Expertise

Hub members Have many expertise, covering most of the fields in bioinformatics and biostatistics. You'll find below a non-exhaustive list of these expertise

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Searched keyword : Algorithmics

Related people (14)

Thomas BIGOT

Group : GIPhy - Embedded : Biology of Infection

I joined the C3BI Hub in 2016 after a curriculum widely dedicated to Bioinformatics studies, and more precisely to Phylogeny and Evolution, topics of my PhD thesis. At Institut Pasteur, I am involved in projects dealing with sequences homology : alignments, hmm profiles, making homologous family databases, kmers signatures. I am also a developer (Python / C++) with a solid interest in optimization as well as in developing usable tools for final user such as automated pipeline for metagenomics sequence analysis. I’m currently embedded in Marc Eloit’s team (80% of my work time). My main task in this team is to develop strategies to identify, in their metagenomics samples, new pathogens, or new combination pathogen / symptoms. The rest of my time, I manage small projects and participate to the Hub life. I am currently experimenting with functional programming (for now, using Python) and its applicability to bioinformatics issues.


Keywords
AlgorithmicsScientific computingSofware development and engineeringParallel computingGraph theory and analysis
Organisms
BacteriaFungiVirus
Projects (11)

Claudia CHICA

Group : GORE - Hub Core

As a computational biologist I have been involved in various projects seeking to answer different biological questions. Those projects have allowed me to define my main research interest, namely the evolutionary study of the emergence, storage and modulation of information in biological systems assisted by computational methods. During my research career I have acquired extensive experience in the analysis of sequence data at the DNA and protein level. I’m trained both in NGS bioinformatic protocols (ChIP-seq, ATAC-seq, RNA-seq, genome assembly) and fine detail sequence analysis. Most importantly, I have gained proficiency in the use of the statistical models that are at the basis of the quantitative analysis of low and high throughput sequence data. Additionally, my experience as a lecturer and instructor has taught me that training researchers about the formal basis of bioinformatic methodologies is the key for a successful collaboration between wet and dry lab. Likewise, I have gained valuable skills by working within two international consortia (TARA Oceans project and TRANSNET): the ability to collaborate with multidisciplinary groups and to coordinate younger researchers.


Keywords
AlgorithmicsGenomicsSequence analysisTranscriptomicsGenome analysisGeneticsEvolutionInteractomics
Organisms

Projects (23)

Freddy CLIQUET


One of my projects consists in developing GRAVITY, a java tool based on Cytoscape to integrate genetic variants within protein-protein interaction networks to allow the visual and statistical interpretation of next-generation sequencing data, ultimately helping geneticists and clinicians to identify causal variants and better diagnose their patients. I’m also involved in several other projects in the lab, taking part in the design of pipelines for the processing and the analysis of genomics data, including SNP arrays, whole-exome and whole-genome sequencing data. This means being confronted to the big data problematic, the unit having to manage hundreds of terabytes of genomics data. Finally, I am now analysing these data in order to identify possible causes for autism, to help clinicians with their diagnosis but also to better understand the biological mechanisms at play in this complex disease. This is done through the project aiming at understanding the genetic architecture of autism in the Faroe Islands, and also with the newly starting IMI2 European project AIMS2-Trials.


Keywords
AlgorithmicsData managementData VisualizationGenomicsMachine learningProteomicsGenome analysisBiostatisticsProgram developmentScientific computingApplication of mathematics in sciencesExploratory data analysisSofware development and engineeringData and text miningGenetics
Organisms

Projects (0)

    Thomas COKELAER

    Group : PLATEFORM - Detached : Biomics

    I joined the Bioinformatics and Biostatistics Hub at Institut Pasteur in 2016 where I am currently developing pipelines related to NGS for the Biomics Pôle. I have an interdisciplinary research experience: after a PhD in Astronomy (gravitational wave data analysis), I joined several research institute to work in the fields of plant modelling (INRIA, Montpellier, 2008-2011), System Biology — in particular logical modelling (EMBL-EBI Cambridge, U.K., 2011-2015), and drug discovery (Sanger Institute, Cambridge, U.K.), 2015). On a daily basis, I use data analysis and machine learning techniques within high-quality software to tackle scientific problems.


