Bioinformatics - recent issues

Phenotypic categorization of genetic skin diseases reveals new relations between phenotypes, genes and pathways

Sadreyev, R. I., Feramisco, J. D., Tsao, H., Grishin, N. V. — Wed, 04 Nov 2009 05:45:49 PST

Motivation: Systematic analysis of connection between proteins, their cellular function and phenotypic manifestations in disease is a central problem of biological and clinical research. The solution to this problem requires the development of new approaches to link the rapidly growing dataset of gene–disease associations with the many complex and overlapping phenotypes of human disease.

Results: We analyze genetic skin disorders and suggest a manually designed set of elementary phenotypes whose combinations define diseases as points in a multidimensional space, providing a basis for phenotypic disease clustering. Placing the known gene–disease associations in the context of this space reveals new patterns that suggest previously unknown functional links between proteins, signaling pathways and disease phenotypes. For example, analysis of telangiectasias (spider vein diseases) reveals a previously unrecognized interplay between the TGF-β signaling pathway and pentose phosphate pathway. This interaction may mediate glucose-dependent regulation of TGF-β signaling, providing a clue to the known association between angiopathies and diabetes and implying new gene candidates for mutational analysis and drug targeting.

Contact: grishin@chop.swmed.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

De novo computational prediction of non-coding RNA genes in prokaryotic genomes

Tran, T. T., Zhou, F., Marshburn, S., Stead, M., Kushner, S. R., Xu, Y. — Wed, 04 Nov 2009 05:45:49 PST

Motivation: The computational identification of non-coding RNA (ncRNA) genes represents one of the most important and challenging problems in computational biology. Existing methods for ncRNA gene prediction rely mostly on homology information, thus limiting their applications to ncRNA genes with known homologues.

Results: We present a novel de novo prediction algorithm for ncRNA genes using features derived from the sequences and structures of known ncRNA genes in comparison to decoys. Using these features, we have trained a neural network-based classifier and have applied it to Escherichia coli and Sulfolobus solfataricus for genome-wide prediction of ncRNAs. Our method has an average prediction sensitivity and specificity of 68% and 70%, respectively, for identifying windows with potential for ncRNA genes in E.coli. By combining windows of different sizes and using positional filtering strategies, we predicted 601 candidate ncRNAs and recovered 41% of known ncRNAs in E.coli. We experimentally investigated six novel candidates using Northern blot analysis and found expression of three candidates: one represents a potential new ncRNA, one is associated with stable mRNA decay intermediates and one is a case of either a potential riboswitch or transcription attenuator involved in the regulation of cell division. In general, our approach enables the identification of both cis- and trans-acting ncRNAs in partially or completely sequenced microbial genomes without requiring homology or structural conservation.

Availability: The source code and results are available at http://csbl.bmb.uga.edu/publications/materials/tran/.

Contact: xyn@bmb.uga.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis

Shen, R., Olshen, A. B., Ladanyi, M. — Wed, 04 Nov 2009 05:45:49 PST

Motivation: The molecular complexity of a tumor manifests itself at the genomic, epigenomic, transcriptomic and proteomic levels. Genomic profiling at these multiple levels should allow an integrated characterization of tumor etiology. However, there is a shortage of effective statistical and bioinformatic tools for truly integrative data analysis. The standard approach to integrative clustering is separate clustering followed by manual integration. A more statistically powerful approach would incorporate all data types simultaneously and generate a single integrated cluster assignment.

Methods: We developed a joint latent variable model for integrative clustering. We call the resulting methodology iCluster. iCluster incorporates flexible modeling of the associations between different data types and the variance–covariance structure within data types in a single framework, while simultaneously reducing the dimensionality of the datasets. Likelihood-based inference is obtained through the Expectation–Maximization algorithm.

Results: We demonstrate the iCluster algorithm using two examples of joint analysis of copy number and gene expression data, one from breast cancer and one from lung cancer. In both cases, we identified subtypes characterized by concordant DNA copy number changes and gene expression as well as unique profiles specific to one or the other in a completely automated fashion. In addition, the algorithm discovers potentially novel subtypes by combining weak yet consistent alteration patterns across data types.

Availability: R code to implement iCluster can be downloaded at http://www.mskcc.org/mskcc/html/85130.cfm

Contact: shenr@mskcc.org

Supplementary information: Supplementary data are available at Bioinformatics online.

Predicting homologous signaling pathways using machine learning

Bostan, B., Greiner, R., Szafron, D., Lu, P. — Wed, 04 Nov 2009 05:45:49 PST

Motivation: In general, each cell signaling pathway involves many proteins, each with one or more specific roles. As they are essential components of cell activity, it is important to understand how these proteins work—and in particular, to determine which of the species' proteins participate in each role. Experimentally determining this mapping of proteins to roles is difficult and time consuming. Fortunately, many pathways are similar across species, so we may be able to use known pathway information of one species to understand the corresponding pathway of another.

Results: We present an automatic approach, Predict Signaling Pathway (PSP), which uses the signaling pathways in well-studied species to predict the roles of proteins in less-studied species. We use a machine learning approach to create a predictor that achieves a generalization F-measure of 78.2% when applied to 11 different pathways across 14 different species. We also show our approach is very effective in predicting the pathways that have not yet been experimentally studied completely.

Availability: The list of predicted proteins for all pathways over all considered species is available at http://www.cs.ualberta.ca/~bioinfo/signaling.

Contact: bioinfo@cs.ualberta.ca; duane@cs.ualberta.ca

Understanding hydrogen-bond patterns in proteins using network motifs

Rahat, O., Alon, U., Levy, Y., Schreiber, G. — Wed, 04 Nov 2009 05:45:49 PST

Summary: Protein structures can be viewed as networks of contacts (edges) between amino-acid residues (nodes). Here we dissect proteins into sub-graphs consisting of six nodes and their corresponding edges, with an edge being either a backbone hydrogen bond (H-bond) or a covalent interaction. Six thousand three hundred and twenty-two such sub-graphs were found in a large non-redundant dataset of high-resolution structures, from which 35 occur much more frequently than in a random model. Many of these significant sub-graphs (also called network motifs) correspond to sub-structures of helices and β-sheets, as expected. However, others correspond to more exotic sub-structures such as 3₁₀ helix, Schellman motif and motifs that were not defined previously. This topological characterization of patterns is very useful for producing a detailed differences map to compare protein structures. Here we analyzed in details the differences between NMR, molecular dynamics (MD) simulations and X-ray structures for Lysozyme, SH3 and the lambda repressor. In these cases, the same structures solved by NMR and simulated by MD showed small but consistent differences in their motif composition from the crystal structures, despite a very small root mean square deviation (RMSD) between them. This may be due to differences in the pair-wise energy functions used and the dynamic nature of these proteins.

Availability: A web-based tool to calculate network motifs is available at http://bioinfo.weizmann.ac.il/protmot/.

Contact: gideon.schreiber@weizmann.ac.il; koby.levy@weizmann.ac.il

Supplementary information: Supplementary data are available at Bioinformatics online.

A boosting approach to structure learning of graphs with and without prior knowledge

Anjum, S., Doucet, A., Holmes, C. C. — Wed, 04 Nov 2009 05:45:49 PST

Motivation: Identifying the network structure through which genes and their products interact can help to elucidate normal cell physiology as well as the genetic architecture of pathological phenotypes. Recently, a number of gene network inference tools have appeared based on Gaussian graphical model representations. Following this, we introduce a novel Boosting approach to learn the structure of a high-dimensional Gaussian graphical model motivated by the applications in genomics. A particular emphasis is paid to the inclusion of partial prior knowledge on the structure of the graph. With the increasing availability of pathway information and large-scale gene expression datasets, we believe that conditioning on prior knowledge will be an important aspect in raising the statistical power of structural learning algorithms to infer true conditional dependencies.

Results: Our Boosting approach, termed BoostiGraph, is conceptually and algorithmically simple. It complements recent work on the network inference problem based on Lasso-type approaches. BoostiGraph is computationally cheap and is applicable to very high-dimensional graphs. For example, on graphs of order 5000 nodes, it is able to map out paths for the conditional independence structure in few minutes. Using computer simulations, we investigate the ability of our method with and without prior information to infer Gaussian graphical models from artificial as well as actual microarray datasets. The experimental results demonstrate that, using our method, it is possible to recover the true network topology with relatively high accuracy.

Availability: This method and all other associated files are freely available from http://www.stats.ox.ac.uk/~anjum/.

Contact: s.anjum@har.mrc.ac.uk; cholmes@stats.ox.ac.uk

Supplementary information: Supplementary data are available at Bioinfomatics online.

Learning gene regulatory networks from gene expression measurements using non-parametric molecular kinetics

Aijo, T., Lahdesmaki, H. — Wed, 04 Nov 2009 05:45:49 PST

Motivation: Regulation of gene expression is fundamental to the operation of a cell. Revealing the structure and dynamics of a gene regulatory network (GRN) is of great interest and represents a considerably challenging computational problem. The GRN estimation problem is complicated by the fact that the number of gene expression measurements is typically extremely small when compared with the dimension of the biological system. Further, because the gene regulation process is intrinsically complex, commonly used parametric models can provide too simple description of the underlying phenomena and, thus, can be unreliable. In this article, we propose a novel methodology for the inference of GRNs from time-series and steady-state gene expression measurements. The presented framework is based on the use of Bayesian analysis with ordinary differential equations (ODEs) and non-parametric Gaussian process modeling for the transcriptional-level regulation.

Results: The performance of the proposed structure inference method is evaluated using a recently published in vivo dataset. By comparing the obtained results with those of existing ODE- and Bayesian-based inference methods we demonstrate that the proposed method provides more accurate network structure learning. The predictive capabilities of the method are examined by splitting the dataset into a training set and a test set and by predicting the test set based on the training set.

Availability: A MATLAB implementation of the method will be available from http://www.cs.tut.fi/~aijo2/gp upon publication

Contact: harri.lahdesmaki@tut.fi

Supplementary information: Supplementary data are available at Bioinformatics online.

Identification of genes involved in the same pathways using a Hidden Markov Model-based approach

Senf, A., Chen, X.-w. — Wed, 04 Nov 2009 05:45:49 PST

Motivation: The sequencing of whole genomes from various species has provided us with a wealth of genetic information. To make use of the vast amounts of data available today it is necessary to devise computer-based analysis techniques.

Results: We propose a Hidden Markov Model (HMM) based algorithm to detect groups of genes functionally similar to a set of input genes from microarray expression data. A subset of experiments from a microarray is selected based on a set of related input genes. HMMs are trained from the input genes and a group of random gene input sets to provide significance estimates. Every gene in the microarray is scored using all HMMs and significant matches with the input genes are retained. We ran this algorithm on the life cycle of Drosophila microarray data set with KEGG pathways for cell cycle and translation factors as input data sets. Results show high functional similarity in resulting gene sets, increasing our biological insight into gene pathways and KEGG annotations. The algorithm performed very well compared to the Signature Algorithm and a purely correlation-based approach.

Availability: Java source codes and data sets are available at http://www.ittc.ku.edu/~xwchen/software.htm

Contact: xwchen@ittc.ku.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Mining gene functional networks to improve mass-spectrometry-based protein identification

Wed, 04 Nov 2009 05:45:49 PST

Motivation: High-throughput protein identification experiments based on tandem mass spectrometry (MS/MS) often suffer from low sensitivity and low-confidence protein identifications. In a typical shotgun proteomics experiment, it is assumed that all proteins are equally likely to be present. However, there is often other evidence to suggest that a protein is present and confidence in individual protein identification can be updated accordingly.

Results: We develop a method that analyzes MS/MS experiments in the larger context of the biological processes active in a cell. Our method, MSNet, improves protein identification in shotgun proteomics experiments by considering information on functional associations from a gene functional network. MSNet substantially increases the number of proteins identified in the sample at a given error rate. We identify 8–29% more proteins than the original MS experiment when applied to yeast grown in different experimental conditions analyzed on different MS/MS instruments, and 37% more proteins in a human sample. We validate up to 94% of our identifications in yeast by presence in ground-truth reference sets.

Availability and Implementation: Software and datasets are available at http://aug.csres.utexas.edu/msnet

Contact: miranker@cs.utexas.edu, marcotte@icmb.utexas.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Simultaneous inference of biological networks of multiple species from genome-wide data and evolutionary information: a semi-supervised approach

Kashima, H., Yamanishi, Y., Kato, T., Sugiyama, M., Tsuda, K. — Wed, 04 Nov 2009 05:45:49 PST

Motivation: The existing supervised methods for biological network inference work on each of the networks individually based only on intra-species information such as gene expression data. We believe that it will be more effective to use genomic data and cross-species evolutionary information from different species simultaneously, rather than to use the genomic data alone.

