A multivariate test of association

Summary: Although genetic association studies often test multiple,related phenotypes, few formal multivariate tests of associationare available. We describe a test of association that can beefficiently applied to large population-based designs. Availability: A C++ implementation can be obtained from theauthors. Contact: manuel.ferreira{at}qimr.edu.au Supplementary information: Supplementary figures are availableat Bioinformatics online. Associate Editor: Alex Bateman     

Align human interactome with phenome to identify causative genes and networks underlying disease families

Motivation: Understanding the complexity in gene–phenotyperelationship is vital for revealing the genetic basis of commondiseases. Recent studies on the basis of human interactome andphenome not only uncovers prevalent phenotypic overlap and geneticoverlap between diseases, but also reveals a modular organizationof the genetic landscape of human diseases, providing new opportunitiesto reduce the complexity in dissecting the gene–phenotypeassociation. Results: We provide systematic and quantitative evidence thatphenotypic overlap implies genetic overlap. With these results,we perform the first heterogeneous alignment of human interactomeand phenome via a network alignment technique and identify 39disease families with corresponding causative gene networks.Finally, we propose AlignPI, an alignment-based framework topredict disease genes, and identify plausible candidates for70 diseases. Our method scales well to the whole genome, asdemonstrated by prioritizing 6154 genes across 37 chromosomeregions for Crohn's disease (CD). Results are consistent witha recent meta-analysis of genome-wide association studies forCD. Availability: Bi-modules and disease gene predictions are freelyavailable at the URL http://bioinfo.au.tsinghua.edu.cn/alignpi/ Contact: ruijiang{at}tsinghua.edu.cn Supplementary information: Supplementary data are availableat Bioinformatics online. Associate Editor: Trey Ideker     

Genetic association analysis with FAMHAP: a major program update

Summary: FAMHAP is an established software for haplotype associationanalysis of nuclear families. We have released a major updatethat comprises various new features for case-control data. Furthermore,weprovide an additional program runFamhap that allows usersto start the same method repeatedly for varying sets of geneticmarkers. In addition, a platform-independent graphical userinterface (GUI) was developed to simplify the usage of bothFAMHAP and runFamhap. The runFamhap program greatly facilitatesthe application of FAMHAP to genome-wide association studies(GWAS) and supports flexible genome-wide haplotype analysis.As an example, we describe application to HapMap data. Availability: The software is available at http://famhap.meb.uni-bonn.de Contact: herold{at}imbie.meb.uni-bonn.de; becker{at}imbie.meb.uni-bonn.de Supplementary information: Supplementary data are availableat Bioinformatics online. Associate Editor: Alex Bateman     

Model-based analysis of non-specific binding for background correction of high-density oligonucleotide microarrays

Motivation: High-density DNA microarrays provide us with usefultools for analyzing DNA and RNA comprehensively. However, thebackground signal caused by the non-specific binding (NSB) betweenprobe and target makes it difficult to obtain accurate measurements.To remove the background signal, there is a set of backgroundprobes on Affymetrix Exon arrays to represent the amount ofnon-specific signals, and an accurate estimation of non-specificsignals using these background probes is desirable for improvementof microarray analyses. Results: We developed a thermodynamic model of NSB on shortnucleotide microarrays in which the NSBs are modeled by duplexformation of probes and multiple hypothetical targets. We fittedthe observed signal intensities of the background probes withthose expected by the model to obtain the model parameters.As a result, we found that the presented model can improve theaccuracy of prediction of non-specific signals in comparisonwith previously proposed methods. This result will provide auseful method to correct for the background signal in oligonucleotidemicroarray analysis. Availability: The software is implemented in the R languageand can be downloaded from our website (http://www-shimizu.ist.osaka-u.ac.jp/shimizu_lab/MSNS/). Contact: furusawa{at}ist.osaka-u.ac.jp Supplementary information: Supplementary data are availableat Bioinformatics online. The authors wish it to be known that, in their opinion, thefirst two authors should be regarded as joint First Authors. Associate Editor: Trey Ideker     

GENOME: a rapid coalescent-based whole genome simulator

Summary: GENOME proposes a rapid coalescent-based approach tosimulate whole genome data. In addition to features of standardcoalescent simulators, the program allows for recombinationrates to vary along the genome and for flexible population histories.Within small regions, we have evaluated samples simulated byGENOME to verify that GENOME provides the expected LD patternsand frequency spectra. The program can be used to study thesampling properties of any statistic for a whole genome study. Availability: The program and C++ source code are availableonline at http://www.sph.umich.edu/csg/liang/genome/ Contact: lianglim{at}umich.edu Supplementary information: Supplementary data are availableat Bioinformatics online. Associate Editor: Martin Bishop     

