Conservation of regulatory elements between two species of Drosophila

BACKGROUND: One of the important goals in the post-genomic era is to determine the regulatory elements within the non-coding DNA of a given organism's genome. The identification of functional cis-regulatory modules has proven difficult since the component factor binding sites are small and the rules governing their arrangement are poorly understood. However, the genomes of suitably diverged species help to predict regulatory elements based on the generally accepted assumption that conserved blocks of genomic sequence are likely to be functional. To judge the efficacy of strategies that prefilter by sequence conservation it is important to know to what extent the converse assumption holds, namely that functional elements common to both species will fall within these conserved blocks. The recently completed sequence of a second Drosophila species provides an opportunity to test this assumption for one of the experimentally best studied regulatory networks in multicellular organisms, the body patterning of the fly embryo. RESULTS: We find that 50%–70% of known binding sites reside in conserved sequence blocks, but these percentages are not greatly enriched over what is expected by chance. Finally, a computational genome-wide search in both species for regulatory modules based on clusters of binding sites suggests that genes central to the regulatory network are consistently recovered. CONCLUSIONS: Our results indicate that binding sites remain clustered for these "core modules" while not necessarily residing in conserved blocks. This is an important clue as to how regulatory information is encoded in the genome and how modules evolve

Accelerating String Set Matching in FPGA Hardware for Bioinformatics Research

<p>Abstract</p> <p>Background</p> <p>This paper describes techniques for accelerating the performance of the string set matching problem with particular emphasis on applications in computational proteomics. The process of matching peptide sequences against a genome translated in six reading frames is part of a proteogenomic mapping pipeline that is used as a case-study. The Aho-Corasick algorithm is adapted for execution in field programmable gate array (FPGA) devices in a manner that optimizes space and performance. In this approach, the traditional Aho-Corasick finite state machine (FSM) is split into smaller FSMs, operating in parallel, each of which matches up to 20 peptides in the input translated genome. Each of the smaller FSMs is further divided into five simpler FSMs such that each simple FSM operates on a single bit position in the input (five bits are sufficient for representing all amino acids and special symbols in protein sequences).</p> <p>Results</p> <p>This bit-split organization of the Aho-Corasick implementation enables efficient utilization of the limited random access memory (RAM) resources available in typical FPGAs. The use of on-chip RAM as opposed to FPGA logic resources for FSM implementation also enables rapid reconfiguration of the FPGA without the place and routing delays associated with complex digital designs.</p> <p>Conclusion</p> <p>Experimental results show storage efficiencies of over 80% for several data sets. Furthermore, the FPGA implementation executing at 100 MHz is nearly 20 times faster than an implementation of the traditional Aho-Corasick algorithm executing on a 2.67 GHz workstation.</p

A haplome alignment and reference sequence of the highly polymorphic Ciona savignyi genome

The high degree of polymorphism in the genome of the sea squirt Ciona savignyi complicated the assembly of sequence contigs, but a new alignment method results in a much improved sequence

Mobilomics in Saccharomyces cerevisiae Strains

Background: Mobile Genetic Elements (MGEs) are selfish DNA integrated in the genomes. Their detection is mainly based on consensus-like searches by scanning the investigated genome against the sequence of an already identified MGE. Mobilomics aims at discovering all the MGEs in a genome and understanding their dynamic behavior: The data for this kind of investigation can be provided by comparative genomics of closely related organisms. The amount of data thus involved requires a strong computational effort, which should be alleviated.Results: Our approach proposes to exploit the high similarity among homologous chromosomes of different strains of the same species, following a progressive comparative genomics philosophy. We introduce a software tool based on our new fast algorithm, called regender, which is able to identify the conserved regions between chromosomes. Our case study is represented by a unique recently available dataset of 39 different strains of S.cerevisiae, which regender is able to compare in few minutes. By exploring the non-conserved regions, where MGEs are mainly retrotransposons called Tys, and marking the candidate Tys based on their length, we are able to locate a priori and automatically all the already known Tys and map all the putative Tys in all the strains. The remaining putative mobile elements (PMEs) emerging from this intra-specific comparison are sharp markers of inter-specific evolution: indeed, many events of non-conservation among different yeast strains correspond to PMEs. A clustering based on the presence/absence of the candidate Tys in the strains suggests an evolutionary interconnection that is very similar to classic phylogenetic trees based on SNPs analysis, even though it is computed without using phylogenetic information.Conclusions: The case study indicates that the proposed methodology brings two major advantages: (a) it does not require any template sequence for the wanted MGEs and (b) it can be applied to infer MGEs also for low coverage genomes with unresolved bases, where traditional approaches are largely ineffective

Hardware Software Co-Design for Protein Identification

Recently new technologies and research in computational bioinformatics have revolutionized the rate of biological data generation. A vast amount of proteomics and genomics data is contributed to the life science society by researchers especially in the domain of high throughput next generation sequencing methods and it is doubling at every 18 months. Protein identification is a fundamental step in protein sequence analysis and it needs efficient solutions to match the data growth. Rapid methods are focused in the quest for faster protein sequence analysis to scan databases and identify a protein accurately. This benefits the discipline of disease biomarker identification and aid disease diagnosis and prognosis

Multiple Anchor Staged Local Sequence Alignment Algorithm - MASAA.

