Publication Date: 2010 Mar 15 PMID: 20231229Authors: Yomtovian, I. - Teerakulkittipong, N. - Lee, B. - Moult, J. - Unger, R.Journal: BioinformaticsMOTIVATION: Intriguingly, sequence analysis of genomes reveals that a large number of genes are unique to each organism. The origin of these genes, termed ORFans, is not known. Here, we explore the origin of ORFan genes by defining a simple measure called "composition bias", based on the deviation of the amino acid composition of a given sequence from the average composition of all proteins of a given genome. RESULTS: For a set of 47 prokaryotic genomes, we show that the amino acid composition bias of real proteins, random "proteins" (created by using the nucleotide frequencies of each genome), and "proteins" translated from intergenic regions are distinct. For ORFans, we observed a correlation between their composition bias and their relative evolutionary age. Recent ORFan proteins have compositions more similar to those of random "proteins", while the compositions of more ancient ORFan proteins are more similar to those of the set of all proteins of the organism. This observation is consistent with an evolutionary scenario wherein ORFan genes emerged and underwent a large number of random mutations and selection, eventually adapting to the composition preference of their organism over time.post to: CiteULike

Publication Date: 2010 Mar 12 PMID: 20228129Authors: Bouckaert, R. R.Journal: BioinformaticsMOTIVATION: Bayesian analysis through programs like BEAST (Drummond and Rumbaut, 2007) and MrBayes (Huelsenbeck et al., 2001) provides a powerful method for reconstruction of evolutionary relationships. One of the benefits of Bayesian methods is that well founded estimates of uncertainty in models can be made available. So, for example not only the mean time of a most recent common ancestor (tMRCA) is estimated, but also the spread. This distribution over model space is represented by a set of trees, which can be rather large and difficult to interpret. DensiTree is a tool that helps navigating these sets of trees. RESULTS: The main idea behind DensiTree is to draw all trees in the set transparently. As a result, areas where a lot of the trees agree in topology and branch lengths show up as highly colored areas, while areas with little agreement show up as webs. This makes it possible to quickly get an impression of properties of the tree set such as well supported clades, distribution of tMRCA and areas of topological uncertainty. Thus, DensiTree provides a quick method for qualitative analysis of tree sets. AVAILABILITY: DensiTree is freely available from http://compevol.auckland.ac.nz/software/DensiTree/. The program is licensed under GPL and source code is available. CONTACT: remco@cs.auckland.ac.nz.post to: CiteULike

Publication Date: 2010 Mar 12 PMID: 20228128Authors: Faust, K. - Dupont, P. - Callut, J. - van Helden, J.Journal: BioinformaticsMOTIVATION: Subgraph extraction is a powerful technique to predict pathways from biological networks and a set of query items (e.g. genes, proteins, compounds...). It can be applied to a variety of different data types, such as gene expression, protein levels, operons or phylogenetic profiles. In this article, we investigate different approaches to extract relevant pathways from metabolic networks. Although these approaches have been adapted to metabolic networks, they are generic enough to be adjusted to other biological networks as well. RESULTS: We comparatively evaluated seven sub-network extraction approaches on 71 known metabolic pathways from S. cerevisiae and a metabolic network obtained from MetaCyc. The best performing approach is a novel hybrid strategy, which combines a random walk-based reduction of the graph with a shortest paths-based algorithm, and which recovers the reference pathways with an accuracy of ~ 77%. AVAILABILITY: Most of the presented algorithms are available as part of the network analysis tool set (NeAT). The kWalks method is released under the GPL3 license. CONTACT: kfaust@ulb.ac.be.post to: CiteULike

Publication Date: 2010 Mar 11 PMID: 20223837Authors: Shiraishi, Y. - Kimura, S. - Okada, M.Journal: BioinformaticsMOTIVATION: Clustering and gene network inference often help to predict the biological functions of gene subsets. Recently, researchers have accumulated a large amount of time-course transcriptome data collected under different treatment conditions to understand the physiological states of cells in response to extracellular stimuli and to identify drug-responsive genes. Although a variety of statistical methods for clustering and inferring gene networks from expression profiles have been proposed, most of these are not tailored to simultaneously treat expression data collected under multiple stimulation conditions. RESULTS: We propose a new statistical method for analyzing temporal profiles under multiple experimental conditions. Our method simultaneously performs clustering of temporal expression profiles and inference of regulatory relationships among gene clusters. We applied this method to MCF7 human breast cancer cells treated with epidermal growth factor and heregulin which induce cellular proliferation and differentiation, respectively. The results showed that the method is useful for extracting biologically relevant information. AVAILABILITY: A MATLAB implementation of the method is available from http://csb.gsc.riken.jp/yshira/software/clusterNetwork.zip. CONTACT: yshira@riken.jp SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.post to: CiteULike