Results: We created a new semi-supervised learning method called Link Propagation for inferring biological networks of multiple species based on genome-wide data and evolutionary information. The new method was applied to simultaneous reconstruction of three metabolic networks of Caenorhabditis elegans, Helicobacter pylori and Saccharomyces cerevisiae, based on gene expression similarities and amino acid sequence similarities. The experimental results proved that the new simultaneous network inference method consistently improves the predictive performance over the individual network inferences, and it also outperforms in accuracy and speed other established methods such as the pairwise support vector machine.

Availability: The software and data are available at http://cbio.ensmp.fr/~yyamanishi/LinkPropagation/.

Contact: kashima@mist.i.u-tokyo.ac.jp

Supplementary information: Supplementary data are available at Bioinformatics online.

Improving peptide identification with single-stage mass spectrum peaks

He, Z., Yu, W. — Wed, 04 Nov 2009 05:45:49 PST

Motivation: Database searching is the major peptide identification method in shotgun proteomics. It searches tandem mass spectrometry (MS/MS) spectra against a protein database to identify target peptides. The success of such a database searching method relies on a scoring algorithm that can evaluate the quality of peptide-spectrum matches (PSMs) accurately. However, current scoring algorithms frequently generate inaccurate assignments due to variations and noises in the MS/MS spectra. To address this issue, we like to improve peptide identification by using additional information from other data sources.

Results: Single-stage MS data is complementary to MS/MS data in the sense that it provides broader mass coverage but less sequence information. In this article, we show that single-stage MS data can be used to re-rank PSMs. The proposed method explores a linear combination of scores between MS and MS/MS data to perform re-ranking. Experimental results on real data show that such a re-ranking strategy improves the identification performance significantly.

Availability: http://bioinformatics.ust.hk/ReRankPSMwMS1.rar

Contact: eezyhe@ust.hk

Supplementary information: Supplementary data are available at Bioinformatics online.

Metabolite and reaction inference based on enzyme specificities

Wed, 04 Nov 2009 05:45:49 PST

Motivation: Many enzymes are not absolutely specific, or even promiscuous: they can catalyze transformations of more compounds than the traditional ones as listed in, e.g. KEGG. This information is currently only available in databases, such as the BRENDA enzyme activity database. In this article, we propose to model enzyme aspecificity by predicting whether an input compound is likely to be transformed by a certain enzyme. Such a predictor has many applications, for example, to complete reconstructed metabolic networks, to aid in metabolic engineering or to help identify unknown peaks in mass spectra.

Results: We have developed a system for metabolite and reaction inference based on enzyme specificities (MaRIboES). It employs structural and stereochemistry similarity measures and molecular fingerprints to generalize enzymatic reactions based on data available in BRENDA. Leave-one-out cross-validation shows that 80% of known reactions are predicted well. Application to the yeast glycolytic and pentose phosphate pathways predicts a large number of known and new reactions, often leading to the formation of novel compounds, as well as a number of interesting bypasses and cross-links.

Availability: Matlab and C++ code is freely available at https://gforge.nbic.nl/projects/mariboes/

Contact: d.deridder@tudelft.nl

Supplementary information: Supplementary data are available at Bioinformatics online.

A dictionary to identify small molecules and drugs in free text

Wed, 04 Nov 2009 05:45:49 PST

Motivation: From the scientific community, a lot of effort has been spent on the correct identification of gene and protein names in text, while less effort has been spent on the correct identification of chemical names. Dictionary-based term identification has the power to recognize the diverse representation of chemical information in the literature and map the chemicals to their database identifiers.

Results: We developed a dictionary for the identification of small molecules and drugs in text, combining information from UMLS, MeSH, ChEBI, DrugBank, KEGG, HMDB and ChemIDplus. Rule-based term filtering, manual check of highly frequent terms and disambiguation rules were applied. We tested the combined dictionary and the dictionaries derived from the individual resources on an annotated corpus, and conclude the following: (i) each of the different processing steps increase precision with a minor loss of recall; (ii) the overall performance of the combined dictionary is acceptable (precision 0.67, recall 0.40 (0.80 for trivial names); (iii) the combined dictionary performed better than the dictionary in the chemical recognizer OSCAR3; (iv) the performance of a dictionary based on ChemIDplus alone is comparable to the performance of the combined dictionary.

Availability: The combined dictionary is freely available as an XML file in Simple Knowledge Organization System format on the web site http://www.biosemantics.org/chemlist.

Contact: k.hettne@erasmusmc.nl

Supplementary information: Supplementary data are available at Bioinformatics online.

Characterization of 1H NMR spectroscopic data and the generation of synthetic validation sets

Wed, 04 Nov 2009 05:45:49 PST

Motivation: Common contemporary practice within the nuclear magnetic resonance (NMR) metabolomics community is to evaluate and validate novel algorithms on empirical data or simplified simulated data. Empirical data captures the complex characteristics of experimental data, but the optimal or most correct analysis is unknown a priori; therefore, researchers are forced to rely on indirect performance metrics, which are of limited value. In order to achieve fair and complete analysis of competing techniques more exacting metrics are required. Thus, metabolomics researchers often evaluate their algorithms on simplified simulated data with a known answer. Unfortunately, the conclusions obtained on simulated data are only of value if the data sets are complex enough for results to generalize to true experimental data. Ideally, synthetic data should be indistinguishable from empirical data, yet retain a known best analysis.

Results: We have developed a technique for creating realistic synthetic metabolomics validation sets based on NMR spectroscopic data. The validation sets are developed by characterizing the salient distributions in sets of empirical spectroscopic data. Using this technique, several validation sets are constructed with a variety of characteristics present in ‘real’ data. A case study is then presented to compare the relative accuracy of several alignment algorithms using the increased precision afforded by these synthetic data sets.

Availability: These data sets are available for download at http://birg.cs.wright.edu/nmr_synthetic_data_sets.

Contact: travis.doom@wright.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Flynet: a genomic resource for Drosophila melanogaster transcriptional regulatory networks

Wed, 04 Nov 2009 05:45:49 PST

Motivation: The highly coordinated expression of thousands of genes in an organism is regulated by the concerted action of transcription factors, chromatin proteins and epigenetic mechanisms. High-throughput experimental data for genome wide in vivo protein–DNA interactions and epigenetic marks are becoming available from large projects, such as the model organism ENCyclopedia Of DNA Elements (modENCODE) and from individual labs. Dissemination and visualization of these datasets in an explorable form is an important challenge.

Results: To support research on Drosophila melanogaster transcription regulation and make the genome wide in vivo protein–DNA interactions data available to the scientific community as a whole, we have developed a system called Flynet. Currently, Flynet contains 101 datasets for 38 transcription factors and chromatin regulator proteins in different experimental conditions. These factors exhibit different types of binding profiles ranging from sharp localized peaks to broad binding regions. The protein–DNA interaction data in Flynet was obtained from the analysis of chromatin immunoprecipitation experiments on one color and two color genomic tiling arrays as well as chromatin immunoprecipitation followed by massively parallel sequencing. A web-based interface, integrated with an AJAX based genome browser, has been built for queries and presenting analysis results. Flynet also makes available the cis-regulatory modules reported in literature, known and de novo identified sequence motifs across the genome, and other resources to study gene regulation.

Contact: grossman@uic.edu

Availability: Flynet is available at https://www.cistrack.org/flynet/.

Supplementary information: Supplementary data are available at Bioinformatics online.

Mobyle: a new full web bioinformatics framework

Wed, 04 Nov 2009 05:45:49 PST

Motivation: For the biologist, running bioinformatics analyses involves a time-consuming management of data and tools. Users need support to organize their work, retrieve parameters and reproduce their analyses. They also need to be able to combine their analytic tools using a safe data flow software mechanism. Finally, given that scientific tools can be difficult to install, it is particularly helpful for biologists to be able to use these tools through a web user interface. However, providing a web interface for a set of tools raises the problem that a single web portal cannot offer all the existing and possible services: it is the user, again, who has to cope with data copy among a number of different services. A framework enabling portal administrators to build a network of cooperating services would therefore clearly be beneficial.

Results: We have designed a system, Mobyle, to provide a flexible and usable Web environment for defining and running bioinformatics analyses. It embeds simple yet powerful data management features that allow the user to reproduce analyses and to combine tools using a hierarchical typing system. Mobyle offers invocation of services distributed over remote Mobyle servers, thus enabling a federated network of curated bioinformatics portals without the user having to learn complex concepts or to install sophisticated software. While being focused on the end user, the Mobyle system also addresses the need, for the bioinfomatician, to automate remote services execution: PlayMOBY is a companion tool that automates the publication of BioMOBY web services, using Mobyle program definitions.

Availability: The Mobyle system is distributed under the terms of the GNU GPLv2 on the project web site (http://bioweb2.pasteur.fr/projects/mobyle/). It is already deployed on three servers: http://mobyle.pasteur.fr, http://mobyle.rpbs.univ-paris-diderot.fr and http://lipm-bioinfo.toulouse.inra.fr/Mobyle. The PlayMOBY companion is distributed under the terms of the CeCILL license, and is available at http://lipm-bioinfo.toulouse.inra.fr/biomoby/PlayMOBY/.

Contact: mobyle-support@pasteur.fr; mobyle-support@rpbs.univ-paris-diderot.fr; letondal@pasteur.fr

Supplementary information:Supplementary data are available at Bioinformatics online.

A method for visualizing CellML models

Wed, 04 Nov 2009 05:45:49 PST

Motivation: The Physiome Project was established in 1997 to develop tools to facilitate international collaboration in the physiological sciences and the sharing of biological models and experimental data. The CellML language was developed to represent and exchange mathematical models of biological processes. CellML models can be very complicated, making it difficult to interpret the underlying physical and biological concepts and relationships captured/described in the mathematical model.

Results: To address this issue a set of ontologies was developed to explicitly annotate the biophysical concepts represented in the CellML models. This article presents a framework that combines a visual language, together with CellML ontologies, to support the visualization of the underlying physical and biological concepts described by the mathematical model and also their relationships with the CellML model. Automated CellML model visualization assists in the interpretation of model concepts and facilitates model communication and exchange between different communities.

Contact: sarala.dissanayake@auckland.ac.nz

Supplementary information: Supplementary data are available at Bioinformatics online.

Comparative analysis and unification of domain-domain interaction networks

Bjorkholm, P., Sonnhammer, E. L. L. — Wed, 04 Nov 2009 05:45:49 PST

Motivation: Certain protein domains are known to preferentially interact with other domains. Several approaches have been proposed to predict domain–domain interactions, and over nine datasets are available. Our aim is to analyse the coverage and quality of the existing resources, as well as the extent of their overlap. With this knowledge, we have the opportunity to merge individual domain interaction networks to construct a comprehensive and reliable database.

Results: In this article we introduce a new approach towards comparing domain–domain interaction networks. This approach is used to compare nine predicted domain and protein interaction networks. The networks were used to generate a database of unified domain interactions, UniDomInt. Each interaction in the dataset is scored according to the benchmarked reliability of the sources. The performance of UniDomInt is an improvement compared to the underlying source networks and to another composite resource, Domine.

Availability: http://sonnhammer.sbc.su.se/download/UniDomInt/

Contact: Erik.Sonnhammer@sbc.su.se

Saint: a lightweight integration environment for model annotation

Lister, A. L., Pocock, M., Taschuk, M., Wipat, A. — Wed, 04 Nov 2009 05:45:49 PST

Summary: Saint is a web application which provides a lightweight annotation integration environment for quantitative biological models. The system enables modellers to rapidly mark up models with biological information derived from a range of data sources.

Availability and Implementation: Saint is freely available for use on the web at http://www.cisban.ac.uk/saint. The web application is implemented in Google Web Toolkit and Tomcat, with all major browsers supported. The Java source code is freely available for download at http://saint-annotate.sourceforge.net. The Saint web server requires an installation of libSBML and has been tested on Linux (32-bit Ubuntu 8.10 and 9.04).

Contact: helpdesk@cisban.ac.uk; a.l.lister@ncl.ac.uk

Supplementary information: Supplementary data are available at Bioinformatics online.

CellClassifier: supervised learning of cellular phenotypes

Ramo, P., Sacher, R., Snijder, B., Begemann, B., Pelkmans, L. — Wed, 04 Nov 2009 05:45:49 PST

Summary:CellClassifier is a tool for classifying single-cell phenotypes in microscope images. It includes several unique and user-friendly features for classification using multiclass support vector machines

Availability: Source code, user manual and SaveObjectSegmentation CellProfiler module available for download at www.cellclassifier.ethz.ch under the GPL license (implemented in Matlab).

