Mclip: motif detection based on cliques of gapped local profile-to-profile alignments

A multitude of motif-finding tools have been published, which can generally be assigned to one of three classes: expectation-maximization, Gibbs-sampling or enumeration. Irrespective of this grouping, most motif detection tools only take into account similarities across ungapped sequence regions, possibly causing short motifs located peripherally and in varying distance to a 'core' motif to be missed. We present a new method, adding to the set of expectation-maximization approaches, that permits the use of gapped alignments for motif elucidation. Availability: The program is available for download from: http://bioinfoserver.rsbs.anu.edu.au/downloads/mclip.jar. Supplementary information: http://bioinfoserver.rsbs.anu.edu.au/utils/mclip/info.php.     

RNAMotifScanX: a graph alignment approach for RNA structural motif identification

RNA structural motifs are recurrent three-dimensional (3D) components found in the RNA architecture. These RNA structural motifs play important structural or functional roles and usually exhibit highly conserved 3D geometries and base-interaction patterns. Analysis of the RNA 3D structures and elucidation of their molecular functions heavily rely on efficient and accurate identification of these motifs. However, efficient RNA structural motif search tools are lacking due to the high complexity of these motifs. In this work, we present RNAMotifScanX, a motif search tool based on a base-interaction graph alignment algorithm. This novel algorithm enables automatic identification of both partially and fully matched motif instances. RNAMotifScanX considers noncanonical base-pairing interactions, base-stacking interactions, and sequence conservation of the motifs, which leads to significantly improved sensitivity and specificity as compared with other state-of-the-art search tools. RNAMotifScanX also adopts a carefully designed branch-and-bound technique, which enables ultra-fast search of large kink-turn motifs against a 23S rRNA. The software package RNAMotifScanX is implemented using GNU C++, and is freely available from http://genome.ucf.edu/RNAMotifScanX.     

3MATRIX and 3MOTIF: a protein structure visualization system for conserved sequence motifs

Computational methods such as sequence alignment and motif construction are useful in grouping related proteins into families, as well as helping to annotate new proteins of unknown function. These methods identify conserved amino acids in protein sequences, but cannot determine the specific functional or structural roles of conserved amino acids without additional study. In this work, we present 3MATRIX (http://3matrix.stanford.edu) and 3MOTIF (http://3motif.stanford.edu), a web-based sequence motif visualization system that displays sequence motif information in its appropriate three-dimensional (3D) context. This system is flexible in that users can enter sequences, keywords, structures or sequence motifs to generate visualizations. In 3MOTIF, users can search using discrete sequence motifs such as PROSITE patterns, eMOTIFs, or any other regular expression-like motif. Similarly, 3MATRIX accepts an eMATRIX position-specific scoring matrix, or will convert a multiple sequence alignment block into an eMATRIX for visualization. Each query motif is used to search the protein structure database for matches, in which the motif is then visually highlighted in three dimensions. Important properties of motifs such as sequence conservation and solvent accessible surface area are also displayed in the visualizations, using carefully chosen color shading schemes.     

ARCS: an aggregated related column scoring scheme for aligned sequences

MOTIVATION: Biologists frequently align multiple biological sequences to determine consensus sequences and/or search for predominant residues and conserved regions. Particularly, determining conserved regions in an alignment is one of the most important activities. Since protein sequences are often several-hundred residues or longer, it is difficult to distinguish biologically important conserved regions (motifs or domains) from others. The widely used tools, Logos, Al2co, Confind, and the entropy-based method, often fail to highlight such regions. Thus a computational tool that can highlight biologically important regions accurately will be highly desired. RESULTS: This paper presents a new scoring scheme ARCS (Aggregated Related Column Score) for aligned biological sequences. ARCS method considers not only the traditional character similarity measure but also column correlation. In an extensive experimental evaluation using 533 PROSITE patterns, ARCS is able to highlight the motif regions with up to 77.7% accuracy corresponding to the top three peaks. AVAILABILITY: The source code is available on http://bio.informatics.indiana.edu/projects/arcs and http://goldengate.case.edu/projects/arcs     