Publication Date: 2010 Mar 11 PMID: 20223836Authors: Xiao, S. J. - Zhang, C. - Ji, Z. L.Journal: BioinformaticsSUMMARY: The tissue-specific genes are a group of genes whose function and expression are preferred in one or several tissues/cell types. Identification of these genes helps better understanding of tissue-gene relationship, etiology and discovery of novel tissue-specific drug targets. In this study, a statistical method is introduced to detect tissue specific genes from more than 123125 gene expression profiles over 107 human tissues, 67 mouse tissues and 30 rat tissues. As a result, a novel subject-specialized repository, namely the Tissue-Specific Genes Database (TiSGeD), is developed to represent the analyzed results. Auxiliary information of tissue-specific genes were also collected from biomedical literatures. AVAILABILITY: http://bioinf.xmu.edu.cn/databases/TISGED/index.html. CONTACT: appo@bioinf.xmu.edu.cn or zhiliang.ji@gmail.com.post to: CiteULike

Publication Date: 2010 Mar 11 PMID: 20223835Authors: Narang, V. - Mittal, A. - Sung, W. K.Journal: BioinformaticsMOTIVATION: Discovery of nucleotide motifs that are localized with respect to a certain biological landmark is important in several applications, such as in regulatory sequences flanking the transcription start site, in the neighborhood of known transcription factor binding sites, and in transcription factor binding regions discovered by massively parallel sequencing (ChIP-Seq). RESULTS: We report an algorithm called LocalMotif to discover such localized motifs. The algorithm is based on a novel scoring function, called spatial confinement score, which can determine the exact interval of localization of a motif. This score is combined with other existing scoring measures including over-representation and relative entropy to determine the overall prominence of the motif. The approach successfully discovers biologically relevant motifs and their intervals of localization in scenarios where the motifs cannot be discovered by general motif finding tools. It is especially useful for discovering multiple co-localized motifs in a set of regulatory sequences, such as those identified by ChIP-Seq. Availability and Implementation: The LocalMotif software is available at http://www.comp.nus.edu.sg/~bioinfo/LocalMotif CONTACT: ksung@comp.nus.edu.sg SUPPLEMENTARY INFORMATION: Supplementary description of the method, algorithm and results is available at Bioinformatics online.post to: CiteULike

Publication Date: 2010 Mar 11 PMID: 20223834Authors: Le Cao, K. A. - Meugnier, E. - McLachlan, G. J.Journal: BioinformaticsMOTIVATION: Microarrays are being increasingly used in cancer research to better characterize and classify tumors by selecting marker genes. However, as very few of these genes have been validated as predictive biomarkers so far, it is mostly conventional clinical and pathological factors which are being used as prognostic indicators of clinical course. Combining clinical data with gene expression data may add valuable information, but it is a challenging task due to their categorical versus continuous characteristics. We have further developed the mixture of experts methodology (ME), a promising approach to tackle complex nonlinear problems. Several variants are proposed in integrative ME as well as the inclusion of various gene selection methods to select a hybrid signature. RESULTS: We show on three cancer studies that prediction accuracy can be improved when combining both types of variables. Furthermore, the selected genes were found to be of high relevance and can be considered as potential biomarkers for the prognostic selection of cancer therapy. AVAILABILITY: Integrative ME is implemented in the R package integrativeME (http://cran.r-project.org/). CONTACT: k.lecao@uq.edu.au.post to: CiteULike

Publication Date: 2010 Mar 10 PMID: 20219865Authors: Bayjanov, J. R. - Siezen, R. J. - van Hijum, S. A.Journal: BioinformaticsSUMMARY: A pangenome is the total of genes present in strains of the same species. Pangenome microarrays allow determining the genomic content of bacterial strains more accurately than conventional comparative genome hybridization microarrays. PanCGHweb is the first tool that effectively calls genotype based on pangenome microarray data. AVAILABILITY: PanCGHweb The web tool is accessible from: http://bamics2.cmbi.ru.nl/websoftware/pancgh/ CONTACT: sacha.vanhijum@nizo.nl.post to: CiteULike