Contact: pelkmans@imsb.biol.ethz.ch

Supplementary information: Supplementary data are available at Bioinformatics online.

PubMed-EX: a web browser extension to enhance PubMed search with text mining features

Tsai, R. T.-H., Dai, H.-J., Lai, P.-T., Huang, C.-H. — Wed, 04 Nov 2009 05:45:49 PST

Summary: PubMed-EX is a browser extension that marks up PubMed search results with additional text-mining information. PubMed-EX's page mark-up, which includes section categorization and gene/disease and relation mark-up, can help researchers to quickly focus on key terms and provide additional information on them. All text processing is performed server-side, freeing up user resources.

Availability: PubMed-EX is freely available at http://bws.iis.sinica.edu.tw/PubMed-EX and http://iisr.cse.yzu.edu.tw:8000/PubMed-EX/.

Contact: thtsai@saturn.yzu.edu.tw

Supplementary information: Supplementary data are available at Bioinformatics online.

digeR: a graphical user interface R package for analyzing 2D-DIGE data

Fan, Y., Murphy, T. B., Watson, R. W. G. — Wed, 04 Nov 2009 05:45:49 PST

Summary: 2D Difference In-Gel Electrophoresis (2D-DIGE) or 2D gel technology is being used as a routine proteomics technique for biomarker discovery. Analyzing such high-dimensional data requires multivariate analysis techniques to be applied. In addition, protein post-translational modification (PTM) information from the 2D gel data is usually overlooked. We report on an R package, digeR, with an easy to use graphical user interface for analyzing 2D-DIGE (2D gel) data. It provides a tool for visually looking for potential PTM changes from different biological states and support biomarker discovery through multivariate analysis techniques.

Availability: digeR package is freely available from the CRAN: http://cran.r-project.org/web/packages/digeR/index.html.

Contact: yue.fan@ucd.ie

Supplementary information: Supplementary data are available at Bioinformatics online.

UniMaP: finding unique mass and peptide signatures in the human proteome

Alexandridou, A., Tsangaris, G. Th., Vougas, K., Nikita, K., Spyrou, G. — Wed, 04 Nov 2009 05:45:49 PST

Summary: The uniqueness of a measured molecular mass or peptide sequence plays a very important role in the fields of protein identification and peptide/protein-biomarker investigation. We present a publicly available web application that offers information concerning the uniqueness of one or more molecular masses and one or more peptide sequences in the human proteome. When a sequence is found to be unique in humans, the application is able to search across all species querying whether this sequence is unique, not only in humans but also in other species found in the Swiss-Prot Database. The application is also able to search for unique protein fragments derived computationally from enzymatic digestion driven by certain enzymes. Furthermore, the application can list all the unique masses and peptides of a given protein. Through this application, researchers are able to find unique tags, either on a molecular mass level or on a sequence level. These unique tags are remarkably important in research related to protein identification or biomarker discovery and measurements.

Availability: UniMaP web-application is available at http://bioserver-1.bioacademy.gr/Bioserver/UniMaP/

Contact: gspyrou@bioacademy.gr

Supplementary information: Supplementary data are available at Bioinformatics online.

Identifying related journals through log analysis

Lu, Z., Xie, N., Wilbur, W. J. — Wed, 04 Nov 2009 05:45:49 PST

Motivation: With the explosion of biomedical literature and the evolution of online and open access, scientists are reading more articles from a wider variety of journals. Thus, the list of core journals relevant to their research may be less obvious and may often change over time. To help researchers quickly identify appropriate journals to read and publish in, we developed a web application for finding related journals based on the analysis of PubMed log data.

Availability: http://www.ncbi.nlm.nih.gov/IRET/Journals

Contact: luzh@ncbi.nlm.nih.gov

Supplementary information: Supplementary data are available at Bioinformatics online.

CMap 1.01: a comparative mapping application for the Internet

Youens-Clark, K., Faga, B., Yap, I. V., Stein, L., Ware, D. — Wed, 04 Nov 2009 05:45:49 PST

Summary:CMap is a web-based tool for displaying and comparing maps of any type and from any species. A user can compare an unlimited number of maps, view pair-wise comparisons of known correspondences, and search for maps or for features by name, species, type and accession. CMap is freely available, can run on a variety of database engines and uses only free and open software components.

Availability: http://www.gmod.org/cmap

Contact: kclark@cshl.edu

Next generation software for functional trend analysis

Berriz, G. F., Beaver, J. E., Cenik, C., Tasan, M., Roth, F. P. — Wed, 04 Nov 2009 05:45:50 PST

Summary: FuncAssociate is a web application that discovers properties enriched in lists of genes or proteins that emerge from large-scale experimentation. Here we describe an updated application with a new interface and several new features. For example, enrichment analysis can now be performed within multiple gene- and protein-naming systems. This feature avoids potentially serious translation artifacts to which other enrichment analysis strategies are subject.

Availability: The FuncAssociate web application is freely available to all users at http://llama.med.harvard.edu/funcassociate.

Contact: fritz_roth@hms.harvard.edu

QuickGO: a web-based tool for Gene Ontology searching

Binns, D., Dimmer, E., Huntley, R., Barrell, D., O'Donovan, C., Apweiler, R. — Wed, 04 Nov 2009 05:45:50 PST

Summary: QuickGO is a web-based tool that allows easy browsing of the Gene Ontology (GO) and all associated electronic and manual GO annotations provided by the GO Consortium annotation groups QuickGO has been a popular GO browser for many years, but after a recent redevelopment it is now able to offer a greater range of facilities including bulk downloads of GO annotation data which can be extensively filtered by a range of different parameters and GO slim set generation.

Availability and Implementation: QuickGO has implemented in JavaScript, Ajax and HTML, with all major browsers supported. It can be queried online at http://www.ebi.ac.uk/QuickGO. The software for QuickGO is freely available under the Apache 2 licence and can be downloaded from http://www.ebi.ac.uk/QuickGO/installation.html

Contact: goa@ebi.ac.uk; dbinns@ebi.ac.uk

Rapid detection, classification and accurate alignment of up to a million or more related protein sequences

Neuwald, A. F. — Wed, 04 Nov 2009 05:45:50 PST

Efficiently finding genome-wide three-way gene interactions from transcript- and genotype-data

Kayano, M., Takigawa, I., Shiga, M., Tsuda, K., Mamitsuka, H. — Fri, 23 Oct 2009 06:34:19 PDT

Motivation: We address the issue of finding a three-way gene interaction, i.e. two interacting genes in expression under the genotypes of another gene, given a dataset in which expressions and genotypes are measured at once for each individual. This issue can be a general, switching mechanism in expression of two genes, being controlled by categories of another gene, and finding this type of interaction can be a key to elucidating complex biological systems. The most suitable method for this issue is likelihood ratio test using logistic regressions, which we call interaction test, but a serious problem of this test is computational intractability at a genome-wide level.

Results: We developed a fast method for this issue which improves the speed of interaction test by around 10 times for any size of datasets, keeping highly interacting genes with an accuracy of ~85%. We applied our method to ~3 x 10⁸ three-way combinations generated from a dataset on human brain samples and detected three-way gene interactions with small P-values. To check the reliability of our results, we first conducted permutations by which we can show that the obtained P-values are significantly smaller than those obtained from permuted null examples. We then used GEO (Gene Expression Omnibus) to generate gene expression datasets with binary classes to confirm the detected three-way interactions by using these datasets and interaction tests. The result showed us some datasets with significantly small P-values, strongly supporting the reliability of the detected three-way interactions.

Availability: Software is available from http://www.bic.kyoto-u.ac.jp/pathway/kayano/bioinfo_three-way.html

Contact: kayano@kuicr.kyoto-u.ac.jp

Supplementary information: Supplementary data are available at Bioinformatics online.

Automated inference of molecular mechanisms of disease from amino acid substitutions

Fri, 23 Oct 2009 06:34:20 PDT

Motivation: Advances in high-throughput genotyping and next generation sequencing have generated a vast amount of human genetic variation data. Single nucleotide substitutions within protein coding regions are of particular importance owing to their potential to give rise to amino acid substitutions that affect protein structure and function which may ultimately lead to a disease state. Over the last decade, a number of computational methods have been developed to predict whether such amino acid substitutions result in an altered phenotype. Although these methods are useful in practice, and accurate for their intended purpose, they are not well suited for providing probabilistic estimates of the underlying disease mechanism.

Results: We have developed a new computational model, MutPred, that is based upon protein sequence, and which models changes of structural features and functional sites between wild-type and mutant sequences. These changes, expressed as probabilities of gain or loss of structure and function, can provide insight into the specific molecular mechanism responsible for the disease state. MutPred also builds on the established SIFT method but offers improved classification accuracy with respect to human disease mutations. Given conservative thresholds on the predicted disruption of molecular function, we propose that MutPred can generate accurate and reliable hypotheses on the molecular basis of disease for ~11% of known inherited disease-causing mutations. We also note that the proportion of changes of functionally relevant residues in the sets of cancer-associated somatic mutations is higher than for the inherited lesions in the Human Gene Mutation Database which are instead predicted to be characterized by disruptions of protein structure.

Availability: http://mutdb.org/mutpred

Contact: predrag@indiana.edu; smooney@buckinstitute.org

Algorithms for optimal protein structure alignment

Poleksic, A. — Fri, 23 Oct 2009 06:34:20 PDT

Motivation: Structural alignment is an important tool for understanding the evolutionary relationships between proteins. However, finding the best pairwise structural alignment is difficult, due to the infinite number of possible superpositions of two structures. Unlike the sequence alignment problem, which has a polynomial time solution, the structural alignment problem has not been even classified as solvable.

Results: We study one of the most widely used measures of protein structural similarity, defined as the number of pairs of residues in two proteins that can be superimposed under a predefined distance cutoff. We prove that, for any two proteins, this measure can be optimized for all but finitely many distance cutoffs. Our method leads to a series of algorithms for optimizing other structure similarity measures, including the measures commonly used in protein structure prediction experiments. We also present a polynomial time algorithm for finding a near-optimal superposition of two proteins. Aside from having a relatively low cost, the algorithm for near-optimal solution returns a superposition of provable quality. In other words, the difference between the score of the returned superposition and the score of an optimal superposition can be explicitly computed and used to determine whether the returned superposition is, in fact, the best superposition.

Contact: poleksic@cs.uni.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

CCHMM_PROF: a HMM-based coiled-coil predictor with evolutionary information

Bartoli, L., Fariselli, P., Krogh, A., Casadio, R. — Fri, 23 Oct 2009 06:34:20 PDT

Motivation:The widespread coiled-coil structural motif in proteins is known to mediate a variety of biological interactions. Recognizing a coiled-coil containing sequence and locating its coiled-coil domains are key steps towards the determination of the protein structure and function. Different tools are available for predicting coiled-coil domains in protein sequences, including those based on position-specific score matrices and machine learning methods.

Results: In this article, we introduce a hidden Markov model (CCHMM_PROF) that exploits the information contained in multiple sequence alignments (profiles) to predict coiled-coil regions. The new method discriminates coiled-coil sequences with an accuracy of 97% and achieves a true positive rate of 79% with only 1% of false positives. Furthermore, when predicting the location of coiled-coil segments in protein sequences, the method reaches an accuracy of 80% at the residue level and a best per-segment and per-protein efficiency of 81% and 80%, respectively. The results indicate that CCHMM_PROF outperforms all the existing tools and can be adopted for large-scale genome annotation.

Availability: The dataset is available at http://www.biocomp.unibo.it/~lisa/coiled-coils. The predictor is freely available at http://gpcr.biocomp.unibo.it/cgi/predictors/cchmmprof/pred_cchmmprof.cgi.

Contact: piero@biocomp.unibo.it

Correlating multiple SNPs and multiple disease phenotypes: penalized non-linear canonical correlation analysis

Waaijenborg, S., Zwinderman, A. H. — Fri, 23 Oct 2009 06:34:20 PDT

Motivation: Canonical correlation analysis (CCA) can be used to capture the underlying genetic background of a complex disease, by associating two datasets containing information about a patient's phenotypical and genetic details. Often the genetic information is measured on a qualitative scale, consequently ordinary CCA cannot be applied to such data. Moreover, the size of the data in genetic studies can be enormous, thereby making the results difficult to interpret.

Results: We developed a penalized non-linear CCA approach that can deal with qualitative data by transforming each qualitative variable into a continuous variable through optimal scaling. Additionally, sparse results were obtained by adapting soft-thresholding to this non-linear version of the CCA. By means of simulation studies, we show that our method is capable of extracting relevant variables out of high-dimensional sets. We applied our method to a genetic dataset containing 144 patients with glial cancer.

