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 : Clustering

Related people (5)


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.

AlgorithmicsClusteringGenome assemblyGenomicsGenotypingPhylogeneticsTaxonomyGenome analysisProgram developmentEvolutionSequence homology analysis

Projects (26)


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.

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

Projects (28)


Group : DETACHED - Hub Core

I am seeking to apply my knowledge in computer science and statistics to understand real world data. I have interdisciplinary background spanning complex systems, Big Data, machine learning, biostatistics and genomics. I have completed a PhD in which I applied clustering and PCA to epigenomics data and discovered new insights on the coupling between replication and epigenetics. I worked at Dataiku, a dynamic start up in which I was actively engaged to help their clients to build their Big Data strategy and draw value from their data. I studied the human microbiota during two years at MetaGenoPolis (MGP), an innovative research center. We aim at improving human health by developing strategies (eg. nutritional, therapeutical, preventive…) to restore dysbiosed microbiota with our industrial and academical partners. I currently work in the statistical genetics group at the Pasteur Institut where I apply my software development and data science skills to quantify the impact of the human genome variation on diverse health parameters.

ClusteringData managementGenomicsGenome analysisExploratory data analysisGeneticsComparative metagenomicsDimensional reductionMultidimensional data analysis

Projects (2)

Christophe MALABAT

Group : HEAD - Hub Core

After a PhD in biochemistry of the rapeseed proteins, during which I developed my first automated scripts for handling data processing and analysis, I join Danone research facility center for developing multivariate models for the prediction of milk protein composition using infrared spectrometry.
As I was already developing my own informatics tools, I decided to join the course of informatic for biology of the Institut Pasteur in 2007. At the end of the course I was recruited by the Institute and integrate the unit of “génétique des interactions macromoléculaires” of Alain Jacquier. Within this group, I learn to handle sequencing data and I developed processing and analysis tools using python and R. I also create a genome browser and database system for storing, retrieving and visualizing microarray data. After 8 years within the Alain Jacquier’s lab, I join the Hub of bioinformatics and biostatistics as co-head of the team.

ClusteringData managementSequence analysisTranscriptomicsWeb developmentDatabaseGenome analysisProgram developmentScientific computingExploratory data analysisData and text miningIllumina HiSeqRead mappingLIMSIllumina MiSeqHigh Throughput ScreeningMultidimensional data analysisWorkflow and pipeline developmentRibosome profilingMotifs and patterns detection

Projects (10)


Group : Stats - Hub Core

Since February 2017 Research engineer, Hub of Bioinformatics and Biostatistics of the C3BI, Institut Pasteur 2015-2017 Post doctoral position, team MISTIS, INRIA Grenoble Topic: Robust clustering and robust non linear regression in high dimension. Collaboration with Florence Forbes (INRIA). 2012-2015 PhD thesis in Statistics, Applied Mathematics Department of Agrocampus-Ouest, IRMAR UMR 6625 CNRS, Rennes Topic: Stability of variable selection in regression and classification issues for correlated data in high dimension. Supervisor: David Causeur (Agrocampus-Ouest, IRMAR). Education 2015 PhD thesis in Statistics, Applied Mathematics Department of Agrocampus-Ouest, IRMAR UMR 6625 CNRS, Rennes 2012 ISUP degree (Institut de Statistique de l’UPMC), Université Pierre et Marie Curie, Paris 2012 Master 2 of Statistics, Université Pierre et Marie Curie, Paris

ClusteringModelingStatistical inferenceTranscriptomicsBiostatisticsExploratory data analysisDimensional reductionStatistical experiment designMultidimensional data analysis

Projects (22)

Related projects (6)

