type="email">nikob@cbs.dtu.dk</inter-ref> Oxford University Press 2007-04-01 00:00:00.0 TEXT text/html http://bioinformatics.oxfordjournals.org/cgi/content/short/23/7/895 http://dx.doi.org/10.1093/bioinformatics/btm020 en Copyright (C) 2007, Oxford University Press

oai:open-archive.highwire.org:bioinfo:23/7/8982013-05-13HighWireOUPbioinfo:23:7

COPYCAT : cophylogenetic analysis tool Meier-Kolthoff, Jan P. Auch, Alexander F. Huson, Daniel H. Göker, Markus PHYLOGENETICS Summary: We have developed the software C<scp>opy</scp>C<scp>at</scp> which provides an easy and fast access to cophylogenetic analyses. It incorporates a wrapper for the program P<scp>ara</scp>F<scp>it</scp>, which conducts a statistical test for the presence of congruence between host and parasite phylogenies. C<scp>opy</scp>C<scp>at</scp> offers various features, such as the creation of customized host–parasite association data and the computation of phylogenetic host/parasite trees based on the NCBI taxonomy. Availability: C<scp>opy</scp>C<scp>at</scp> and its manual are freely available at <inter-ref locator="http://www-ab.informatik.uni-tuebingen.de/software/copycat" locator-type="url">http://www-ab.informatik.uni-tuebingen.de/software/copycat</inter-ref>. Contact: <inter-ref locator="auch@informatik.uni-tuebingen.de" locator-type="email">auch@informatik.uni-tuebingen.de</inter-ref> Supplementary information: Results of the real-world example can be found at <inter-ref locator="http://www-ab.informatik.uni-tuebingen.de/software/copycat" locator-type="url">http://www-ab.informatik.uni-tuebingen.de/software/copycat</inter-ref> or <it>Bioinformatics</it> online. Oxford University Press 2007-04-01 00:00:00.0 TEXT text/html http://bioinformatics.oxfordjournals.org/cgi/content/short/23/7/898 http://dx.doi.org/10.1093/bioinformatics/btm027 en Copyright (C) 2007, Oxford University Press

oai:open-archive.highwire.org:bioinfo:23/7/9012013-05-13HighWireOUPbioinfo:23:7

DFprot: a webtool for predicting local chain deformability Garzón, José Ignacio Kovacs, Julio Abagyan, Ruben Chacón, Pablo STRUCTURAL BIOINFORMATICS Summary: DFprot is a web-based server for predicting main-chain deformability from a single protein conformation. The server automatically performs a normal-mode analysis (NMA) of the uploaded structure and calculates its capability to deform at each of its residues. Non-specialists can easily and rapidly obtain a quantitative first approximation of the flexibility of their structures with a simple and efficient interface. Availability: <inter-ref locator="http://sbg.cib.csic.es/Software/DFprot" locator-type="url">http://sbg.cib.csic.es/Software/DFprot</inter-ref> Contact: <inter-ref locator="pablo@cib.csic.es" locator-type="email">pablo@cib.csic.es</inter-ref> Oxford University Press 2007-04-01 00:00:00.0 TEXT text/html http://bioinformatics.oxfordjournals.org/cgi/content/short/23/7/901 http://dx.doi.org/10.1093/bioinformatics/btm014 en Copyright (C) 2007, Oxford University Press

oai:open-archive.highwire.org:bioinfo:23/7/9032013-05-13HighWireOUPbioinfo:23:7

Automatic correspondence of tags and genes (ACTG): a tool for the analysis of SAGE, MPSS and SBS data Galante, Pedro A. F. Trimarchi, Jeff Cepko, Constance L. de Souza, Sandro J. Ohno-Machado, Lucila Kuo, Winston P. GENE EXPRESSION Summary: A critical step in any SAGE, MPSS and SBS data analysis is tag-to-gene assignment. Current available tools are limited by a tag-by-tag annotation process and/or do not provide the dataset that is used to produce a complete tag-to-gene mapping. We developed ACTG, a web-based application that allows a large-scale tag-to-gene mapping using several reference datasets. ACTG can annotate SAGE (14 or 21 bp), MPSS (17 or 20 bp) and SBS (16 bp) data for both human and mouse organisms. Availability: <inter-ref locator="http://retina.med.harvard.edu/ACTG/" locator-type="url">http://retina.med.harvard.edu/ACTG/</inter-ref> Contact: <inter-ref locator="pgalante@ludwig.org.br" locator-type="email">pgalante@ludwig.org.br</inter-ref> Supplementary information: Supplementary data are available at <it>Bioinformatics</it> online. Oxford University Press 2007-04-01 00:00:00.0 TEXT text/html http://bioinformatics.oxfordjournals.org/cgi/content/short/23/7/903 http://dx.doi.org/10.1093/bioinformatics/btm023 en Copyright (C) 2007, Oxford University Press