Contact: s.waaijenborg@amc.uva.nl

The effects of probe binding affinity differences on gene expression measurements and how to deal with them

Dannemann, M., Lorenc, A., Hellmann, I., Khaitovich, P., Lachmann, M. — Fri, 23 Oct 2009 06:34:20 PDT

Motivation: When comparing gene expression levels between species or strains using microarrays, sequence differences between the groups can cause false identification of expression differences. Our simulated dataset shows that a sequence divergence of only 1% between species can lead to falsely reported expression differences for >50% of the transcripts—similar levels of effect have been reported previously in comparisons of human and chimpanzee expression. We propose a method for identifying probes that cause such false readings, using only the microarray data, so that problematic probes can be excluded from analysis. We then test the power of the method to detect sequence differences and to correct for falsely reported expression differences. Our method can detect 70% of the probes with sequence differences using human and chimpanzee data, while removing only 18% of probes with no sequence differences. Although only 70% of the probes with sequence differences are detected, the effect of removing probes on falsely reported expression differences is more dramatic: the method can remove 98% of the falsely reported expression differences from a simulated dataset. We argue that the method should be used even when sequence data are available.

Contact: lachmann@eva.mpg.de

Supplementary information: Supplementary data are available at Bioinformatics online.

Statistical methods for gene set co-expression analysis

Choi, Y., Kendziorski, C. — Fri, 23 Oct 2009 06:34:20 PDT

Motivation: The power of a microarray experiment derives from the identification of genes differentially regulated across biological conditions. To date, differential regulation is most often taken to mean differential expression, and a number of useful methods for identifying differentially expressed (DE) genes or gene sets are available. However, such methods are not able to identify many relevant classes of differentially regulated genes. One important example concerns differentially co-expressed (DC) genes.

Results: We propose an approach, gene set co-expression analysis (GSCA), to identify DC gene sets. The GSCA approach provides a false discovery rate controlled list of interesting gene sets, does not require that genes be highly correlated in at least one biological condition and is readily applied to data from individual or multiple experiments, as we demonstrate using data from studies of lung cancer and diabetes.

Availability: The GSCA approach is implemented in R and available at www.biostat.wisc.edu/~kendzior/GSCA/.

Contact: kendzior@biostat.wisc.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

A novel algorithm for detecting differentially regulated paths based on gene set enrichment analysis

Fri, 23 Oct 2009 06:34:20 PDT

Motivation: Deregulated signaling cascades are known to play a crucial role in many pathogenic processes, among them are tumor initiation and progression. In the recent past, modern experimental techniques that allow for measuring the amount of mRNA transcripts of almost all known human genes in a tissue or even in a single cell have opened new avenues for studying the activity of the signaling cascades and for understanding the information flow in the networks.

Results: We present a novel dynamic programming algorithm for detecting deregulated signaling cascades. The so-called FiDePa (Finding Deregulated Paths) algorithm interprets differences in the expression profiles of tumor and normal tissues. It relies on the well-known gene set enrichment analysis (GSEA) and efficiently detects all paths in a given regulatory or signaling network that are significantly enriched with differentially expressed genes or proteins. Since our algorithm allows for comparing a single tumor expression profile with the control group, it facilitates the detection of specific regulatory features of a tumor that may help to optimize tumor therapy. To demonstrate the capabilities of our algorithm, we analyzed a glioma expression dataset with respect to a directed graph that combined the regulatory networks of the KEGG and TRANSPATH database. The resulting glioma consensus network that encompasses all detected deregulated paths contained many genes and pathways that are known to be key players in glioma or cancer-related pathogenic processes. Moreover, we were able to correlate clinically relevant features like necrosis or metastasis with the detected paths.

Availability: C++ source code is freely available, BiNA can be downloaded from http://www.bnplusplus.org/.

Contact: ack@bioinf.uni-sb.de

Supplementary information: Supplementary data are available at Bioinformatics online.

Bi-correlation clustering algorithm for determining a set of co-regulated genes

Bhattacharya, A., De, R. K. — Fri, 23 Oct 2009 06:34:20 PDT

Motivation: Biclustering has been emerged as a powerful tool for identification of a group of co-expressed genes under a subset of experimental conditions (measurements) present in a gene expression dataset. Several biclustering algorithms have been proposed till date. In this article, we address some of the important shortcomings of these existing biclustering algorithms and propose a new correlation-based biclustering algorithm called bi-correlation clustering algorithm (BCCA).

Results: BCCA has been able to produce a diverse set of biclusters of co-regulated genes over a subset of samples where all the genes in a bicluster have a similar change of expression pattern over the subset of samples. Moreover, the genes in a bicluster have common transcription factor binding sites in the corresponding promoter sequences. The presence of common transcription factors binding sites, in the corresponding promoter sequences, is an evidence that a group of genes in a bicluster are co-regulated. Biclusters determined by BCCA also show highly enriched functional categories. Using different gene expression datasets, we demonstrate strength and superiority of BCCA over some existing biclustering algorithms.

Availability: The software for BCCA has been developed using C and Visual Basic languages, and can be executed on the Microsoft Windows platforms. The software may be downloaded as a zip file from http://www.isical.ac.in/~rajat. Then it needs to be installed. Two word files (included in the zip file) need to be consulted before installation and execution of the software.

Contact: rajat@isical.ac.in

Supplementary information: Supplementary data are available at Bioinformatics online.

Multiple testing in genome-wide association studies via hidden Markov models

Wei, Z., Sun, W., Wang, K., Hakonarson, H. — Fri, 23 Oct 2009 06:34:20 PDT

Motivation: Genome-wide association studies (GWAS) interrogate common genetic variation across the entire human genome in an unbiased manner and hold promise in identifying genetic variants with moderate or weak effect sizes. However, conventional testing procedures, which are mostly P-value based, ignore the dependency and therefore suffer from loss of efficiency. The goal of this article is to exploit the dependency information among adjacent single nucleotide polymorphisms (SNPs) to improve the screening efficiency in GWAS.

Results: We propose to model the linear block dependency in the SNP data using hidden Markov models (HMMs). A compound decision–theoretic framework for testing HMM-dependent hypotheses is developed. We propose a powerful data-driven procedure [pooled local index of significance (PLIS)] that controls the false discovery rate (FDR) at the nominal level. PLIS is shown to be optimal in the sense that it has the smallest false negative rate (FNR) among all valid FDR procedures. By re-ranking significance for all SNPs with dependency considered, PLIS gains higher power than conventional P-value based methods. Simulation results demonstrate that PLIS dominates conventional FDR procedures in detecting disease-associated SNPs. Our method is applied to analysis of the SNP data from a GWAS of type 1 diabetes. Compared with the Benjamini–Hochberg (BH) procedure, PLIS yields more accurate results and has better reproducibility of findings.

Conclusion: The genomic rankings based on our procedure are substantially different from the rankings based on the P-values. By integrating information from adjacent locations, the PLIS rankings benefit from the increased signal-to-noise ratio, hence our procedure often has higher statistical power and better reproducibility. It provides a promising direction in large-scale GWAS.

Availability: An R package PLIS has been developed to implement the PLIS procedure. Source codes are available upon request and will be available on CRAN (http://cran.r-project.org/).

Contact: zhiwei@njit.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Quantifying cancer progression with conjunctive Bayesian networks

Gerstung, M., Baudis, M., Moch, H., Beerenwinkel, N. — Fri, 23 Oct 2009 06:34:20 PDT

Motivation: Cancer is an evolutionary process characterized by accumulating mutations. However, the precise timing and the order of genetic alterations that drive tumor progression remain enigmatic.

Results: We present a specific probabilistic graphical model for the accumulation of mutations and their interdependencies. The Bayesian network models cancer progression by an explicit unobservable accumulation process in time that is separated from the observable but error-prone detection of mutations. Model parameters are estimated by an Expectation-Maximization algorithm and the underlying interaction graph is obtained by a simulated annealing procedure. Applying this method to cytogenetic data for different cancer types, we find multiple complex oncogenetic pathways deviating substantially from simplified models, such as linear pathways or trees. We further demonstrate how the inferred progression dynamics can be used to improve genetics-based survival predictions which could support diagnostics and prognosis.

Availability: The software package ct-cbn is available under a GPL license on the web site cbg.ethz.ch/software/ct-cbn

Contact: moritz.gerstung@bsse.ethz.ch

Accessible methods for the dynamic time-scale decomposition of biochemical systems

Surovtsova, I., Simus, N., Lorenz, T., Konig, A., Sahle, S., Kummer, U. — Fri, 23 Oct 2009 06:34:20 PDT

Motivation: The growing complexity of biochemical models asks for means to rationally dissect the networks into meaningful and rather independent subnetworks. Such foregoing should ensure an understanding of the system without any heuristics employed. Important for the success of such an approach is its accessibility and the clarity of the presentation of the results.

Results: In order to achieve this goal, we developed a method which is a modification of the classical approach of time-scale separation. This modified method as well as the more classical approach have been implemented for time-dependent application within the widely used software COPASI. The implementation includes different possibilities for the representation of the results including 3D-visualization.

Availability: The methods are included in COPASI which is free for academic use and available at www.copasi.org.

Contact: irina.surovtsova@bioquant.uni-heidelberg.de

Supplementary information: Supplementary data are available at Bioinformatics online.

Modified variational Bayes EM estimation of hidden Markov tree model of cell lineages

Fri, 23 Oct 2009 06:34:20 PDT

Motivation: Human pluripotent stem cell lines persist in culture as a heterogeneous population of SSEA3 positive and SSEA3 negative cells. Tracking individual stem cells in real time can elucidate the kinetics of cells switching between the SSEA3 positive and negative substates. However, identifying a cell's substate at all time points within a cell lineage tree is technically difficult.

Results: A variational Bayesian Expectation Maximization (EM) with smoothed probabilities (VBEMS) algorithm for hidden Markov trees (HMT) is proposed for incomplete tree structured data. The full posterior of the HMT parameters is determined and the underflow problems associated with previous algorithms are eliminated. Example results for the prediction of the types of cells in synthetic and real stem cell lineage trees are presented.

Availability:The Matlab code for the VBEMS algorithm is freely available at http://www.acse.dept.shef.ac.uk/repository/vbems_lineage_tree/VBEMS.ZIP

Contact: visakan@sheffield.ac.uk

Supplementary information: Supplementary data are available at Bioinformatics online.

A hypergraph-based learning algorithm for classifying gene expression and arrayCGH data with prior knowledge

Tian, Z., Hwang, T., Kuang, R. — Fri, 23 Oct 2009 06:34:20 PDT

Motivation: Incorporating biological prior knowledge into predictive models is a challenging data integration problem in analyzing high-dimensional genomic data. We introduce a hypergraph-based semi-supervised learning algorithm called HyperPrior to classify gene expression and array-based comparative genomic hybridization (arrayCGH) data using biological knowledge as constraints on graph-based learning. HyperPrior is a robust two-step iterative method that alternatively finds the optimal labeling of the samples and the optimal weighting of the features, guided by constraints encoding prior knowledge. The prior knowledge for analyzing gene expression data is that cancer-related genes tend to interact with each other in a protein–protein interaction network. Similarly, the prior knowledge for analyzing arrayCGH data is that probes that are spatially nearby in their layout along the chromosomes tend to be involved in the same amplification or deletion event. Based on the prior knowledge, HyperPrior imposes a consistent weighting of the correlated genomic features in graph-based learning.

Results: We applied HyperPrior to test two arrayCGH datasets and two gene expression datasets for both cancer classification and biomarker identification. On all the datasets, HyperPrior achieved competitive classification performance, compared with SVMs and the other baselines utilizing the same prior knowledge. HyperPrior also identified several discriminative regions on chromosomes and discriminative subnetworks in the PPI, both of which contain cancer-related genomic elements. Our results suggest that HyperPrior is promising in utilizing biological prior knowledge to achieve better classification performance and more biologically interpretable findings in gene expression and arrayCGH data.

Availability: http://compbio.cs.umn.edu/HyperPrior

Contact: kuang@cs.umn.edu

Supplementary information: Supplementary data are available at bioinformatics online.

TagDust--a program to eliminate artifacts from next generation sequencing data

Lassmann, T., Hayashizaki, Y., Daub, C. O. — Fri, 23 Oct 2009 06:34:20 PDT

Motivation: Next-generation parallel sequencing technologies produce large quantities of short sequence reads. Due to experimental procedures various types of artifacts are commonly sequenced alongside the targeted RNA or DNA sequences. Identification of such artifacts is important during the development of novel sequencing assays and for the downstream analysis of the sequenced libraries.

