Data mining of protein families using common peptides

Predicting the function of a protein from its sequence is typically addressed using sequence-similarity. Here we propose a motif-based approach, using supervised motif extraction from protein sequences belonging to one functional family. The resulting deterministic motifs form Common Peptides (CPs) that characterize this family, allow for data mining of its proteins and facilitate further partition of the family into cluster

WildSpan: mining structured motifs from protein sequences

<p>Abstract</p> <p>Background</p> <p>Automatic extraction of motifs from biological sequences is an important research problem in study of molecular biology. For proteins, it is desired to discover sequence motifs containing a large number of wildcard symbols, as the residues associated with functional sites are usually largely separated in sequences. Discovering such patterns is time-consuming because abundant combinations exist when long gaps (a gap consists of one or more successive wildcards) are considered. Mining algorithms often employ constraints to narrow down the search space in order to increase efficiency. However, improper constraint models might degrade the sensitivity and specificity of the motifs discovered by computational methods. We previously proposed a new constraint model to handle large wildcard regions for discovering functional motifs of proteins. The patterns that satisfy the proposed constraint model are called W-patterns. A W-pattern is a structured motif that groups motif symbols into pattern blocks interleaved with large irregular gaps. Considering large gaps reflects the fact that functional residues are not always from a single region of protein sequences, and restricting motif symbols into clusters corresponds to the observation that short motifs are frequently present within protein families. To efficiently discover W-patterns for large-scale sequence annotation and function prediction, this paper first formally introduces the problem to solve and proposes an algorithm named WildSpan (sequential pattern mining across large wildcard regions) that incorporates several pruning strategies to largely reduce the mining cost.</p> <p>Results</p> <p>WildSpan is shown to efficiently find W-patterns containing conserved residues that are far separated in sequences. We conducted experiments with two mining strategies, protein-based and family-based mining, to evaluate the usefulness of W-patterns and performance of WildSpan. The protein-based mining mode of WildSpan is developed for discovering functional regions of a single protein by referring to a set of related sequences (e.g. its homologues). The discovered W-patterns are used to characterize the protein sequence and the results are compared with the conserved positions identified by multiple sequence alignment (MSA). The family-based mining mode of WildSpan is developed for extracting sequence signatures for a group of related proteins (e.g. a protein family) for protein function classification. In this situation, the discovered W-patterns are compared with PROSITE patterns as well as the patterns generated by three existing methods performing the similar task. Finally, analysis on execution time of running WildSpan reveals that the proposed pruning strategy is effective in improving the scalability of the proposed algorithm.</p> <p>Conclusions</p> <p>The mining results conducted in this study reveal that WildSpan is efficient and effective in discovering functional signatures of proteins directly from sequences. The proposed pruning strategy is effective in improving the scalability of WildSpan. It is demonstrated in this study that the W-patterns discovered by WildSpan provides useful information in characterizing protein sequences. The WildSpan executable and open source codes are available on the web (<url>http://biominer.csie.cyu.edu.tw/wildspan</url>).</p

SLIDER: Mining correlated motifs in protein-protein interaction networks

Abstract—Correlated motif mining (CMM) is the problem to find overrepresented pairs of patterns, called motif pairs, in interacting protein sequences. Algorithmic solutions for CMM thereby provide a computational method for predicting binding sites for protein interaction. In this paper, we adopt a motif-driven approach where the support of candidate motif pairs is evaluated in the network. We experimentally establish the superiority of the Chi-square-based support measure over other support measures. Furthermore, we obtain that CMM is an NP-hard problem for a large class of support measures (including Chi-square) and reformulate the search for correlated motifs as a combinatorial optimization problem. We then present the method SLIDER which uses local search with a neighborhood function based on sliding motifs and employs the Chi-square-based support measure. We show that SLIDER outperforms existing motif-driven CMM methods and scales to large protein-protein interaction networks

Multiple instance learning for sequence data with across bag dependencies

In Multiple Instance Learning (MIL) problem for sequence data, the instances inside the bags are sequences. In some real world applications such as bioinformatics, comparing a random couple of sequences makes no sense. In fact, each instance may have structural and/or functional relations with instances of other bags. Thus, the classification task should take into account this across bag relation. In this work, we present two novel MIL approaches for sequence data classification named ABClass and ABSim. ABClass extracts motifs from related instances and use them to encode sequences. A discriminative classifier is then applied to compute a partial classification result for each set of related sequences. ABSim uses a similarity measure to discriminate the related instances and to compute a scores matrix. For both approaches, an aggregation method is applied in order to generate the final classification result. We applied both approaches to solve the problem of bacterial Ionizing Radiation Resistance prediction. The experimental results of the presented approaches are satisfactory