Publication Date: 2010 Mar 9 PMID: 20215462Authors: Li, Y. - Patra, J. C.Journal: BioinformaticsMOTIVATION: Clinical diseases are characterized by distinct phenotypes. To identify disease genes is to elucidate the genephenotype relationships. Mutations in functionally related genes may result in similar phenotypes. It is reasonable to predict disease causing genes by integrating phenotypic data and genomic data. Some genetic diseases are genetically or phenotypic similar. They may share the common pathogenetic mechanisms. Identifying the relationship between diseases will facilitate better understanding of the pathogenetic mechanism of diseases. RESULTS: In this paper, we constructed a heterogeneous network by connecting the gene network and phenotype network using the phenotype-gene relationship information from the OMIM database. We extended the random walk with restart algorithm to the heterogeneous network. The algorithm prioritizes the genes and phenotypes simultaneously. We use leave one out cross validation to evaluate the ability of finding the gene-phenotype relationship. Results showed improved performance than previous works. We also used the algorithm to disclose hidden disease associations that cannot be found by gene network or phenotype network alone. We identified 18 hidden disease associations, most of which were supported by literature evidence. AVAILABILITY: The MATLAB code of the program is available at: http://www3.ntu.edu.sg/home/aspatra/research/Yongjin_BI2010.zip. CONTACT: yongjin.li@gmail.com; aspatra@ntu.edu.sg.post to: CiteULike

Publication Date: 2010 Mar 8 PMID: 20212019Authors: Tuna, S. - Niranjan, M.Journal: BioinformaticsMOTIVATION: High throughput measurements of mRNA abundances from microarrays involve several stages of preprocessing. At each stage, a user has access to a large number of algorithms with no universally agreed guidance on which of these to use. We show that binary representations of gene expressions, retaining only information on whether a gene is expressed or not, reduces the variability in results caused by algorithmic choice, while also improving the quality of inference drawn from microarray studies. RESULTS: Binary representation of transcriptome data has the desirable property of reducing the variability introduced at the preprocessing stages due to algorithmic choice. We compare the effect of the choice of algorithms on different problems and suggest that using binary representation of microarray data with Tanimoto kernel for SVM reduces the effect of the choice of algorithm and simultaneously improves the performance of classification of phenotypes. AVAILABILITY: Supplementary material is available online at Bioinformatics Webpage. CONTACT: mn@ecs.soton.ac.uk.post to: CiteULike

Publication Date: 2010 Mar 5 PMID: 20208069Authors: Grabherr, M. G. - Russell, P. - Meyer, M. - Mauceli, E. - Alfoldi, J. - Dipalma, F. - Lindblad-Toh, K.Journal: BioinformaticsMOTIVATION: Comparative genomics heavily relies on alignments of large and often complex DNA sequences. From an engineering perspective, the problem here is to provide maximum sensitivity (to find all there is to find), specificity (to only find real homology), and speed (to accommodate the billions of base pairs of vertebrate genomes). RESULTS: Satsuma addresses all three issues through novel strategies: a) cross-correlation, implemented via Fast Fourier Transform, b) a match scoring scheme that eliminates almost all false hits, and c) an asynchronous 'battleship'-like search, that allows for aligning two entire fish genomes (470 Mb and 217 Mb) in 120 CPU hours using 15 processors on a single machine. AVAILABILITY: Satsuma is part of the Spines software package, implemented in C++ on Linux. The latest version of Spines can be freely downloaded under the LGPL license from http://www.broadinstitute.org/science/programs/genome-biology/spines/ CONTACT: grabherr@broadinstitute.org.post to: CiteULike