Results: Here we present TagDust, a program identifying artifactual sequences in large sequencing runs. Given a user-defined cutoff for the false discovery rate, TagDust identifies all reads explainable by combinations and partial matches to known sequences used during library preparation. We demonstrate the quality of our method on sequencing runs performed on Illumina's Genome Analyzer platform.

Availability: Executables and documentation are available from http://genome.gsc.riken.jp/osc/english/software/.

Contact: timolassmann@gmail.com

Updates to the RMAP short-read mapping software

Fri, 23 Oct 2009 06:34:20 PDT

Summary: We report on a major new version of the RMAP software for mapping reads from short-read sequencing technology. General improvements to accuracy and space requirements are included, along with novel functionality. Included in the RMAP software package are tools for mapping paired-end reads, mapping using more sophisticated use of quality scores, collecting ambiguous mapping locations and mapping bisulfite-treated reads.

Availability: The applications described in this note are available for download at http://www.cmb.usc.edu/people/andrewds/rmap and are distributed as Open Source software under the GPLv3.0. The software has been tested on Linux and OS X platforms.

Contact: andrewds@usc.edu; mzhang@cshl.edu

The RMAP algorithm was introduced by (Smith et al., 2008) as one of the earliest available programs for mapping reads from the Illumina second-generation sequencing technology. One important contribution of RMAP was to incorporate the use of quality scores directly into the mapping process: read positions with too low a quality score were not considered while mapping, and that quality score cutoff could be adjusted by the user. Subsequently, numerous mapping algorithm have appeared (Langmead et al., 2009; Li,H. et al., 2008; Li,R. et al., 2008; Lin et al., 2008; Schatz, 2009; Yanovsky et al., 2008), with improvements in both efficiency and breadth of functionality (e.g. ability to map paired-end reads; integrated SNP calling). Investigators requiring solutions to mapping problems now have many options. As new applications of short-read sequencing emerge, many variations on the analysis task of read mapping emerge. Diversity in performance characteristics of existing mapping tools becomes potentially valuable.

We report the first major update to RMAP. The basic algorithmic framework in RMAP is still to preprocess reads and scan the genome, but several modifications have been made and much additional functionality has been included. Importantly, RMAP has a memory footprint that depends on the number of reads being mapped. This feature allows RMAP to be used effectively in cluster environments with commodity nodes, because partitioning the reads allows natural parallelizations with linear reduction in memory requirements per processor core used.

Included in this release of the RMAP software package is functionality for mapping paired-end reads, making more sophisticated use of quality scores, collecting mapping locations for ambiguously mapping reads and mapping bisulfite-treated reads.

3D-SURFER: software for high-throughput protein surface comparison and analysis

Fri, 23 Oct 2009 06:34:20 PDT

Summary: We present 3D-SURFER, a web-based tool designed to facilitate high-throughput comparison and characterization of proteins based on their surface shape. As each protein is effectively represented by a vector of 3D Zernike descriptors, comparison times for a query protein against the entire PDB take, on an average, only a couple of seconds. The web interface has been designed to be as interactive as possible with displays showing animated protein rotations, CATH codes and structural alignments using the CE program. In addition, geometrically interesting local features of the protein surface, such as pockets that often correspond to ligand binding sites as well as protrusions and flat regions can also be identified and visualized.

Availability: 3D-SURFER is a web application that can be freely accessed from: http://dragon.bio.purdue.edu/3d-surfer

Contact: dkihara@purdue.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

COMPASS: a program for generating serial samples under an infinite sites model

Jakobsson, M. — Fri, 23 Oct 2009 06:34:20 PDT

Summary: The program COMPASS can generate samples that have been collected at various points in time from a population that is evolving according to a Wright–Fisher model. The samples are generated using coalescence simulations permitting various demographic scenarios and the program uses an infinite sites model to generate polymorphism data for the samples. By generating serially sampled population-genetic data, COMPASS allows investigating properties of polymorphism data that has been collected at different time points, and aid in making inference from ancient polymorphism data.

Availability: The program and the manual are available at: http://www.egs.uu.se/evbiol/Research/JakobssonLab/compass.html

Contact: mattias.jakobsson@ebc.uu.se

iBioSim: a tool for the analysis and design of genetic circuits

Myers, C. J., Barker, N., Jones, K., Kuwahara, H., Madsen, C., Nguyen, N.-P. D. — Fri, 23 Oct 2009 06:34:20 PDT

Summary: iBioSim is a tool that supports learning of genetic circuit models, efficient abstraction-based analysis of these models and the design of synthetic genetic circuits. iBioSim includes project management features and a graphical user interface that facilitate the development and maintenance of genetic circuit models as well as both experimental and simulation data records.

Availability: iBioSim is available for download for Windows, Linux, and MacOS at http://www.async.ece.utah.edu/iBioSim/

Contact: myers@ece.utah.edu

WEbcoli: an interactive and asynchronous web application for in silico design and analysis of genome-scale E.coli model

Jung, T.-S., Yeo, H. C., Reddy, S. G., Cho, W.-S., Lee, D.-Y. — Fri, 23 Oct 2009 06:34:20 PDT

Summary: WEbcoli is a WEb application for in silico designing, analyzing and engineering Escherichia coli metabolism. It is devised and implemented using advanced web technologies, thereby leading to enhanced usability and dynamic web accessibility. As a main feature, the WEbcoli system provides a user-friendly rich web interface, allowing users to virtually design and synthesize mutant strains derived from the genome-scale wild-type E.coli model and to customize pathways of interest through a graph editor. In addition, constraints-based flux analysis can be conducted for quantifying metabolic fluxes and charactering the physiological and metabolic states under various genetic and/or environmental conditions.

Availability: WEbcoli is freely accessible at http://webcoli.org.

Contact: cheld@nus.edu.sg

ERNEST: a toolbox for chemical reaction network theory

Soranzo, N., Altafini, C. — Fri, 23 Oct 2009 06:34:20 PDT

Summary: ERNEST Reaction Network Equilibria Study Toolbox is a MATLAB package which, by checking various different criteria on the structure of a chemical reaction network, can exclude the multistationarity of the corresponding reaction system. The results obtained are independent of the rate constants of the reactions, and can be used for model discrimination.

Availability and Implementation: The software, implemented in MATLAB, is available under the GNU GPL free software license from http://people.sissa.it/~altafini/papers/SoAl09/. It requires the MATLAB Optimization Toolbox.

Contact: altafini@sissa.it

integrOmics: an R package to unravel relationships between two omics datasets

Le Cao, K.-A., Gonzalez, I., Dejean, S. — Fri, 23 Oct 2009 06:34:20 PDT

Motivation: With the availability of many ‘omics’ data, such as transcriptomics, proteomics or metabolomics, the integrative or joint analysis of multiple datasets from different technology platforms is becoming crucial to unravel the relationships between different biological functional levels. However, the development of such an analysis is a major computational and technical challenge as most approaches suffer from high data dimensionality. New methodologies need to be developed and validated.

Results: integrOmics efficiently performs integrative analyses of two types of ‘omics’ variables that are measured on the same samples. It includes a regularized version of canonical correlation analysis to enlighten correlations between two datasets, and a sparse version of partial least squares (PLS) regression that includes simultaneous variable selection in both datasets. The usefulness of both approaches has been demonstrated previously and successfully applied in various integrative studies.

Availability: integrOmics is freely available from http://CRAN.R-project.org/ or from the web site companion (http://math.univ-toulouse.fr/biostat) that provides full documentation and tutorials.

Contact: k.lecao@uq.edu.au

Supplementary information: Supplementary data are available at Bioinformatics online.

Analyzing biological network parameters with CentiScaPe

Scardoni, G., Petterlini, M., Laudanna, C. — Fri, 23 Oct 2009 06:34:21 PDT

Summary: The increasing availability of large network datasets along with the progresses in experimental high-throughput technologies have prompted the need for tools allowing easy integration of experimental data with data derived form network computational analysis. In order to enrich experimental data with network topological parameters, we have developed the Cytoscape plug-in CentiScaPe. The plug-in computes several network centrality parameters and allows the user to analyze existing relationships between experimental data provided by the users and node centrality values computed by the plug-in. CentiScaPe allows identifying network nodes that are relevant from both experimental and topological viewpoints. CentiScaPe also provides a Boolean logic-based tool that allows easy characterization of nodes whose topological relevance depends on more than one centrality. Finally, different graphic outputs and the included description of biological significance for each computed centrality facilitate the analysis by the end users not expert in graph theory, thus allowing easy node categorization and experimental prioritization.

Availability: CentiScaPe can be downloaded via the Cytoscape web site: http://chianti.ucsd.edu/cyto_web/plugins/index.php. Tutorial, centrality descriptions and example data are available at: http://profs.sci.univr.it/~scardoni/centiscape/centiscapepage.php

Contact: giovanni.scardoni@gmail.com

Supplementary information: Supplementary data are available at Bioinformatics online.

PathBuilder--open source software for annotating and developing pathway resources

Fri, 23 Oct 2009 06:34:21 PDT

Summary: We have developed PathBuilder, an open-source web application to annotate biological information pertaining to signaling pathways and to create web-based pathway resources. PathBuilder enables annotation of molecular events including protein–protein interactions, enzyme–substrate relationships and protein translocation events either manually or through automated importing of data from other databases. Salient features of PathBuilder include automatic validation of data formats, built-in modules for visualization of pathways, automated import of data from other pathway resources, export of data in several standard data exchange formats and an application programming interface for retrieving existing pathway datasets.

Availability: PathBuilder is freely available for download at http://pathbuilder.sourceforge.net/ under the terms of GNU lesser general public license (LGPL: http://www.gnu.org/copyleft/lesser.html). The software is platform independent and has been tested on Windows and Linux platforms.

Contact: pandey@jhmi.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

A report on the 2009 SIG on short read sequencing and algorithms (Short-SIG)

Brudno, M., Medvedev, P., Stoye, J., De La Vega, F. M. — Fri, 23 Oct 2009 06:34:21 PDT

Pindel: a pattern growth approach to detect break points of large deletions and medium sized insertions from paired-end short reads

Ye, K., Schulz, M. H., Long, Q., Apweiler, R., Ning, Z. — Fri, 23 Oct 2009 06:34:21 PDT

Motivation: There is a strong demand in the genomic community to develop effective algorithms to reliably identify genomic variants. Indel detection using next-gen data is difficult and identification of long structural variations is extremely challenging.

Results: We present Pindel, a pattern growth approach, to detect breakpoints of large deletions and medium-sized insertions from paired-end short reads. We use both simulated reads and real data to demonstrate the efficiency of the computer program and accuracy of the results.

Availability: The binary code and a short user manual can be freely downloaded from http://www.ebi.ac.uk/~kye/pindel/.

Contact: k.ye@lumc.nl; zn1@sanger.ac.uk

De novo transcriptome assembly with ABySS

Fri, 23 Oct 2009 06:34:21 PDT

Motivation: Whole transcriptome shotgun sequencing data from non-normalized samples offer unique opportunities to study the metabolic states of organisms. One can deduce gene expression levels using sequence coverage as a surrogate, identify coding changes or discover novel isoforms or transcripts. Especially for discovery of novel events, de novo assembly of transcriptomes is desirable.

Results: Transcriptome from tumor tissue of a patient with follicular lymphoma was sequenced with 36 base pair (bp) single- and paired-end reads on the Illumina Genome Analyzer II platform. We assembled ~194 million reads using ABySS into 66 921 contigs 100 bp or longer, with a maximum contig length of 10 951 bp, representing over 30 million base pairs of unique transcriptome sequence, or roughly 1% of the genome.

Availability and Implementation: Source code and binaries of ABySS are freely available for download at http://www.bcgsc.ca/platform/bioinfo/software/abyss. Assembler tool is implemented in C++. The parallel version uses Open MPI. ABySS-Explorer tool is implemented in Java using the Java universal network/graph framework.

Contact: ibirol@bcgsc.ca

Increasing the coverage of a metapopulation consensus genome by iterative read mapping and assembly

Dutilh, B. E., Huynen, M. A., Strous, M. — Fri, 23 Oct 2009 06:34:21 PDT

Motivation: Most microbial species can not be cultured in the laboratory. Metagenomic sequencing may still yield a complete genome if the sequenced community is enriched and the sequencing coverage is high. However, the complexity in a natural population may cause the enrichment culture to contain multiple related strains. This diversity can confound existing strict assembly programs and lead to a fragmented assembly, which is unnecessary if we have a related reference genome available that can function as a scaffold.