Model-based deconvolution of genome-wide DNA binding

Motivation: Chromatin immunoprecipitation followed by hybridizationto a genomic tiling microarray (ChIP-chip) is a routinely usedprotocol for localizing the genomic targets of DNA-binding proteins.The resolution to which binding sites in this assay can be identifiedis commonly considered to be limited by two factors: (1) theresolution at which the genomic targets are tiled in the microarrayand (2) the large and variable lengths of the immunoprecipitatedDNA fragments. Results: We have developed a generative model of binding sitesin ChIP-chip data and an approach, MeDiChI, for efficientlyand robustly learning that model from diverse data sets. Wehave evaluated MeDiChI's performance using simulated data, aswell as on several diverse ChIP-chip data sets collected onwidely different tiling array platforms for two different organisms(Saccharomyces cerevisiae and Halobacterium salinarium NRC-1).We find that MeDiChI accurately predicts binding locations toa resolution greater than that of the probe spacing, even foroverlapping peaks, and can increase the effective resolutionof tiling array data by a factor of 5x or better. Moreover,the method's performance on simulated data provides insightsinto effectively optimizing the experimental design for increasedbinding site localization accuracy and efficacy. Availability: MeDiChI is available as an open-source R package,including all data, from http://baliga.systemsbiology.net/medichi. Contact: dreiss{at}systemsbiology.org Supplementary information: Supplementary data are availableat Bioinformatics online. Associate Editor: Martin Bishop     

A new algorithm for cluster analysis of genomic methylation: the Helicobacter pylori case

Motivation: The genomic methylation analysis is useful to typebacteria that have a high number of expressed type II methyltransferases.Methyltransferases are usually committed to Restriction andModification (R-M) systems, in which the restriction endonucleaseimposes high pressure on the expression of the cognate methyltransferasethat hinder R-M system loss. Conventional cluster methods donot reflect this tendency. An algorithm was developed for dendrogramconstruction reflecting the propensity for conservation of R-MType II systems. Results: The new algorithm was applied to 52 Helicobacter pyloristrains from different geographical regions and compared withconventional clustering methods. The algorithm works by firstgrouping strains that share a common minimum set of R-M systemsand gradually adds strains according to the number of the R-Msystems acquired. Dendrograms revealed a cluster of Africanstrains, which suggest that R-M systems are present in H.pylorigenome since its human host migrates from Africa. Availability: The software files are available at http://www.ff.ul.pt/paginas/jvitor/Bioinformatics/MCRM_algorithm.zip Contact: filipavale{at}fe.ucp.pt Supplementary information: Supplementary data are availableat Bioinformatics online. Associate Editor: Martin Bishop     

Unequal group variances in microarray data analyses

Motivation: In searching for differentially expressed (DE) genesin microarray data, we often observe a fraction of the genesto have unequal variability between groups. This is not an issuein large samples, where a valid test exists that uses individualvariances separately. The problem arises in the small-samplesetting, where the approximately valid Welch test lacks sensitivity,while the more sensitive moderated t-test assumes equal variance. Methods: We introduce a moderated Welch test (MWT) that allowsunequal variance between groups. It is based on (i) weightingof pooled and unpooled standard errors and (ii) improved estimationof the gene-level variance that exploits the information fromacross the genes. Results: When a non-trivial proportion of genes has unequalvariability, false discovery rate (FDR) estimates based on thestandard t and moderated t-tests are often too optimistic, whilethe standard Welch test has low sensitivity. The MWT is shownto (i) perform better than the standard t, the standard Welchand the moderated t-tests when the variances are unequal betweengroups and (ii) perform similarly to the moderated t, and betterthan the standard t and Welch tests when the group variancesare equal. These results mean that MWT is more reliable thanother existing tests over wider range of data conditions. Availability: R package to perform MWT is available at http://www.meb.ki.se/~yudpaw Contact: yudi.pawitan{at}ki.se Supplementary information: Supplementary data are availableat Bioinformatics online. Associate Editor: Martin Bishop     