Publication Date: 2010 Mar 5 PMID: 20208068Authors: Johannes, F. - Wardenaar, R. - Colome-Tatche, M. - Mousson, F. - de Graaf, P. - Mokry, M. - Guryev, V. - Timmers, H. T. - Cuppen, E. - Jansen, R. C.Journal: BioinformaticsMOTIVATION: ChIP-chip and ChIP-seq technologies provide genomewide measurements of various types of chromatin marks at an unprecedented resolution. With ChIP samples collected from different tissue types and/or individuals, we can now begin to characterize stochastic or systematic changes in epigenetic patterns during development (intra-individual) or at the population level (inter-individual). This requires statistical methods that permit a simultaneous comparison of multiple ChIP samples on a global as well as locus-specific scale. Current analytical approaches are mainly geared towards single sample investigations, and therefore have limited applicability in this comparative setting. This shortcoming presents a bottleneck in biological interpretations of multiple sample data. RESULTS: To address this limitation, we introduce a parametric classification approach for the simultaneous analysis of two (or more) ChIP samples. We consider several competing models that reflect alternative biological assumptions about the global distribution of the data. Inferences about locus-specific and genomewide chromatin differences are reached through the estimation of multivariate mixtures. Parameter estimates are obtained using a version of the Incremental Expectation Maximization algorithm (IEM). We demonstrate efficient scalability and application to three very diverse ChIP-chip and ChIP-seq experiments. The proposed approach is evaluated against several published ChIP-chip and ChIPseq software packages. We recommend its use as a first-pass algorithm to identify candidate regions in the epigenome, possibly followed by some type of second-pass algorithm to fine-tune detected peaks in accordance with biological or technological criteria. AVAILABILITY: R source code is available at http://gbic.biol.rug.nl/supplementary/2009/ChromatinProfiles/ Access to Chip-seq data: GEO repository GSE17937 CONTACT: f.johannes@rug.nl.post to: CiteULike

Publication Date: 2010 Mar 15 PMID: 20207696Authors: Sasson, A. - Michael, T. P.Journal: BioinformaticsSUMMARY: Here, we report the development of a filtering framework designed for efficient identification of both polyclonal and independent errors within SOLiD sequence data. The filtering utilizes the quality values reported by SOLiD's primary analysis for the identification of the two different types of errors. The filtering framework facilitates the passage of high-quality data into a variety of functional genomics applications, including de novo assemblers and sequence matching programs for SNP calling, improving the output quality and reducing resources necessary for analysis. AVAILABILITY: This error analysis framework is written in Perl and runs on Mac OS and Linux/Unix systems. The filter, documentation and sample Excel files for quality analysis are available at http://hts.rutgers.edu/filter and are distributed as Open Source software under the GPLv3.0. CONTACT: tmichael@waksman.rutgers.edu SUPPLEMENTARY INFORMATION: Supplementary data is available at Bioinformatics online.post to: CiteULike

Publication Date: 2010 Mar 3 PMID: 20202974Authors: Johnson, A. D.Journal: BioinformaticsThe International Union of Pure and Applied Chemistry (IUPAC) code specified nearly 25 years ago provides a nomenclature for incompletely specified nucleic acids (Cornish-Bowden 1984). The IUPAC code has been applied in a wide-ranging manner, contributing to many biologically and chemically meaningful representations, including: 1) recognition sequences (e.g., restriction enzymes, protein and RNA binding sites, consensus signals), 2) codon degeneracy, 3) sequence base calling ambiguity 4) representation of ancestral states in phylogenetics and 5) to a vast extent in the fields of genetics and genomics in representing polymorphic nucleic acids, e.g., single nucleotide polymorphisms (SNPs). However, no system currently exists that allows for the informatics representation of the relative abundance at polymorphic nucleic acids (e.g., SNPs) in a single specified character, or a string of characters. Here I propose such an information code as a natural extension to the IUPAC nomenclature code, and present some potential uses and limitations to such a code. The original IUPAC code remains useful in all its previous applications and is also compatible as a subset of the extended code proposed here. The extended IUPAC code allows for new nucleic acid representations, in single characters or character strings, with potential applications in genetics, cross-species or cross-strain comparison, sequence alignment, bioinformatics, genome assembly, database design and querying, and chemical sequencing and synthesis. The primary anticipated use of this extended nomenclature code is to assist in the representation of the rapidly growing space of information on human genetic variation.post to: CiteULike

Publication Date: 2010 Mar 4 PMID: 20202973Authors: Clarke, J. - Seo, P. - Clarke, B.Journal: BioinformaticsMOTIVATION: Global expression patterns within cells are used for purposes ranging from the identification of disease biomarkers to basic understanding of cellular processes. Unfortunately tissue samples used in cancer studies are usually composed of multiple cell types and the non-cancerous portions can significantly affect expression profiles. This severely limits the conclusions that can be made about the specificity of gene expression in the cell-type of interest. However, statistical analysis can be used to identify differentially expressed genes that are related to the biological question being studied. RESULTS: We propose a statistical approach to expression deconvolution from mixed tissue samples in which the proportion of each component cell type is unknown. Our method estimates the proportion of each component in a mixed tissue sample; this estimate can be used to provide estimates of gene expression from each component. We demonstrate our technique on xenograft samples from breast cancer research and publicly available experimental data sets found in the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) repository. AVAILABILITY: R code (http://www.r-project.org/) for estimating sample proportions is freely available to non-commercial users and available at http://www.med.miami.edu/medicine/x2691.xml CONTACT: jclarke@med.miami.edu; pseo@med.miami.edu; bclarke2@med.miami.edu.post to: CiteULike