Results: Here, we map short metagenomic sequencing reads from a population of strains to a related reference genome, and compose a genome that captures the consensus of the population's sequences. We show that by iteration of the mapping and assembly procedure, the coverage increases while the similarity with the reference genome decreases. This indicates that the assembly becomes less dependent on the reference genome and approaches the consensus genome of the multi-strain population.

Contact: dutilh@cmbi.ru.nl

Supplementary Information: Supplementary data are available at Bioinformatics online.

ISOLATE: a computational strategy for identifying the primary origin of cancers using high-throughput sequencing

Quon, G., Morris, Q. — Fri, 23 Oct 2009 06:34:21 PDT

Motivation: One of the most deadly cancer diagnoses is the carcinoma of unknown primary origin. Without the knowledge of the site of origin, treatment regimens are limited in their specificity and result in high mortality rates. Though supervised classification methods have been developed to predict the site of origin based on gene expression data, they require large numbers of previously classified tumors for training, in part because they do not account for sample heterogeneity, which limits their application to well-studied cancers.

Results: We present ISOLATE, a new statistical method that simultaneously predicts the primary site of origin of cancers and addresses sample heterogeneity, while taking advantage of new high-throughput sequencing technology that promises to bring higher accuracy and reproducibility to gene expression profiling experiments. ISOLATE makes predictions de novo, without having seen any training expression profiles of cancers with identified origin. Compared with previous methods, ISOLATE is able to predict the primary site of origin, de-convolve and remove the effect of sample heterogeneity and identify differentially expressed genes with higher accuracy, across both synthetic and clinical datasets. Methods such as ISOLATE are invaluable tools for clinicians faced with carcinomas of unknown primary origin.

Availability: ISOLATE is available for download at: http://morrislab.med.utoronto.ca/software

Contact: gerald.quon@utoronto.ca; quaid.morris@utoronto.ca

Supplementary information: Supplementary data are available at Bioinformatics online.

TargetMiner: microRNA target prediction with systematic identification of tissue-specific negative examples

Bandyopadhyay, S., Mitra, R. — Thu, 08 Oct 2009 22:47:51 PDT

Motivation: Prediction of microRNA (miRNA) target mRNAs using machine learning approaches is an important area of research. However, most of the methods suffer from either high false positive or false negative rates. One reason for this is the marked deficiency of negative examples or miRNA non-target pairs. Systematic identification of non-target mRNAs is still not addressed properly, and therefore, current machine learning approaches are compelled to rely on artificially generated negative examples for training.

Results: In this article, we have identified ~300 tissue-specific negative examples using a novel approach that involves expression profiling of both miRNAs and mRNAs, miRNA–mRNA structural interactions and seed-site conservation. The newly generated negative examples are validated with pSILAC dataset, which elucidate the fact that the identified non-targets are indeed non-targets.These high-throughput tissue-specific negative examples and a set of experimentally verified positive examples are then used to build a system called TargetMiner, a support vector machine (SVM)-based classifier. In addition to assessing the prediction accuracy on cross-validation experiments, TargetMiner has been validated with a completely independent experimental test dataset. Our method outperforms 10 existing target prediction algorithms and provides a good balance between sensitivity and specificity that is not reflected in the existing methods. We achieve a significantly higher sensitivity and specificity of 69% and 67.8% based on a pool of 90 feature set and 76.5% and 66.1% using a set of 30 selected feature set on the completely independent test dataset.

In order to establish the effectiveness of the systematically generated negative examples, the SVM is trained using a different set of negative data generated using the method in Yousef et al. A significantly higher false positive rate (70.6%) is observed when tested on the independent set, while all other factors are kept the same. Again, when an existing method (NBmiRTar) is executed with the our proposed negative data, we observe an improvement in its performance. These clearly establish the effectiveness of the proposed approach of selecting the negative examples systematically.

Availability: TargetMiner is now available as an online tool at www.isical.ac.in/~bioinfo_miu

Contact: sanghami@isical.ac.in; rmitra_t@isical.ac.in

Supplementary information: Supplementary data are available at Bioinformatics online.

T-REKS: identification of Tandem REpeats in sequences with a K-meanS based algorithm

Jorda, J., Kajava, A. V. — Thu, 08 Oct 2009 22:47:51 PDT

Motivation: Over the last years a number of evidences have been accumulated about high incidence of tandem repeats in proteins carrying fundamental biological functions and being related to a number of human diseases. At the same time, frequently, protein repeats are strongly degenerated during evolution and, therefore, cannot be easily identified. To solve this problem, several computer programs which were based on different algorithms have been developed. Nevertheless, our tests showed that there is still room for improvement of methods for accurate and rapid detection of tandem repeats in proteins.

Results: We developed a new program called T-REKS for ab initio identification of the tandem repeats. It is based on clustering of lengths between identical short strings by using a K-means algorithm. Benchmark of the existing programs and T-REKS on several sequence datasets is presented. Our program being linked to the Protein Repeat DataBase opens the way for large-scale analysis of protein tandem repeats. T-REKS can also be applied to the nucleotide sequences.

Availability: The algorithm has been implemented in JAVA, the program is available upon request at http://bioinfo.montp.cnrs.fr/?r=t-reks. Protein Repeat DataBase generated by using T-REKS is accessible at http://bioinfo.montp.cnrs.fr/?r=repeatDB.

Contact: julien.jorda@crbm.cnrs.fr; andrey.kajava@crbm.cnrs.fr

Supplementary information: Supplementary data are available at Bioinformatics online.

Enhancement of beta-sheet assembly by cooperative hydrogen bonds potential

Levy-Moonshine, A., Amir, E.-a. D., Keasar, C. — Thu, 08 Oct 2009 22:47:51 PDT

Motivation: The roughness of energy landscapes is a major obstacle to protein structure prediction, since it forces conformational searches to spend much time struggling to escape numerous traps. Specifically, beta-sheet formation is prone to stray, since many possible combinations of hydrogen bonds are dead ends in terms of beta-sheet assembly. It has been shown that cooperative terms for backbone hydrogen bonds ease this problem by augmenting hydrogen bond patterns that are consistent with beta sheets. Here, we present a novel cooperative hydrogen-bond term that is both effective in promoting beta sheets and computationally efficient. In addition, the new term is differentiable and operates on all-atom protein models.

Results: Energy optimization of poly-alanine chains under the new term led to significantly more beta-sheet content than optimization under a non-cooperative term. Furthermore, the optimized structure included very few non-native patterns.

Availability: The new term is implemented within the MESHI package and is freely available at http://cs.bgu.ac.il/~meshi.

Contact: chen.keasar@gmail.com

Supplementary information: Supplementary data are available at Bioinformatics online.

Partition function and base pairing probabilities for RNA-RNA interaction prediction

Huang, F. W. D., Qin, J., Reidys, C. M., Stadler, P. F. — Thu, 08 Oct 2009 22:47:51 PDT

Motivation: The RNA–RNA interaction problem (RIP) consists in finding the energetically optimal structure of two RNA molecules that bind to each other. The standard model allows secondary structures in both partners as well as additional base pairs between the two RNAs subject to certain restrictions that ensure that RIP is solvabale by a polynomial time dynamic programming algorithm. RNA–RNA binding, like RNA folding, is typically not dominated by the ground state structure. Instead, a large ensemble of alternative structures contributes to the interaction thermodynamics.

Results: We present here an O(N⁶) time and O(N⁴) dynamics programming algorithm for computing the full partition function for RIP which is based on the combinatorial notion of ‘tight structures’. Albeit equivalent to recent work by H. Chitsaz and collaborators, our approach in addition provides a full-fledged computation of the base pairing probabilities, which relies on the notion of a decomposition tree for joint structures. In practise, our implementation is efficient enough to investigate, for instance, the interactions of small bacterial RNAs and their target mRNAs.

Availability: The program rip is implemented in C. The source code is available for download from http://www.combinatorics.cn/cbpc/rip.html and http://www.bioinf.uni-leipzig.de/Software/rip.html.

Contact: duck@santafe.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

A new taxonomy-based protein fold recognition approach based on autocross-covariance transformation

Dong, Q., Zhou, S., Guan, J. — Thu, 08 Oct 2009 22:47:51 PDT

Motivation: Fold recognition is an important step in protein structure and function prediction. Traditional sequence comparison methods fail to identify reliable homologies with low sequence identity, while the taxonomic methods are effective alternatives, but their prediction accuracies are around 70%, which are still relatively low for practical usage.

Results: In this study, a simple and powerful method is presented for taxonomic fold recognition, which combines support vector machine (SVM) with autocross-covariance (ACC) transformation. The evolutionary information represented in the form of position-specific score matrices is converted into a series of fixed-length vectors by ACC transformation and these vectors are then input to a SVM classifier for fold recognition. The sequence-order effect can be effectively captured by this scheme. Experiments are performed on the widely used D-B dataset and the corresponding extended dataset, respectively. The proposed method, called ACCFold, gets an overall accuracy of 70.1% on the D-B dataset, which is higher than major existing taxonomic methods by 2–14%. Furthermore, the method achieves an overall accuracy of 87.6% on the extended dataset, which surpasses major existing taxonomic methods by 9–17%. Additionally, our method obtains an overall accuracy of 80.9% for 86-folds and 77.2% for 199-folds. These results demonstrate that the ACCFold method provides the state-of-the-art performance for taxonomic fold recognition.

Availability: The source code for ACC transformation is freely available at http://www.iipl.fudan.edu.cn/demo/accpkg.html.

Contact: sgzhou@fudan.edu.cn

Supplementary information: Supplementary data are available at Bioinformatics online.

MISTRAL: a tool for energy-based multiple structural alignment of proteins

Micheletti, C., Orland, H. — Thu, 08 Oct 2009 22:47:51 PDT

Motivation: The steady growth of the number of available protein structures has constantly motivated the development of new algorithms for detecting structural correspondences in proteins. Detecting structural equivalences in two or more proteins is computationally demanding as it typically entails the exploration of the combinatorial space of all possible amino acid pairings in the parent proteins. The search is often aided by the introduction of various constraints such as considering protein fragments, rather than single amino acids, and/or seeking only sequential correspondences in the given proteins. An additional challenge is represented by the difficulty of associating to a given alignment, a reliable a priori measure of its statistical significance.

Results: Here, we present and discuss MISTRAL (Multiple STRuctural ALignment), a novel strategy for multiple protein alignment based on the minimization of an energy function over the low-dimensional space of the relative rotations and translations of the molecules. The energy minimization avoids combinatorial searches and returns pairwise alignment scores for which a reliable a priori statistical significance can be given.

Availability: MISTRAL is freely available for academic users as a standalone program and as a web service at http://ipht.cea.fr/protein.php.

Contact: michelet@sissa.it

Supplementary information: Supplementary data are available at Bioinformatics online.

Statistical lower bounds on protein copy number from fluorescence expression images

Zamparo, L., Perkins, T. J. — Thu, 08 Oct 2009 22:47:51 PDT

Motivation: Fluorescence imaging has become a commonplace for quantitatively measuring mRNA or protein expression in cells and tissues. However, such expression data are usually relative—absolute concentrations or molecular copy numbers are typically not known. While this is satisfactory for many applications, for certain kinds of quantitative network modeling and analysis of expression noise, absolute measures of expression are necessary.

Results: We propose two methods for estimating molecular copy numbers from single uncalibrated expression images of tissues. These methods rely on expression variability between cells, due either to steady-state fluctuations or unequal distribution of molecules during cell division, to make their estimates. We apply these methods to 152 protein fluorescence expression images of Drosophila melanogaster embryos during early development, generating copy number estimates for 14 genes in the segmentation network. We also analyze the effects of noise on our estimators and compare with empirical findings. Finally, we confirm an observation of Bar-Even et al., made in the much different setting of Saccharomyces cerevisiae, that steady-state expression variance tends to scale with mean expression.

Availability: The data are all drawn from FlyEx (explained within), and is available at http://flyex.ams.sunysb.edu/FlyEx/. Data and MATLAB codes for all algorithms described in this article are available at http://www.perkinslab.ca/pubs/ZP2009.html.

Contact: tperkins@ohri.ca

Multi-dimensional correlations for gene coexpression and application to the large-scale data of Arabidopsis

Kinoshita, K., Obayashi, T. — Thu, 08 Oct 2009 22:47:51 PDT

Background: Recent improvements in DNA microarray techniques have made a large variety of gene expression data available in public databases. This data can be used to evaluate the strength of gene coexpression by calculating the correlation of expression patterns among different genes between many experiments. However, gene expression levels differ significantly across various tissues in higher organisms, as well as in different cellular location in eukaryotes in different cell state. Thus the usual correlation measure can only evaluate the difference of tissues or cellular localizations, and cannot adequately elucidate the functional relationship from the coexpression of genes.