Mining Representative Unsubstituted Graph Patterns Using Prior Similarity Matrix

One of the most powerful techniques to study protein structures is to look for recurrent fragments (also called substructures or spatial motifs), then use them as patterns to characterize the proteins under study. An emergent trend consists in parsing proteins three-dimensional (3D) structures into graphs of amino acids. Hence, the search of recurrent spatial motifs is formulated as a process of frequent subgraph discovery where each subgraph represents a spatial motif. In this scope, several efficient approaches for frequent subgraph discovery have been proposed in the literature. However, the set of discovered frequent subgraphs is too large to be efficiently analyzed and explored in any further process. In this paper, we propose a novel pattern selection approach that shrinks the large number of discovered frequent subgraphs by selecting the representative ones. Existing pattern selection approaches do not exploit the domain knowledge. Yet, in our approach we incorporate the evolutionary information of amino acids defined in the substitution matrices in order to select the representative subgraphs. We show the effectiveness of our approach on a number of real datasets. The results issued from our experiments show that our approach is able to considerably decrease the number of motifs while enhancing their interestingness

Prediction of peptides binding to MHC class I alleles by partial periodic pattern mining

MHC (Major Histocompatibility Complex) is a key player in the immune response of an organism. It is important to be able to predict which antigenic peptides will bind to a spe-cific MHC allele and which will not, creating possibilities for controlling immune response and for the applications of immunotherapy. However a problem encountered in the computational binding prediction methods for MHC class I is the presence of bulges and loops in the peptides, changing the total length. Most machine learning methods in use to-day require the sequences to be of same length to success-fully mine the binding motifs. We propose the use of time-based data mining methods in motif mining to be able to mine motifs position-independently. Also, the information for both binding and non-binding peptides are used on the contrary to the other methods which only rely on binding peptides. The prediction results are between 70-80% for the tested alleles

WebTraceMiner: a web service for processing and mining EST sequence trace files

Expressed sequence tags (ESTs) remain a dominant approach for characterizing the protein-encoding portions of various genomes. Due to inherent deficiencies, they also present serious challenges for data quality control. Before GenBank submission, EST sequences are typically screened and trimmed of vector and adapter/linker sequences, as well as polyA/T tails. Removal of these sequences presents an obstacle for data validation of error-prone ESTs and impedes data mining of certain functional motifs, whose detection relies on accurate annotation of positional information for polyA tails added posttranscriptionally. As raw DNA sequence information is made increasingly available from public repositories, such as NCBI Trace Archive, new tools will be necessary to reanalyze and mine this data for new information. WebTraceMiner (www.conifergdb.org/software/wtm) was designed as a public sequence processing service for raw EST traces, with a focus on detection and mining of sequence features that help characterize 3′ and 5′ termini of cDNA inserts, including vector fragments, adapter/linker sequences, insert-flanking restriction endonuclease recognition sites and polyA or polyT tails. WebTraceMiner complements other public EST resources and should prove to be a unique tool to facilitate data validation and mining of error-prone ESTs (e.g. discovery of new functional motifs)

Unveiling combinatorial regulation through the combination of ChIP information and in silico cis-regulatory module detection

Computationally retrieving biologically relevant cis-regulatory modules (CRMs) is not straightforward. Because of the large number of candidates and the imperfection of the screening methods, many spurious CRMs are detected that are as high scoring as the biologically true ones. Using ChIP-information allows not only to reduce the regions in which the binding sites of the assayed transcription factor (TF) should be located, but also allows restricting the valid CRMs to those that contain the assayed TF (here referred to as applying CRM detection in a query-based mode). In this study, we show that exploiting ChIP-information in a query-based way makes in silico CRM detection a much more feasible endeavor. To be able to handle the large datasets, the query-based setting and other specificities proper to CRM detection on ChIP-Seq based data, we developed a novel powerful CRM detection method 'CPModule'. By applying it on a well-studied ChIP-Seq data set involved in self-renewal of mouse embryonic stem cells, we demonstrate how our tool can recover combinatorial regulation of five known TFs that are key in the self-renewal of mouse embryonic stem cells. Additionally, we make a number of new predictions on combinatorial regulation of these five key TFs with other TFs documented in TRANSFAC

Automated DNA Motif Discovery

Ensembl's human non-coding and protein coding genes are used to automatically find DNA pattern motifs. The Backus-Naur form (BNF) grammar for regular expressions (RE) is used by genetic programming to ensure the generated strings are legal. The evolved motif suggests the presence of Thymine followed by one or more Adenines etc. early in transcripts indicate a non-protein coding gene. Keywords: pseudogene, short and microRNAs, non-coding transcripts, systems biology, machine learning, Bioinformatics, motif, regular expression, strongly typed genetic programming, context-free grammar.Comment: 12 pages, 2 figure