MOTIVATION: Integration of different omics data could markedly help to identify biological signatures, understand the missing heritability of complex diseases and ultimately achieve personalized medicine. Standard regression models used in Genome-Wide Association Studies (GWAS) identify loci with a strong effect size, whereas GWAS meta-analyses are often needed to capture weak loci contributing to the missing heritability. Development of novel machine learning algorithms for merging genotype data with other omics data is highly needed as it could enhance the prioritization of weak loci. RESULTS: We developed cNMTF (corrected non-negative matrix tri-factorization), an integrative algorithm based on clustering techniques of biological data. This method assesses the inter-relatedness between genotypes, phenotypes, the damaging effect of the variants and gene networks in order to identify loci-trait associations. cNMTF was used to prioritize genes associated with lipid traits in two population cohorts. We replicated 129 genes reported in GWAS world-wide and provided evidence that supports 85% of our findings (226 out of 265 genes), including recent associations in literature (NLGN1), regulators of lipid metabolism (DAB1) and pleiotropic genes for lipid traits (CARM1). Moreover, cNMTF performed efficiently against strong population structures by accounting for the individuals' ancestry. As the method is flexible in the incorporation of diverse omics data sources, it can be easily adapted to the user's research needs. AVAILABILITY AND IMPLEMENTATION: An R package (cnmtf) is available at https://lgl15.github.io/cnmtf_web/index.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Motivation: Genome-wide association studies have identified thousands of loci associated with human disease, but identifying the causal genes at these loci is often difficult. Several methods prioritize genes most likely to be disease causing through the integration of biological data, including protein-protein interaction and phenotypic data. Data availability is not the same for all genes however, potentially influencing the performance of these methods. Results: We demonstrate that whilst disease genes tend to be associated with greater numbers of data, this may be at least partially a result of them being better studied. With this observation we develop PhenoRank, which prioritizes disease genes whilst avoiding being biased towards genes with more available data. Bias is avoided by comparing gene scores generated for the query disease against gene scores generated using simulated sets of phenotype terms, which ensures that differences in data availability do not affect the ranking of genes. We demonstrate that whilst existing prioritization methods are biased by data availability, PhenoRank is not similarly biased. Avoiding this bias allows PhenoRank to effectively prioritize genes with fewer available data and improves its overall performance. PhenoRank outperforms three available prioritization methods in cross-validation (PhenoRank area under receiver operating characteristic curve [AUC]=0.89, DADA AUC = 0.87, EXOMISER AUC = 0.71, PRINCE AUC = 0.83, P < 2.2 × 10-16). Availability and implementation: PhenoRank is freely available for download at https://github.com/alexjcornish/PhenoRank. Supplementary information: Supplementary data are available at Bioinformatics online.

BACKGROUND: Despite being hugely important in biological processes, allostery is poorly understood and no universal mechanism has been discovered. Allosteric drugs are a largely unexplored prospect with many potential advantages over orthosteric drugs. Computational methods to predict allosteric sites on proteins are needed to aid the discovery of allosteric drugs, as well as to advance our fundamental understanding of allostery. RESULTS: AlloPred, a novel method to predict allosteric pockets on proteins, was developed. AlloPred uses perturbation of normal modes alongside pocket descriptors in a machine learning approach that ranks the pockets on a protein. AlloPred ranked an allosteric pocket top for 23 out of 40 known allosteric proteins, showing comparable and complementary performance to two existing methods. In 28 of 40 cases an allosteric pocket was ranked first or second. The AlloPred web server, freely available at http://www.sbg.bio.ic.ac.uk/allopred/home, allows visualisation and analysis of predictions. The source code and dataset information are also available from this site. CONCLUSIONS: Perturbation of normal modes can enhance our ability to predict allosteric sites on proteins. Computational methods such as AlloPred assist drug discovery efforts by suggesting sites on proteins for further experimental study.

We have developed RNNI, a global alignment method for protein-protein interaction networks between species, using a random neural network model (RNN) tailored for the alignment problem. The benchmark of the method in comparison with other available alignment approaches was performed using a range of measurements. The alignment results of the human and yeast pair showed that RNNI is capable of generating alignments with large conserved networks with functionally-related protein pairs while maintaining the closeness to the naive- sequence homology approach (BLAST).