MOODel: Modeling Mood Disorders

Mood disorders such as bipolar and major depressive illnesses are among the most severe psychiatric disorders. They have high prevalence and chronic course, and are associated with significant mental and somatic comorbidities and high personal and societal costs (lost productivity and increased medical expenses). Patients with bipolar disorder (BD), for example, exhibit a reduced lifespan compared with the general population, a finding that cannot only be explained by high suicide risk, reduced access to medical care and lifestyle factors. However, the pathophysiological mechanisms of BD are poorly understood, and patients often have incomplete treatment response. Advanced mathematical approaches such as machine learning techniques are increasingly being used to generate predictions based on complex data, and it has been successfully used to detect a number of clinical outcomes and to predict behaviours. In combination with mobile technologies (e.g. smartphones, wearables) to collect behavioural, physiological and environmental data, these big data predictive approaches may provide a much richer and deeper understanding of phenomenology and pathophysiological mechanisms of mood and bipolar disorders. By taking advantage of the high-standard bioinformatics expertise offered by the C3BI, this multidisciplinary, collaborative project aims to explore how clinical and biological factors, may contribute for better characterizing BD patients as well as to identify predictors of treatment response in BD. Our project also aims to explore how daily behavioural and physiological parameters may influence mood and behaviour in individuals at-risk or suffering from mood disorders.

Project status : Closed

Clinical and biological characterisation of auto-antibodies to the nicotinic receptors in major psychiatric disorder patient

In recent years, immune dysfunctions, including auto-immune mechanisms and peripheral inflammation, have been clearly associated with severe neuropsychiatric disorders like bipolar disorders (BD) and schizophrenia (SZ). These findings are supported by an extensive litterature highlighting a higher risk to develop neuropsychiatric disorders in patients suffering from auto-immune diseases and also the presence of low-grade inflammation and abnormal immunoglobulin rates in patients with psychosis. It is now well-established, since the description of paraneoplastic and auto-immune encephalitis, that antibodies targeting self-antigens such as membrane receptors or intracellular proteins could trigger psychiatric symptoms and severe inflammation. Therefore, an in-depth characterisation of circulating auto-antibodies and their clinical and/or biological implications has become a critical issue to stratify specific subgroups of patients with auto-immune psychosis in order to adjust and adapt the treatment and care, and develop novel approaches. Nicotinic acetylcholine receptors (nAChRs) have been linked to severe neuropsychiatric disorders by clinical and genome-wide association studies (GWAS). In addition to their key roles in neuronal function, nAChRs are also involved in the complex regulation of immuno-inflammatory processes both in the brain and the periphery, making them prime candidates to study the link between inflammation and major psychiatric disorders. Furthermore, nAChRs have already been identified as the target of autoimmune mechanisms leading to the destruction of the neuromuscular junction in the well-described autoimmune disease, myasthenia gravis. Based on these findings, we have started to dissect auto-immune mechanisms against nAChRs involved in SZ and BD. In brief, anti-nAChR auto-antibodies contribute to cognitive dysfunction and psychotic symptoms through peripheral inflammation. Here, our goals are (1) to extend the current knowledge by dissecting the relationship between clinical features and peripheral inflammation (cytokines, chemokines, ...) (2) to stratify patients with anti-nAChR auto-immune psychosis by using cluster analyse

Project status : Awaiting Publication

A genome-wide RNAi screening for mitochondrial fission factors

Mitochondria are dynamic organelles that undergo constant morphological changes, resulting from fusion and fission events. Mitochondrial fission is crucial for mitochondrial function, apoptosis, mitophagy, and mitochondrial segregation during mitosis. While core mitochondrial fission factors have been elucidated and characterized, it is unclear if additional molecules participate or are main players of the fission process. To solve this question, we setup a genome wide, high content imaging (HCI) screening to identify suppressors of mitochondrial fragmentation in Opa1-/- cells. The principle is that using cells deficient for mitochondrial fusion (ablated for the core fusion protein Opa1), we may identify fission factors by screening for genes for which the loss of function is able to complement Opa1-/- phenotype. This was validated in preliminary experiments of silencing of the core fission protein Drp1, where confocal and electron microscopy confirmed that ablation of Drp1 resulted in mitochondrial elongation in Opa1-/- cells without causing mitochondrial fusion in a classic polyethylene glycol fusion assay. Following miniaturization of the assay, we set up an efficient pipeline to perform an automated HCI screening in Opa1-/- MEFs transfected with a pooled siRNAs library targeting >19,000 genes. Automated imaging and high content image quantification allowed us to generate a list of potential hits, that we aim to process in collaboration with the lab of Timothy Wai and the C3BI HUB, in order to identify promising genes that will be validated and investigated in the future.

Project status : Closed