Publication Date: 2010 Mar 2 PMID: 20200011Authors: Moghaddas Gholami, A. - Fellenberg, K.Journal: BioinformaticsMOTIVATION: Cross-species meta-analyses of microarray data usually require prior affiliation of genes based on orthology information that often relies on sequence similarity. RESULTS: We present an algorithm merging microarray datasets on the basis of co-expression alone, without any requirement for orthology information to affiliate genes. Combining existing methods such as co-inertia analysis, back-transformation, Hungarian matching, and majority voting in an iterative non-greedy hill-climbing approach, it affiliates arrays and genes at the same time, maximizing the co-structure between the datasets. To introduce the method, we demonstrate its performance on two closely and two distantly related datasets of different experimental context and produced on different platforms. Each pair stems from two different species. The resulting cross-species dynamic Bayesian gene networks improve on the networks inferred from each dataset alone by yielding more significant network motifs, as well as more of the interactions already recorded in KEGG and other databases. Also, it is shown that our algorithm converges on the optimal number of nodes for network inference. Being readily extendable to more than two datasets, it provides the opportunity to infer extensive gene regulatory networks. Availability and Implementation: Source code (MATLAB and R) freely available for download at http://www.mchips.org/supplements/moghaddasi_source.tgz CONTACT: kurt@tum.de SUPPLEMENTARY INFORMATION: Supplementary data are available at http://www.mchips.org/supplements/moghaddasi_supp.pdf.post to: CiteULike

Publication Date: 2010 Mar 3 PMID: 20200010Authors: Rebholz-Schuhmann, D. - Kavaliauskas, S. - Pezik, P.Journal: BioinformaticsMOTIVATION: The automatic analysis of scientific literature can support authors in writing their manuscripts. Implementation: PaperMaker is a novel IT solution that receives a scientific manuscript via a Web interface, automatically analyses the publication, evaluates consistency parameters and interactively delivers feedback to the author. It analyses the proper use of acronyms and their definitions, and the use of specialized terminology. It provides GO and MeSH categorization of text passages, the retrieval of relevant publications from public scientific literature repositories, and the identification of missing or unused references. RESULT: The author receives a summary of findings, the manuscript in its corrected form and a digital abstract containing the GO and MeSH annotations in the NLM/PubMed format. AVAILABILITY: http://www.ebi.ac.uk/Rebholz-srv/PaperMaker.post to: CiteULike

Publication Date: 2010 Mar 3 PMID: 20200009Authors: Malone, J. - Holloway, E. - Adamusiak, T. - Kapushesky, M. - Zheng, J. - Kolesnikov, N. - Zhukova, A. - Brazma, A. - Parkinson, H.Journal: BioinformaticsMOTIVATION: Describing biological sample variables with ontologies is complex due to the cross-domain nature of experiments. Ontologies provide annotation solutions, however, for cross-domain investigations, multiple ontologies are needed to represent the data. These are subject to rapid change, are often not interoperable and present complexities that are a barrier to biological resource users. RESULTS: We present the Experimental Factor Ontology (EFO), designed to meet cross-domain, application focused use cases for gene expression data. We describe our methodology and open source tools used to create the ontology. These include tools for creating ontology mappings, ontology views, detecting ontology changes and using ontologies in interfaces to enhance querying. The application of reference ontologies to data is a key problem and this work presents guidelines on how community ontologies can be presented in an application ontology in a data driven way. AVAILABILITY: http://www.ebi.ac.uk/efo CONTACT: malone@ebi.ac.uk.post to: CiteULike

