The seminars take place on Tuesdays at 2 pm in the CGFB conference room (unless otherwise specified)

Upcoming Seminar

22 Octobre 2019  - Guillaume Bernard (Sorbonne university, MNHN, Paris)

Next-generation phylogenomics: alignment-free approaches, sequence similarity networks and more.

From the 2000’s with the development of Next Generation Sequencing (NGS), biologists have been able to sequence microbes directly from the environment. The ‘microbial dark matter’ represented in metagenomes could contain a huge amount of new information about Earth’s microbial diversity and the origins of life, as well as solutions to medical problems or the adaptation to climate changes. NGS brought a deluge of collected sequence data in which we found a huge diversity and uncharacterized organisms that weren’t cultivated in the laboratory. Alternative approaches to the classical phylogenetic methods based on multiple sequence alignment (MSA), such as sequence similarity networks (SSN) and alignment-free (AF) methods, have been increasingly used in evolutionary analyses to cope with the increasingly large amount of data. These latter approaches are faster and more scalable than their MSA- based counterpart, and can be applied to a broader range of data (sequencing reads, whole genomes, etc). I will start with a brief introduction to the AF approaches followed by an overview of the different methods available. Next, I will show the network-based methods and their applications. Finally, I will present a novel approach combining the SSN and the AF methods to quickly identify gene/proteins of interest in metagenomic data and infer proxies of phylogenies, robust to long branch attraction, when the data are too large or divergent to perform a MSA.

Seminars to come

10 December 10 2019 Laura Cantini (IBENS, ENS, Paris)

February 11 2020 - Anais Baudot (Marseille Medical Genetics, Aix-Marseille Université)


Past seminars

October 82019 - Laurent Brehelin (LIRMM, Montpellier)

Probing transcriptional regulation with statistical models

Gene expression in Eukaryotes is orchestrated by distinct regulatory mechanisms to ensure a wide variety of cell types and functions. While these regulations include actors as different as transcription factors (TFs), histone marks, or chromatin structure, the DNA sequence itself is invariably involved in the different processes. Hence, a key challenge in regulatory genomics is to decipher the links between gene regulation and DNA sequence. In this talk, I will present two attempts to this
problem based on statistical machine learning and feature selection approaches.

In the first work [1], we probe sequence-level instructions for gene expression and develop a method to explain mRNA levels based solely on nucleotide features. Our method positions nucleotide composition as a critical component of gene expression. In the second one [2], we study TF combinations involved in the binding of a target TF in a particular cell type. We show that TF combinations are different between promoters and enhancers, but similar for promoters of mRNAs, lncRNAs and pri-miRNAs.


May 21 2019 - Eduardo Rocha (Institut Pasteur)

Horizontal gene transfer: from acquisition to functional innovation

May 7 2019 - Florian Thibord (UPMC Université Paris 6)

Alignement des données miRseq

March 26 2019 - Julien Chiquet (AgroParisTech)

A collection of Poisson lognormal models for multivariate analysis of count data

February 19 2019 - Magali Champion (Université Paris Descartes)

AMARETTO: Multi-omics data fusion for cancer data

January 8 2019 - Warren Francis (University of Southern Denmark)

Comparative genomics and the nature of placozoan species

November 29 2018 - Antonio Marco (University of Essex, UK), TBA

On sex, mothers and microRNA

November 11 2018 - Clovis Galliez (Université de Grenoble)

Making sense of the metagenomics mixture: identifying bacterial hosts from phage sequences and binning billions of contigs