Lossless seeds for searching short patterns with high error rates

International audienceWe address the problem of approximate pattern matching using the Levenshtein distance. Given a text T and a pattern P , find alllocations in T that differ by at most k errors from P . For that purpose, we propose a filtration algorithm that is based on a novel type of seeds,combining exact parts and parts with a fixed number of errors. Experimental tests show that the method is specifically well-suited for short patterns with a large number of error

Algorithmique pour la recherche de motifs approchée et application à la recherche de cibles de microARN

Approximate string matching consists in identifying the occurrences of a motif within a text, modulo a given distance. This problem has many applications in bioinformatics for the analysis of biological sequences. For instance, microRNAs are short RNA molecules regulating the expression of genes by specific recognition of their sequence motif on the target gene. Understanding the mode of action of microRNAs requires the ability to identify short motifs, around 21 nucleotides in size, comprising up to 3-4 errors in a text whose size is in the order of 108-109 , representing a genome. In this thesis, Ihave proposed an efficient algorithm for the approximate search of short motifs. This algorithm is based on a new type of seeds containing errors, the 01∗ 0 seeds, and uses a compressed index structure, the FM-index. I have implemented this algorithm in a freely available software, Bwolo. I demonstrate experimentally the advantage of this approach and compare it to the state of the art of existing tools. I also show how Bwolo can be used and have set up an original study on the distribution of potential miRNA target sites in two plant genomes, Arabidopsis thaliana and Arabidopsis lyrata.La recherche de motifs approchée consiste à identifier les occurrences d’un motif modulo une certaine distance au sein d’un texte. Ce problème trouve de nombreuses applications en bio-informatique pour l’analyse de séquences biologiques. Par exemple, les microARN sont des petits ARN qui régulent l’expression des gènes par reconnaissance d’un motisimilaire. Comprendre le mode d’action des microARN demande de pouvoir localiser de courts motifs, environ 21 nucléotides, comprenant jusqu’à 3 ou 4 erreurs dans un texte de l’ordre de 108 à 109 nucléotides, représentant un génome. Dans cette thèse, nous proposons un algorithme efficace pour la recherche de motifs approchée, qui se base sula définition d’un nouveau type de graines avec erreurs, les graines 01∗0, et qui exploite une structure d’index compressée, le FM-index. Cet algorithme a été mis en œuvre dans un logiciel librement disponible, appelé Bwolo. Nous démontrons expérimentalemenl’avantage de cette approche en nous comparant à l’état de l’art des outils existants. Nous montrons également comment utiliser Bwolo pour mettre en place une analyse originale sur l’étude de la distribution des cibles potentielles de miARN dans deux génomes de plantes, Arabidopsis thaliana et Arabidopsis lyrata

Détection des protéines de surface de Propionibacterium freudenreichii

il s'agit d'un type de produit dont les métadonnées ne correspondent pas aux métadonnées attendues dans les autres types de produit : DISSERTATIONPropionibacterium freudenreichii is a gram-positive bacterium known as probiotic. A probiotic is alive microorganism which, when administered in adequate amounts, confers a health benefit on thehost. The beneficial effects are ensured by different interactions with other microorganisms andhost’s cells, including the immune system. These interactions are performed largely by the surfacecompounds. Among these compounds, it was shown in P. freudenreichii that surface proteins areimportant. An in vivo labeling method of surface proteins using CyDye fluorescent labels isdeveloped here. A Western blot shows that no lysis is observed during the labeling, but is observedduring treatment with lysozyme, treatment needed to extract proteins. Observation of cells by TEMreveals that lysozyme has only destabilized the wall. The spots observed on a 2D gel byfluorescence and visible by staining with Coomassie blue were analyzed by MS-MS. The majorityof spots contains several proteins. The spots contain often at least one surface protein. It will thus benecessary to use more resolving gels and to combine this method with another based on the shavingof surface proteins by trypsinPropionibacterium freudenreichii est une bactérie à Gram positif dite probiotique. Un probiotiqueest un microorganisme vivant, qui lorsqu’il est administré en quantité adéquat, confère un bénéficepour la santé de l’hôte. Les actions bénéfiques sont assurées par différentes interactions avec lesautres microorganismes et les cellules de l’hôte, dont le système immunitaire. Ces interactions sontassurées en grande partie par les composés de surface. Parmi ces composés, il a été démontré queles protéines de surface chez P. freudenreichii sont importantes. Une méthode de marquage desprotéines de surface réalisé in vivo à l’aide des marqueurs fluorescents CyDye est développée ici.Un Western Blot montre qu’aucune lyse n’est observée durant le marquage, mais elle est observéedurant le traitement au lysozyme, traitement nécessaire à l’extraction de protéines. L’observationdes cellules au MET révèle que le lysozyme a uniquement déstabilisé la paroi. Les spots observéssur un gel 2D apparaissant fluorescent et étant visible grâce à une coloration au bleu de coomassieont été analysé par MS-MS. La majorité des spots contient plusieurs protéines, dont bien souvent aumoins une de surface. Il sera donc nécessaire de réaliser des gels plus résolutif et de compléter cetteméthode avec une autre basée sur le rasage des protéines de surface par de la trypsi