MOTIVATION: Analysis of protein-protein interaction networks (PPINs) at the system level has become increasingly important in understanding biological processes. Comparison of the interactomes of different species not only provides a better understanding of species evolution but also helps with detecting conserved functional components and in function prediction. Method and RESULTS: Here we report a PPIN alignment method, called PINALOG, which combines information from protein sequence, function and network topology. Alignment of human and yeast PPINs reveals several conserved subnetworks between them that participate in similar biological processes, notably the proteasome and transcription related processes. PINALOG has been tested for its power in protein complex prediction as well as function prediction. Comparison with PSI-BLAST in predicting protein function in the twilight zone also shows that PINALOG is valuable in predicting protein function. AVAILABILITY AND IMPLEMENTATION: The PINALOG web-server is freely available from http://www.sbg.bio.ic.ac.uk/~pinalog. The PINALOG program and associated data are available from the Download section of the web-server. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: There is a need for automated methods to learn general features of the interactions of a ligand class with its diverse set of protein receptors. An appropriate machine learning approach is Inductive Logic Programming (ILP), which automatically generates comprehensible rules in addition to prediction. The development of ILP systems which can learn rules of the complexity required for studies on protein structure remains a challenge. In this work we use a new ILP system, ProGolem, and demonstrate its performance on learning features of hexose-protein interactions. RESULTS: The rules induced by ProGolem detect interactions mediated by aromatics and by planar-polar residues, in addition to less common features such as the aromatic sandwich. The rules also reveal a previously unreported dependency for residues cys and leu. They also specify interactions involving aromatic and hydrogen bonding residues. This paper shows that Inductive Logic Programming implemented in ProGolem can derive rules giving structural features of protein/ligand interactions. Several of these rules are consistent with descriptions in the literature. CONCLUSIONS: In addition to confirming literature results, ProGolem's model has a 10-fold cross-validated predictive accuracy that is superior, at the 95% confidence level, to another ILP system previously used to study protein/hexose interactions and is comparable with state-of-the-art statistical learners.

MOTIVATION: Databases of sequenced genomes are widely used to characterize the structure, function and evolutionary relationships of proteins. The ability to discern such relationships is widely expected to grow as sequencing projects provide novel information, bridging gaps in our map of the protein universe. RESULTS: We have plotted our progress in protein sequencing over the last two decades and found that the rate of novel sequence discovery is in a sustained period of decline. Consequently, PSI-BLAST, the most widely used method to detect remote evolutionary relationships, which relies upon the accumulation of novel sequence data, is now showing a plateau in performance. We interpret this trend as signalling our approach to a representative map of the protein universe and discuss its implications.

MOTIVATION: Reliable structural modelling of protein-protein complexes has widespread application, from drug design to advancing our knowledge of protein interactions and function. This work addresses three important issues in protein-protein docking: implementing backbone flexibility, incorporating prior indications from experiment and bioinformatics, and providing public access via a server. 3D-Garden (Global And Restrained Docking Exploration Nexus), our benchmarked and server-ready flexible docking system, allows sophisticated programming of surface patches by the user via a facet representation of the interactors' molecular surfaces (generated with the marching cubes algorithm). Flexibility is implemented as a weighted exhaustive conformer search for each clashing pair of molecular branches in a set of 5000 models filtered from around approximately 340,000 initially. RESULTS: In a non-global assessment, carried out strictly according to the protocols for number of models considered and model quality of the Critical Assessment of Protein Interactions (CAPRI) experiment, over the widely-used Benchmark 2.0 of 84 complexes, 3D-Garden identifies a set of ten models containing an acceptable or better model in 29/45 test cases, including one with large conformational change. In 19/45 cases an acceptable or better model is ranked first or second out of 340,000 candidates. AVAILABILITY: http://www.sbg.bio.ic.ac.uk/3dgarden (server).

MOTIVATION: The success of genome sequencing has resulted in many protein sequences without functional annotation. We present ConFunc, an automated Gene Ontology (GO)-based protein function prediction approach, which uses conserved residues to generate sequence profiles to infer function. ConFunc split sets of sequences identified by PSI-BLAST into sub-alignments according to their GO annotations. Conserved residues are identified for each GO term sub-alignment for which a position specific scoring matrix is generated. This combination of steps produces a set of feature (GO annotation) derived profiles from which protein function is predicted. RESULTS: We assess the ability of ConFunc, BLAST and PSI-BLAST to predict protein function in the twilight zone of sequence similarity. ConFunc significantly outperforms BLAST & PSI-BLAST obtaining levels of recall and precision that are not obtained by either method and maximum precision 24% greater than BLAST. Further for a large test set of sequences with homologues of low sequence identity, at high levels of presicision, ConFunc obtains recall six times greater than BLAST. These results demonstrate the potential for ConFunc to form part of an automated genomics annotation pipeline. AVAILABILITY: http://www.sbg.bio.ic.ac.uk/confunc

