Showing papers in "Algorithms for Molecular Biology in 2007"

Efficient and accurate P-value computation for Position Weight Matrices

Abstract: Background: Position Weight Matrices (PWMs) are probabilistic representations of signals in sequences. They are widely used to model approximate patterns in DNA or in protein sequences. The usage of PWMs needs as a prerequisite to knowing the statistical significance of a word according to its score. This is done by defining the P-value of a score, which is the probability that the background model can achieve a score larger than or equal to the observed value. This gives rise to the following problem: Given a P-value, find the corresponding score threshold. Existing methods rely on dynamic programming or probability generating functions. For many examples of PWMs, they fail to give accurate results in a reasonable amount of time. Results: The contribution of this paper is two fold. First, we study the theoretical complexity of the problem, and we prove that it is NP-hard. Then, we describe a novel algorithm that solves the P-value problem efficiently. The main idea is to use a series of discretized score distributions that improves the final result step by step until some convergence criterion is met. Moreover, the algorithm is capable of calculating the exact P-value without any error, even for matrices with noninteger coefficient values. The same approach is also used to devise an accurate algorithm for the reverse problem: finding the P-value for a given score. Both methods are implemented in a software called TFM-PVALUE, that is freely available. Conclusion: We have tested TFM-PVALUE on a large set of PWMs representing transcription factor binding sites. Experimental results show that it achieves better performance in terms of computational time and precision than existing tools.

Finding coevolving amino acid residues using row and column weighting of mutual information and multi-dimensional amino acid representation

Abstract: Some amino acid residues functionally interact with each other. This interaction will result in an evolutionary co-variation between these residues – coevolution. Our goal is to find these coevolving residues. We present six new methods for detecting coevolving residues. Among other things, we suggest measures that are variants of Mutual Information, and measures that use a multidimensional representation of each residue in order to capture the physico-chemical similarities between amino acids. We created a benchmarking system, in silico, able to evaluate these methods through a wide range of realistic conditions. Finally, we use the combination of different methods as a way of improving performance. Our best method (Row and Column Weighed Mutual Information) has an estimated accuracy increase of 63% over Mutual Information. Furthermore, we show that the combination of different methods is efficient, and that the methods are quite sensitive to the different conditions tested.

Consistency of the Neighbor-Net Algorithm

Abstract: Background: Neighbor-Net is a novel method for phylogenetic analysis that is currently being widely used in areas such as virology, bacteriology, and plant evolution. Given an input distance matrix, Neighbor-Net produces a phylogenetic network, a generalization of an evolutionary or phylogenetic tree which allows the graphical representation of conflicting phylogenetic signals. Results: In general, any network construction method should not depict more conflict than is found in the data, and, when the data is fitted well by a tree, the method should return a network that is close to this tree. In this paper we provide a formal proof that Neighbor-Net satisfies both of these requirements so that, in particular, Neighbor-Net is statistically consistent on circular distances.

Exact p-value calculation for heterotypic clusters of regulatory motifs and its application in computational annotation of cis-regulatory modules.

Abstract: cis-Regulatory modules (CRMs) of eukaryotic genes often contain multiple binding sites for transcription factors. The phenomenon that binding sites form clusters in CRMs is exploited in many algorithms to locate CRMs in a genome. This gives rise to the problem of calculating the statistical significance of the event that multiple sites, recognized by different factors, would be found simultaneously in a text of a fixed length. The main difficulty comes from overlapping occurrences of motifs. So far, no tools have been developed allowing the computation of p-values for simultaneous occurrences of different motifs which can overlap. We developed and implemented an algorithm computing the p-value that s different motifs occur respectively k1, ..., k s or more times, possibly overlapping, in a random text. Motifs can be represented with a majority of popular motif models, but in all cases, without indels. Zero or first order Markov chains can be adopted as a model for the random text. The computational tool was tested on the set of cis-regulatory modules involved in D. melanogaster early development, for which there exists an annotation of binding sites for transcription factors. Our test allowed us to correctly identify transcription factors cooperatively/competitively binding to DNA. The algorithm that precisely computes the probability of simultaneous motif occurrences is inspired by the Aho-Corasick automaton and employs a prefix tree together with a transition function. The algorithm runs with the O(n|Σ|(m| | + K|σ| K ) ∏ i k i ) time complexity, where n is the length of the text, |Σ| is the alphabet size, m is the maximal motif length, | | is the total number of words in motifs, K is the order of Markov model, and k i is the number of occurrences of the i th motif. The primary objective of the program is to assess the likelihood that a given DNA segment is CRM regulated with a known set of regulatory factors. In addition, the program can also be used to select the appropriate threshold for PWM scanning. Another application is assessing similarity of different motifs. Project web page, stand-alone version and documentation can be found at http://bioinform.genetika.ru/AhoPro/