Algorithmique pour la recherche de motifs approchée et application à la recherche de cibles de microARN

Approximate string matching consists in identifying the occurrences of a motif within a text, modulo a given distance. This problem has many applications in bioinformatics for the analysis of biological sequences. For instance, microRNAs are short RNA molecules regulating the expression of genes by specific recognition of their sequence motif on the target gene. Understanding the mode of action of microRNAs requires the ability to identify short motifs, around 21 nucleotides in size, comprising up to 3-4 errors in a text whose size is in the order of 108-109 , representing a genome. In this thesis, Ihave proposed an efficient algorithm for the approximate search of short motifs. This algorithm is based on a new type of seeds containing errors, the 01∗ 0 seeds, and uses a compressed index structure, the FM-index. I have implemented this algorithm in a freely available software, Bwolo. I demonstrate experimentally the advantage of this approach and compare it to the state of the art of existing tools. I also show how Bwolo can be used and have set up an original study on the distribution of potential miRNA target sites in two plant genomes, Arabidopsis thaliana and Arabidopsis lyrata.La recherche de motifs approchée consiste à identifier les occurrences d’un motif modulo une certaine distance au sein d’un texte. Ce problème trouve de nombreuses applications en bio-informatique pour l’analyse de séquences biologiques. Par exemple, les microARN sont des petits ARN qui régulent l’expression des gènes par reconnaissance d’un motisimilaire. Comprendre le mode d’action des microARN demande de pouvoir localiser de courts motifs, environ 21 nucléotides, comprenant jusqu’à 3 ou 4 erreurs dans un texte de l’ordre de 108 à 109 nucléotides, représentant un génome. Dans cette thèse, nous proposons un algorithme efficace pour la recherche de motifs approchée, qui se base sula définition d’un nouveau type de graines avec erreurs, les graines 01∗0, et qui exploite une structure d’index compressée, le FM-index. Cet algorithme a été mis en œuvre dans un logiciel librement disponible, appelé Bwolo. Nous démontrons expérimentalemenl’avantage de cette approche en nous comparant à l’état de l’art des outils existants. Nous montrons également comment utiliser Bwolo pour mettre en place une analyse originale sur l’étude de la distribution des cibles potentielles de miARN dans deux génomes de plantes, Arabidopsis thaliana et Arabidopsis lyrata

Algorithmic for approximate string matching and application for the search of microRNA targets

La recherche de motifs approchée consiste à identifier les occurrences d’un motif modulo une certaine distance au sein d’un texte. Ce problème trouve de nombreuses applications en bio-informatique pour l’analyse de séquences biologiques. Par exemple, les microARN sont des petits ARN qui régulent l’expression des gènes par reconnaissance d’un motif similaire. Comprendre le mode d’action des microARN demande de pouvoir localiser de courts motifs, environ 21 nucléotides, comprenant jusqu’à 3 ou 4 erreurs dans un texte de l’ordre de 108 à 109 nucléotides, représentant un génome. Dans cette thèse, nous proposons un algorithme efficace pour la recherche de motifs approchée, qui se base sur la définition d’un nouveau type de graines avec erreurs, les graines 01*0, et qui exploite une structure d’index compressée, le FM-index. Cet algorithme a été mis en œuvre dans un logiciel librement disponible, appelé Bwolo. Nous démontrons expérimentalement l’avantage de cette approche en nous comparant à l’état de l’art des outils existants. Nous montrons également comment utiliser Bwolo pour mettre en place une analyse originale sur l’étude de la distribution des cibles potentielles de miARN dans deux génomes de plantes, Arabidopsis thaliana et Arabidopsis lyrata.Approximate string matching consists in identifying the occurrences of a motif within a text, modulo a given distance. This problem has many applications in bioinformatics for the analysis of biological sequences. For instance, microRNAs are short RNA molecules regulating the expression of genes by specific recognition of their sequence motif on the target gene. Understanding the mode of action of microRNAs requires the ability to identify short motifs, around 21 nucleotides in size, comprising up to 3-4 errors in a text whose size is in the order of 108-109 , representing a genome. In this thesis, I have proposed an efficient algorithm for the approximate search of short motifs. This algorithm is based on a new type of seeds containing errors, the 01*0 seeds, and uses a compressed index structure, the FM-index. I have implemented this algorithm in a freely available software, Bwolo. I demonstrate experimentally the advantage of this approach and compare it to the state of the art of existing tools. I also show how Bwolo can be used and have set up an original study on the distribution of potential miRNA target sites in two plant genomes, Arabidopsis thaliana and Arabidopsis lyrata

Recherche et annotation des structures de CRISPR dans l'ensemble des génomes procaryotes