Publication Date: 2010 Mar 1 PMID: 20197286Authors: Shimamura, T. - Imoto, S. - Yamaguchi, R. - Nagasaki, M. - Miyano, S.Journal: BioinformaticsMOTIVATION: Elucidating the differences between cellular responses to various biological conditions or external stimuli is an important challenge in systems biology. Many approaches have been developed to reverse-engineer a cellular system, called gene network, from time-series microarray data in order to understand a transcriptomic response under a condition of interest. Comparative topological analysis has also been applied based on the gene networks inferred independently from each of the multiple time-series datasets under varying conditions to find critical differences between these networks. However, these comparisons often lead to misleading results, because each network contains considerable noise due to the limited length of the time-series. RESULTS: We propose an integrated approach for inferring multiple gene networks from time-series expression data under varying conditions. To the best of our knowledge, our approach is the first reverse-engineering method that is intended for transcriptomic network comparison between varying conditions. Furthermore, we propose a state-of-the-art parameter estimation method, relevanceweighted recursive elastic net, for providing higher precision and recall than existing reverse-engineering methods. We analyze experimental data of MCF-7 human breast cancer cells stimulated by EGF or HRG with several doses and provide novel biological hypotheses through network comparison. AVAILABILITY: The software NETCOMP is available at http://bonsai.ims.u-tokyo.ac.jp/~shima/NETCOMP/. CONTACT: shima@ims.u-tokyo.ac.jp SUPPLEMENTARY INFORMATION: All supplementary information can be accessed online.post to: CiteULike

Publication Date: 2010 Mar 1 PMID: 20194627Authors: Chevenet, F. - Croce, O. - Hebrard, M. - Christen, R. - Berry, V.Journal: BioinformaticsSUMMARY: There is a large amount of tools for interactive display of phylogenetic trees. However, there is a shortage of tools for the automation of tree rendering. Scripting phylogenetic graphics would enable the saving of graphical analyses involving numerous and complex tree handling operations and would allow the automation of repetitive tasks. ScripTree is a tool intended to fill this gap. It is an interpreter to be used in batch mode. Phylogenetic graphics instructions, related to tree rendering as well as tree annotation, are stored in a text file and processed in a sequential way. AVAILABILITY: ScriptTree can be used online or downloaded at www.scriptree.org, under the GPL license. Implementation: ScriptTree is written in Tcl/Tk is a cross-platform application available for Windows and Unix-like systems including OS X. It can be used either as a standalone package or included in a bioinformatic pipeline and linked to a HTTP server. CONTACT: chevenet@ird.fr.post to: CiteULike

Publication Date: 2010 Mar 1 PMID: 20194626Authors: Hemmerich, C. - Buechlein, A. - Podicheti, R. - Revanna, K. V. - Dong, Q.Journal: BioinformaticsSUMMARY: Ergatis is a flexible workflow management system for designing and executing complex bioinformatics pipelines. However, its complexity restricts its usage to only highly skilled bioinformaticians. We have developed a web-based prokaryotic genome annotation server, Integrative Services for Genomics Analysis (ISGA), which builds upon the Ergatis workflow system, integrates other dynamic analysis tools, and provides intuitive web interfaces for biologists to customize and execute their own annotation pipelines. ISGA is designed to be installed at genomics core facilities and be used directly by biologists. AVAILABILITY: ISGA is accessible at http://isga.cgb.indiana.edu/ and the system is also freely available for local installation. CONTACT: Qunfeng.Dong@unt.edu SUPPLEMENTARY INFORMATION: Supplementary figures are available at Bioinformatics online.post to: CiteULike

Publication Date: 2010 Mar 1 PMID: 20194625Authors: Tanaka, N. - Waki, K. - Kaneda, H. - Suzuki, T. - Yamada, I. - Furuse, T. - Kobayashi, K. - Motegi, H. - Toki, H. - Inoue, M. - Minowa, O. - Noda, T. - Takao, K. - Miyakawa, T. - Takahashi, A. - Koide, T. - Wakana, S. - Masuya, H.Journal: BioinformaticsSUMMARY: This paper reports the development of SDOP-DB, which can provide definite, detailed, and easy comparison of experimental protocols used in mouse phenotypic analyses among institutes or laboratories. Because SDOP-DB is fully compliant with international standards, it can act as a practical foundation for international sharing and integration of mouse phenotypic information. AVAILABILITY: SDOP-DB (http://www.brc.riken.jp/lab/bpmp/SDOP/) CONTACT: knowledge-base@brc.riken.jp SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.post to: CiteULike