PhyloScan: identification of transcription factor binding sites using cross-species evidence

Abstract: When transcription factor binding sites are known for a particular transcription factor, it is possible to construct a motif model that can be used to scan sequences for additional sites. However, few statistically significant sites are revealed when a transcription factor binding site motif model is used to scan a genome-scale database. We have developed a scanning algorithm, PhyloScan, which combines evidence from matching sites found in orthologous data from several related species with evidence from multiple sites within an intergenic region, to better detect regulons. The orthologous sequence data may be multiply aligned, unaligned, or a combination of aligned and unaligned. In aligned data, PhyloScan statistically accounts for the phylogenetic dependence of the species contributing data to the alignment and, in unaligned data, the evidence for sites is combined assuming phylogenetic independence of the species. The statistical significance of the gene predictions is calculated directly, without employing training sets. In a test of our methodology on synthetic data modeled on seven Enterobacteriales, four Vibrionales, and three Pasteurellales species, PhyloScan produces better sensitivity and specificity than MONKEY, an advanced scanning approach that also searches a genome for transcription factor binding sites using phylogenetic information. The application of the algorithm to real sequence data from seven Enterobacteriales species identifies novel Crp and PurR transcription factor binding sites, thus providing several new potential sites for these transcription factors. These sites enable targeted experimental validation and thus further delineation of the Crp and PurR regulons in E. coli. Better sensitivity and specificity can be achieved through a combination of (1) using mixed alignable and non-alignable sequence data and (2) combining evidence from multiple sites within an intergenic region.

Local sequence alignments statistics: deviations from Gumbel statistics in the rare-event tail

Abstract: The optimal score for ungapped local alignments of infinitely long random sequences is known to follow a Gumbel extreme value distribution. Less is known about the important case, where gaps are allowed. For this case, the distribution is only known empirically in the high-probability region, which is biologically less relevant. We provide a method to obtain numerically the biologically relevant rare-event tail of the distribution. The method, which has been outlined in an earlier work, is based on generating the sequences with a parametrized probability distribution, which is biased with respect to the original biological one, in the framework of Metropolis Coupled Markov Chain Monte Carlo. Here, we first present the approach in detail and evaluate the convergence of the algorithm by considering a simple test case. In the earlier work, the method was just applied to one single example case. Therefore, we consider here a large set of parameters: We study the distributions for protein alignment with different substitution matrices (BLOSUM62 and PAM250) and affine gap costs with different parameter values. In the logarithmic phase (large gap costs) it was previously assumed that the Gumbel form still holds, hence the Gumbel distribution is usually used when evaluating p-values in databases. Here we show that for all cases, provided that the sequences are not too long (L > 400), a "modified" Gumbel distribution, i.e. a Gumbel distribution with an additional Gaussian factor is suitable to describe the data. We also provide a "scaling analysis" of the parameters used in the modified Gumbel distribution. Furthermore, via a comparison with BLAST parameters, we show that significance estimations change considerably when using the true distributions as presented here. Finally, we study also the distribution of the sum statistics of the k best alignments. Our results show that the statistics of gapped and ungapped local alignments deviates significantly from Gumbel in the rare-event tail. We provide a Gaussian correction to the distribution and an analysis of its scaling behavior for several different scoring parameter sets, which are commonly used to search protein data bases. The case of sum statistics of k best alignments is included.

RNAstrand: reading direction of structured RNAs in multiple sequence alignments

Abstract: Genome-wide screens for structured ncRNA genes in mammals, urochordates, and nematodes have predicted thousands of putative ncRNA genes and other structured RNA motifs. A prerequisite for their functional annotation is to determine the reading direction with high precision. While folding energies of an RNA and its reverse complement are similar, the differences are sufficient at least in conjunction with substitution patterns to discriminate between structured RNAs and their complements. We present here a support vector machine that reliably classifies the reading direction of a structured RNA from a multiple sequence alignment and provides a considerable improvement in classification accuracy over previous approaches. RNAstrand is freely available as a stand-alone tool from http://www.bioinf.uni-leipzig.de/Software/RNAstrand and is also included in the latest release of RNAz, a part of the Vienna RNA Package.

Evaluating deterministic motif significance measures in protein databases

Abstract: Background: Assessing the outcome of motif mining algorithms is an essential task, as the number of reported motifs can be very large. Significance measures play a central role in automatically ranking those motifs, and therefore alleviating the analysis work. Spotting the most interesting and relevant motifs is then dependent on the choice of the right measures. The combined use of several measures may provide more robust results. However caution has to be taken in order to avoid spurious evaluations. Results: From the set of conducted experiments, it was verified that several of the selected significance measures show a very similar behavior in a wide range of situations therefore providing redundant information. Some measures have proved to be more appropriate to rank highly conserved motifs, while others are more appropriate for weakly conserved ones. Support appears as a very important feature to be considered for correct motif ranking. We observed that not all the measures are suitable for situations with poorly balanced class information, like for instance, when positive data is significantly less than negative data. Finally, a visualization scheme was proposed that, when several measures are applied, enables an easy identification of high scoring motifs. Conclusion: In this work we have surveyed and categorized 14 significance measures for pattern evaluation. Their ability to rank three types of deterministic motifs was evaluated. Measures were applied in different testing conditions, where relations were identified. This study provides some pertinent insights on the choice of the right set of significance measures for the evaluation of deterministic motifs extracted from protein databases.

Transcriptional regulatory network discovery via multiple method integration: application to e. coli K12

Abstract: Transcriptional regulatory network (TRN) discovery from one method (e.g. microarray analysis, gene ontology, phylogenic similarity) does not seem feasible due to lack of sufficient information, resulting in the construction of spurious or incomplete TRNs. We develop a methodology, TRND, that integrates a preliminary TRN, microarray data, gene ontology and phylogenic similarity to accurately discover TRNs and apply the method to E. coli K12. The approach can easily be extended to include other methodologies. Although gene ontology and phylogenic similarity have been used in the context of gene-gene networks, we show that more information can be extracted when gene-gene scores are transformed to gene-transcription factor (TF) scores using a preliminary TRN. This seems to be preferable over the construction of gene-gene interaction networks in light of the observed fact that gene expression and activity of a TF made of a component encoded by that gene is often out of phase. TRND multi-method integration is found to be facilitated by the use of a Bayesian framework for each method derived from its individual scoring measure and a training set of gene/TF regulatory interactions. The TRNs we construct are in better agreement with microarray data. The number of gene/TF interactions we discover is actually double that of existing networks.

A basic analysis toolkit for biological sequences.

Abstract: This paper presents a software library, nicknamed BATS, for some basic sequence analysis tasks. Namely, local alignments, via approximate string matching, and global alignments, via longest common subsequence and alignments with affine and concave gap cost functions. Moreover, it also supports filtering operations to select strings from a set and establish their statistical significance, via z-score computation. None of the algorithms is new, but although they are generally regarded as fundamental for sequence analysis, they have not been implemented in a single and consistent software package, as we do here. Therefore, our main contribution is to fill this gap between algorithmic theory and practice by providing an extensible and easy to use software library that includes algorithms for the mentioned string matching and alignment problems. The library consists of C/C++ library functions as well as Perl library functions. It can be interfaced with Bioperl and can also be used as a stand-alone system with a GUI. The software is available at http://www.math.unipa.it/~raffaele/BATS/ under the GNU GPL.