    Keywords
    AlgorithmicsData managementData VisualizationGenome assemblyGenomicsMachine learningModelingScientific computingDatabases and ontologiesSofware development and engineeringData and text miningIllumina HiSeqGraph theory and analysisIllumina MiSeq
    Organisms

    Projects (2)

    Alexis CRISCUOLO

    Group : GIPhy - Embedded : PIßnet

    | work as a research engineer in the ßioinƒormatics and ßiostatistics HUß of the |nstitut Pasteur. Holder of a PhD in bioinƒormatics, my main interest is on ƒast but robust phylogenetic inƒerence algorithms and methods ƒrom large genome-scaled datasets. |n consequence, | am oƒten involved in related bioinƒormatics projects, such as perƒorming de novo or ab initio genome assemblies, designing and processing core genome †yping schemes, building and analysing phylogenomics datasets, or implementing and distributing novel tools and methods.


    Keywords
    AlgorithmicsClusteringGenome assemblyGenomicsGenotypingPhylogeneticsTaxonomyGenome analysisProgram developmentEvolutionSequence homology analysis
    Organisms

    Projects (26)

    Amine GHOZLANE

    Group : SINGLE - Hub Core

    After a PhD in informatics on graph analysis (metabolic networks and sRNA-mRNA interaction graphs) at the LaBRI (Université de Bordeaux), I joined the DSIMB team (INTS) for a post-doc on structural modeling. Then, I performed a second post-doc at Metagenopolis – INRA Jouy-en-Josas, where I was initiated to the analysis of metagenomic data. I was recruited at the HUB in 2015, and since I pursue the development of methods dedicated to the treatment of metagenomic data by combining either the treatment of sequencing data, the statistics, the protein structural modeling and the graph analysis.


    Keywords
    AlgorithmicsClusteringGenome assemblyGenomicsMetabolomicsModelingNon coding RNASequence analysisStructural bioinformaticsTargeted metagenomicsDatabaseGenome analysisBiostatisticsProgram developmentScientific computingDatabases and ontologiesExploratory data analysisData and text miningIllumina HiSeqComparative metagenomicsRead mappingIllumina MiSeqSequence homology analysisGene predictionMultidimensional data analysisSequencingShotgun metagenomics
    Organisms

    Projects (28)

    Bernd JAGLA

    Group : PLATEFORM - Detached : Biomarker Discovery

    Bernd Jagla received his PhD in bioinformatics (department of Biology, Chemistry, and Parmacy) from the Free University in Berlin, Germany in 1999. Before joining the Institut Pasteur, he worked for almost ten years in New York City, including as an associate research scientist in the Joint Centers for System Biology (Columbia University) and at the Columbia University Screening Center led by Dr J.E. Rothman. He joined the Institut Pasteur in 2009 to take charge of the bioinformatic needs at the Transcriptome et Epigenome platform, focusing on Next Generation Sequencing. As of 2016 he is member of the C3BI – HUB Team detached to the Human immunology center (CIH) and provides support for cytometry, next generation sequencing, and microarray data analysis. His areas of interest include the quality assurance and data analysis and visualization at the facility. He also has strong expertise in developing algorithms for function prediction from sequence data, image analysis, analysis of mass spectrometry data, workflow management systems. While at Pasteur he developed: KNIME extensions for Next Generation Sequencing (Link) Post Alignment Visualization and Characterization of High-Throughput Sequencing Experiments (Link) Post Alignment statistics of Illumina reads (Link)


    Keywords
    AlgorithmicsChIP-seqData managementData VisualizationImage analysisMachine learningSequence analysisDatabaseGenome analysisBiostatisticsProgram developmentScientific computingData and text miningIllumina HiSeqGraphics and Image ProcessingIllumina MiSeqHigh Throughput ScreeningFlow cytometry/cell sortingPac Bio
    Organisms

    Projects (2)

    Rachel LEGENDRE

    Group : GORE - Hub Core

    Rachel Legendre is a bioinformatics engineer. She completed her master degree in apprenticeship for two years at INRA in Jouy-en-Josas in the Genetic Animal department. She was involved in a project aiming at the detection and the expression analysis of micro-RNA involved in an equine disease. In 2012, she joined the Genomic, Structure and Translation Team at Paris-Sud (Paris XI) university. She worked principally on Ribosome Profiling data analysis, a new technique that allows to identify the position of the ribosome on the mRNA at the nucleotide level. Since november 2015, she worked at Institut Pasteur. During 4 years, she was detached to the Biomics Platform, where she was in charge of the bioinformatics analyses for transcriptomics and epigenomics projects. She was also involved in Long Reads (PacBio and Nanopore) developments with other bioinformaticians of Biomics. Since november 2019, she has joined the Hub of Bioinformatics and Biostatistics, et more precisely the Genome Organization Regulation and Expression group.