Les structures de CRISPR sont des suites d'unités répétées largement présentes dans les génomes procaryotes mais absentes des génomes eucaryotes. Nous avons déjà réalisé une base de données relationnelle, Crispi, rassemblant l'ensemble des CRISPR, et alimentée en permanence par une mise à jour automatique. Il s'agit ici d'enrichir la base en ajoutant l'annotation des structures présentes dans les éléments CRISPR. L'identification de ces structures palindromiques (tige-boucles) a été réalisée à l'aide d'un modèle grammatical, en utilisant le logiciel de pattern-matching Logol. La définition du modèle de tige-boucle adapté aux CRISPRS a constitué un des points sensibles de ces travaux

Approximate search of short patterns with high error rates using the 01⁎0 lossless seeds

International audienceApproximate pattern matching is an important computational problem that has a wide range of applications in computational biology and in information retrieval. However, searching a short pattern in a text with high error rates (10–20%) under the Levenshtein distance is a task for which few efficient solutions exist. Here we address this problem by introducing a new type of seeds: the 01⁎0 seeds. These seeds are made of two exact parts separated by parts with exactly one error. We show that those seeds are lossless, and we apply them to two filtration algorithms for two popular applications, one where a compressed index is built on the text and another one where the patterns are indexed. We also demonstrate experimentally the advantages of our approach compared to alternative methods implementing other types of seeds. This work opens the way to the design of more efficient and more sensitive text algorithms

The probiotic[i] Propionibacterium freudenreichii[/i] surface proteome

Surface proteins are key actors of the complex interactions between bacteria (pathogens, commensals or symbionts) and their host. In beneficial (probiotic) bacteria, they participate in competition with pathogens, adhesion to the host cells, and immunomodulation. We investigated such proteins in the beneficial bacterium Propionibacterium freudenreichii, consumed both in Swiss-type cheeses and probiotic preparations. P. freudenreichii genome was sequenced and annotated, the localization of the encoded proteins was predicted using SurfG+. A combination of 3 biochemical methods confirmed surface exposure of P. freudenreichii proteins: shedding, shaving and labelling. Shedding consisted in the extraction of cell-wall associated proteins using guanidine, followed by trypsinolysis of the extracted proteins. Shaving consisted in enzymatic hydrolysis of surface protruding proteins which were accessible to trypsin in situ on live bacteria. For labeling, an NHS-ester-cyanine was added to live bacteria in order to label surface proteins, prior to 2-D electrophoresis and detection of fluorescent protein spots. For the 3 methods, the resulting tryptic peptides were identified by NanoLC-MS/MS on a Q-TOF mass spectrometer This combination of methods allowed identification of surface layer type-proteins, lipoproteins, proteins associated to the cell wall, to the membrane, or predicted to be secreted, as well as moonlighting proteins predicted to be cytoplasmic. Some of these proteins are known to participate in adhesion and in the modulation of the immune response by probiotics. This work constitutes a decisive step in the elucidation of P. freudenreichii ability to interact with host cells and in the understanding of protein sorting in this bacterium

The probiotic[i] Propionibacterium freudenreichii[/i] surface proteome

Surface proteins are key actors of the complex interactions between bacteria (pathogens, commensals or symbionts) and their host. In beneficial (probiotic) bacteria, they participate in competition with pathogens, adhesion to the host cells, and immunomodulation. We investigated such proteins in the beneficial bacterium Propionibacterium freudenreichii, consumed both in Swiss-type cheeses and probiotic preparations. P. freudenreichii genome was sequenced and annotated, the localization of the encoded proteins was predicted using SurfG+. A combination of 3 biochemical methods confirmed surface exposure of P. freudenreichii proteins: shedding, shaving and labelling. Shedding consisted in the extraction of cell-wall associated proteins using guanidine, followed by trypsinolysis of the extracted proteins. Shaving consisted in enzymatic hydrolysis of surface protruding proteins which were accessible to trypsin in situ on live bacteria. For labeling, an NHS-ester-cyanine was added to live bacteria in order to label surface proteins, prior to 2-D electrophoresis and detection of fluorescent protein spots. For the 3 methods, the resulting tryptic peptides were identified by NanoLC-MS/MS on a Q-TOF mass spectrometer This combination of methods allowed identification of surface layer type-proteins, lipoproteins, proteins associated to the cell wall, to the membrane, or predicted to be secreted, as well as moonlighting proteins predicted to be cytoplasmic. Some of these proteins are known to participate in adhesion and in the modulation of the immune response by probiotics. This work constitutes a decisive step in the elucidation of P. freudenreichii ability to interact with host cells and in the understanding of protein sorting in this bacterium

Surface proteins of [i]Propionibacterium freudenreichii[/i] are involved in its anti-inflammatory properties.

Propionibacterium freudenreichii is a food grade bacterium consumed both in cheeses and in probiotic preparations. Its promising immunomodulatory potential largely relies on the presence of specific and strain-dependent surface compounds. The CIRM BIA 129 strain was selected for immunomodulatory properties, its surfaceome deciphered using an integrative approach. Surface Layer Associated Proteins (SLAPs) are involved in the anti-inflammatory properties