cWINNOWER Algorithm for Finding Fuzzy DNA Motifs

The cWINNOWER algorithm detects fuzzy motifs in DNA sequences rich in protein-binding signals. A signal is defined as any short nucleotide pattern having up to d mutations differing from a motif of length l. The algorithm finds such motifs if multiple mutated copies of the motif (i.e., the signals) are present in the DNA sequence in sufficient abundance. The cWINNOWER algorithm substantially improves the sensitivity of the winnower method of Pevzner and Sze by imposing a consensus constraint, enabling it to detect much weaker signals. We studied the minimum number of detectable motifs qc as a function of sequence length N for random sequences. We found that qc increases linearly with N for a fast version of the algorithm based on counting three-member sub-cliques. Imposing consensus constraints reduces qc, by a factor of three in this case, which makes the algorithm dramatically more sensitive. Our most sensitive algorithm, which counts four-member sub-cliques, needs a minimum of only 13 signals to detect motifs in a sequence of length N = 12000 for (l,d) = (15,4)

A survey of DNA motif finding algorithms

Background: Unraveling the mechanisms that regulate gene expression is a major challenge in biology. An important task in this challenge is to identify regulatory elements, especially the binding sites in deoxyribonucleic acid (DNA) for transcription factors. These binding sites are short DNA segments that are called motifs. Recent advances in genome sequence availability and in high-throughput gene expression analysis technologies have allowed for the development of computational methods for motif finding. As a result, a large number of motif finding algorithms have been implemented and applied to various motif models over the past decade. This survey reviews the latest developments in DNA motif finding algorithms.Results: Earlier algorithms use promoter sequences of coregulated genes from single genome and search for statistically overrepresented motifs. Recent algorithms are designed to use phylogenetic footprinting or orthologous sequences and also an integrated approach where promoter sequences of coregulated genes and phylogenetic footprinting are used. All the algorithms studied have been reported to correctly detect the motifs that have been previously detected by laboratory experimental approaches, and some algorithms were able to find novel motifs. However, most of these motif finding algorithms have been shown to work successfully in yeast and other lower organisms, but perform significantly worse in higher organisms.Conclusion: Despite considerable efforts to date, DNA motif finding remains a complex challenge for biologists and computer scientists. Researchers have taken many different approaches in developing motif discovery tools and the progress made in this area of research is very encouraging. Performance comparison of different motif finding tools and identification of the best tools have proven to be a difficult task because tools are designed based on algorithms and motif models that are diverse and complex and our incomplete understanding of the biology of regulatory mechanism does not always provide adequate evaluation of underlying algorithms over motif models.Peer reviewedComputer Scienc

Finding exact optimal motifs in matrix representation by partitioning

Motivation: Finding common patterns, or motifs, in the promoter regions of co-expressed genes is an important problem in bioinformatics. A common representation of the motif is by probability matrix or PSSM (position specific scoring matrix). However, even for a motif of length six or seven, there is no algorithm that can guarantee finding the exact optimal matrix from an infinite number of possible matrices. Results: T his paper introduces the first algorithm, called EOMM, for finding the exact optimal matrix-represented motif, or simply optimal motif. Based on branch-and-bound searching by partitioning the solution space recursively, EOMM can find the optimal motif of size up to eight or nine, and a motif of larger size with any desired accuracy on the principle that the smaller the error bound, the longer the running time. Experiments show that for some real and simulated data sets, EOMM finds the motif despite very weak signals when existing software, such as MEME and MITRA-PSSM, fails to do so. © The Author 2005. Published by Oxford University Press. All rights reserved.postprin

BLSSpeller : exhaustive comparative discovery of conserved cis-regulatory elements

Motivation: The accurate discovery and annotation of regulatory elements remains a challenging problem. The growing number of sequenced genomes creates new opportunities for comparative approaches to motif discovery. Putative binding sites are then considered to be functional if they are conserved in orthologous promoter sequences of multiple related species. Existing methods for comparative motif discovery usually rely on pregenerated multiple sequence alignments, which are difficult to obtain for more diverged species such as plants. As a consequence, misaligned regulatory elements often remain undetected. Results: We present a novel algorithm that supports both alignment-free and alignment-based motif discovery in the promoter sequences of related species. Putative motifs are exhaustively enumerated as words over the IUPAC alphabet and screened for conservation using the branch length score. Additionally, a confidence score is established in a genome-wide fashion. In order to take advantage of a cloud computing infrastructure, the MapReduce programming model is adopted. The method is applied to four monocotyledon plant species and it is shown that high-scoring motifs are significantly enriched for open chromatin regions in Oryza sativa and for transcription factor binding sites inferred through protein-binding microarrays in O. sativa and Zea mays. Furthermore, the method is shown to recover experimentally profiled ga2ox1-like KN1 binding sites in Z. mays