Technology advancements have helped biologists gather massive amount of biological data including genomic sequences of various species today. Sequence alignment techniques play a central role in investigating the adaptive significance of organism traits and revealing evolutionary relations among organisms by comparing these biological data. This thesis presents an algorithm to perform pairwise local sequence alignment. Recent pairwise local sequence alignment algorithms are either slow and sensitive or fast and less sensitive. Our algorithm is faster and at the same time sensitive. The algorithm employs suffix tree data structure to accurately identify long common subsequences in the two given sequences quickly. Regions of high similarity are again identified between segments of long subsequences already found. Several measures are taken into consideration to design the algorithm, such that the output is biologically meaningful. Data sets are carefully chosen and the output is compared with a well known algorithm, BLASTZ. Experiments conducted demonstrate that our algorithm performs better than BLASTZ in computation time, while either preserving or exceeding the accuracy of alignments at times.The original print copy of this thesis may be available here: http://wizard.unbc.ca/record=b160049

Application of motif scoring algorithms for enhancer prediction in distantly related species

Although many studies proposed methods for the identification of enhancers, reliable prediction on a genome-wide scale is still an unsolved problem. One of the reasons for this is the highly flexible regulatory logic underlying a detectable enhancer activity. In each cell type or tissue and at any given time, a mostly unknown set of transcription factors activates specific regulatory elements by coordinated binding to the corresponding genomic region. Position, spacing, and orientation of the individual bound factors can thereby vary between different enhancers yet result in a highly similar spatio-temporal activity. Due to this inner flexibility, so-called “alignment-free” methods have been proposed for enhancer prediction, as they are able to cope with rearrangements by comparison of word profiles rather than linear sequence. However, the problems caused by allowing for permutation in sequence comparison have not been investigated so far. In this study I implemented several published alignment-free metrics and analysed, which parameters affect their ability to successfully predict regulatory regions. As results show, single point mutations and the increasing amount of spurious matches with decreasing word size pose the biggest challenge to alignment-free techniques, especially when applied on a genome-wide scale. Alignment algorithms usually solve these problems quite efficiently but cannot handle permutation. I therefore implemented a new technique for enhancer prediction that combines the advantages of both algorithm types and used it for the identification of regulatory regions in the teleost fish Oryzias latipes (Medaka) based on a set of known and validated human enhancers. Predicted medaka regions and human enhancers were subsequently used in an in vivo enhancer assay and analysed for their activity. In total, 12 predicted regions corresponding to 9 human enhancers showed clear enhancing activity in the fish. This shows that the principle implemented here is able to predict active enhancers at a high rate on a genome-wide scale even in species as diverged as human and fish. Furthermore, evidence for motif-level conservation between some of the human and medaka enhancers could be found that was invisible for most of the alignment-algorithms used for comparison

Enabling comparative genomics at the scale of hundreds of species

Comparing related (homologous) subsequences between genomes from different species gives insight into their function. This information is captured in ``genome alignments'', which are essential for almost all comparative genomics analyses. However, most existing methods to create a genome alignment suffer from reference-bias (where only one genome is fully aligned to all others), or ignore duplication events. Though the Cactus genome aligner avoided these restrictions, it could not align more than a few genomes without becoming cost-prohibitive as well as losing accuracy. I developed and refined a “progressive alignment” extension to Cactus to allow it to produce a full alignment in time linear in the number of input genomes while maintaining similar, or often improved, quality. This new method allows Cactus to align hundreds of large vertebrate genomes---enabling comparative genomics at an unprecedented scale. During its development I used Cactus as an essential component of several successful comparative genomics projects. Working closely with the 200 Mammals and Bird 10K projects, I have used Cactus to create an alignment of over 600 bird and mammal genomes, which is by far the largest genome alignment ever created. Finally, I have utilized this alignment to provide a highest-possible-resolution annotation of mammalian and avian evolutionary constraint, using the uniquely large number of taxa to enable the examination of weak effects of purifying selection

