STATISTICAL METHODS FOR MOLECULAR DYNAMICS AND INTERACTION ANALYSIS IN FLUORESCENCE MICROSCOPY

EVENT : C3BI Seminars


Main speaker : Charles KERVRANN, from Inria Rennes – Bretagne Atlantique / SERPICO Project-Team Date : 27-02-2020 at 02:00 pm Location : Auditorium Francois Jacob – BIME (26) ,Institut Pasteur, Paris


The characterization of biomolecule dynamics and interactions in living cells is essential to decipher biological mechanisms and processes. This topic is usually addressed in fluorescent video-microscopy from particle trajectories computed by object tracking algorithms. However, classifying individual trajectories into predefined diffusion classes (e.g. sub-diffusion, free diffusion (or Brownian motion), super-diffusion), estimating diffusion model parameters, or detecting diffusion mode switches, is a difficult task in most cases. Meanwhile, colocalization is generally applied to detect interactions between two biomolecules observed in an image pair. Colocalization aims at characterizing spatial associations between two fluorescently-tagged biomolecules by quantifying the co- occurrence and correlation between the two channels acquired in fluorescence microscopy. This problem remains an open issue in diffraction-limited microscopy and raises new challenges with the emergence of super-resolution imaging. To address these challenging issues, we propose a computational framework based on statistical tests to both classify biomolecule trajectories and to detect spatially-varying colocalization in single molecule imaging (PALM, STORM). The methodological approach is well-grounded in statistics and is more robust than previous techniques. In this talk, I will present the underlying concepts and methods. The resulting algorithms are flexible in most cases, with a minimal number of control parameters to be tuned (p-values). They can be applied to a large range of problems in cell imaging and can be integrated in generic image- based workflows, including for high content screening applications.

References: 1. V. Briane, C. Kervrann, M. Vimond. Statistical analysis of particle trajectories in living cells, Phys. Rev. E 97, 062121 2. V. Briane, M. Vimond, C. Valades Cruz, A. Salomon, C. Wunder, C. Kervrann. A sequential algorithm to detect diffusion switching along intracellular particle trajectories, Bioinformatics, btz489, 2019. 3. V. Briane, M. Vimond, C. Kervrann. An overview of diffusion models for intracellular dynamics analysis, Briefings in Bioinformatics, bbz052, 2019 4. V. Briane, M. Vimond, A. Salomon, C. Kervrann. A computational approach for detecting micro-domains and confinement domains in cells: a simulation study, 2019. 5. F. Lavancier, T. Pécot, L. Zengzhen, C. Kervrann, Testing independence between two random sets for the analysis of colocalization in bio-imaging. Biometrics, doi:10.1111/BIOM.13115, 2019.

Due to security policy in Institut Pasteur, please register before if you plan to come to this meeting

STATISTICAL METHODS FOR MOLECULAR DYNAMICS AND INTERACTION ANALYSIS IN FLUORESCENCE MICROSCOPY

EVENT : C3BI Seminars


Main speaker : Charles KERVRANN, from Inria Rennes – Bretagne Atlantique / CNRS-UMR 144 Paris Inria  

Institut Curie  CNRS  UPMC  PSL Research University SERPICO Project Team Date : 27-02-2020 at 02:00 pm Location : Auditorium Francois Jacob – BIME (26) , Institut Pasteur, Paris


The characterization of biomolecule dynamics and interactions in living cells is essential to decipher biological mechanisms and processes. This topic is usually addressed in fluorescent video-microscopy from particle trajectories computed by object tracking algorithms. However, classifying individual trajectories into predefined diffusion classes (e.g. sub-diffusion, free diffusion (or Brownian motion), super-diffusion), estimating diffusion model parameters, or detecting diffusion mode switches, is a difficult task in most cases. Meanwhile, colocalization is generally applied to detect interactions between two biomolecules observed in an image pair. Colocalization aims at characterizing spatial associations between two fluorescently-tagged biomolecules by quantifying the co-occurrence and correlation between the two channels acquired in fluorescence microscopy. This problem remains an open issue in diffraction-limited microscopy and raises new challenges with the emergence of super-resolution imaging.

