Morgane Thomas-Chollier

  • Computational biologist
  • 2 children (2012, 2017)
  • Associate professor (maître de conférence) at Ecole normale supérieure ENS-PSL (Paris, France)
  • Junior Member of Institut Universitaire de France (IUF, 2017-2022)
  • President of the Société française de Bioinformatique (SFBI, 2016-2020).
  • High-throughput functional genomics & single-cell, core facility service, Transcriptional regulation

Education and Academic positions

Complementary education

  • 2021-2022 : courses in Management "Animer une équipe et des hommes" from Dauphine-PSL (Université PSL école Interne)


As co-head of a genomics core facility, I am interested in how next-generation sequencing data are generated, shared and analyzed. On a technical level, I am interested in the approaches for single-cell analyses, particularly for poorly-annoted genomes and with long-read sequencing.

In addition to teaching at university, training of researchers/engineers to bioinformatics approaches is crucial for me.

My projects often involve predictions of binding regions for transcription factors (motif detection, de-novo motif discovery, ChIP-seq, ChIP-exo) and I am co-leader of the software suite RSAT.

I keep a biological interest for the evo-devo field.


List of publications with HAL: ORCID : 0000-0003-2608-476X

International and National conferences

Software projects

Santana-Garcia*,Castro-Mondragon* et al, NAR, 2022
Santana-Garcia et al, Comput Struct Biotechnol J, 2019
Nguyen*,Contreras-Moreira* et al, NAR, 2018
Castro-Mondragon JA et al, NAR, 2017
Medina-Rivera A*, Defrance M*, Sand O* et al, NAR, 2015
Thomas-Chollier M et al, Nature Protocols, 2012
Thomas-Chollier M et al, NAR, 2012
Thomas-Chollier M et al, NAR, 2011
Thomas-Chollier M*, Sand O* et al, NAR, 2008
Medina-Rivera A, Abreu-Goodger C, Thomas-Chollier M et al, NAR, 2011
Sand O, Thomas-Chollier M, van Helden J.Bioinformatics, 2009
Sand O, Thomas-Chollier M et al. Nature Protocols, 2008
Thomas-Chollier M*, Turatsinze J-V* et al. Nature Protocols, 2008
Thomas-Chollier M et al, BMC Evol Biol, 2010
Thomas-Chollier M et Ledent V, BMC Genomics, 2008
Thomas-Chollier M et al, BMC Bioinformatics, 2007
Starick S*, Ibn-Salem J*, Jurk M*, Hernandez C, Love MI, Chung H, Vingron M, Thomas-Chollier M#, Meijsing SH#, Genome Research, 2015
Thomas-Chollier M et al, Nature Protocols, 2012


    Classes at Ecole normale supérieure :
  • L3 : Ateliers méthodologiques
  • L3 : Génétique – Génomique – Bioinformatique
  • L3 : Introduction aux sciences du vivant (Biologie pour non biologistes)
  • M1 : Computational biology project
  • M1+M2 : Mathematics and programming training
  • M1+M2 : Soft skills
  • M2: Functional genomic data analysis: epigenomics

    Current classes outside Ecole normale supérieure :
  • Institut Pasteur : Ethics, good scientific practices and plagiarism

    Trainings for graduate students/researchers/engineers:
  • Single-cell transcriptomics and epigenomics (SincellTE, Roscoff)
  • Initiation au traitement des données de génomique obtenues par séquençage à haut débit (Ecole de bioinformatique AVIESAN IFB, Roscoff)
  • ChIP-seq data analysis (UAEM, Cuernavaca, Mexico ; National University of Singapore ; VIB Bits, Leuven, Belgium ; Institut Pasteur cours Analyse des Génomes)

Being a bioinformatician in 2016 is both thrilling and frustrating.

Foreword HDR thesis.