BACKGROUND: Microarray experimentation requires the application of complex analysis methods as well as the use of non-trivial computer technologies to manage the resultant large data sets. This, together with the proliferation of tools and techniques for microarray data analysis, makes it very challenging for a laboratory scientist to keep up-to-date with the latest developments in this field. Our aim was to develop a distributed e-support system for microarray data analysis and management. RESULTS: EMAAS (Extensible MicroArray Analysis System) is a multi-user rich internet application (RIA) providing simple, robust access to up-to-date resources for microarray data storage and analysis, combined with integrated tools to optimise real time user support and training. The system leverages the power of distributed computing to perform microarray analyses, and provides seamless access to resources located at various remote facilities. The EMAAS framework allows users to import microarray data from several sources to an underlying database, to pre-process, quality assess and analyse the data, to perform functional analyses, and to track data analysis steps, all through a single easy to use web portal. This interface offers distance support to users both in the form of video tutorials and via live screen feeds using the web conferencing tool EVO. A number of analysis packages, including R-Bioconductor and Affymetrix Power Tools have been integrated on the server side and are available programmatically through the Postgres-PLR library or on grid compute clusters. Integrated distributed resources include the functional annotation tool DAVID, GeneCards and the microarray data repositories GEO, CELSIUS and MiMiR. EMAAS currently supports analysis of Affymetrix 3' and Exon expression arrays, and the system is extensible to cater for other microarray and transcriptomic platforms. CONCLUSION: EMAAS enables users to track and perform microarray data management and analysis tasks through a single easy-to-use web application. The system architecture is flexible and scalable to allow new array types, analysis algorithms and tools to be added with relative ease and to cope with large increases in data volume.

MOTIVATION: The output of a bioinformatic tool such as BLAST must usually be interpreted by an expert before reliable conclusions can be drawn. This may be based upon the expert's experience, additional data and statistical analysis. Often the process is laborious, goes unrecorded and may be biased. Argumentation is an established technique for reasoning about situations where absolute truth or precise probability is impossible to determine. RESULTS: We demonstrate the application of argumentation to 3D-PSSM, a protein structure prediction tool. The expert's interpretation of results is represented as an argumentation framework. Given a 3D-PSSM result, an automated procedure constructs arguments for and against the conclusion that the result is a good predictor of protein structure. In addition to capturing the unique expertise of the author of 3D-PSSM for distribution to users, an improvement in recall of 5-10 percentage points is achieved. This technique can be applied to a wide range of bioinformatic tools. AVAILABILITY: Example public server and benchmarking data are available at http://www.sbg.bio.ic.ac.uk/~brj03/argumentation/paper/. Source code available on request.

MOTIVATION: In recent years graph-theoretic descriptions have been applied to aid the analysis of a number of complex biological systems. However, such an approach has only just begun to be applied to examine protein structures and the network of interactions between residues with promising results. Here we examine whether a graph measure known as closeness is capable of predicting regions where a protein can be split to form a viable circular permutant. Circular permutants are a powerful experimental tool to probe folding mechanisms and more recently have been used to design split enzyme reporter proteins. RESULTS: We test our method on an extensive set of experiments carried out on dihydrofolate reductase in which circular permutants were constructed for every amino acid position in the sequence, together with partial data from studies on other proteins. Results show that closeness is capable of correctly identifying significantly more residues which are suitable for circular permutation than solvent accessibility. This has potential implications for the design of successful split enzymes having particular importance for the development of protein-protein interaction screening methods and offers new perspectives on protein folding. More generally, the method illustrates the success with which graph-theoretic measures encapsulate the variety of long and short range interactions between residues during the folding process.

MOTIVATION: Sequence database search methods often identify putative sub-threshold hits of known function or structure for a given query sequence. It is widespread practice to filter these hits by hand using knowledge of function and other factors; to the expert, some hits may appear more sensible than others. SAWTED (Structure Assignment With Text Description) is an automated solution to this post-filtering problem which will be applicable to large scale genome assignments. RESULTS: A standard document comparison algorithm is applied to text descriptions extracted from SWISS-PROT annotations. The added value of SAWTED in combination with PSI-BLAST has been shown with a benchmark of difficult remote homologues taken from the SCOP structure database. AVAILABILITY: A WAWTED PSI-BLAST Web server is available to perform sensitive searches against the protein structure database (http://www.bmm.icnet.uk/servers/sawted). CONTACT: [email protected]

A method (3D-PSSM) to recognise remote protein sequence homologues is described.The method uses homologous proteins of similar three-dimensional structure in the SCOP database (Murzin, A. G. et al., 1995, J. Mol.Biol.247, 536-540) to obtain a structural equivalence of residues.These equivalences are used to extend multiply-aligned sequences obtained by standard sequence searches (i.e.lDprofiles).The resultant 3D profile is converted into a position specific scoring matrix (a 3D-PSSM).The approach is benchmarked on recognising remote homologues in the SCOP database and comparing the hit and error rates.3D-PSSMs are compared with lD-PSSMs and with two widely-used sensitive search approaches -PSI-BLAST (Altschul, S. F., et al

Ross D. King, Mansoor Saqi, Roger Sayle, Michael J.E. Sternberg; DSC: public domain protein secondary structure prediction, Bioinformatics, Volume 13, Issue 4,

Information about the three-dimensional structure or function of a newly determined protein sequence can be obtained if the protein is found to contain a characterized motif or pattern of residues. Recently a database (PROSITE) has been established that contains 337 known motifs encoded as a list of allowed residue types at specific positions along the sequence. PROMOT is a FORTRAN computer program that takes a protein sequence and examines if it contains any of the motifs in PROSITE. The program also extends the definitions of patterns beyond those used in PROSITE to provide a simple, yet flexible, method to scan either a PROSITE or a user-defined pattern against a protein sequence database.