In the first year of the post-baccalaureate programme, the teaching is centred on the use of Information and Communication Technologies, both within the institution and in a professional perspective. The objectives are as follows:

•     Basic knowledge of computers (hardware, operating system, networks),
•     Setting up and configuring one's working environment
•     Principles of use of common software (organisation of personal computer data, office automation, information search on the Internet),
•     Publication on the Internet
•     Ethical and legal context of the use of tools and in particular the Internet.
•     Management of one's digital identity and protection of personal data.

In the first year of the post-baccalaureate programme, interventions in other modules allow the introduction of the first notions of scientific computing:

•     Discovery of programming through electrical and electronic activities on Arduino in conjunction with the physics teacher
•     First notions of data management within the framework of the Campus Biodiversity Analysis with the ecology teachers

In the second year of the post-baccalaureate curriculum, the Introduction to Programming course aims to provide students with tools that allow them to automate data processing tasks or numerically solve problems for which no analytical solution necessarily exists. Thus, the module focuses on the notion of problem solving, from a computer science point of view. The different steps are covered:

• Introduction to the principles of problem analysis,
• Basics of algorithmics as a method of problem solving,
• Basics of a programming language, to express the algorithms in a form understandable by the computer,
• Iterative error analysis: hypothesis on the origin of the malfunction, solutions, tests...

Application problems are provided by other disciplines: ecology, economics, physics, chemistry, etc.

In the third year of the post-baccalaureate course, the “Databases and information system” course addresses information systems and the place of databases in these systems when they are computerized. Content includes:

• Computerization process for the company, possible infrastructures and architectures,
• Essential principles of database design,
• Basics allowing to go from a theoretical model to an implementation, associated with the set up of graphical interfaces intended for the end-users.

In the first year of the post-preparatory class course, the “Computer Science” module aims to provide the same skills as the three modules of the post-baccalaureate course. The objectives remain the same:

• Basic computer skills (hardware, operating system, networks),
• Principles of effective use of current tools (organization of personal computer data, office automation, information retrieval on the Internet),
• Ethical and legal context of the use of tools and in particular the Internet,
• Business IT: programming and databases.

I co-pilot an optional module named "Initiation to Bioinformatics" for M1 students in Horticulture. The objective is to introduce the methods and tools of bioinformatics from the point of view of the user, i.e. the biologist, by:
     • Presenting the main principles of bioinformatics and the use of these approaches in plant industries (R&D, breeding, etc.).
     • Presenting the methods of obtaining and processing different types of data (genomics, transcriptomics, phenotypics, etc.) in biology.
     • Providing a first overview of methods to exploit these data according to standard R protocols, image analysis software or bioinformatics tools available free of charge.

I am co-responsible for a M2 "Experimentation and exploitation of massive data" module, intended for engineering students in horticulture in the "Plant science and engineering" specialty, "Seeds and plants" option. This module is shared with the Master " Plant's biology". The objective is to make students aware of the diversity and complexity of phenotyping and genotyping data in light of their analysis…. This involves showing them the underlying biological problem and the way in which the data are produced through the design of experimental devices and the visit of platforms so that they can implement the appropriate processing tools (analysis, modelization).

The IRHS bioinformatics team develops tools for the management of biological data, developments to which I am contributing. Thus, ELVIS (Experiment and Laboratory on Vegetal Information System) brings together the databases and server layer common to the various data management / processing tools developed in the team. ELVIS takes the form of a PostgreSQL database and a data access web service layer developed in Python. ELVIS is broken down into a set of thematic modules. Several business applications developed by the team are based on ELVIS.

The ELVIS project page on ForgeMIA

PREMS is the business application oriented towards laboratory management based on ELVIS. PREMS consists of a set of bricks including the management of projects, samples and experimental results.

The PREMS project page on ForgeMIA

Elterm is the terminology management application based on ELVIS.

In ELVIS, the content of many fields is controlled by lists of possible values, which are generally derived from terminologies:

• recognized domain terminologies, possibly derived from publicly available taxonomies or ontologies (Plant Ontology, Crop Ontology, etc.)
• specific terminologies that we can consider disseminating

We therefore store a set of terminologies each covering a theme: morphology of organisms, development stages, growth conditions, etc. The general principle of what we want to store is similar to what is found in standard representations of terminologies in XML format like TermBase Exchange, but in the form of a database. Elterm provides a set of graphical interfaces allowing users to manipulate terminologies stored in ELVIS.

The ELTerm project page on ForgeMIA

Thanks to the wider spread of high-throughput experimental techniques, biologists are accumulating large amounts of datasets which often mix quantitative and qualitative variables and are not always complete, in particular when they regard phenotypic traits. In order to get a first insight into these datasets and reduce the data matrices size scientists often rely on multivariate analysis techniques. However such approaches are not always easily practicable in particular when faced with mixed datasets. Moreover displaying large numbers of individuals leads to cluttered visualisations which are difficult to interpret.

We developed a new methodology to overcome these limits. Its main feature is a new semantic distance tailored for both quantitative and qualitative variables which allows for a realistic representation of the relationships between individuals (phenotypic descriptions in our case). This semantic distance is based on ontologies which are engineered to represent real life knowledge regarding the underlying variables. For easier handling by biologists, we incorporated its use into a complete tool, from raw data file to visualisation. Following the distance calculation, the next steps performed by the tool consist in (i) grouping similar individuals, (ii) representing each group by emblematic individuals we call archetypes and (iii) building sparse visualisations based on these archetypes.Our approach is implemented as a Python pipeline and applied to  a rosebush dataset including passport and phenotypic data.

The DIVIS project page on ForgeMIA

As part of the DIVIS project, we were faced with the need to characterized groups of individuals according to the values of variables in the dataset. Such a method has been developed by F. Husson et al with the catdes() function as part of the FactoMiner R package. However were not completely satisfied with the output of this function regarding both the result data table and the visualisation. Therefore we developped our own Python implementation, with extras...

The QuaDS project page on ForgeMIA