    Keywords
    AlgorithmicsChIP-seqEpigenomicsNon coding RNATranscriptomicsGenome analysisProgram developmentScientific computingSofware development and engineeringIllumina HiSeqRead mappingSequencingWorkflow and pipeline developmentChromatin accessibility assaysPac BioRibosome profiling
    Organisms
    BacteriaFungiParasiteHumanInsect or arthropodOther animal
    Projects (25)

    Frédéric LEMOINE

    Group : DETACHED - Hub Core

    After a Master degree in bioinformatics and biostatistics, I did a PhD in computer science / bioinformatics at University Paris-Sud (now in University Paris-Saclay), where I worked on integration and analysis of comparative genomics data. After a postdoc in Lausanne, Switzerland where I worked on small-RNA sequencing data, I joined GenoSplice where I was responsible for the development of bioinformatics projects related to next generation sequencing. I joined Institut Pasteur in Nov. 2015, to work in the Evolutionary Bioinformatics Unit and participate in the development of new tools and algorithms that are able to tackle efficiently the ever increasing amount of sequencing data.


    Keywords
    AlgorithmicsData managementPhylogeneticsSequence analysisDatabaseGenome analysisProgram developmentScientific computingDatabases and ontologiesSequencingWorkflow and pipeline development
    Organisms

    Projects (1)

    Nicolas MAILLET

    Group : SINGLE - Embedded : Structural Virology

    After a PhD in bioinformatics at Inria/IRISA, Université de Rennes 1, Rennes (France), under the supervision of Dominique Lavenier and Pierre Peterlongo, I did a postdoc in bioinformatics at Laboratory of Ecology and Evolution of Plankton in Stazione Zoologica Anton Dohrn of Naples, Italy. Both my thesis and my postdoc were about the Tara Oceans projet and the development of new software to analyze huge quantities of raw reads coming from metagenomics sample. I am currently occupying a research engineer position at the Hub as leader of ALPS group and focus on several different computing problems including metagenomics, protein assembly and several short term developments.


    Keywords
    AlgorithmicsData managementProteomicsDatabaseProgram developmentScientific computingSofware development and engineeringComparative metagenomics
    Organisms

    Projects (8)

    Natalia PIETROSEMOLI

    Group : SysBio - Hub Core

    Dr. Natalia Pietrosemoli is an Engineer with a M. Sc. in Modeling and Simulation of Complex Realities from the International Center for Theoretical Physics, ICTP and the International School of Advanced Studies, SISSA (Triest, Italy). During her M. Sc. internships she mostly worked in modeling, optimization, combinatorics and information theory applied to medical imaging. In 2012 she got a Ph. D in Computational Biology from the School of Bioengineering of Rice University (Houston, TX, US), where she specialized in computational structural biology and functional genomics. Her doctoral thesis “Protein functional features extracted with from primary sequences : a focus on disordered regions”, contributed to a better understanding of the functional and evolutionary role of intrinsic disorder in protein plasticity, complexity and adaptation to stress conditions. As part of her Ph. D., Natalia was a visiting scholar in two labs in Madrid: the Structural Computational Biology Group at the Spanish National Cancer Research Centre (CNIO), where she mainly worked in sequence analysis and the functional-structural relationships of proteins, and the Computational Systems Biology Group at the Spanish National Centre for Biotechnology (CNB-CSIC ), where she studied the functional implications of intrinsically disordered proteins at the genomic level for several organisms, collaborating with different experimental and theoretical groups. In 2013, she joined the Swiss Institute of Bioinformatics as a postdoctoral fellow in the Bioinformactics Core Facility. Her main project consisted in the molecular classification of a rare type of lymphoma, which involved the integration of transcriptomic, clinical and mutational data for the identification of molecular markers for classification, diagnosis and prognosis. This work was performed in collaboration with the Pathology Institute at the University Hospital of Lausanne (CHUV). In November of 2015 Natalia joined the Hub Team @ Pasteur C3BI as a Senior Bioinformatician. Natalia is especially interested in the integrative analysis of different omics data, both at large-scale and for small datasets, and loves collaborating in interdisciplinary environments and having feedback from her fellow experimental colleagues. Currently, she’s coordinating several projects performing functional and pathway analysis at the genomic level. By grouping genes, proteins and other biological molecules into the pathways they are involved in, the complexity of the analyses is significantly reduced, while the explanatory power increases with respect to having a list of differentially expressed genes or proteins.