To address these challenging issues, we propose a computational framework based on statistical tests to both classify biomolecule trajectories and to detect spatially-varying colocalization in single molecule imaging (PALM, STORM). The methodological approach is well-grounded in statistics and is more robust than previous techniques. In this talk, I will present the underlying concepts and methods. The resulting algorithms are flexible in most cases, with a minimal number of control parameters to be tuned (p-values). They can be applied to a large range of problems in cell imaging and can be integrated in generic image-based workflows, including for high content screening applications.     1. V. Briane, C. Kervrann, M. Vimond. Statistical analysis of particle trajectories in living cells, Phys. Rev. E 97, 062121 2. V. Briane, M. Vimond, C. Valades Cruz, A. Salomon, C. Wunder, C. Kervrann. A sequential algorithm to detect diffusion switching along intracellular particle trajectories, Bioinformatics, btz489, 2019. 3. V. Briane, M. Vimond, C. Kervrann. An overview of diffusion models for intracellular dynamics analysis, Briefings in Bioinformatics, bbz052, 2019 4. V. Briane, M. Vimond, A. Salomon, C. Kervrann. A computational approach for detecting micro-domains and confinement domains in cells: a simulation study, 2019. 5. F. Lavancier, T. Pécot, L. Zengzhen, C. Kervrann, Testing independence between two random sets for the analysis of colocalization in bio-imaging. Biometrics, doi:10.1111/BIOM.13115, 2019.

Due to security policy in Institut Pasteur, please register before if you plan to come to this meeting

Inferring the parameters of random walks without tracking

EVENT : C3BI Seminars


Main speaker : Till Kletti, from Former member of the Decision and Bayesian Computation Group at the DBC Date : 30-01-2020 at 02:00 pm Location : Auditorium Francois Jacob – BIME (26), Institut Pasteur, Paris


We consider the problem of inferring random walk models (e.g. spatial maps of diffusivity, drift) of a set of moving particles (e.g. biomolecules) using discrete-time snapshots of their positions (a movie). A main difficulty stems from the fact that the particles are not labelled, which makes the particle matching between two consecutive snapshots uncertain. We describe how to account for this uncertainty using the belief propagation (BP) algorithm, which outperforms explicit tracking methods when the particle density is high. Furthermore, we describe procedures allowing us to account for blinking of the particles. Finally, we show applications of the method to mapping heterogeneous diffusivity fields experienced by biomolecules in the plasma membrane of living cells.


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Inferring interaction partners and evolutionary constraints from protein sequences

EVENT : C3BI Seminars


Main speaker : Anne-Florence Bitbol, from CNRS – Sorbonne Universite Date : 16-01-2020 at 02:00 pm Location : Auditorium Francois Jacob – BIME (26) ,Institut Pasteur, Paris


Proteins and multi-protein complexes play crucial roles in our cells. The amino-acid sequence of a protein encodes its function, including its structure and its possible interactions. In evolution, random mutations affect the sequence, while natural selection acts at the level of function. Hence, shedding light on the sequence-function mapping of proteins is central to a systems-level understanding of cells, and has far-reaching applications in synthetic biology and drug targeting. The current explosion of available sequence data has inspired data-driven approaches to discover the principles of protein operation. At the root of these approaches is the observation that amino-acid residues which possess related functional roles often evolve in a correlated way.

First, I will present two novel methods to predict protein-protein interactions from sequence data. One method is based on the maximum-entropy inference approach that has already allowed to infer protein structures from sequences, and the other one is based on information theory. These methods accurately identify which proteins are functional interaction partners among the paralogous proteins of two families, starting from sequence data alone. They also provide signatures of the existence of interactions between protein families. I will further discuss the role of correlations arising from the shared evolutionary history of interacting partners in the success of these methods. Then, I will propose a simple interpretation of the origin of the “sectors” of collectively correlated amino acids that have been discovered in several protein families through statistical analyses of sequence alignments. I will show that selection acting on any functional property of a protein, represented by an additive trait, can give rise to such a sector.