Method: We propose a new measure of coexpression by expanding the generally used correlation into a multidimensional one. We used principal component analyses to identify the major factors of gene expression correlation, and then re-calculate the correlation by subtracting the major components in order to remove biases cased by a few experiments. The repeated subtractions of the major components yielded a set of correlation values for each pair of genes. We observed the correlation changes when the first ten principal components were subtracted step-by-step in large-scale Arabidopsis expression data.

Results: We found two extreme patterns of correlation changes, corresponding to stable and fragile coexpression. Our new indexes provided a good means to determine the functional relationships of the genes, by examining a few examples, and higher performance of Gene Ontology term prediction by using the support vector machine and the multidimensional correlation.

Availability: The results are available from the expression detail pages in ATTED-II (http://atted.jp).

Contact: kinosita@hgc.jp

Supplementary information: Supplementary data are available at Bioinformatics online.

A modified LOESS normalization applied to microRNA arrays: a comparative evaluation

Risso, D., Massa, M. S., Chiogna, M., Romualdi, C. — Thu, 08 Oct 2009 22:47:51 PDT

Motivation: Microarray normalization is a fundamental step in removing systematic bias and noise variability caused by technical and experimental artefacts. Several approaches, suitable for large-scale genome arrays, have been proposed and shown to be effective in the reduction of systematic errors. Most of these methodologies are based on specific assumptions that are reasonable for whole-genome arrays, but possibly unsuitable for small microRNA (miRNA) platforms. In this work, we propose a novel normalization (loessM), and we investigate, through simulated and real datasets, the influence that normalizations for two-colour miRNA arrays have on the identification of differentially expressed genes.

Results: We show that normalizations usually applied to large-scale arrays, in several cases, modify the actual structure of miRNA data, leading to large portions of false positives and false negatives. Nevertheless, loessM is able to outperform other techniques in most experimental scenarios. Moreover, when usual assumptions on differential expression distribution are missed, channel effect has a strikingly negative influence on small arrays, bias that cannot be removed by normalizations but rather by an appropriate experimental design. We find that the combination of loessM with eCADS, an experimental design based on biological replicates dye-swap recently proposed for channel-effect reduction, gives better results in most of the experimental conditions in terms of specificity/sensitivity both on simulated and real data.

Availability: LoessM R function is freely available at http://gefu.cribi.unipd.it/papers/miRNA-simulation/

Contact: chiara.romualdi@unipd.it

Supplementary information: Supplementary data are available at Bioinformatics online.

Moderated effect size and P-value combinations for microarray meta-analyses

Marot, G., Foulley, J.-L., Mayer, C.-D., Jaffrezic, F. — Thu, 08 Oct 2009 22:47:51 PDT

Motivation: With the proliferation of microarray experiments and their availability in the public domain, the use of meta-analysis methods to combine results from different studies increases. In microarray experiments, where the sample size is often limited, meta-analysis offers the possibility to considerably increase the statistical power and give more accurate results.

Results: A moderated effect size combination method was proposed and compared with other meta-analysis approaches. All methods were applied to real publicly available datasets on prostate cancer, and were compared in an extensive simulation study for various amounts of inter-study variability. Although the proposed moderated effect size combination improved already existing effect size approaches, the P-value combination was found to provide a better sensitivity and a better gene ranking than the other meta-analysis methods, while effect size methods were more conservative.

Availability: An R package metaMA is available on the CRAN.

Contact: guillemette.marot@jouy.inra.fr

Gene ranking and biomarker discovery under correlation

Zuber, V., Strimmer, K. — Thu, 08 Oct 2009 22:47:51 PDT

Motivation: Biomarker discovery and gene ranking is a standard task in genomic high-throughput analysis. Typically, the ordering of markers is based on a stabilized variant of the t-score, such as the moderated t or the SAM statistic. However, these procedures ignore gene–gene correlations, which may have a profound impact on the gene orderings and on the power of the subsequent tests.

Results: We propose a simple procedure that adjusts gene-wise t-statistics to take account of correlations among genes. The resulting correlation-adjusted t-scores (‘cat’ scores) are derived from a predictive perspective, i.e. as a score for variable selection to discriminate group membership in two-class linear discriminant analysis. In the absence of correlation the cat score reduces to the standard t-score. Moreover, using the cat score it is straightforward to evaluate groups of features (i.e. gene sets). For computation of the cat score from small sample data, we propose a shrinkage procedure. In a comparative study comprising six different synthetic and empirical correlation structures, we show that the cat score improves estimation of gene orderings and leads to higher power for fixed true discovery rate, and vice versa. Finally, we also illustrate the cat score by analyzing metabolomic data.

Availability: The shrinkage cat score is implemented in the R package ‘st’, which is freely available under the terms of the GNU General Public License (version 3 or later) from CRAN (http://cran.r-project.org/web/packages/st/).

Contact: strimmer@uni-leipzig.de

Methods for labeling error detection in microarrays based on the effect of data perturbation on the regression model

Zhang, C., Wu, C., Blanzieri, E., Zhou, Y., Wang, Y., Du, W., Liang, Y. — Thu, 08 Oct 2009 22:47:51 PDT

Motivation: Mislabeled samples often appear in gene expression profile because of the similarity of different sub-type of disease and the subjective misdiagnosis. The mislabeled samples deteriorate supervised learning procedures. The LOOE-sensitivity algorithm is an approach for mislabeled sample detection for microarray based on data perturbation. However, the failure of measuring the perturbing effect makes the LOOE-sensitivity algorithm a poor performance. The purpose of this article is to design a novel detection method for mislabeled samples of microarray, which could take advantage of the measuring effect of data perturbations.

Results: To measure the effect of data perturbation, we define an index named perturbing influence value (PIV), based on the support vector machine (SVM) regression model. The Column Algorithm (CAPIV), Row Algorithm (RAPIV) and progressive Row Algorithm (PRAPIV) based on the PIV value are proposed to detect the mislabeled samples. Experimental results obtained by using six artificial datasets and five microarray datasets demonstrate that all proposed methods in this article are superior to LOOE-sensitivity. Moreover, compared with the simple SVM and CL-stability, the PRAPIV algorithm shows an increase in precision and high recall.

Availability: The program and source code (in JAVA) are publicly available at http://ccst.jlu.edu.cn/CSBG/PIVS/index.htm

Contact: blanzier@dit.unitn.it; ycliang@jlu.edu.cn

info-gibbs: a motif discovery algorithm that directly optimizes information content during sampling

Defrance, M., van Helden, J. — Thu, 08 Oct 2009 22:47:51 PDT

Motivation: Discovering cis-regulatory elements in genome sequence remains a challenging issue. Several methods rely on the optimization of some target scoring function. The information content (IC) or relative entropy of the motif has proven to be a good estimator of transcription factor DNA binding affinity. However, these information-based metrics are usually used as a posteriori statistics rather than during the motif search process itself.

Results: We introduce here info-gibbs, a Gibbs sampling algorithm that efficiently optimizes the IC or the log-likelihood ratio (LLR) of the motif while keeping computation time low. The method compares well with existing methods like MEME, BioProspector, Gibbs or GAME on both synthetic and biological datasets. Our study shows that motif discovery techniques can be enhanced by directly focusing the search on the motif IC or the motif LLR.

Availability: http://rsat.ulb.ac.be/rsat/info-gibbs

Contact: defrance@bigre.ulb.ac.be

Supplementary information: Supplementary data are available at Bioinformatics online.

An optimization model for metabolic pathways

Planes, F. J., Beasley, J. E. — Thu, 08 Oct 2009 22:47:51 PDT

Motivation: Different mathematical methods have emerged in the post-genomic era to determine metabolic pathways. These methods can be divided into stoichiometric methods and path finding methods. In this paper we detail a novel optimization model, based upon integer linear programming, to determine metabolic pathways. Our model links reaction stoichiometry with path finding in a single approach. We test the ability of our model to determine 40 annotated Escherichia coli metabolic pathways. We show that our model is able to determine 36 of these 40 pathways in a computationally effective manner.

Contact: john.beasley@brunel.ac.uk

Supplementary information: Supplementary data are available at Bioinformatics online.

The Integrated Genome Browser: free software for distribution and exploration of genome-scale datasets

Nicol, J. W., Helt, G. A., Blanchard, S. G., Raja, A., Loraine, A. E. — Thu, 08 Oct 2009 22:47:51 PDT

Summary: Experimental techniques that survey an entire genome demand flexible, highly interactive visualization tools that can display new data alongside foundation datasets, such as reference gene annotations. The Integrated Genome Browser (IGB) aims to meet this need. IGB is an open source, desktop graphical display tool implemented in Java that supports real-time zooming and panning through a genome; layout of genomic features and datasets in moveable, adjustable tiers; incremental or genome-scale data loading from remote web servers or local files; and dynamic manipulation of quantitative data via genome graphs.

Availability: The application and source code are available from http://igb.bioviz.org and http://genoviz.sourceforge.net.

Contact: aloraine@uncc.edu

SnoopCGH: software for visualizing comparative genomic hybridization data

Thu, 08 Oct 2009 22:47:51 PDT

Summary: Array-based comparative genomic hybridization (CGH) technology is used to discover and validate genomic structural variation, including copy number variants, insertions, deletions and other structural variants (SVs). The visualization and summarization of the array CGH data outputs, potentially across many samples, is an important process in the identification and analysis of SVs. We have developed a software tool for SV analysis using data from array CGH technologies, which is also amenable to short-read sequence data.

Availability and implementation: SnoopCGH is written in java and is available from http://snoopcgh.sourceforge.net/

Contact: jg10@sanger.ac.uk; tc5@sanger.ac.uk

YADA: a tool for taking the most out of high-resolution spectra

Carvalho, P. C., Xu, T., Han, X., Cociorva, D., Barbosa, V. C., Yates, J. R. — Thu, 08 Oct 2009 22:47:51 PDT

Summary: YADA can deisotope and decharge high-resolution mass spectra from large peptide molecules, link the precursor monoisotopic peak information to the corresponding tandem mass spectrum, and account for different co-fragmenting ion species (multiplexed spectra). We describe how YADA enables a pipeline consisting of ProLuCID and DTASelect for analyzing large-scale middle-down proteomics data.

Availability: http://fields.scripps.edu/yada

Contact: paulo@pcarvalho.com

Supplementary information: Supplementary data are available at Bioinformatics online.

ShotgunFunctionalizeR: an R-package for functional comparison of metagenomes

Kristiansson, E., Hugenholtz, P., Dalevi, D. — Thu, 08 Oct 2009 22:47:51 PDT

Summary: Microorganisms are ubiquitous in nature and constitute intrinsic parts of almost every ecosystem. A culture-independent and powerful way to study microbial communities is metagenomics. In such studies, functional analysis is performed on fragmented genetic material from multiple species in the community. The recent advances in high-throughput sequencing have greatly increased the amount of data in metagenomic projects. At present, there is an urgent need for efficient statistical tools to analyse these data. We have created ShotgunFunctionalizeR, an R-package for functional comparison of metagenomes. The package contains tools for importing, annotating and visualizing metagenomic data produced by shotgun high-throughput sequencing. ShotgunFunctionalizeR contains several statistical procedures for assessing functional differences between samples, both for individual genes and for entire pathways. In addition to standard and previously published methods, we have developed and implemented a novel approach based on a Poisson model. This procedure is highly flexible and thus applicable to a wide range of different experimental designs. We demonstrate the potential of ShotgunFunctionalizeR by performing a regression analysis on metagenomes sampled at multiple depths in the Pacific Ocean.

Availability: http://shotgun.zool.gu.se

Contact: dalevi@chalmers.se; erik.kristiansson@zool.gu.se

Supplementary information: supplementary data are available at Bioinformatics online.

Retrieve-ensembl-seq: user-friendly and large-scale retrieval of single or multi-genome sequences from Ensembl

Sand, O., Thomas-Chollier, M., van Helden, J. — Thu, 08 Oct 2009 22:47:51 PDT

Summary: The preparation of an appropriate sequence dataset is the starting point of all genomic analyses. We present retrieve-ensembl-seq, an application that considerably eases the retrieval of sequences from the Ensembl database, via our user-friendly web site or web services. The user provides Ensembl identifiers or gene names, and the program returns corresponding upstream, downstream, intronic, exonic, UTR or whole gene sequences. retrieve-ensembl-seq also offers a multiple organism mode to retrieve sequences from homologous genes at any taxonomical level. And we introduce various original filters such as the masking of coding fragments and the avoidance of sequence redundancy for genes with multiple transcripts. retrieve-ensembl-seq is included in the software suite regulatory sequence analysis tools (RSAT), allowing instant submission of retrieved sequences to further analysis tools.