GOGOT: a method for the identification of differentially expressed fragments from cDNA-AFLP data

Abstract: Background One-dimensional (1-D) electrophoretic data obtained using the cDNA-AFLP method have attracted great interest for the identification of differentially expressed transcript-derived fragments (TDFs). However, high-throughput analysis of the cDNA-AFLP data is currently limited by the need for labor-intensive visual evaluation of multiple electropherograms. We would like to have high-throughput ways of identifying such TDFs.

A spatio-temporal mining approach towards summarizing and analyzing protein folding trajectories

Abstract: Understanding the protein folding mechanism remains a grand challenge in structural biology. In the past several years, computational theories in molecular dynamics have been employed to shed light on the folding process. Coupled with high computing power and large scale storage, researchers now can computationally simulate the protein folding process in atomistic details at femtosecond temporal resolution. Such simulation often produces a large number of folding trajectories, each consisting of a series of 3D conformations of the protein under study. As a result, effectively managing and analyzing such trajectories is becoming increasingly important. In this article, we present a spatio-temporal mining approach to analyze protein folding trajectories. It exploits the simplicity of contact maps, while also integrating 3D structural information in the analysis. It characterizes the dynamic folding process by first identifying spatio-temporal association patterns in contact maps, then studying how such patterns evolve along a folding trajectory. We demonstrate that such patterns can be leveraged to summarize folding trajectories, and to facilitate the detection and ordering of important folding events along a folding path. We also show that such patterns can be used to identify a consensus partial folding pathway across multiple folding trajectories. Furthermore, we argue that such patterns can capture both local and global structural topology in a 3D protein conformation, thereby facilitating effective structural comparison amongst conformations. We apply this approach to analyze the folding trajectories of two small synthetic proteins-BBA5 and GSGS (or Beta3S). We show that this approach is promising towards addressing the above issues, namely, folding trajectory summarization, folding events detection and ordering, and consensus partial folding pathway identification across trajectories.

Data Mining in Bioinformatics (BIOKDD)

Abstract: Data Mining is the process of automatic discovery of novel and understandable models and patterns from large amounts of data. Bioinformatics is the science of storing, analyzing, and utilizing information from biological data such as sequences, molecules, gene expressions, and pathways. Development of novel data mining methods will play a fundamental role in understanding these rapidly expanding sources of biological data. Data mining approaches seem ideally suited for bioinformatics, which is data-rich, but lacks a comprehensive theory of life's organization at the molecular level. The extensive databases of biological information create both challenges and opportunities for developing novel data mining methods. The 6th Workshop on Data Mining in Bioinformatics (BIOKDD) was held on August 20th, 2006, Philadelphia, PA, USA, in conjunction with the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. The goal of the workshop was to encourage KDD researchers to take on the numerous challenges that Bioinformatics offers. The BIOKDD workshops have been held annually in conjunction with the ACM SIGKDD Conferences, since 2001. Additional information about BIOKDD can be obtained online [1]. Five revised and expanded papers were selected from the BIOKDD workshop, out of a total of 18 submissions, to appear in Algorithms for Molecular Biology (AMB). These papers underwent another round of external reviewing prior to being accepted for AMB. An overview of each paper is given below. In the paper titled Automatic Layout and Visualization of Biclusters, Gregory A. Grothaus, Adeel Mufti and T. M. Murali [2], present a novel method to display biclusters mined from gene expression data. The approach allows querying and visual exploration of the clusters/sub-matrices. The software is also available as open-source. In ExMotif: Efficient Structured Motif Extraction, Yongqiang Zhang and Mohammed J. Zaki [3], describe a new algorithm called EXMOTIF to extract frequent motifs from DNA sequences. The method can mine structured motifs and profiles which have variable gaps between different elements. The demonstrate the efficiency of the method compared to state-of-the-art methods, and also demonstrate an application in mining composite transcription factor binding sites. In the paper Refining Motifs by Improving Information Content Scores using Neighborhood Profile Search, Chandan K. Reddy, Yao-Chung Weng and Hsiao-Dong Chiang [4], show how one can refine the profile motifs discovered via Expectation Maximization and Gibbs Sampling based methods. They search the neighborhood regions of the initial alignments to obtain locally optimal solutions, which improve the information content of the discovered profiles. In their paper, A Novel Functional Module Detection Algorithm for Protein-Protein Interaction Networks, Woochang Hwang, Young-Rae Cho, Aidong Zhang and Murali Ramanathan [5], describe the unexpected properties of the protein-protein interaction (PPI) networks and their use in a clustering method to detect biologically relevant functional modules. They propose a new method called STM (signal transduction model) to detect the PPI modules, and compare it with previous approaches to demonstrate its effectiveness in discovering large and arbitrary shaped clusters. In A Spatio-temporal Mining Approach towards Summarizing and Analyzing Protein Folding Trajectories, Hui Yang, Srinivasan Parthasarathy and Duygu Ucar [6], describe a method to mine protein folding molecular dynamics simulations datasets. They describe a spatio-temporal association discovery approach to mine protein folding trajectories, to identify critical events and common pathways.