Due to security policy in Institut Pasteur, please register before if you plan to come to this meeting

Cancelled – Inferring interaction partners and evolutionary constraints from protein sequences

EVENT : C3BI Seminars


Main speaker : Anne-Florence Bitbol, from CNRS – Sorbonne Université Date : Cancelled at XX pm Location : Retrovirus room – LWOFF (22) ,Institut Pasteur, Paris


Proteins and multi-protein complexes play crucial roles in our cells. The amino-acid sequence of a protein encodes its function, including its structure and its possible interactions. In evolution, random mutations affect the sequence, while natural selection acts at the level of function. Hence, shedding light on the sequence-function mapping of proteins is central to a systems-level understanding of cells, and has far-reaching applications in synthetic biology and drug targeting. The current explosion of available sequence data has inspired data-driven approaches to discover the principles of protein operation. At the root of these approaches is the observation that amino-acid residues which possess related functional roles often evolve in a correlated way.

First, I will present two novel methods to predict protein-protein interactions from sequence data. One method is based on the maximum-entropy inference approach that has already allowed to infer protein structures from sequences, and the other one is based on information theory. These methods accurately identify which proteins are functional interaction partners among the paralogous proteins of two families, starting from sequence data alone. They also provide signatures of the existence of interactions between protein families. I will further discuss the role of correlations arising from the shared evolutionary history of interacting partners in the success of these methods. Then, I will propose a simple interpretation of the origin of the “sectors” of collectively correlated amino acids that have been discovered in several protein families through statistical analyses of sequence alignments. I will show that selection acting on any functional property of a protein, represented by an additive trait, can give rise to such a sector.

Due to security policy in Institut Pasteur, please register before if you plan to come to this meeting

Bioinformatics program for PhD students 2019-2020 – ALL CLASSES CANCELLED

A TARGETED CURRICULUM DEVELOPED BY THE COMPUTATIONAL BIOLOGY DEPARTMENT

In accordance with a request in 2016 from the former General Board of Directors, the Computational Biology Department has developed this training program dedicated to Institut Pasteur PhD students. Each and every student at the institute must attend at least 50 hours of training. At the minimum, he or she is required to validate the statistical modules (which, depending on their background, some students may skip), and possibly choose additional modules according to his or her background and field of research.  

A CORE FOUNDATION AND THE OPTION TO SPECIALIZE

This 50-hour training program required for all Institut Pasteur PhD students begins with a group of common core courses including a knowledge base in reproducible research, R Programming and Statistics. Each student then chooses a track of additional modules— Bioinformatics or Image Analysis—according to their background and field of research. Students already proficient in R programming and statistics can skip certain modules or the entire track.  

INSTITUTIONAL RECOGNITION

This PhD program has been acknowledged by the doctoral schools at the Sorbonne Bio, CDV, Ecole doctorale interdisciplinaire européenne Frontières du Vivant, and ED-SDSV. Check out the course’s flyer for more information and and visit the Pasteur course web page!
   

COURSE PROGRAM

Mandatory Common Core

This 6h course is mandatory for all PhD students. They will learn how to get help and assistance from the Department of Computational Biology and from the Hubs for questions in programming, experimental design, data analysis and image analysis. They will attend three short lectures in programming and reproducible resaerch. Finally, they will have the opportunity to assess their level in R programming and statistics and to register to the modules of their choice in the R programming and statistics, Bioinformatics and Image analysis tracks. Two sessions will be organized, on Nov 12th, 2019 and Jan 8th, 2020. Students already on the campus are strongly encouraged to attend the first session.  
Program of the mandatory common core module, session 2
   

Other modules

The figure below shows all the available modules. To attend these lectures you need to bring your own laptop. No special configuration is required. Just avoid to come with your grandma’s machine.  
Description of the Bioinformatics program
  A detailed description of each and every module can be found here.

UPDATE : New dates and additional sessions !!!

  These two modules were shifted by one week:  
  • • Introduction to R and Statistics, session 1: December 10-12
  • • Hypothesis testing, session 1: December 16-17, meeting room, 2nd floor, Yersin building
  Following remarks that were made during the first common core course, and to take into account the high number of registrations in all modules, a new session will be organized for most modules of the PhD program. New dates are given below :

R programming and statistics

A third session IN FRENCH will be organized in June :
  • • Introduction to R and stats : 9-11 june
  • • Hypothesis testing : 22-23 june
  • • Linear models : 25-26 june
  • • Multivariate analyses : 29-30 june
Students who have registered to the second session (in march) and wish to switch to the third one can send an email to c3bi-teaching@pasteur.fr    