BetaSearch: a new method for querying beta-residue motifs

ABSTRACT: BACKGROUND: Searching for structural motifs across known protein structures can be useful for identifying unrelated proteins with similar function and characterising secondary structures such as beta-sheets. This is infeasible using conventional sequence alignment because linear protein sequences do not contain spatial information. beta-residue motifs are beta-sheet substructures that can be represented as graphs and queried using existing graph indexing methods, however, these approaches are designed for general graphs that do not incorporate the inherent structural constraints of beta-sheets and require computationally-expensive filtering and verification procedures. 3D substructure search methods, on the other hand, allow beta-residue motifs to be queried in a three-dimensional context but at significant computational costs. RESULTS: We developed a new method for querying beta-residue motifs, called BetaSearch, which leverages the natural planar constraints of beta-sheets by indexing them as 2D matrices, thus avoiding much of the computational complexities involved with structural and graph querying. BetaSearch demonstrates faster filtering, verification, and overall query time than existing graph indexing approaches whilst producing comparable index sizes. Compared to 3D substructure search methods, BetaSearch achieves 33 and 240 times speedups over index-based and pairwise alignment-based approaches, respectively. Furthermore, we have presented case-studies to demonstrate its capability of motif matching in sequentially dissimilar proteins and described a method for using BetaSearch to predict beta-strand pairing. CONCLUSIONS: We have demonstrated that BetaSearch is a fast method for querying substructure motifs. The improvements in speed over existing approaches make it useful for efficiently performing high-volume exploratory querying of possible protein substructural motifs or conformations. BetaSearch was used to identify a nearly identical beta-residue motif between an entirely synthetic (Top7) and a naturally-occurring protein (Charcot-Leyden crystal protein), as well as identifying structural similarities between biotin-binding domains of avidin, streptavidin and the lipocalin gamma subunit of human C8. AVAILABILITY: The web-interface, source code, and datasets for BetaSearch can be accessed from http://www.csse.unimelb.edu.au/~hohkhkh1/betasearch.     

PMS: A Panoptic Motif Search Tool

Background

Identification of DNA/Protein motifs is a crucial problem for biologists. Computational techniques could be of great help in this identification. In this direction, many computational models for motifs have been proposed in the literature.

Methods

One such important model is the motif model. In this paper we describe a motif search web tool that predominantly employs this motif model. This web tool exploits the state-of-the art algorithms for solving the motif search problem.

Results

The online tool has been helping scientists identify many unknown motifs. Many of our predictions have been successfully verified as well. We hope that this paper will expose this crucial tool to many more scientists.

Availability and requirements

Project name: PMS - Panoptic Motif Search Tool. Project home page: http://pms.engr.uconn.edu or http://motifsearch.com. Licence: PMS tools will be readily available to any scientist wishing to use it for non-commercial purposes, without restrictions. The online tool is freely available without login.     

MAVisto: a tool for the exploration of network motifs

SUMMARY: MAVisto is a tool for the exploration of motifs in biological networks. It provides a flexible motif search algorithm and different views for the analysis and visualization of network motifs. These views help to explore interesting motifs: the frequency of motif occurrences can be compared with randomized networks, a list of motifs along with information about structure and number of occurrences depending on the reuse of network elements shows potentially interesting motifs, a motif fingerprint reveals the overall distribution of motifs of a given size and the distribution of a particular motif in the network can be visualized using an advanced layout algorithm. AVAILABILITY: MAVisto is platform independent and available free of charge as a Java webstart application at http://mavisto.ipk-gatersleben.de/ CONTACT: schwoebb@ipk-gatersleben.de SUPPLEMENTARY INFORMATION: Can be found at http://mavisto.ipk-gatersleben.de/     

