QPath: a method for querying pathways in a protein-protein interaction network

Background  

Sequence comparison is one of the most prominent tools in biological research, and is instrumental in studying gene function and evolution. The rapid development of high-throughput technologies for measuring protein interactions calls for extending this fundamental operation to the level of pathways in protein networks.     

Kavosh: a new algorithm for finding network motifs

Background  

Complex networks are studied across many fields of science and are particularly important to understand biological processes. Motifs in networks are small connected sub-graphs that occur significantly in higher frequencies than in random networks. They have recently gathered much attention as a useful concept to uncover structural design principles of complex networks. Existing algorithms for finding network motifs are extremely costly in CPU time and memory consumption and have practically restrictions on the size of motifs.     

Bioinformatics--The new home for protein sequence motifs

Protein domains and sequence motifs have been very influentialin the field of molecular biology. These units are the commoncurrency of protein structure and function. The     

Discover 1: a new program to search for unusually represented DNA motifs.

DISCOVER1 (DIStribution COunter VERsion 1) is a new program that can identify DNA motifs occurring with a high deviation from the expected frequency. The program generates families of patterns, each family having a common set of defined bases. Undefined bases are inserted amongst the defined bases in different ways, thus generating the diverse patterns of each family. The occurrences of the different patterns are then compared and analysed within each family, assuming that all patterns should have the same probability of occurrence. An extensive use of computer memory, combined with the immediate sorting of counts by address calculation allow a complete counting of all DNA motifs on a single pass on the DNA sequence. This approach offers a very fast way to search for unusually distributed patterns and can identify inexact patterns as well as exact patterns.     

Motif programming: a microgene-based method for creating synthetic proteins containing multiple functional motifs

The presence of peptide motifs within the proteins provides the synthetic biologist with the opportunity to fabricate novel proteins through the programming of these motifs. Here we describe a method that enables one to combine multiple peptide motifs to generate a combinatorial protein library. With this method, a set of sense and antisense oligonucleotide primers were prepared. These primers were mixed and polymerized, so that the resultant DNA consisted of combinatorial polymers of multiple microgenes created from the stochastic assembly of the sense and antisense primers. With this motif-mixing method, we prepared a protein library from the BH1-4 motifs shared among Bcl-2 family proteins. Among the 41 clones created, 70% of clones had a stable, presumably folded expression product in human cells, which was detectable by immunohistochemistry and western blot. The proteins obtained varied with respect to both the number and the order of the four motifs. The method enables homology-independent polymerization of DNA blocks that coded motif sequences, and the frequency of each motif within a library can be adjusted in a tailor-made manner. This motif programming has a potential for creating a library with a large proportion of folded/functional proteins.     

GSearcher: agile attribute querying for biological networks

SUMMARY: GSearcher provides a highly interactive user experience in navigating attribute data associated with large and complex biological networks. The user may either perform a quick search using keywords, phrases or regular expressions, or build a complex query with a group of filters for efficient and flexible exploration of large datasets. AVAILABILITY: http://brainarray.mbni.med.umich.edu/gsearcher/.     

SPARQLGraph: a web-based platform for graphically querying biological Semantic Web databases

Background

Semantic Web has established itself as a framework for using and sharing data across applications and database boundaries. Here, we present a web-based platform for querying biological Semantic Web databases in a graphical way.

Results

SPARQLGraph offers an intuitive drag & drop query builder, which converts the visual graph into a query and executes it on a public endpoint. The tool integrates several publicly available Semantic Web databases, including the databases of the just recently released EBI RDF platform. Furthermore, it provides several predefined template queries for answering biological questions. Users can easily create and save new query graphs, which can also be shared with other researchers.

Conclusions

This new graphical way of creating queries for biological Semantic Web databases considerably facilitates usability as it removes the requirement of knowing specific query languages and database structures. The system is freely available at http://sparqlgraph.i-med.ac.at.     

A vector projection method for predicting supersecondary motifs

For the 11 types of most frequently occurring supersecondary motifs, we used a new method—the vector projection method—to predict a protein's supersecondary structure. In a training set of peptides and a test set of peptides we obtained a satisfactory result, with a prediction accuracy of about 90%. The high prediction accuracy indicates that this method is reasonable for predicting the folding motifs of proteins. This work provides insight into the problem of predicting a protein's local structure accurately, and is of particular value in protein modeling, prediction, and molecule design.     

BioGuideSRS: querying multiple sources with a user-centric perspective

Biologists are frequently faced with the problem of integrating information from multiple heterogeneous sources with their own experimental data. Given the large number of public sources, it is difficult to choose which sources to integrate without assistance. When doing this manually, biologists differ in their preferences concerning the sources to be queried as well as the strategies, i.e. the querying process they follow for navigating through the sources. In response to these findings, we have developed BioGuide to assist scientists search for relevant data within external sources while taking their preferences and strategies into account. In this article, we present BioGuideSRS, a user-friendly system which automatically retrieves instances of data by using BioGuide on top of the sequence retrieval system (SRS). BioGuideSRS is an Applet that can be run from its web page on any system with Java 5.0. AVAILABILITY: http://www.bioguide-project.net.     