CRONOS: the cross-reference navigation server

Summary: Cross-mapping of gene and protein identifiers betweendifferent databases is a tedious and time-consuming task. Toovercome this, we developed CRONOS, a cross-reference serverthat contains entries from five mammalian organisms presentedby major gene and protein information resources. Sequence similarityanalysis of the mapped entries shows that the cross-referencesare highly accurate. In total, up to 18 different identifiertypes can be used for identification of cross-references. Thequality of the mapping could be improved substantially by exclusionof ambiguous gene and protein names which were manually validated.Organism-specific lists of ambiguous terms, which are valuablefor a variety of bioinformatics applications like text miningare available for download. Availability: CRONOS is freely available to non-commercial usersat http://mips.gsf.de/genre/proj/cronos/index.html, web servicesare available at http://mips.gsf.de/CronosWSService/CronosWS?wsdl. Contact: brigitte.waegele{at}helmholtz-muenchen.de Supplementary information: Supplementary data are availableat Bioinformatics online. The online Supplementary Materialcontains all figures and tables referenced by this article. Associate Editor: Martin Bishop     

3D-Garden: a system for modelling protein-protein complexes based on conformational refinement of ensembles generated with the marching cubes algorithm

Motivation: Reliable structural modelling of protein–proteincomplexes has widespread application, from drug design to advancingour knowledge of protein interactions and function. This workaddresses three important issues in protein–protein docking:implementing backbone flexibility, incorporating prior indicationsfrom experiment and bioinformatics, and providing public accessvia a server. 3D-Garden (Global And Restrained Docking ExplorationNexus), our benchmarked and server-ready flexible docking system,allows sophisticated programming of surface patches by the uservia a facet representation of the interactors’ molecularsurfaces (generated with the marching cubes algorithm). Flexibilityis implemented as a weighted exhaustive conformer search foreach clashing pair of molecular branches in a set of 5000 modelsfiltered from around 340 000 initially. Results: In a non-global assessment, carried out strictly accordingto the protocols for number of models considered and model qualityof the Critical Assessment of Protein Interactions (CAPRI) experiment,over the widely-used Benchmark 2.0 of 84 complexes, 3D-Gardenidentifies a set of ten models containing an acceptable or bettermodel in 29/45 test cases, including one with large conformationalchange. In 19/45 cases an acceptable or better model is rankedfirst or second out of 340 000 candidates. Availability: http://www.sbg.bio.ic.ac.uk/3dgarden (server) Contact: v.lesk{at}ic.ac.uk Supplementary information: Supplementary data are availableat Bioinformatics online. Associate Editor: Burkhard Rost     

Combining multiple positive training sets to generate confidence scores for protein-protein interactions

Motivation: High-throughput experimental and computational methodsare generating a wealth of protein–protein interactiondata for a variety of organisms. However, data produced by currentstate-of-the-art methods include many false positives, whichcan hinder the analyses needed to derive biological insights.One way to address this problem is to assign confidence scoresthat reflect the reliability and biological significance ofeach interaction. Most previously described scoring methodsuse a set of likely true positives to train a model to scoreall interactions in a dataset. A single positive training set,however, may be biased and not representative of true interactionspace. Results: We demonstrate a method to score protein interactionsby utilizing multiple independent sets of training positivesto reduce the potential bias inherent in using a single trainingset. We used a set of benchmark yeast protein interactions toshow that our approach outperforms other scoring methods. Ourapproach can also score interactions across data types, whichmakes it more widely applicable than many previously proposedmethods. We applied the method to protein interaction data fromboth Drosophila melanogaster and Homo sapiens. Independent evaluationsshow that the resulting confidence scores accurately reflectthe biological significance of the interactions. Contact: rfinley{at}wayne.edu Supplementary information: Supplementary data are availableat Bioinformatics Online. Associate Editor: Burkhard Rost     

ConFunc--functional annotation in the twilight zone

Motivation: The success of genome sequencing has resulted inmany protein sequences without functional annotation. We presentConFunc, an automated Gene Ontology (GO)-based protein functionprediction approach, which uses conserved residues to generatesequence profiles to infer function. ConFunc split sets of sequencesidentified by PSI-BLAST into sub-alignments according to theirGO annotations. Conserved residues are identified for each GOterm sub-alignment for which a position specific scoring matrixis generated. This combination of steps produces a set of feature(GO annotation) derived profiles from which protein functionis predicted. Results: We assess the ability of ConFunc, BLAST and PSI-BLASTto predict protein function in the twilight zone of sequencesimilarity. ConFunc significantly outperforms BLAST & PSI-BLASTobtaining levels of recall and precision that are not obtainedby either method and maximum precision 24% greater than BLAST.Further for a large test set of sequences with homologues oflow sequence identity, at high levels of presicision, ConFuncobtains recall six times greater than BLAST. These results demonstratethe potential for ConFunc to form part of an automated genomicsannotation pipeline. Availability: http://www.sbg.bio.ic.ac.uk/confunc Contact: m.sternberg{at}imperial.ac.uk Supplementary information: Supplementary data are availableat Bioinformatics online. Associate Editor: Dmitrij Frishman     