Discriminative motif finding for predicting protein subcellular localization

Many methods have been described to predict the subcellular location of proteins from sequence information. However, most of these methods either rely on global sequence properties or use a set of known protein targeting motifs to predict protein localization. Here, we develop and test a novel method that identifies potential targeting motifs using a discriminative approach based on hidden Markov models (discriminative HMMs). These models search for motifs that are present in a compartment but absent in other, nearby, compartments by utilizing an hierarchical structure that mimics the protein sorting mechanism. We show that both discriminative motif finding and the hierarchical structure improve localization prediction on a benchmark data set of yeast proteins. The motifs identified can be mapped to known targeting motifs and they are more conserved than the average protein sequence. Using our motif-based predictions, we can identify potential annotation errors in public databases for the location of some of the proteins. A software implementation and the data set described in this paper are available from http://murphylab.web.cmu.edu/software/2009_TCBB_motif/.     

RNAMotifScan: automatic identification of RNA structural motifs using secondary structural alignment

Recent studies have shown that RNA structural motifs play essential roles in RNA folding and interaction with other molecules. Computational identification and analysis of RNA structural motifs remains a challenging task. Existing motif identification methods based on 3D structure may not properly compare motifs with high structural variations. Other structural motif identification methods consider only nested canonical base-pairing structures and cannot be used to identify complex RNA structural motifs that often consist of various non-canonical base pairs due to uncommon hydrogen bond interactions. In this article, we present a novel RNA structural alignment method for RNA structural motif identification, RNAMotifScan, which takes into consideration the isosteric (both canonical and non-canonical) base pairs and multi-pairings in RNA structural motifs. The utility and accuracy of RNAMotifScan is demonstrated by searching for kink-turn, C-loop, sarcin-ricin, reverse kink-turn and E-loop motifs against a 23S rRNA (PDBid: 1S72), which is well characterized for the occurrences of these motifs. Finally, we search these motifs against the RNA structures in the entire Protein Data Bank and the abundances of them are estimated. RNAMotifScan is freely available at our supplementary website (http://genome.ucf.edu/RNAMotifScan).     

Automated classification of RNA 3D motifs and the RNA 3D Motif Atlas

The analysis of atomic-resolution RNA three-dimensional (3D) structures reveals that many internal and hairpin loops are modular, recurrent, and structured by conserved non-Watson–Crick base pairs. Structurally similar loops define RNA 3D motifs that are conserved in homologous RNA molecules, but can also occur at nonhomologous sites in diverse RNAs, and which often vary in sequence. To further our understanding of RNA motif structure and sequence variability and to provide a useful resource for structure modeling and prediction, we present a new method for automated classification of internal and hairpin loop RNA 3D motifs and a new online database called the RNA 3D Motif Atlas. To classify the motif instances, a representative set of internal and hairpin loops is automatically extracted from a nonredundant list of RNA-containing PDB files. Their structures are compared geometrically, all-against-all, using the FR3D program suite. The loops are clustered into motif groups, taking into account geometric similarity and structural annotations and making allowance for a variable number of bulged bases. The automated procedure that we have implemented identifies all hairpin and internal loop motifs previously described in the literature. All motif instances and motif groups are assigned unique and stable identifiers and are made available in the RNA 3D Motif Atlas (http://rna.bgsu.edu/motifs), which is automatically updated every four weeks. The RNA 3D Motif Atlas provides an interactive user interface for exploring motif diversity and tools for programmatic data access.     