Publication Date: 2010 Feb 26 PMID: 20190251Authors: Chou, W. Y. - Chou, W. I. - Pai, T. W. - Lin, S. C. - Jiang, T. Y. - Tang, C. Y. - Chang, M. D.Journal: BioinformaticsMOTIVATION: Carbohydrate binding modules (CBMs) share similar secondary and tertiary topology, but their primary sequence identity is low. Computational identification of ligand-binding residues allows biologists to better understand the protein-carbohydrate binding mechanism. In general functional characterization can be alternatively solved by alignment-based manners. As alignment accuracy based on conventional methods is often sensitive to sequence identity, low sequence identity among query sequences makes it difficult to precisely locate small portions of relevant features. Therefore, we propose a feature-incorporated alignment (FIA) to flexibly align conserved signatures in CBMs. Then, an FIA-based target-template prediction model was further implemented to identify functional ligand-binding residues. RESULTS: Arabidopsis thaliana CBM45 and CBM53 were used to validate the FIA-based prediction model. The predicted ligand-binding residues residing on the surface in the hypothetical structures were verified to be ligand-binding residues. In the absence of three dimensional structural information, FIA demonstrated significant improvement in the estimation of sequence similarity and identity for a total of 808 sequences from 11 different CBM families as compared with six leading tools by Friedman rank test. CONTACT: dtchang@life.nthu.edu.tw.post to: CiteULike

Publication Date: 2010 Feb 26 PMID: 20190250Authors: Malhis, N. - Jones, S. J.Journal: BioinformaticsMOTIVATION: Detection of single nucleotide polymorphisms (SNPs) has been a major application in processing second generation sequencing (SGS) data. In principle, SNPs are called on single base differences between a reference genome and a sequence generated from SGS short reads of a sample genome. However, this exercise is far from trivial; several parameters related to sequencing quality, and/or reference genome properties, play essential effect on the accuracy of called SNPs especially at shallow coverage data. In this work, we present Slider II, an alignment and SNP calling approach that demonstrates improved algorithmic approaches enabling larger number of called SNPs with lower false positive rate. In addition to the regular alignment and SNP calling, as an optional feature, Slider II is capable of utilizing information about known SNPs of a target genome, as priors, in the alignment and SNPs calling to enhance it's capability of detecting these known SNPs and novel SNPs and mutations in their vicinity. CONTACT: nmalhis@bcgsc.ca Supplementary information and availability: http://www.bcgsc.ca/platform/bioinfo/software/SliderII.post to: CiteULike

Publication Date: 2010 Feb 25 PMID: 20189941Authors: Trudgian, D. C. - Thomas, B. - McGowan, S. J. - Kessler, B. M. - Salek, M. - Acuto, O.Journal: BioinformaticsSUMMARY: The Central Proteomics Facilities Pipeline (CPFP) provides identification, validation, and quantitation of peptides and proteins from LC-MS/MS datasets through an easy to use web interface. It is the first analysis pipeline targeted specifically at the needs of proteomics core facilities, reducing the data-analysis load on staff, and allowing facility clients to easily access and work with their data. Identification of peptides is performed using multiple search engines, their output combined and validated using state-of-the-art techniques for improved results. Cluster execution of jobs allows analysis capacity to be increased easily as demand grows. AVAILABILITY: Released under the Common Development and Distribution License (CDDL) at http://cpfp.sourceforge.net/. Demonstration available at https://cpfp-master.molbiol.ox.ac.uk/cpfp_demo CONTACT: dctrud@ccmp.ox.ac.uk.post to: CiteULike

Publication Date: 2010 Feb 25 PMID: 20189940Authors: Corel, E. - Pitschi, F. - Morgenstern, B.Journal: BioinformaticsMOTIVATION: Multiple sequence alignments can be constructed on the basis of pairwise local sequence similarities. This approach is rather flexible and can combine the advantages of global and local alignment methods. The restriction to pairwise alignments as building blocks, however, can lead to misalignments since weak homologies may be missed if only pairs of sequences are compared. RESULTS: Herein, we propose a graph-theoretical approach to find local multiple sequence similarities. Starting with pairwise alignements produced by DIALIGN, we use a min-cut algorithm to find potential (partial) alignment columns that we use to construct a final multiple alignment. On real and simulated benchmark data, our approach consistently outperforms the standard version of DIALIGN where local pairwise alignments are greedily incorporated into a multiple alignment. AVAILABILITY: The prototype is freely available under GNU Public Licence from the first author. CONTACT: ecorel@gwdg.de.post to: CiteULike