Bayesian models based on test statistics for multiple hypothesis testing problems

Motivation: We propose a Bayesian method for the problem ofmultiple hypothesis testing that is routinely encountered inbioinformatics research, such as the differential gene expressionanalysis. Our algorithm is based on modeling the distributionsof test statistics under both null and alternative hypotheses.We substantially reduce the complexity of the process of definingposterior model probabilities by modeling the test statisticsdirectly instead of modeling the full data. Computationally,we apply a Bayesian FDR approach to control the number of rejectionsof null hypotheses. To check if our model assumptions for thetest statistics are valid for various bioinformatics experiments,we also propose a simple graphical model-assessment tool. Results: Using extensive simulations, we demonstrate the performanceof our models and the utility of the model-assessment tool.In the end, we apply the proposed methodology to an siRNA screeningand a gene expression experiment. Contact: yuanji{at}mdanderson.org Supplementary information: Supplementary data are availableat Bioinformatics online. Associate Editor: Chris Stoeckert     

MMG: a probabilistic tool to identify submodules of metabolic pathways

Motivation: A fundamental task in systems biology is the identificationof groups of genes that are involved in the cellular responseto particular signals. At its simplest level, this often reducesto identifying biological quantities (mRNA abundance, enzymeconcentrations, etc.) which are differentially expressed intwo different conditions. Popular approaches involve using t-teststatistics, based on modelling the data as arising from a mixturedistribution. A common assumption of these approaches is thatthe data are independent and identically distributed; however,biological quantities are usually related through a complex(weighted) network of interactions, and often the more pertinentquestion is which subnetworks are differentially expressed,rather than which genes. Furthermore, in many interesting cases(such as high-throughput proteomics and metabolomics), onlyvery partial observations are available, resulting in the needfor efficient imputation techniques. Results: We introduce Mixture Model on Graphs (MMG), a novelprobabilistic model to identify differentially expressed submodulesof biological networks and pathways. The method can easily incorporateinformation about weights in the network, is robust againstmissing data and can be easily generalized to directed networks.We propose an efficient sampling strategy to infer posteriorprobabilities of differential expression, as well as posteriorprobabilities over the model parameters. We assess our methodon artificial data demonstrating significant improvements overstandard mixture model clustering. Analysis of our model resultson quantitative high-throughput proteomic data leads to theidentification of biologically significant subnetworks, as wellas the prediction of the expression level of a number of enzymes,some of which are then verified experimentally. Availability: MATLAB code is available from http://www.dcs.shef.ac.uk/~guido/software.html Contact: guido{at}dcs.shef.ac.uk Supplementary information: Supplementary data are availableat Bioinformatics online. Associate Editor: Jonathan Wren     

From pull-down data to protein interaction networks and complexes with biological relevance

Motivation: Recent improvements in high-throughput Mass Spectrometry(MS) technology have expedited genome-wide discovery of protein–proteininteractions by providing a capability of detecting proteincomplexes in a physiological setting. Computational inferenceof protein interaction networks and protein complexes from MSdata are challenging. Advances are required in developing robustand seamlessly integrated procedures for assessment of protein–proteininteraction affinities, mathematical representation of proteininteraction networks, discovery of protein complexes and evaluationof their biological relevance. Results: A multi-step but easy-to-follow framework for identifyingprotein complexes from MS pull-down data is introduced. It assessesinteraction affinity between two proteins based on similarityof their co-purification patterns derived from MS data. It constructsa protein interaction network by adopting a knowledge-guidedthreshold selection method. Based on the network, it identifiesprotein complexes and infers their core components using a graph-theoreticalapproach. It deploys a statistical evaluation procedure to assessbiological relevance of each found complex. On Saccharomycescerevisiae pull-down data, the framework outperformed othermore complicated schemes by at least 10% in F₁-measure and identified610 protein complexes with high-functional homogeneity basedon the enrichment in Gene Ontology (GO) annotation. Manual examinationof the complexes brought forward the hypotheses on cause offalse identifications. Namely, co-purification of differentprotein complexes as mediated by a common non-protein molecule,such as DNA, might be a source of false positives. Protein identificationbias in pull-down technology, such as the hydrophilic bias couldresult in false negatives. Contact: samatovan{at}ornl.gov Supplementary information: Supplementary data are availableat Bioinformatics online. Associate Editor: Jonathan Wren Present address: Department of Biomedical Informatics, VanderbiltUniversity, Nashville, TN 37232. The authors wish it to be known that, in their opinion, thefirst two authors should be regarded as joint First Authors.