Dragon Promoter Mapper (DPM): a Bayesian framework for modelling promoter structures

SUMMARY: Dragon Promoter Mapper (DPM) is a tool to model promoter structure of co-regulated genes using methodology of Bayesian networks. DPM exploits an exhaustive set of motif features (such as motif, its strand, the order of motif occurrence and mutual distance between the adjacent motifs) and generates models from the target promoter sequences, which may be used to (1) detect regions in a genomic sequence which are similar to the target promoters or (2) to classify other promoters as similar or not to the target promoter group. DPM can also be used for modelling of enhancers and silencers. AVAILABILITY: http://defiant.i2r.a-star.edu.sg/projects/BayesPromoter/ CONTACT: vlad@sanbi.ac.za SUPPLEMENTARY INFORMATION: Manual for using DPM web server is provided at http://defiant.i2r.a-star.edu.sg/projects/BayesPromoter/html/manual/manual.htm.     

GOPred: GO Molecular Function Prediction by Combined Classifiers

Functional protein annotation is an important matter for in vivo and in silico biology. Several computational methods have been proposed that make use of a wide range of features such as motifs, domains, homology, structure and physicochemical properties. There is no single method that performs best in all functional classification problems because information obtained using any of these features depends on the function to be assigned to the protein. In this study, we portray a novel approach that combines different methods to better represent protein function. First, we formulated the function annotation problem as a classification problem defined on 300 different Gene Ontology (GO) terms from molecular function aspect. We presented a method to form positive and negative training examples while taking into account the directed acyclic graph (DAG) structure and evidence codes of GO. We applied three different methods and their combinations. Results show that combining different methods improves prediction accuracy in most cases. The proposed method, GOPred, is available as an online computational annotation tool (http://kinaz.fen.bilkent.edu.tr/gopred).     

TELS: A Novel Computational Framework for Identifying Motif Signatures of Transcribed Enhancers

In mammalian cells, transcribed enhancers (TrEns) play important roles in the initiation of gene expression and maintenance of gene expression levels in a spatiotemporal manner. One of the most challenging questions is how the genomic characteristics of enhancers relate to enhancer activities. To date, only a limited number of enhancer sequence characteristics have been investigated, leaving space for exploring the enhancers’ DNA code in a more systematic way. To address this problem, we developed a novel computational framework, Transcribed Enhancer Landscape Search (TELS), aimed at identifying predictive cell type/tissue-specific motif signatures of TrEns. As a case study, we used TELS to compile a comprehensive catalog of motif signatures for all known TrEns identified by the FANTOM5 consortium across 112 human primary cells and tissues. Our results confirm that combinations of different short motifs characterize in an optimized manner cell type/tissue-specific TrEns. Our study is the first to report combinations of motifs that maximize classification performance of TrEns exclusively transcribed in one cell type/tissue from TrEns exclusively transcribed in different cell types/tissues. Moreover, we also report 31 motif signatures predictive of enhancers’ broad activity. TELS codes and material are publicly available at http://www.cbrc.kaust.edu.sa/TELS.     

mmsearch: a motif arrangement language and search program.

This paper presents a language for describing arrangements of motifs in biological sequences, and a program that uses the language to find the arrangements in motif match databases. The program does not by itself search for the constituent motifs, and is thus independent of how they are detected, which allows it to use motif match data of various origins. AVAILABILITY: The program can be tested online at http://hits.isb-sib.ch and the distribution is available from ftp://ftp.isrec.isb-sib.ch/pub/software/unix/mmsearch-1.0.tar.gz CONTACT: Thomas.Junier@isrec.unil.ch SUPPLEMENTARY INFORMATION: The full documentation about mmsearchis available from http://hits.isb-sib.ch/~tjunier/mmsearch/doc.     

3MOTIF: visualizing conserved protein sequence motifs in the protein structure database

SUMMARY: 3MOTIF is a web application that visually maps conserved sequence motifs onto three-dimensional protein structures in the Protein Data Bank (PDB; Berman et al., Nucleic Acids Res., 28, 235-242, 2000). Important properties of motifs such as conservation strength and solvent accessible surface area at each position are visually represented on the structure using a variety of color shading schemes. Users can manipulate the displayed motifs using the freely available Chime plugin. AVAILABILITY: http://motif.stanford.edu/3motif/