Suffix Tree Characterization of Maximal Motifs in Biological Sequences

Finding motifs in biological sequences is one of the most intriguing problems for string algorithms designers as it is necessary to deal with approximations and this complicates the problem. Existing algorithms run in time linear with the input size. Nevertheless, the output size can be very large due to the approximation. This makes the output often unreadable, next to slowing down the inference itself. Since only a subset of the motifs, i.e. the \emph{maximal} motifs, could be enough to give the information of all of them, in this paper, we aim at removing such redundancy. We define notions of maximality that we characterize in the suffix tree data structure. Given that this is used by a whole class of motifs extraction tools, we show how these tools can be modified to include the maximality requirement on the fly without changing the asymptotical complexity

Inference of Network Expressions

No abstract availabl

High Performance Implementation of Planted Motif Problem using Suffix trees

In this paper we present a high performance implementation of suffix tree based solution to the planted motif problem on two different parallel architectures: NVIDIA GPU and Intel Multicore machines. An (l,d) planted motif problem(PMP) is defined as: Given a sequence of n DNA sequences, each of length L, find M, the set of sequences(or motifs) of length l which have atleast one d-neighbor in each of the n sequences. Here, a d-neighbor of a sequence is a sequence of same length that differs in at-most d positions. PMP is a well studied problem in computational biology. It is useful in developing methods for finding transcription factor binding sites, sequence classification and for building phylogenetic trees. The problem is computationally challenging to solve, for example a (19,7) PMP takes 9.9 hours on a sequential machine. Many approaches to solve planted motif problem can be found in literature. One approach is based on use of suffix tree data structure. Though suffix tree based methods are the most efficient ones for solving large planted motif problems on sequential machines, they are quite difficult to parallelize. We present suffix tree based parallel solutions for PMP on NVIDIA GPU and Intel Multicore architectures that are efficient and scalable. The solutions are based on a suffix tree algorithm previously presented but use extensive adaptation to individual architectures to ensure that the implementations work efficiently and scale well

Output-Sensitive Pattern Extraction in Sequences

Genomic Analysis, Plagiarism Detection, Data Mining, Intrusion Detection, Spam Fighting and Time Series Analysis are just some examples of applications where extraction of recurring patterns in sequences of objects is one of the main computational challenges. Several notions of patterns exist, and many share the common idea of strictly specifying some parts of the pattern and to don\u27t care about the remaining parts. Since the number of patterns can be exponential in the length of the sequences, pattern extraction focuses on statistically relevant patterns, where any attempt to further refine or extend them causes a loss of significant information (where the number of occurrences changes). Output-sensitive algorithms have been proposed to enumerate and list these patterns, taking polynomial time O(n^c) per pattern for constant c > 1, which is impractical for massive sequences of very large length n. We address the problem of extracting maximal patterns with at most k don\u27t care symbols and at least q occurrences. Our contribution is to give the first algorithm that attains a stronger notion of output-sensitivity, borrowed from the analysis of data structures: the cost is proportional to the actual number of occurrences of each pattern, which is at most n and practically much smaller than n in real applications, thus avoiding the aforementioned cost of O(n^c) per pattern

A polynomial time biclustering algorithm for finding approximate expression patterns in gene expression time series