oai:open-archive.highwire.org:bioinfo:23/7/9062013-05-13HighWireOUPbioinfo:23:7

Enabling high-throughput data management for systems biology: The Bioinformatics Resource Manager Shah, Anuj R. Singhal, Mudita Klicker, Kyle R. Stephan, Eric G. Wiley, H. Steven Waters, Katrina M. SYSTEMS BIOLOGY Summary: The Bioinformatics Resource Manager (BRM) is a software environment that provides the user with data management, retrieval and integration capabilities. Designed in collaboration with biologists, BRM simplifies mundane analysis tasks of merging microarray and proteomic data across platforms, facilitates integration of users’ data with functional annotation and interaction data from public sources and provides connectivity to visual analytic tools through reformatting of the data for easy import or dynamic launching capability. BRM is developed using Java™ and other open-source technologies for free distribution. Availability: BRM, sample data sets and a user manual can be downloaded from <inter-ref locator="http://www.sysbio.org/dataresources/brm.stm" locator-type="url">http://www.sysbio.org/dataresources/brm.stm</inter-ref> Contact: <inter-ref locator="anuj.shah@pnl.gov" locator-type="email">anuj.shah@pnl.gov</inter-ref>, <inter-ref locator="brm@pnl.gov" locator-type="email">brm@pnl.gov</inter-ref> Oxford University Press 2007-04-01 00:00:00.0 TEXT text/html http://bioinformatics.oxfordjournals.org/cgi/content/short/23/7/906 http://dx.doi.org/10.1093/bioinformatics/btm031 en Copyright (C) 2007, Oxford University Press

oai:open-archive.highwire.org:bioinfo:23/7/9102013-05-13HighWireOUPbioinfo:23:7

NetMatch: a Cytoscape plugin for searching biological networks Ferro, A. Giugno, R. Pigola, G. Pulvirenti, A. Skripin, D. Bader, G. D. Shasha, D. SYSTEMS BIOLOGY Summary: NetMatch is a Cytoscape plugin which allows searching biological networks for subcomponents matching a given query. Queries may be approximate in the sense that certain parts of the subgraph-query may be left unspecified. To make the query creation process easy, a drawing tool is provided. Cytoscape is a bioinformatics software platform for the visualization and analysis of biological networks. Availability: The full package, a tutorial and associated examples are available at the following web sites: <inter-ref locator="http://alpha.dmi.unict.it/~ctnyu/netmatch.html" locator-type="url">http://alpha.dmi.unict.it/~ctnyu/netmatch.html</inter-ref>, <inter-ref locator="http://baderlab.org/Software/NetMatch" locator-type="url">http://baderlab.org/Software/NetMatch</inter-ref> Contact: <inter-ref locator="ferro@dmi.unict.it" locator-type="email">ferro@dmi.unict.it</inter-ref> Oxford University Press 2007-04-01 00:00:00.0 TEXT text/html http://bioinformatics.oxfordjournals.org/cgi/content/short/23/7/910 http://dx.doi.org/10.1093/bioinformatics/btm032 en Copyright (C) 2007, Oxford University Press

oai:open-archive.highwire.org:bioinfo:23/7/9132013-05-13HighWireOUPbioinfo:23:7

Ontology development for biological systems: immunology Diehl, Alexander D. Lee, Jamie A. Scheuermann, Richard H. Blake, Judith A. DATABASES AND ONTOLOGIES Summary: We recently implemented improvements to the representation of immunology content of the biological process branch of the Gene Ontology (GO). The aims of the revision were to provide a comprehensive representation of immunological processes and to improve the organization of immunology related terms in the GO to match current concepts in the field of immunology. With these improvements, the GO will better reflect current understanding in the field of immunology and thus prove to be a more valuable resource for knowledge representation in gene annotation and analysis in the areas of immunology related to genomics and bioinformatics. Availability: <inter-ref locator="http://www.geneontology.org" locator-type="url">http://www.geneontology.org</inter-ref> Contact: <inter-ref locator="adiehl@informatics.jax.org" locator-type="email">adiehl@informatics.jax.org</inter-ref> Oxford University Press 2007-04-01 00:00:00.0 TEXT text/html http://bioinformatics.oxfordjournals.org/cgi/content/short/23/7/913 http://dx.doi.org/10.1093/bioinformatics/btm029 en Copyright (C) 2007, Oxford University Press

oai:open-archive.highwire.org:bioinfo:23/7/9162013-05-13HighWireOUPbioinfo:23:7

Modular organization of protein interaction networks Luo, Feng Yang, Yunfeng Chen, Chin-Fu Chang, Roger Zhou, Jizhong Scheuermann, Richard H. CORRIGENDUM Oxford University Press 2007-04-01 00:00:00.0 TEXT text/html http://bioinformatics.oxfordjournals.org/cgi/content/short/23/7/916 http://dx.doi.org/10.1093/bioinformatics/btm037 en Copyright (C) 2007, Oxford University Press