Publication Date: 2010 Feb 25 PMID: 20189939Authors: Beisser, D. - Klau, G. W. - Dandekar, T. - Mueller, T. - Dittrich, M.Journal: BioinformaticsMOTIVATION: Increasing quantity and quality of data in transcriptomics and interactomics create the need for integrative approaches to network analysis. Here we present a comprehensive R-package for the analysis of biological networks including an exact and a heuristic approach to identify functional modules. RESULTS: The BioNet package provides an extensive framework for integrated network analysis in R. This includes the statistics for the integration of transcriptomic and functional data with biological networks, the scoring of nodes as well as methods for network search and visualization. AVAILABILITY: The BioNet package and a tutorial are available from http://bionet.bioapps.biozentrum.uni-wuerzburg.de. CONTACT: marcus.dittrich@biozentrum.uni-wuerzburg.de, tobias.mueller@biozentrum.uni-wuerzburg.de.post to: CiteULike

Publication Date: 2010 Feb 25 PMID: 20189938Authors: Zehetmayer, S. - Posch, M.Journal: BioinformaticsBACKGROUND: The statistical power or multiple Type II error rate in large scale multiple testing problems as, for example, in gene expression microarray experiments, depends on typically unknown parameters and is therefore difficult to assess a priori. However, it has been suggested to estimate the multiple Type II error rate post-hoc, based on the observed data. METHODS: We consider a class of post-hoc estimators that are functions of the estimated proportion of true null hypotheses among all hypotheses. Numerous estimators for this proportion have been proposed and we investigate the statistical properties of the derived multiple Type II error rate estimators in an extensive simulation study. RESULTS: The performance of the estimators in terms of the mean squared error depends sensitively on the distributional scenario. Estimators based on empirical distributions of the null hypotheses are superior in the presence of strongly correlated test statistics. AVAILABILITY: R-code (R Development Core Team, 2008) to compute all considered estimators based on p-values and supplementary material is available from the authors web page http://statistics.msi.meduniwien.ac.at/index.php?page=pageszfnr CONTACT: martin.posch@meduniwien.ac.at.post to: CiteULike

Publication Date: 2010 Feb 25 PMID: 20189937Authors: Wong, G. - Leckie, C. - Gorringe, K. L. - Haviv, I. - Campbell, I. G. - Kowalczyk, A.Journal: BioinformaticsMOTIVATION: High-density single nucleotide polymorphism (SNP) genotyping arrays are efficient and cost effective platforms for the detection of copy number variation. To ensure accuracy in probe synthesis and to minimise production costs, short oligonucleotide probe sequences are used. The use of short probe sequences limits the specificity of binding targets in the human genome. The specificity of these short probeset sequences has yet to be fully analysed against a normal reference human genome. Sequence similarity can artificially elevate or suppress copy number measurements and hence reduce the reliability of affected probe readings. For the purpose of detecting narrow copy number variations reliably down to the width of a single probeset, sequence similarity is an important issue that needs to be addressed. RESULTS: We surveyed the Affymetrix Human Mapping SNP arrays for probeset sequence similarity against the reference human genome. Utilising sequence similarity results, we identified a collection of fine-scaled putative copy number variations between gender from autosomal probesets whose sequence matches various loci on the sex chromosomes. To detect these variations, we utilised our statistical approach, DRECS, and showed that its performance was superior and more stable than the t-test in detecting copy number variations. Through the application of DRECS on the HapMap population datasets with multi-matching probesets filtered, we identified biologically relevant SNPs in aberrant regions across populations with known association to physical traits, such as height, covered by the span of a single probe. This provided empirical confirmation of the existence of naturally occurring narrow copy number variations as well as the sensitivity of the Affymetrix SNP array technology in detecting them. AVAILABILITY: The MATLAB implementation of DRECS is available at http://ww2.cs.mu.oz.au/~gwong/DRECS/index.html CONTACT: gwong@csse.unimelb.edu.au.post to: CiteULike

Publication Date: 2010 Feb 24 PMID: 20185407Authors: Loriot, S. - Cazals, F. - Bernauer, J.Journal: BioinformaticsSUMMARY: The ever increasing number of structural biological data calls for robust and efficient software for analysis. ESBTL (Easy Structural Biology Template Library) is a lightweight C++ library that allows the handling of PDB data and provides a data structure suitable for geometric constructions and analyses. The parser and data model provided by this ready-to-use include-only library allows adequate treatment of usually discarded information (insertion code, atom occupancy...) while still being able to detect badly formatted files. The template-based structure allows rapid design of new computational structural biology applications and is fully compatible with the new remediated PDB archive format. It also allows the code to be easy-to-use while being versatile enough to allow advanced user developments. AVAILABILITY: ESBTL is freely available under the GNU General Public License from http://esbtl.sf.net. The website provides the source code, examples, code snippets, and documentation. CONTACT: julie.bernauer@inria.fr.post to: CiteULike