MOPAT: a graph-based method to predict recurrent cis-regulatory modules from known motifs

The identification of cis-regulatory modules (CRMs) can greatly advance our understanding of eukaryotic regulatory mechanism. Current methods to predict CRMs from known motifs either depend on multiple alignments or can only deal with a small number of known motifs provided by users. These methods are problematic when binding sites are not well aligned in multiple alignments or when the number of input known motifs is large. We thus developed a new CRM identification method MOPAT (motif pair tree), which identifies CRMs through the identification of motif modules, groups of motifs co-occurring in multiple CRMs. It can identify 'orthologous' CRMs without multiple alignments. It can also find CRMs given a large number of known motifs. We have applied this method to mouse developmental genes, and have evaluated the predicted CRMs and motif modules by microarray expression data and known interacting motif pairs. We show that the expression profiles of the genes containing CRMs of the same motif module correlate significantly better than those of a random set of genes do. We also show that the known interacting motif pairs are significantly included in our predictions. Compared with several current methods, our method shows better performance in identifying meaningful CRMs.     

An efficient network querying method based on conditional random fields

MOTIVATION: A large amount of biomolecular network data for multiple species have been generated by high-throughput experimental techniques, including undirected and directed networks such as protein-protein interaction networks, gene regulatory networks and metabolic networks. There are many conserved functionally similar modules and pathways among multiple biomolecular networks in different species; therefore, it is important to analyze the similarity between the biomolecular networks. Network querying approaches aim at efficiently discovering the similar subnetworks among different species. However, many existing methods only partially solve this problem. RESULTS: In this article, a novel approach for network querying problem based on conditional random fields (CRFs) model is presented, which can handle both undirected and directed networks, acyclic and cyclic networks and any number of insertions/deletions. The CRF method is fast and can query pathways in a large network in seconds using a PC. To evaluate the CRF method, extensive computational experiments are conducted on the simulated and real data, and the results are compared with the existing network querying methods. All results show that the CRF method is very useful and efficient to find the conserved functionally similar modules and pathways in multiple biomolecular networks.     

SMotif: a server for structural motifs in proteins

SMotif is a server that identifies important structural segments or motifs for a given protein structure(s) based on conservation of both sequential as well as important structural features such as solvent inaccessibility, secondary structural content, hydrogen bonding pattern and residue packing. This server also provides three-dimensional orientation patterns of the identified motifs in terms of inter-motif distances and torsion angles. These motifs may form the common core and therefore, can also be employed to design and rationalize protein engineering and folding experiments. AVAILABILITY: SMotif server is available via the URL http://caps.ncbs.res.in/SMotif/index.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

PATIKAweb: a Web interface for analyzing biological pathways through advanced querying and visualization

Patikaweb provides a Web interface for retrieving and analyzing biological pathways in the Patika database, which contains data integrated from various prominent public pathway databases. It features a user-friendly interface, dynamic visualization and automated layout, advanced graph-theoretic queries for extracting biologically important phenomena, local persistence capability and exporting facilities to various pathway exchange formats.     

RNA interference: a tool for querying nervous system function and an emerging therapy

RNA interference (RNAi), a mediator of gene silencing, has swiftly become one of the most exciting and applicable biological discoveries. There has been rapid progress in identifying RNAi pathway components and elucidating the mechanisms of microRNA (miRNA) biogenesis and gene suppression. As a result, RNAi technologies have been successfully employed in a variety of systems as biological tools, and studies are underway to test the therapeutic utility of RNAi. In the span of several years, significant advances in the delivery of inhibitory RNAs in the nervous system have been made. We have glimpses into how endogenous miRNAs interface with neuronal development and function; in addition, RNAi has shown therapeutic efficacy in several mouse models of human neurological conditions. In this review, we summarize the current state-of-the-art of RNAi and its utility to neuroscientists.     

indel-Seq-Gen: a new protein family simulator incorporating domains, motifs, and indels

Reconstructing the evolutionary history of protein sequences will provide a better understanding of divergence mechanisms of protein superfamilies and their functions. Long-term protein evolution often includes dynamic changes such as insertion, deletion, and domain shuffling. Such dynamic changes make reconstructing protein sequence evolution difficult and affect the accuracy of molecular evolutionary methods, such as multiple alignments and phylogenetic methods. Unfortunately, currently available simulation methods are not sufficiently flexible and do not allow biologically realistic dynamic protein sequence evolution. We introduce a new method, indel-Seq-Gen (iSG), that can simulate realistic evolutionary processes of protein sequences with insertions and deletions (indels). Unlike other simulation methods, iSG allows the user to simulate multiple subsequences according to different evolutionary parameters, which is necessary for generating realistic protein families with multiple domains. iSG tracks all evolutionary events including indels and outputs the "true" multiple alignment of the simulated sequences. iSG can also generate a larger sequence space by allowing the use of multiple related root sequences. With all these functions, iSG can be used to test the accuracy of, for example, multiple alignment methods, phylogenetic methods, evolutionary hypotheses, ancestral protein reconstruction methods, and protein family classification methods. We empirically evaluated the performance of iSG against currently available methods by simulating the evolution of the G protein-coupled receptor and lipocalin protein families. We examined their true multiple alignments, reconstruction of the transmembrane regions and beta-strands, and the results of similarity search against a protein database using the simulated sequences. We also presented an example of using iSG for examining how phylogenetic reconstruction is affected by high indel rates.     

GeneSplicer: a new computational method for splice site prediction

GeneSplicer is a new, flexible system for detecting splice sites in the genomic DNA of various eukaryotes. The system has been tested successfully using DNA from two reference organisms: the model plant Arabidopsis thaliana and human. It was compared to six programs representing the leading splice site detectors for each of these species: NetPlantGene, NetGene2, HSPL, NNSplice, GENIO and SpliceView. In each case GeneSplicer performed comparably to the best alternative, in terms of both accuracy and computational efficiency.