Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions

MOTIVATION: Polymorphisms in human genes are being described in remarkable numbers. Determining which polymorphisms and which environmental factors are associated with common, complex diseases has become a daunting task. This is partly because the effect of any single genetic variation will likely be dependent on other genetic variations (gene-gene interaction or epistasis) and environmental factors (gene-environment interaction). Detecting and characterizing interactions among multiple factors is both a statistical and a computational challenge. To address this problem, we have developed a multifactor dimensionality reduction (MDR) method for collapsing high-dimensional genetic data into a single dimension thus permitting interactions to be detected in relatively small sample sizes. In this paper, we describe the MDR approach and an MDR software package. RESULTS: We developed a program that integrates MDR with a cross-validation strategy for estimating the classification and prediction error of multifactor models. The software can be used to analyze interactions among 2-15 genetic and/or environmental factors. The dataset may contain up to 500 total variables and a maximum of 4000 study subjects. AVAILABILITY: Information on obtaining the executable code, example data, example analysis, and documentation is available upon request. SUPPLEMENTARY INFORMATION: All supplementary information can be found at http://phg.mc.vanderbilt.edu/Software/MDR.     

An efficiency analysis of high-order combinations of gene–gene interactions using multifactor-dimensionality reduction

Background

Multifactor dimensionality reduction (MDR) is widely used to analyze interactions of genes to determine the complex relationship between diseases and polymorphisms in humans. However, the astronomical number of high-order combinations makes MDR a highly time-consuming process which can be difficult to implement for multiple tests to identify more complex interactions between genes. This study proposes a new framework, named fast MDR (FMDR), which is a greedy search strategy based on the joint effect property.

Results

Six models with different minor allele frequencies (MAFs) and different sample sizes were used to generate the six simulation data sets. A real data set was obtained from the mitochondrial D-loop of chronic dialysis patients. Comparison of results from the simulation data and real data sets showed that FMDR identified significant gene–gene interaction with less computational complexity than the MDR in high-order interaction analysis.

Conclusion

FMDR improves the MDR difficulties associated with the computational loading of high-order SNPs and can be used to evaluate the relative effects of each individual SNP on disease susceptibility. FMDR is freely available at http://bioinfo.kmu.edu.tw/FMDR.rar.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1717-8) contains supplementary material, which is available to authorized users.     

Parallelized multiple alignment

SUMMARY: Multiple sequence alignment is a frequently used technique for analyzing sequence relationships. Compilation of large alignments is computationally expensive, but processing time can be considerably reduced when the computational load is distributed over many processors. Parallel processing functionality in the form of single-instruction multiple-data (SIMD) technology was implemented into the multiple alignment program Praline by using 'message passing interface' (MPI) routines. Over the alignments tested here, the parallelized program performed up to ten times faster on 25 processors compared to the single processor version. AVAILABILITY: Example program code for parallelizing pairwise alignment loops is available from http://mathbio.nimr.mrc.ac.uk/~jkleinj/tools/mpicode. The 'message passing interface' package (MPICH) is available from http:/www.unix.mcs.anl.gov/mpi/mpich. CONTACT: jhering@nimr.mrc.ac.uk SUPPLEMENTARY INFORMATION: Praline is accessible at http://mathbio.nimr.mrc.ac.uk/praline.     

Protein interactions study through proximity-labeling

Introduction: The proteome is a dynamic system in which protein-protein interactions play a crucial part in shaping the cell phenotype. However, given the current limitations of available technologies to describe the dynamic nature of these interactions, the identification of protein-protein interactions has long been a major challenge in proteomics. In recent years, the development of BioID and APEX, two proximity-tagging technologies, have opened-up new perspectives and have already started to change our conception of protein-protein interactions, and more generally, of the proteome. With a broad range of application encompassing health, these new technologies are currently setting milestones crucial to understand fine cellular mechanisms.

Area covered: In this article, we describe both the recent and the more conventional available tools to study protein-protein interactions, compare the advantages and the limitations of these techniques, and discuss the recent advancements led by the proximity tagging techniques to refine our conception of the proteome.

Expert opinion: The recent development of proximity labeling techniques emphasizes the growing importance of such technologies to decipher cellular mechanism. Although several challenges still need to be addressed, many fields can benefit from these tools and notably the detection of new therapeutic targets for patient care     

ParAlign: a parallel sequence alignment algorithm for rapid and sensitive database searches

There is a need for faster and more sensitive algorithms for sequence similarity searching in view of the rapidly increasing amounts of genomic sequence data available. Parallel processing capabilities in the form of the single instruction, multiple data (SIMD) technology are now available in common microprocessors and enable a single microprocessor to perform many operations in parallel. The ParAlign algorithm has been specifically designed to take advantage of this technology. The new algorithm initially exploits parallelism to perform a very rapid computation of the exact optimal ungapped alignment score for all diagonals in the alignment matrix. Then, a novel heuristic is employed to compute an approximate score of a gapped alignment by combining the scores of several diagonals. This approximate score is used to select the most interesting database sequences for a subsequent Smith-Waterman alignment, which is also parallelised. The resulting method represents a substantial improvement compared to existing heuristics. The sensitivity and specificity of ParAlign was found to be as good as Smith-Waterman implementations when the same method for computing the statistical significance of the matches was used. In terms of speed, only the significantly less sensitive NCBI BLAST 2 program was found to outperform the new approach. Online searches are available at http://dna.uio.no/search/     