Bioinformatics

The Basic concepts in NGS data analyses lecture – Feb 3rd – will be extended to 35 seats. A second session has been organized at the following dates:
  • • Unix basic commands: 2-3 April
  • • Introduction to sequence analysis: 19-20 March
  • • Proteomics data analysis: 14-15 April
  • • NGS week: 6-10 April
    • * Basic concepts in NGS data analysis: 6 April
    • * ChIP-seq: 7 April
    • * Variant calling: 8 April
    • * Expression quantification, differential analysis: 9 April
    • * Functional analysis: 10 April
   

Image analysis

A second session will be organized end of April (28-30)
  • • Fiji : 28 April
  • • Icy : 29 April morning
  • • Advanced Icy : 29 April afternoon
  • • Super resolution images : 30 April morning
  • • Machine learning : 30 April afternoon
Registration form is available here

COURSE MATERIAL

  Mandatory common core:   R Programming and Statistics    

CANCELLED : Computational Biology in the Crossroad of Big Data, Artificial Intelligence and High Performance Computing

EVENT : C3BI Seminars


Main speaker : Alfonso Valencia, from Barcelona Super Computing Center (BSC-CNS) Date : 26-09-2019 at 02:00 pm


Due to security policy in Institut Pasteur, please register before if you plan to come to this meeting

Genomic enzymology web tools for functional assignment: Generating and analyzing Sequence Similarity Networks (SSNs) and Genome Neighborhood Networks (GNNs) with the EFI suite

EVENT : C3BI Seminars


Main speaker : Rémy Zallot, from University of Illinois Date : 20-06-2019 at 02:00 pm Location : Duclaux room down groundfloor – DUCLAUX (01), Institut Pasteur, Paris


Protein databases contain an exponentially growing number of sequences as a result of the decrease of cost and difficulty of genome sequencing. The rate of data accumulation far exceeds the rate of functional studies, producing an increase in genomic ‘dark matter’, sequences for which no precise and validated function is defined. Strategies to leverage the protein and genome databases for discovery of the functions of novel enzymes belonging to the dark matter are needed. “Genomic enzymology” is the integration of relationships among sequence-function space in protein families and the genome context of their bacterial, archaeal, and fungal members to propose function. The Enzyme Function Initiative suite of webtools (https://efi.igb.illinois.edu) include the EFI-Enzyme Similarity Tool (EFI-EST) for generating sequence similarity networks (SSNs) for protein families and the EFI-Genome Neighborhood Tool (EFI-GNT) producing Genome Neighborhood Networks (GNNs) and Genome Neighborhood Diagrams (GND) for analyzing and visualizing genome context of SSNs clusters. Together, these tools facilitate the “Genomic enzymology” application to the ‘dark matter’ problem. A detailed overview of the principle of SSNs, GNNs and GNDs generation will be presented. The identification of an unexpected reaction in the Queuosine biosynthesis pathway will illustrate the approach.


Due to security policy in Institut Pasteur, please register before if you plan to come to this meeting

New computational tools for the analysis of microbiome dynamics

EVENT : C3BI Seminars


Main speaker : Eran Halperin, from UCLA (Computer Science Department & Departments of Human Genetics, Biomathematics & Department of Anesthesiology) Date : 25-06-2019 at 11:00 am Location : Auditorium Jaques Monod – MONOD (66) ,Institut Pasteur, Paris


High-throughput microbiome analysis has become ubiquitous over the last few years. However, the interpretation of the data is often non-trivial and highly depends on the methodology used for the analysis. I will describe a few methods for the analysis of microbiome in contexts that are typical in such analyses. First, I will describe a new method for microbial source tracking, that is, finding the sources of a microbiome sample. I will demonstrate how using the method one can reach very different conclusions (that make more biological sense) than using previous methods. Specifically, I will show examples on the dynamics of gut microbiome in babies, and on the usage of source tracking as a tool for disease diagnosis. Second, I will discuss novel approaches for the analysis of time-series micorbiome data, and here too, I will show how this new approach results in new biological insights, particularly on the dependence of microbiome in the future on the current composition of the microbiome. The talk will be self contained, and I will not assume any expert knowledge in computer science or statistics.


Due to security policy in Institut Pasteur, please register before if you plan to come to this meeting