    Keywords
    AlgorithmicsData managementGenomicsImage analysisMachine learningModelingProteomicsSequence analysisStructural bioinformaticsTranscriptomicsDatabaseGenome analysisBiostatisticsScientific computingDatabases and ontologiesApplication of mathematics in sciencesData and text miningGeneticsGraphics and Image ProcessingBiosensors and biomarkersClinical researchCell biology and developmental biologyInteractomicsBioimage analysis
    Organisms

    Projects (32)

    Johann DRÉO


    As a senior research engineer, I have explored many corners of computer science and artificial intelligence. I can most notably help you on the following topics. Skills Decision support systems Boxes and arrows design and implementation of decision-aid software (web-based as well as native interfaces and backends), visualization and diagrams (how to summarize complex data/concepts in a visual way), integration of third-party modules (how to design API to use external services, how to integrate software that does not really want to be integrated). Automated decision A black-box with a black-box inside score function modelling (how to design a metric defining a quality for a solution to a decision problem, while maintaining good mathematical properties), optimization problem modelling (how to design a formal model of a decision problem to be automatically solved by a computer), solving automated configuration problems (how to set parameters of a complex system so as to maximize its performances), Scientific computing Lego blocks and arrows efficient algorithmics (how to cope with combinatorial explosion or curse of dimension when implementing complex algorithms), highly modular software architectures (how to structure your code to allow efficient —and automated— exploration of your ideas), modern C++ (how to program with C++ using —almost— the same concepts than in Python), shell scripting (how to use the existing Unix tools to —very— efficiently automatize any task). Artificial Intelligence search heuristics, metaheuristics or evolutionary computation (how to solve hard optimization problems), design of experiments for randomized algorithmics (how to design experiments involving modern AI, using rigorous statistics), automated planning (how to compute shortest paths, and more generally optimize sequences of actions), semantic graph mining (how to find patterns in an ontology).


    Keywords
    AlgorithmicsData Visualization
    Organisms
    Non applicable
    Projects (0)

      Related projects (2)

      Implémentation d’un algorithme rapide de génotypage cgMLST

      Le génotypage MLST (Multi-Locus Sequence Typing) est une technique standard qui permet une caractérisation génotypique précise et reproductible des souches bactériennes. Elle consiste à déterminer la séquence nucléotidique de différents gènes répartis dans le génome (généralement entre 5 et 10). L’Institut Pasteur développe depuis de nombreuses années des schémas MLST pour différentes souches d’intérêt biomédical (e.g. Bordetella, Klebsiella, Listeria, Escherichia, Salmonella). Ces schémas consistent en la définition des différents loci et, pour chacun d’entre eux, en l’identification des allèles observés dans les différentes souches isolées (cf. bigsdb.pasteur.fr). Ainsi, en pratique, le génotypage d’une nouvelle souche s’effectue en déterminant le numéro de l’allèle observé au sein de son génome pour chaque locus du schéma MLST associé. Plus récemment, cette approche de classification de souches a été étendue à l’ensemble des gènes communs aux différents génomes d’une espèce donnée (i.e. core-gene) afin d’observer une meilleure discrimination entre souches proches (e.g. issues d’un même foyer épidémiologique). Ce nouveau système de typage cgMLST (core-gene MLST) s’articule ainsi sur un nombre beaucoup plus important de loci que l’approche MLST standard (e.g. plusieurs centaines ou milliers de loci, chacun contenant entre une dizaine et une centaine d’allèles). L’apparition des nouveaux schémas cgMLST implique en pratique des temps calculs relativement importants lorsque plusieurs centaines de génomes doivent être génotypés en même temps. Malheureusement, les solutions bioinformatiques actuellement disponibles pour déterminer l’ensemble des allèles à partir d’un génome assemblé s’articulent uniquement sur des recherches de type BLAST (e.g. LOCUST ; mlst), alors que de nouveaux algorithmes rapides sont actuellement développés mais uniquement pour effectuer cette tâche à partir de fichiers de reads séquencés (e.g. MentaLIST ; stringMLST). Or, l’utilisation de recherches BLAST pour déterminer les occurrences exactes d’un ensemble de séquences nucléotidiques pré-déterminées n’est trivialement pas la solution la plus optimale. Ainsi, dans le contexte actuel où le séquençage et l’assemblage de centaines de génomes bactériens est devenu routinier, il serait utile et pertinent de disposer de l’implémentation d’un algorithme très rapide de recherche des occurrences exactes d’un très grand nombre de séquences alléliques au sein d’un génome. Un tel logiciel permettrait d’accélérer significativement les missions de surveillance épidémiologique (Bordetella pertussis, mais également Klebsiella pneumoniae et Corynebacterium diphteriae) au sein de l’unité BEBP (Biodiversité et Epidémiologie des Bactéries Pathogènes), mais permettrait également de faciliter certaines analyses bioinformatiques basées sur la recherche exacte d’un grand nombres de motifs nucléotidiques au sein d’un génome.



      Project status : Closed