GOAnno: GO annotation based on multiple alignment

SUMMARY: GOAnno is a web tool that automatically annotates proteins according to the Gene Ontology (GO) using evolutionary information available in hierarchized multiple alignments. GO terms present in the aligned functional subfamily can be cross-validated and propagated to obtain highly reliable predicted GO annotation based on the GOAnno algorithm. AVAILABILITY: The web tool and a reduced version for local installation are freely available at http://igbmc.u-strasbg.fr/GOAnno/GOAnno.html SUPPLEMENTARY INFORMATION: The website supplies a detailed explanation and illustration of the algorithm at http://igbmc.u-strasbg.fr/GOAnno/GOAnnoHelp.html.     

A mouse protein interactome through combined literature mining with multiple sources of interaction evidence

Protein–protein interactions (PPIs) play crucial roles in a number of biological processes. Recently, protein interaction networks (PINs) for several model organisms and humans have been generated, but few large-scale researches for mice have ever been made neither experimentally nor computationally. In the work, we undertook an effort to map a mouse PIN, in which protein interactions are hidden in enormous amount of biomedical literatures. Following a co-occurrence-based text-mining approach, a probabilistic model—naïve Bayesian was used to filter false-positive interactions by integrating heterogeneous kinds of evidence from genomic and proteomic datasets. A support vector machine algorithm was further used to choose protein pairs with physical interactions. By comparing with the currently available PPI datasets from several model organisms and humans, it showed that the derived mouse PINs have similar topological properties at the global level, but a high local divergence. The mouse protein interaction dataset is stored in the Mouse protein–protein interaction DataBase (MppDB) that is useful source of information for system-level understanding of gene function and biological processes in mammals. Access to the MppDB database is public available at http://bio.scu.edu.cn/mppi.     

The domain-server: direct prediction of protein domain-homologies from BLAST search.

RESULTS: A WWW server for protein domain homology prediction, based on BLAST search and a simple data-mining algorithm (Hegyi,H. and Pongor,S. (1993) Comput. Appl. Biosci., 9, 371-372), was constructed providing a tabulated list and a graphic plot of similarities. AVAILABILITY: http://www.icgeb.trieste.it/domain. Mirror site is available at http://sbase.abc.hu/domain. A standalone programme will be available on request. SUPPLEMENTARY INFORMATION: A series of help files is available at the above addresses.     

A Java applet for visualizing protein-protein interaction.

A web applet for browsing protein-protein interactions was implemented. It enables the display of interaction relationships, based upon neighboring distance and biological function. AVAILABILITY: The Java applet is available at http://www.charite.de/bioinformatics     

MultiPLX: automatic grouping and evaluation of PCR primers

SUMMARY: MultiPLX is a new program for automatic grouping of PCR primers. It can use many different parameters to estimate the compatibility of primers, such as primer-primer interactions, primer-product interactions, difference in melting temperatures, difference in product length and the risk of generating alternative products from the template. A unique feature of the MultiPLX is the ability to perform automatic grouping of large number (thousands) of primer pairs. AVAILABILITY: Binaries for Windows, Linux and Solaris are available from http://bioinfo.ebc.ee/download/. A graphical version with limited capabilities can be used through a web interface at http://bioinfo.ebc.ee/multiplx/. The source code of the program is available on request for academic users. CONTACT: maido.remm@ut.ee.     

Three-way interaction model with switching mechanism as an effective strategy for tracing functionally-related genes

Introduction: Identification of functionally-related genes is an important step in understanding biological systems. The most popular strategy to infer functional dependence is to study pairwise correlations between gene expression levels. However, certain functionally-related genes may have a low expression correlation due to their nonlinear interactions. The use of a three-way interaction (3WI) model with switching mechanism (SM) is a relatively new strategy to trace functionally-related genes. The 3WI model traces the dynamic and nonlinear nature of the co-expression relationship of two genes by introducing their link to the expression level of a third gene.

Areas covered: In this paper, we reviewed a variety of existing methods for tracing the 3WIs. Furthermore, we provide a comprehensive review of the previous biological studies based on 3WI models.

Expert commentary: Comparison of features of these methods indicates that the modified liquid association algorithm has the best efficiency for tracing 3WI between others. The limited number of biological studies based on the 3WI suggests that high computational demand of the available algorithms is a major challenge to apply this approach for analyzing high-throughput omics data.     

Predicting interactions in protein networks by completing defective cliques

Datasets obtained by large-scale, high-throughput methods for detecting protein-protein interactions typically suffer from a relatively high level of noise. We describe a novel method for improving the quality of these datasets by predicting missed protein-protein interactions, using only the topology of the protein interaction network observed by the large-scale experiment. The central idea of the method is to search the protein interaction network for defective cliques (nearly complete complexes of pairwise interacting proteins), and predict the interactions that complete them. We formulate an algorithm for applying this method to large-scale networks, and show that in practice it is efficient and has good predictive performance. More information can be found on our website http://topnet.gersteinlab.org/clique/ CONTACT: Mark.Gerstein@yale.edu SUPPLEMENTARY INFORMATION: Supplementary Materials are available at Bioinformatics online.     