<p>Abstract</p> <p>Background</p> <p>The ability to monitor the change in expression patterns over time, and to observe the emergence of coherent temporal responses using gene expression time series, obtained from microarray experiments, is critical to advance our understanding of complex biological processes. In this context, biclustering algorithms have been recognized as an important tool for the discovery of local expression patterns, which are crucial to unravel potential regulatory mechanisms. Although most formulations of the biclustering problem are NP-hard, when working with time series expression data the interesting biclusters can be restricted to those with contiguous columns. This restriction leads to a tractable problem and enables the design of efficient biclustering algorithms able to identify all maximal contiguous column coherent biclusters.</p> <p>Methods</p> <p>In this work, we propose <it>e</it>-CCC-Biclustering, a biclustering algorithm that finds and reports all maximal contiguous column coherent biclusters with approximate expression patterns in time polynomial in the size of the time series gene expression matrix. This polynomial time complexity is achieved by manipulating a discretized version of the original matrix using efficient string processing techniques. We also propose extensions to deal with missing values, discover anticorrelated and scaled expression patterns, and different ways to compute the errors allowed in the expression patterns. We propose a scoring criterion combining the statistical significance of expression patterns with a similarity measure between overlapping biclusters.</p> <p>Results</p> <p>We present results in real data showing the effectiveness of <it>e</it>-CCC-Biclustering and its relevance in the discovery of regulatory modules describing the transcriptomic expression patterns occurring in <it>Saccharomyces cerevisiae </it>in response to heat stress. In particular, the results show the advantage of considering approximate patterns when compared to state of the art methods that require exact matching of gene expression time series.</p> <p>Discussion</p> <p>The identification of co-regulated genes, involved in specific biological processes, remains one of the main avenues open to researchers studying gene regulatory networks. The ability of the proposed methodology to efficiently identify sets of genes with similar expression patterns is shown to be instrumental in the discovery of relevant biological phenomena, leading to more convincing evidence of specific regulatory mechanisms.</p> <p>Availability</p> <p>A prototype implementation of the algorithm coded in Java together with the dataset and examples used in the paper is available in <url>http://kdbio.inesc-id.pt/software/e-ccc-biclustering</url>.</p

The Gapped-Factor Tree

International audienceWe present a data structure to index a specific kind of factors, that is of substrings, called gapped-factors. A gapped-factor is a factor containing a gap that is ignored during the indexation. The data structure presented is based on the suffix tree and indexes all the gapped-factors of a text with a fixed size of gap, and only those. The construction of this data structure is done online in linear time and space. Such a data structure may play an important role in various pattern matching and motif inference problems, for instance in text filtration

Novel Algorithms for LDD Motif Search

Background: Motifs are crucial patterns that have numerous applications including the identification of transcription factors and their binding sites, composite regulatory patterns, similarity between families of proteins, etc. Several motif models have been proposed in the literature. The (l,d)-motif model is one of these that has been studied widely. However, this model will sometimes report too many spurious motifs than expected. We interpret a motif as a biologically significant entity that is evolutionarily preserved within some distance. It may be highly improbable that the motif undergoes the same number of changes in each of the species. To address this issue, in this paper, we introduce a new model which is more general than (l,d)-motif model. This model is called (l,d1,d2)-motif model (LDDMS) and is NP-hard as well. We present three elegant as well as efficient algorithms to solve the LDDMS problem, i.e., LDDMS1, LDDMS2 and LDDMS3. They are all exact algorithms. Results: We did both theoretical analyses and empirical tests on these algorithms. Theoretical analyses demonstrate that our algorithms have less computational cost than the pattern driven approach. Empirical results on both simulated datasets and real datasets show that each of the three algorithms has some advantages on some (l,d1,d2) instances. Conclusions: We proposed LDDMS model which is more practically relevant. We also proposed three exact efficient algorithms to solve the problem. Besides, our algorithms can be nicely parallelized. We believe that the idea in this new model can also be extended to other motif search problems such as Edit-distance-based Motif Search (EMS) and Simple Motif Search (SMS)

EXMOTIF: efficient structured motif extraction

BACKGROUND: Extracting motifs from sequences is a mainstay of bioinformatics. We look at the problem of mining structured motifs, which allow variable length gaps between simple motif components. We propose an efficient algorithm, called EXMOTIF, that given some sequence(s), and a structured motif template, extracts all frequent structured motifs that have quorum q. Potential applications of our method include the extraction of single/composite regulatory binding sites in DNA sequences. RESULTS: EXMOTIF is efficient in terms of both time and space and is shown empirically to outperform RISO, a state-of-the-art algorithm. It is also successful in finding potential single/composite transcription factor binding sites. CONCLUSION: EXMOTIF is a useful and efficient tool in discovering structured motifs, especially in DNA sequences. The algorithm is available as open-source at: