Jean-Stéphane Varré

Sorting by Reversals with Common Intervals

Studying rearrangements from gene order data is a standard approach in evolutionary analysis. Gene order data are usually modeled as signed permutations. The computation of the minimal number of reversals between two signed permutations produced a lot of literature during the last decade. Algorithms designed were first approximative, then polynomial and were further improved to give a linear one. Several extensions were investigated authorizing for example deletion or insertion of genes during the sorting process. We propose to revisit the ’sorting by reversals’ problem by adding constraints on allowed reversals. We do not allow to break conserved clusters of genes usually called Common Intervals. We show that this problem is NP-complete. Assuming special conditions, we propose a polynomial algorithm.

TFM-Explorer: mining cis-regulatory regions in genomes

DNA-binding transcription factors (TFs) play a central role in transcription regulation, and computational approaches that help in elucidating complex mechanisms governing this basic biological process are of great use. In this perspective, we present the TFM-Explorer web server that is a toolbox to identify putative TF binding sites within a set of upstream regulatory sequences of genes sharing some regulatory mechanisms. TFM-Explorer finds local regions showing overrepresentation of binding sites. Accepted organisms are human, mouse, rat, chicken and drosophila. The server employs a number of features to help users to analyze their data: visualization of selected binding sites on genomic sequences, and selection of cis-regulatory modules. TFM-Explorer is available at http://bioinfo.lifl.fr/TFM.

Bit-parallel multiple pattern matching

Text matching with errors is a regular task in computational biology. We present an extension of the bit-parallel Wu-Manber algorithm [16] to combine several searches for a pattern into a collection of fixed-length words. We further present an OpenCL parallelization of a redundant index on massively parallel multicore processors, within a framework of searching for similarities with seed-based heuristics. We successfully implemented and ran our algorithms on GPU and multicore CPU. Some speedups obtained are more than 60×.

Genome dedoubling by DCJ and reversal

BackgroundSegmental duplications in genomes have been studied for many years. Recently, several studies have highlighted a biological phenomenon called breakpoint-duplication that apparently associates a significant proportion of segmental duplications in Mammals, and the Drosophila species group, to breakpoints in rearrangement events.ResultsIn this paper, we introduce and study a combinatorial problem, inspired from the breakpoint-duplication phenomenon, called the Genome Dedoubling Problem. It consists of finding a minimum length rearrangement scenario required to transform a genome with duplicated segments into a non-duplicated genome such that duplications are caused by rearrangement breakpoints. We show that the problem, in the Double-Cut-and-Join (DCJ) and the reversal rearrangement models, can be reduced to an APX-complete problem, and we provide algorithms for the Genome Dedoubling Problem with 2-approximable parts. We apply the methods for the reconstruction of a non-duplicated ancestor of Drosophila yakuba.ConclusionsWe present the Genome Dedoubling Problem, and describe two algorithms solving the problem in the DCJ model, and the reversal model. The usefulness of the problems and the methods are showed through an application to real Drosophila data.

ProCARs: Progressive Reconstruction of Ancestral Gene Orders

BackgroundIn the context of ancestral gene order reconstruction from extant genomes, there exist two main computational approaches: rearrangement-based, and homology-based methods. The rearrangement-based methods consist in minimizing a total rearrangement distance on the branches of a species tree. The homology-based methods consist in the detection of a set of potential ancestral contiguity features, followed by the assembling of these features into Contiguous Ancestral Regions (CARs).ResultsIn this paper, we present a new homology-based method that uses a progressive approach for both the detection and the assembling of ancestral contiguity features into CARs. The method is based on detecting a set of potential ancestral adjacencies iteratively using the current set of CARs at each step, and constructing CARs progressively using a 2-phase assembling method.ConclusionWe show the usefulness of the method through a reconstruction of the boreoeutherian ancestral gene order, and a comparison with three other homology-based methods: AnGeS, InferCARs and GapAdj. The program, written in Python, and the dataset used in this paper are available at http://bioinfo.lifl.fr/procars/.

Parallel Position Weight Matrices Algorithms

Position Weight Matrices (PWMs) are broadly used in computational biology. The basic problem, SCAN, aims to find the occurrences of a given PWM in large sequences. Some other PWM tasks share a common NP-hard subproblem, SCOREDISTRIBUTION. The existing algorithms rely on the enumeration on a large set of scores or words, and they are mostly not suitable for parallelization.We propose a new algorithm, BUCKETSCOREDISTRIBUTION, that is both very efficient and suitable for parallelization.We bound the error induced by this algorithm. We realized a GPU prototype for SCAN and BUCKETSCOREDISTRIBUTION with the CUDA libraries, and report for the different problems speedups of 21x and 77x on a Nvidia GTX 280.

Parallel Position Weight Matrices algorithms

Position Weight Matrices (PWMs) are broadly used in computational biology. The basic problems, Scan and MultipleScan, aim to find all the occurrences of a given PWM or a set of PWMs in long sequences. Some other PWM tasks share a common NP-hard subproblem, ScoreDistribution. The existing algorithms rely on the enumeration on a large set of scores or words, and they are mostly not suitable for parallelization. We propose a new algorithm, BucketScoreDistribution, that is both very efficient and suitable for parallelization. We bound the error induced by this algorithm. We realized a GPU prototype for Scan, MultipleScan and BucketScoreDistribution with the CUDA libraries, and report for the different problems speedups larger than 10x on several Nvidia cards.

Perfect Hashing Structures for Parallel Similarity Searches

Seed-based heuristics have proved to be efficient for studying similarity between genetic databases with billions of base pairs. This paper focuses on algorithms and data structures for the filtering phase in seed-based heuristics, with an emphasis on efficient parallel GPU/many cores implementation. We propose a 2-stage index structure which is based on neighborhood indexing and perfect hashing techniques. This structure performs a filtering phase over the neighborhood regions around the seeds in constant time and avoid as much as possible random memory accesses and branch divergences. Moreover, it fits particularly well on parallel SIMD processors, because it requires intensive but homogeneous computational operations. Using this data structure, we developed a fast and sensitive Open CL prototype read mapper.

Tandem halving problems by DCJ

We address the problem of reconstructing a non-duplicated ancestor to a partially duplicated genome in a model where duplicated content is caused by several tandem duplications throughout its evolution and the only allowed rearrangement operations are DCJ. As a starting point, we consider a variant of the Genome Halving Problem, aiming at reconstructing a tandem duplicated genome instead of the traditional perfectly duplicated genome. We provide a distance in

Single Tandem Halving by Block Interchange

mathcal{O}(n)