Coexpressionfinder: a new algorithm for finding groups of coexpressed genes

RESULTS: A new algorithm is developed which is intended to find groups of genes whose expression values change in a concordant manner in a series of experiments with DNA arrays. This algorithm is named as CoexpressionFinder. It can find more complete and internally coordinated groups of gene expression vectors than hierarchical clustering. Also, it finds more genes having coordinated expression. The algorithm's design allows parallel execution. AVAILABILITY: The algorithm is implemented as a Java application which is freely available at: http://www.bioinformatics.ru/cf/index.jsp and http://bioinformatics.ru/cf/index.jsp.     

MetaRoute: fast search for relevant metabolic routes for interactive network navigation and visualization

We present MetaRoute, an efficient search algorithm based on atom mapping rules and path weighting schemes that returns relevant or textbook-like routes between a source and a product metabolite within seconds for genome-scale networks. Its speed allows the algorithm to be used interactively through a web interface to visualize relevant routes and local networks for one or multiple organisms based on data from KEGG. AVAILABILITY: http://www-bs.informatik.uni-tuebingen.de/Services/MetaRoute. SUPPLEMENTARY INFORMATION: Supplementary details are available at http://www-bs.informatik.uni-tuebingen.de/Services/MetaRoute.     

TM or not TM: transmembrane protein prediction with low false positive rate using DAS-TMfilter

Web-based servers implementing the DAS-TMfilter algorithm have been launched at three mirror sites and their usage is described. The underlying computer program is an upgraded and modified version of the DAS-prediction method. The new server is (approximately 1 among 100 unrelated queries) while the high efficiency of the original algorithm locating TM segments in queries is preserved (sensitivity of approximately 95% among documented proteins with helical TM regions). AVAILABILITY: The server operates at three mirror sites: http://mendel.imp.univie.ac.at/sat/DAS/DAS.html, http://wooster.bip.bham.ac.uk/DAS.html and http://www.enzim.hu/DAS/DAS.html. The program is available on request.     

IPPRED: server for proteins interactions inference

SUMMARY: IPPRED is a web based server to infer protein-protein interactions through homology search between candidate proteins and those described as interacting. This simple inference allows to propose or to validate potential interactions. AVAILABILITY: IPPRED is freely available at http://cbi.labri.fr/outils/ippred/.     

A simple and efficient algorithm for gene selection using sparse logistic regression

MOTIVATION: This paper gives a new and efficient algorithm for the sparse logistic regression problem. The proposed algorithm is based on the Gauss-Seidel method and is asymptotically convergent. It is simple and extremely easy to implement; it neither uses any sophisticated mathematical programming software nor needs any matrix operations. It can be applied to a variety of real-world problems like identifying marker genes and building a classifier in the context of cancer diagnosis using microarray data. RESULTS: The gene selection method suggested in this paper is demonstrated on two real-world data sets and the results were found to be consistent with the literature. AVAILABILITY: The implementation of this algorithm is available at the site http://guppy.mpe.nus.edu.sg/~mpessk/SparseLOGREG.shtml Supplementary Information: Supplementary material is available at the site http://guppy.mpe.nus.edu.sg/~mpessk/SparseLOGREG.shtml     

Antibiotic Resistance Genes Online (ARGO): a Database on vancomycin and beta-lactam resistance genes

Vancomycin and beta-lactams are antibiotics that inhibit gram positive bacteria by interfering with cell wall synthesis. However, continuous use of antibiotics results in the emergence of multi-drug resistant (MDR) bacterial strains. Here, we describe ARGO, a database containing gene sequences conferring resistance to these two classes of antibiotics. It is designed as a resource to enhance research on the prevalence and spread of antibiotic resistance genes. ARGO is the first attempt to compile the resistance gene sequence data with state specific information. AVAILABILITY: AGRO is available for free at http://www.argodb.org/     

Structural insights into the inhibitor binding and new inhibitor design to Polo-like kinase-1 Polo-box domain using computational studies

Polo box domain (PBD) from Polo-Like Kinase-1 (PLK-1) a cell cycle regulator is one of the important non-kinase targets implicated in various cancers. The crystal structure of PLK-1 PBD bound to phosphopeptide inhibitor is available and acylthiourea derivatives have been reported as potent PBD inhibitors. In this work, structure and ligand-based pharmacophore methods have been used to identify new PBD inhibitors. The binding of acylthiourea analogs and new inhibitors to PBD were assessed using molecular docking and molecular dynamics simulations to understand their binding interactions, investigate the complex stability and reveal the molecular basis for inhibition. This study provides the binding free energies and residue-wise contributions to decipher the essential interactions in the protein-inhibitor complementarity for complex formation and the design of new PBD inhibitors with better binding.

Communicated by Ramaswamy H. Sarma