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 : Targeted metagenomics

Related people (2)

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)

Stevenn VOLANT

Group : Stats - Hub Core

After a diploma of statistician engineer from the Ensai (Ecole Nationale de la Statistique et de l’Analyse de l’Information) and a Ph.D in applied mathematics in the Statistics & Genome lab (AgroParisTech), I worked as a developer for the XLSTAT software. I have implemented some statistical methods such as mixture models, log-linear regression, mood test, bayesian hierarchical modeling CBC/HB, … Then I worked as a head teacher in statistics for one year. I was recruited in the Bioinformatic and biostatistic hub of the C3BI (Center of Bioinformatics, Biostatistics and Integrative Biology) in 2014, I am in charge of the statistical analysis and the development of R/R shiny pipelines.


Keywords
Machine learningStatistical inferenceTargeted metagenomicsBiostatisticsApplication of mathematics in sciencesStatistical experiment design
Organisms

Projects (34)

Related projects (5)

Assessing the role of gut microbiota in spondyloarthritis patients and impact of anti-TNF treament on its composition

Our hypothesis is that gut microbiota could define predictive markers of response and tolerance to biologics. A. Preliminary results: gut bacteria predicting response to TNF blockers. A proof-of-concept study has been performed on 58 patients who were recruited according to the following criteria: active disease despite NSAIDs intake; no history of inflammatory bowel disease; no antibiotics intake within 3 months prior recruitment. Bacterial 16S rRNA gene sequencing region was performed on stools samples before and after TNF-blocker treatment. Diversity metrics and custom LefSe were used to explore the relationship between the composition of the intestinal microbiota and the efficacy of TNF-blockers. A lower alpha diversity at baseline was unexpectedly associated with better treatment response, HLA-B27 genotype and smoking behavior. Meanwhile, beta diversity was associated with smoking behavior and HLA-B27 genotype before and after treatment. Beta diversity at baseline was associated with the BASDAI index after treatment, and the response to the treatment. These results indicate a potential regulatory role for the gut microbiota on the underlying mechanisms involved in the response to TNF-blockers. Moreover, a LefSe-like approach identified 6 bacterial species as potential biomarkers for the treatment response, despite the absence of global changes (beta diversity) in the microbiota composition following a 3-month TNF-blockers intake. B. Current project: ITS2 fungal rDNA sequencing analyses In order to establish a causal link between host-microbe interactions and clinical efficacy of anti-TNF, we expect the following research endpoints from our experimental and translation approach (cohorts/clinical trials): 1. Defining the impact of anti-TNF on fungal microbiota and on the relative representation of fungal/bacterial components 2. Defining correlations between gut fungal composition and clinical outcome, with the aim of identifying stool microbial fingerprint of durable responses and/or primary resistance to anti-TNFα in SpA patients. The analyses will be performed on the same 58 SpA patients that were previously analyzed for their 16S bacterial component. These patients perfectly described regarding their disease characteristics, demographics, ongoing treatments, response to anti-TNF after a 3-month treatment period.



Project status : Closed