Bioinformatics - current issue

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