Thrilling, as in less than a decade (barely since my PhD), we have been propelled into the ”Big Data” era of Biology [Stephens et al., 2015]. Improvements in sequencing technologies have led to an explosion of Genomics data. These billions of Terabytes (”Zettabytes”) of sequence data are raising challenges for computer scientists : data compression and storage, accessibility and distribution, development of more efficient algorithms to process these large datasets. The challenge for bioinformaticians is to keep up with these perpetual new developments to obtain biological insights from all these datasets, bridging the gap between computer scientists and experimental biologists. In just a few years, the global demand in bioinformatics skills has exploded, with several job advertisements posted every single day, solely in France (source: !
Today, it is obvious that there are not enough bioinformaticians. It has become ordinary to be approached by experimental biologists desperate to find ”someone to analyse their data”. That is when the frustration comes in, as bioinformaticians are too often considered as a mere service provider, contacted once the raw data are already produced to apply routine pipelines, regardless of the fact that most projects require customised analyses [Chang, 2015]. Frustration also comes from the lack of consensual definition of ’bioinformatician’ [Smith, 2015]. Within the spectrum of bioinformaticians, I came to consider myself as a computational (or dry) biologist, motivated by biological questions and using a computer as my bench. In this new Big Data era, collaboration between wet and dry biologists is becoming the new standard. Bioinformaticians should be involved early in the experimental design, and fair co-authorship on the publications should be customary. Evaluation criteria should be adapted for bioinformatician careers [Chang, 2015], acknowledging that working with multiple collaborators on very diverse biological questions is actually a sign of success rather than dispersion. The evaluation criteria need to be broadened to not only include the production of scientific software, but also recognize the maintenance of these software for the community [Singh Chawla, 2016]. Last, the frustration also comes when reading high-impact journal articles that have questionable and often unreproducible bioinformatic data analyses. During the peer-reviewing process, editors should enforce policies to ask reviewers if the manuscript should be sent to a bioinformatics specialist, similar to the policies often in place for statistics.
Training in bioinformatics has become crucial in recent years. On the one hand, by providing courses and training material [Lewitter, 2006] dedicated to researchers, to alleviate the current bottleneck of sequence data analysis. It is also important to provide user-friendly computer tools to experimentalists, who have the biological expertise to analyse their data, but often lack bioinformatics skills. On the other hand, it is necessary to engage the undergraduate biology students into interdisciplinary work and computational biology, so that the next generation of biologists and clinicians will have essential bioinformatics skills [Brazas et al., 2014].
Even if this dissertation focuses on my research work, teaching takes a huge part of my activity and motivation to be associate professor. I am gladly contributing to the above-mentioned teaching aspects by (i) my engagement in the AVIESAN/IFB school of bioinformatics for researchers, as well as in various trainings for biologists (Belgium, France, Singapore), (ii) developing usable bioinformatics tools (mainly RSAT) and training users via published protocols and workshops, (iii) as vice-president of the French Society of Bioinformatics (SFBI), co-organising the first national meet- ing dedicated to the teaching of bioinformatics at the undergraduate level, and (iv) at ENS, teaching computational biology to all biology students, and introduce them to the current challenges of the Big Data era.


Brazas, M. D., Lewitter, F., Schneider, M. V., van Gelder, C. W. G., and Palagi, P. M. (2014). A Quick Guide to Genomics and Bioinformatics Training for Clinical and Public Audiences. PLoS computational biology, 10(4):e1003510.
Chang, J. (2015). Core services: Reward bioinformaticians. Nature, 520(7546):151–152.
Lewitter, F. (2006). Welcome to plos computational biology “education”. PLoS computational biology.
Singh Chawla, D. (2016). The unsung heroes of scientific software. Nature, 529(7584):115–116.
Smith, D. R. (2015). Broadening the definition of a bioinformatician. Frontiers in genetics, 6:258.
Stephens, Z. D., Lee, S. Y., Faghri, F., Campbell, R. H., Zhai, C., Efron, M. J., Iyer, R., Schatz, M. C., Sinha, S., and Robinson, G. E. (2015). Big Data: Astronomical or Genomical? PLoS Biology, 13(7):e1002195.

Women in Science

In 2004 when I started my PhD in bioinformatics, I ordered a Mac laptop as my working machine. When I received it, a senior postdoc in the lab told me "hey, you got a Linux for Barbie !".
Probably a great joke for the guys, but that day, I painfully discovered that being a woman would make a difference. That I would need to prove my skills to be considered. That I was not necessarily welcome in "their circle". After this, I have been extremely lucky with three open-minded mentors, who supported and guided me, lowering this bad feeling that nevertheless grew back as I became "older" in research.

Many discussions with female (and male !) scientists later, I'm convinced that this feeling is shared by many young women scientists, not just in bioinformatics, within France and abroad. I wished to do something about it, at my own level. As part of the SFBI, I have contributed to awareness of the low number of female invited speakers and chairs at Jobim, and acted to improve this. As a postdoc in Berlin, I gave a talk on women in sciences to raise awareness. In 2015, I attended the conference "Les Femmes dans le monde académique" to gain a more comprehensive understanding of the issues. I encouraged and helped my PhD students to apply to female-oriented funding schemes. In 2021, I gave a videoconference at the high school in my city to encourage girls to pursue studies in STEM, and explain unconscious biaises.

I am convinced mentoring programs are a way to be part of a community of women, if you don't find incidentally great women on your path... Big thank you to Alexandra and Hélène.
Check out : Programme de mentorat des doctorantes Femmes & Sciences
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