Characteristics of predictor sets found using differential prioritization

Abstract: Feature selection plays an undeniably important role in classification problems involving high dimensional datasets such as microarray datasets. For filter-based feature selection, two well-known criteria used in forming predictor sets are relevance and redundancy. However, there is a third criterion which is at least as important as the other two in affecting the efficacy of the resulting predictor sets. This criterion is the degree of differential prioritization (DDP), which varies the emphases on relevance and redundancy depending on the value of the DDP. Previous empirical works on publicly available microarray datasets have confirmed the effectiveness of the DDP in molecular classification. We now propose to establish the fundamental strengths and merits of the DDP-based feature selection technique. This is to be done through a simulation study which involves vigorous analyses of the characteristics of predictor sets found using different values of the DDP from toy datasets designed to mimic real-life microarray datasets. A simulation study employing analytical measures such as the distance between classes before and after transformation using principal component analysis is implemented on toy datasets. From these analyses, the necessity of adjusting the differential prioritization based on the dataset of interest is established. This conclusion is supported by comparisons against both simplistic rank-based selection and state-of-the-art equal-priorities scoring methods, which demonstrates the superiority of the DDP-based feature selection technique. Reapplying similar analyses to real-life multiclass microarray datasets provides further confirmation of our findings and of the significance of the DDP for practical applications. The findings have been achieved based on analytical evaluations, not empirical evaluation involving classifiers, thus providing further basis for the usefulness of the DDP and validating the need for unequal priorities on relevance and redundancy during feature selection for microarray datasets, especially highly multiclass datasets.

Mapping sequences by parts.

Abstract: We present the N-map method, a pairwise and asymmetrical approach which allows us to compare sequences by taking into account evolutionary events that produce shuffled, reversed or repeated elements. Basically, the optimal N-map of a sequence s over a sequence t is the best way of partitioning the first sequence into N parts and placing them, possibly complementary reversed, over the second sequence in order to maximize the sum of their gapless alignment scores. We introduce an algorithm computing an optimal N-map with time complexity O (|s| × |t| × N) using O (|s| × |t| × N) memory space. Among all the numbers of parts taken in a reasonable range, we select the value N for which the optimal N-map has the most significant score. To evaluate this significance, we study the empirical distributions of the scores of optimal N-maps and show that they can be approximated by normal distributions with a reasonable accuracy. We test the functionality of the approach over random sequences on which we apply artificial evolutionary events. The method is illustrated with four case studies of pairs of sequences involving non-standard evolutionary events.