An efficient algorithm for the extended (l,d)-motif problem with unknown number of binding sites

Finding common patterns, or motifs, from a set of DNA sequences is an important problem in molecular biology. Most motif-discovering algorithms/software require the length of the motif as input. Motivated by the fact that the motif's length is usually unknown in practice, Styczynsfd et al. introduced the Extended (l,d)-Motif Problem (EMP), where the motif's length is not an input parameter. Unfortunately, the algorithm given by Styczynski et al. to solve EMP can take an unacceptably long time to run, e.g. over 3 months to discover a length-14 motif. This paper makes two main contributions. First, we eliminate another input parameter from EMP: the minimum number of binding sites in the DNA sequences. Fewer input parameters not only reduces the burden of the user, but also may give more realistic/robust results since restrictions on length or on the number of binding sites make little sense when the best motif may not be the longest nor have the largest number of binding sites. Second, we develop an efficient algorithm to solve our redefined problem. The algorithm is also a fast solution for EMP (without any sacrifice to accuracy) making EMP practical. © 2005 IEEE.published_or_final_versio

Improving ChIP-seq peak-calling for functional co-regulator binding by integrating multiple sources of biological information

Background: Chromatin immunoprecipitation coupled with massively parallel sequencing (ChIP-seq) is increasingly being applied to study genome-wide binding sites of transcription factors. There is an increasing interest in understanding the mechanism of action of co-regulator proteins, which do not bind DNA directly, but exert their effects by binding to transcription factors such as the estrogen receptor (ER). However, due to the nature of detecting indirect protein-DNA interaction, ChIP-seq signals from co-regulators can be relatively weak and thus biologically meaningful interactions remain difficult to identify

De Novo Transcription Factor Binding Site Discovery: A Machine Learning And Model Selection Approach

Computational methods have been widely applied to the problem of predicting regulatory elements. Many tools have been proposed. Each has taken a different approach and has been based on different underlying sets of assumptions, frequently similar to those of other tools. To date, the accuracy of each individual tool has been relatively poor. Noting that different tools often report different results, common practice is to analyze a given set of regulatory regions using more than one tool and to manually compare the results. Recently, ensemble approaches have been proposed that automate the execution of a set of tools and aggregate the results. This has been seen to provide some improvement but is still handled in an ad hoc manner since tool outputs are often in dissimilar formats. Another approach to improve accuracy has been to investigate the objective functions currently in use and identify additional informational statistics to incorporate into them. As a result of this investigation, one statistical measure of positional specificity has been demonstrated to be informative. In this context, this thesis explores the application of three simple models for the positional distribution of transcription factor binding sites (TFBS) to the problem of TFBS discovery. As alternate measures of positional specificity, log-likelihood ratios for the three models are calculated and treated as features to classify TFBSs as biologically relevant or irrelevant. As a verification step, randomly generated positional distributions are analyzed to demonstrate the robustness and accuracy of the log-likelihood ratios at classifying data from known distributions using a simple classifier. To improve classification accuracy, a support vector machine (SVM) approach is used. Subsequently, randomly generated sequences seeded with TFBSs at positions chosen to conform to one of the three models are analyzed as an additional verification step. Finally, two types of sets of real regulatory region sequences are analyzed. First, results consistent with the literature are obtained in three cases for genes experimentally determined to be co-expressed during mouse thymocyte maturation, and a novel role is predicted for three families of TFBSs in single positive (SP) T-cells. Second, the mouse and human ―real‖ sets from Tompa et al’s ―Assessment of Computational Motif Discovery Tools‖ are analyzed, and the results are reported

Localización de motivos de secuencias de ADN usando un algoritmo genético

"La evolución genética siempre ha sido un tema de relevancia desde principios del siglo XX, cuando se comenzó a hablar de partículas, factores, caracteres y genes, se comenzaron a esclarecer las bases moleculares, es decir, cómo estaban compuestos los lugares que contenían esa información y dónde estaba localizada. El proceso de traspaso de información o de material genético de una generación a otra, depende completamente de cómo la célula crece y se divide. El descubrimiento de motivos es uno de los problemas del análisis de secuencias de ADN, los motivos representan secuencias conservadas que pueden ser biológicamente significativas, el descubrimiento de motivos consiste en encontrar sitios de unión en aminoácidos, encontrando reguladores de información dentro de secuencias de ADN o ARN. Un motivo de ADN se define como un patrón de secuencia de ácido nucleico que tiene algún significado biológico como lo son sitios de unión de ADN para una proteína reguladora, es decir, un factor de transcripción. La presente tesis tiene como objetivo implementar un algoritmo genético para la búsqueda de motivos en secuencias de ADN, con la finalidad de mejorar la búsqueda, haciéndola más eficiente y precisa, así como facilitar su uso en diferentes investigaciones"