Statistique des comparaisons de génomes complets bactériens

La génomique comparative est l'étude des relations structurales et fonctionnelles entre des génomes appartenant à différentes souches ou espèces. Cette discipline offre ainsi la possibilité d'étudier et de comprendre les processus qui façonnent les génomes au cours de l'évolution. Dans le cadre de cette thèse, nous nous sommes intéressés à la génomique comparative des bactéries et plus particulièrement aux méthodes relatives à la comparaison des séquences complètes d'ADN des génomes bactériens. Ces dix dernières années, le développement d'outils informatiques permettant de comparer des génomes entiers à l'échelle de l'ADN est devenu une thématique de recherche à part entière. Actuellement, il existe de nombreux outils dédiés à cette tâche. Cependant, jusqu'à présent, la plupart des efforts ont été dirigés vers la réduction du temps de calcul et l'optimisation de la mémoire au détriment de l'évaluation de la qualité des résultats obtenus. Pour combler ce vide, nous avons travaillé sur différents problèmes statistiques soulevés par la comparaison de génomes complets bactériens. Notre travail se divise en deux axes de recherche. Dans un premier temps, nous nous sommes employés à évaluer la robustesse des alignements de génomes complets bactériens. Nous avons proposé une méthode originale fondée sur l'application de perturbations aléatoires sur les génomes comparés. Trois scores différents sont alors calculés pour estimer la robustesse des alignements de génomes à différentes échelles, allant des nucléotides aux séquences entières des génomes. Notre méthode a été expérimentée sur des données génomiques bactériennes réelles. Nos scores permettent d'identifier à la fois les alignements robustes et non robustes. Ils peuvent être employés pour corriger un alignement ou encore pour comparer plusieurs alignements obtenus à partir de différents outils. Dans un second temps, nous avons étudié le problème de la paramétrisation des outils de comparaisons de génomes entiers. En effet, la plupart des outils existants manquent à la fois de documentation et de valeurs par défaut fiables pour initialiser leurs paramètres. Conséquemment, il y a un besoin crucial de méthodes spécifiques pour aider les utilisateurs à définir des valeurs appropriées pour les paramètres de ces outils. Une grande partie des outils de comparaisons de génomes complets est fondée sur la détection des matches (mots communs exacts). Le paramètre essentiel pour ces méthodes est la longueur des matches à considérer. Au cours de cette thèse, nous avons développé deux méthodes statistiques pour estimer une valeur optimale pour la taille des matches. Notre première approche utilise un modèle de mélange de lois géométriques pour caractériser la distribution de la taille des matches obtenus lorsque l'on compare deux séquences génomiques. La deuxième approche est fondée sur une approximation de Poisson de la loi du comptage des matches entre deux chaînes de Markov. Ces méthodes statistiques nous permettent d'identifier facilement une taille optimale de matches à la fois pour des séquences simulées et pour des données génomiques réelles. Nous avons également montré que cette taille optimale dépend des caractéristiques des génomes comparés telles que leur taille, leur composition en base ou leur divergence relative. Cette thèse représente une des toutes premières études dont l'objectif est d'évaluer et d'améliorer la qualité des comparaisons des génomes complets. L'intérêt et les limites de nos différentes approches sont discutés et plusieurs perspectives d'évolution sont proposées.Comparative genomics is the study of the structural and functional relationships between genomes belonging to different strains or species. This discipline offers great opportunities to investigate and to understand the processes that shape genomes across the evolution. In this thesis, we focused on the comparative genomics of bacteria and more precisely, on methods dedicated to the comparison of the complete DNA sequences of bacterial genomes. This last decade, the design of specific computerized methods to compare complete genomes at the DNA scale has become a subject of first concern. Now, there exist many tools and methods dedicated to this task. However, until now, most of the efforts were directed to reduce execution time and memory usage at the expense of the evaluation of the quality of the results. To fill this gap, we worked on different statistical issues related to the comparison of complete bacterial genomes. Our work was conducted into two directions. In the first one, we investigated the assessment of the robustness of complete bacterial genome alignments. We proposed an original method based on random perturbations of the compared genomes. Three different scores were derived to estimate the robustness of genome alignments at different scales, from nucleotides to the complete genome sequences. Our method was trained on bacterial genomic data. Our scores allow us to identify robust and non robust genome alignments. They can be used to correct an alignment or to compare alignments performed with different tools. Secondly, we studied the problem of the parametrization of comparison tools. Briefly, most of the existing tools suffer from a lack of information and of reliable default values to set their parameters. Consequently, there is a crucial need of methods to help users to define reliable parameter values for these tools. Most of the comparison tools are rooted on the detection of word matches. The key parameter for all these tools is the length of the matches to be considered. During this thesis, we developed two statistical methods to estimate an optimal length for these matches. Our first approach consisted in using a mixture model of geometric distributions to characterize the distribution of the length of matches retrieved from the comparison of two genomic sequences. The second approach is rooted on a Poisson approximation of the number of matches between two Markov chains. These statistical methods allow us to easily identify an optimal length for the matches from both simulated and real genomic data. We also showed that this optimal length depends on the characteristics of the compared genomes such as their length, their nucleotide composition, and their relative divergence. This thesis represents one of the earliest attempts to statistically evaluate and to improve the quality of complete genome comparisons. The interest and limitations of our different methods are discussed and some perspectives are proposed.EVRY-Bib. électronique (912289901) / SudocSudocFranceF