Availability: retrieve-ensembl-seq is integrated in the RSAT suite: http://rsat.ulb.ac.be/rsat. Web site: http://rsat.ulb.ac.be/rsat/retrieve-ensembl-seq_form.cgi. Web services: http://rsat.ulb.ac.be/rsat/web_services/RSATWS.wsdl. Stand-alone distribution: freely available under an academic licence to download from the RSAT web site. The complete manual, a convenient tutorial and demos are available from the RSAT website. Additional help can be found on the RSAT public forum.

Contact: oly@bigre.ulb.ac.be

massXpert 2: a cross-platform software environment for polymer chemistry modelling and simulation/analysis of mass spectrometric data

Rusconi, F. — Thu, 08 Oct 2009 22:47:51 PDT

Summary: Since the middle of the 90s, mass spectrometry has evolved into an almost indispensable tool in structural studies on an ever-growing variety of (bio-)polymers, of which proteins, sugars and nucleic acids are the most prominent. Since the first public release of massXpert, the advances of mass spectrometry have motivated continuous and thorough maintenance of that software, in the form of two full software rewrites, culminating with massXpert 2, which we describe in this report. We shall describe the profound changes in massXpert that were performed so as to keep up with the technical advances in mass spectrometry since a decade.

Availability: The massXpert 2 software is an open source and free software project hosted at http://www.massxpert.org.

Contact: rusconi@mnhn.fr

Supplementary information: Supplementary data are available at Bioinformatics online.

PiSQRD: a web server for decomposing proteins into quasi-rigid dynamical domains

Aleksiev, T., Potestio, R., Pontiggia, F., Cozzini, S., Micheletti, C. — Thu, 08 Oct 2009 22:47:51 PDT

Summary: The PiSQRD web resource can be used to subdivide protein structures in quasi-rigid dynamical domains. The latter are groups of amino acids behaving as approximately rigid units in the course of protein equilibrium fluctuations. The PiSQRD server takes as input a biomolecular structure and the desired fraction of protein internal fluctuations that must be accounted for by the relative rigid-body motion of the dynamical domains. Next, the lowest energy modes of fluctuation of the protein (optionally provided by the user) are calculated and used to identify the rigid subunits. The resulting optimal subdivision is returned through a web page containing both interactive graphics and detailed data output.

Availability: The PiSQRD web server, which requires Java, is available free of charge for academic users at http://pisqrd.escience-lab.org.

Contact: potestio@sissa.it

ANCHOR: web server for predicting protein binding regions in disordered proteins

Dosztanyi, Z., Meszaros, B., Simon, I. — Thu, 08 Oct 2009 22:47:51 PDT

Summary: ANCHOR is a web-based implementation of an original method that takes a single amino acid sequence as an input and predicts protein binding regions that are disordered in isolation but can undergo disorder-to-order transition upon binding. The server incorporates the result of a general disorder prediction method, IUPred and can carry out simple motif searches as well.

Availability: The web server is available at http://anchor.enzim.hu. The program package is freely available for academic users.

Contact: zsuzsa@enzim.hu

PopABC: a program to infer historical demographic parameters

Lopes, J. S., Balding, D., Beaumont, M. A. — Thu, 08 Oct 2009 22:47:51 PDT

Summary: PopABC is a computer package for inferring the pattern of demographic divergence of closely related populations and species. The software performs coalescent simulation in the framework of approximate Bayesian computation (ABC). PopABC can also be used to perform Bayesian model choice to discriminate between different demographic scenarios. The program can be used either for research or for education and teaching purposes.

Availability and Implementation: Source code and binaries are freely available at http://www.reading.ac.uk/~sar05sal/software.htm. The program was implemented in C and can run on UNIX, MacOSX and Windows operating systems.

Contact: joao.lopes@reading.ac.uk

Supplementary information: Supplementary data are available at Bioinformatics online.

GRIMP: a web- and grid-based tool for high-speed analysis of large-scale genome-wide association using imputed data

Thu, 08 Oct 2009 22:47:51 PDT

Summary: The current fast growth of genome-wide association studies (GWAS) combined with now common computationally expensive imputation requires the online access of large user groups to high-performance computing resources capable of analyzing rapidly and efficiently millions of genetic markers for ten thousands of individuals. Here, we present a web-based interface—called GRIMP—to run publicly available genetic software for extremely large GWAS on scalable super-computing grid infrastructures. This is of major importance for the enlargement of GWAS with the availability of whole-genome sequence data from the 1000 Genomes Project and for future whole-population efforts.

Contact: ta.knoch@taknoch.org; f.rivadeneira@erasmusmc.nl

CoCoa: a software tool for estimating the coefficient of coancestry from multilocus genotype data

Maenhout, S., De Baets, B., Haesaert, G. — Thu, 08 Oct 2009 22:47:52 PDT

Motivation: Phenotypic data collected in breeding programs and marker-trait association studies are often analyzed by means of linear mixed models. In these models, the covariance between the genetic background effects of all genotypes under study is modeled by means of pairwise coefficients of coancestry. Several marker-based coancestry estimation procedures allow to estimate this covariance matrix, but generally introduce a certain amount of bias when the examined genotypes are part of a breeding program. CoCoa implements the most commonly used marker-based coancestry estimation procedures and as such, allows to select the best fitting covariance structure for the phenotypic data at hand. This better model fit translates into an increased power and improved type I error control in association studies and an improved accuracy in phenotypic prediction studies. The presented software package also provides an implementation of the new Weighted Alikeness in State (WAIS) estimator for use in hybrid breeding programs. Besides several matrix manipulation tools, CoCoa implements two different bending heuristics, in case the inverse of an ill-conditioned coancestry matrix estimate is needed.

Availability and Implementation: The software package CoCoa is freely available at http://webs.hogent.be/cocoa. Source code, manual, binaries for 32 and 64-bit Linux systems and an installer for Microsoft Windows are provided. The core components of CoCoa are written in C++, while the graphical user interface is written in Java.

Contact: steven.maenhout@hogent.be

FBA-SimVis: interactive visualization of constraint-based metabolic models

Grafahrend-Belau, E., Klukas, C., Junker, B. H., Schreiber, F. — Thu, 08 Oct 2009 22:47:52 PDT

Summary: FBA-SimVis is a VANTED plug-in for the constraint-based analysis of metabolic models with special focus on the visual exploration of metabolic flux data resulting from model analysis. The program provides a user-friendly environment for model reconstruction, constraint-based model analysis, and interactive visualization of the simulation results. With the ability to quantitatively analyse metabolic fluxes in an interactive and visual manner, FBA-SimVis supports a comprehensive understanding of constraint-based metabolic flux models in both overview and detail.

Availability: Software with manual and tutorials are freely available at http://fbasimvis.ipk-gatersleben.de/

Contact: grafahr@ipk-gatersleben.de

Supplementary information: Examples and supplementary data are available at http://fbasimvis.ipk-gatersleben.de/

FRED--a framework for T-cell epitope detection

Feldhahn, M., Donnes, P., Thiel, P., Kohlbacher, O. — Thu, 08 Oct 2009 22:47:52 PDT

Summary: Over the last decade, immunoinformatics has made significant progress. Computational approaches, in particular the prediction of T-cell epitopes using machine learning methods, are at the core of modern vaccine design. Large-scale analyses and the integration or comparison of different methods become increasingly important. We have developed FRED, an extendable, open source software framework for key tasks in immunoinformatics. In this, its first version, FRED offers easily accessible prediction methods for MHC binding and antigen processing as well as general infrastructure for the handling of antigen sequence data and epitopes. FRED is implemented in Python in a modular way and allows the integration of external methods.

Availability: FRED is freely available for download at http://www-bs.informatik.uni-tuebingen.de/Software/FRED.

Contact: feldhahn@informatik.uni-tuebingen.de

Caleydo: connecting pathways and gene expression

Streit, M., Lex, A., Kalkusch, M., Zatloukal, K., Schmalstieg, D. — Thu, 08 Oct 2009 22:47:52 PDT

Summary: Understanding the relationships between pathways and the altered expression of their components in disease conditions can be addressed in a visual data analysis process. Caleydo uses novel visualization techniques to support life science experts in their analysis of gene expression data in the context of pathways and functions of individual genes. Pathways and gene expression visualizations are placed in a 3D scene where selected entities (i.e. genes) are visually connected. This allows Caleydo to seamlessly integrate interactive gene expression visualization with cross-database pathway exploration.

Availability: The Caleydo visualization framework is freely available on www.caleydo.org for non-commercial use. It runs on Windows and Linux and requires a 3D capable graphics card.

Contact: caleydo@icg.tugraz.at; streit@icg.tugraz.at

Supplementary information: Supplementary data are available at Bioinformatics online.

The SNP ratio test: pathway analysis of genome-wide association datasets

Thu, 08 Oct 2009 22:47:52 PDT

Summary: We present a tool that assesses the enrichment of significant associations from genome-wide association studies (GWAS) in a pathway context. The SNP ratio test (SRT) compares the proportion of significant to all SNPs within genes that are part of a pathway and computes an empirical P-value based on comparisons to ratios in datasets where the assignment of case/control status has been randomized. We applied the SRT to a Parkinson's disease GWAS dataset, using the KEGG database, revealing significance for Parkinson's disease and related pathways.

Availability: https://sourceforge.net/projects/snpratiotest/

Contact: codushlaine@gmail.com; colm.odushlaine@tcd.ie

Supplementary information: Supplementary data are available at Bioinformatics online.

Expertomica metabolite profiling: getting more information from LC-MS using the stochastic systems approach

Urban, J., Vanek, J., Soukup, J., Stys, D. — Thu, 08 Oct 2009 22:47:52 PDT

Mass spectrometers are sophisticated, fine instruments which are essential in a variety applications. However, the data they produce are usually interpreted in a rather primitive way, without considering the accuracy of this data and the potential errors in identifying peaks. Our new approach corrects this situation by dividing the LC-MS output into three components: (i) signature of the analyte, (ii) random noise and (iii) systemic noise. The systemic noise is related to the instrument and to the particular experiment; its characteristics change in time and depend on the analyzed substance. Working with these components allows us to quantify the probability of peak errors and, at the same time, to retrieve some peaks which get lost in the noise when using the existing methods. Our software tool, Expertomica metabolite profiling, automatically evaluates the given instrument, detects compounds and calculates the probability of individual peaks. It does not need any artificial user-defined parameters or thresholds.

Availability: MATLAB scripts with a simple graphical user interface are free to download from http://sourceforge.net/projects/expertomica-eda/. The software reads data exported by most Thermo and Agilent spectrometers, and it can also read the more general JCAMP-DX ASCII format. Other formats will be supported on request, assuming that the user can provide representative data samples.

Contact: urban@greentech.cz

A System for Information Management in BioMedical Studies--SIMBioMS

Thu, 08 Oct 2009 22:47:52 PDT

Summary: SIMBioMS is a web-based open source software system for managing data and information in biomedical studies. It provides a solution for the collection, storage, management and retrieval of information about research subjects and biomedical samples, as well as experimental data obtained using a range of high-throughput technologies, including gene expression, genotyping, proteomics and metabonomics. The system can easily be customized and has proven to be successful in several large-scale multi-site collaborative projects. It is compatible with emerging functional genomics data standards and provides data import and export in accepted standard formats. Protocols for transferring data to durable archives at the European Bioinformatics Institute have been implemented.

Availability: The source code, documentation and initialization scripts are available at http://simbioms.org.

Contact: support@simbioms.org; mariak@ebi.ac.uk

Comment on 'MeSH-up: effective MeSH text classification for improved document retrieval'

Neveol, A., Mork, J. G., Aronson, A. R. — Thu, 08 Oct 2009 22:47:52 PDT

Contact: neveola@ncbi.nlm.nih.gov

Supplementary information: Supplementary data are available at Bioinformatics online.

Response to comment on 'MeSH-up: effective MeSH text classification for improved document retrieval'

Thu, 08 Oct 2009 22:47:52 PDT

Contact: trieschn@ewi.utwente.nl; dolf@trieschnigg.nl

As developers and primary users of MTI and MetaMap, Névéol et al. made a number of interesting comments on our recent publication in Bioinformatics. However, some of the results and conclusions found in the reply seem premature and lack proper clarification.

Corrigendum for Elliott,B. et al., 'PathCase pathways database system', Bioinformatics, Nov. 2008, 24(21), pp. 2526-2533

Thu, 08 Oct 2009 22:47:52 PDT

Contact: tekin@case.edu