M2 student - Computational Regulatory Genomics

SFBI; Illkirch-Graffenstaden; Sur place; EUR 8 900 - 18 000; Plein temps

Résumé du poste

SFBI in France is offering an M2 internship in Computational Regulatory Genomics focusing on Perturb-seq analysis of transcription factors. You will build, test and validate a complete bioinformatic pipeline for single-cell RNA-seq data, perform QC, and analyze gene expression changes with respect to nucleotide composition.

Collaboration with the Anaïs Bardet lab and IGBMC will provide an international, research-intensive environment.

Qualifications

• Education in Computational Biology, Bioinformatics or related field.
• Good programming skills in Linux (bash) and scripting languages.
• Good knowledge of statistics and biology; gen genomics interest.

Responsabilités

• Analyze Perturb-seq datasets to study TF effects on gene expression.
• Build, test and validate a complete bioinformatic pipeline for single-cell RNA-seq data.
• Perform quality control on perturbation efficacy and sequencing depth.
• Collaborate with computational and experimental biologists in a team environment.

Connaissances

bash

Python

R

Statistics

Sequencing data analysis

Team collaboration

English proficiency

Formation

Education in Computational Biology, Bioinformatics or related field

Outils

bash

Python

R

Description du poste

M2 student - Computational Regulatory Genomics

Genomics single-cell RNA-seq Transcription Factors Pipeline

Description

Uncovering the gene regulatory potential of atypical transcription factors using single-cell omics (Perturb-seq)

Multicellular organisms include thousands of cell types which are characterized by distinct identities and unique biological functions. Cellular identity is established and maintained by the action of transcription factors (TFs), which are key proteins responsible for the interpretation of the genome and the regulation of transcriptional programs. Many TFs have a strong specificity for DNA motifs localized within regulatory elements (such as enhancers or promoters), allowing genes to be switched ‘on’ or ‘off’. In contrast, some TFs recognize short and repetitive sequences dispersed across the genome, and therefore regulate gene expression more globally. Despite being intensively studied, the mechanism by which TFs regulate gene expression programs remains poorly understood, both in physiological context and in human diseases such as cancer or (neuro-)developmental disorders. Recent advances in genomics approaches have enabled the study of gene regulation in heterogeneous samples at the single cell level. In particular, the “Perturb-seq” methodology combines the power of a high-throughput genetic screen by CRISPR/Cas9 with a single-cell RNA output. A clever barcoding approach allows the attribution of a specific genetic perturbation to each cell, and the interpretation of its transcriptome relative to other samples within the population. This technology is particularly suited for the investigation of transcription factors function and cooperativity, which is traditionally explored at low throughput in genetically engineered cell lines. During this internship, you will analyze Perturb-seq datasets in order to dissect the effects of perturbing transcription factors on gene expression programs. We are particularly interested in the action of proteins recognizing short AT- or GC-rich DNA motifs, which could modulate transcription proportionally to nucleotide composition. Analyses will initially be performed on previously published data, and subsequently applied on new sequencing datasets generated by the Host laboratory. You will work in the computational team of Anaïs Bardet, in collaboration with Raphaël Pantier in the department of Functional Genomics and Cancer of IGBMC. The IGBMC is a world leading scientific institute in biomedical research providing both a stimulating and international environment. You will benefit from the support and expertise in computational biology necessary for the success of this project.

You are expected to build, test and validate a complete bioinformatic pipeline for the analysis of Perturb-seq data, specific tasks will include:

• Extensive quality control: proportion of cells carrying a single genetic perturbation, efficacy of gene disruption by CRISPRi, optimal sequencing depth to perform transcriptome-wide analyses, minimum number of targeted cells for sensitive detection of transcriptional defects.


• Develop custom analyses to explore transcriptional defects in relationship with nucleotide composition (%AT/GC over gene units).


• Determine the unique and redundant transcriptional programs affected by TFs


• Explore the cell state of perturbed cells and the biological pathways affected.

Required skills:

• Education in Computational Biology, Bioinformatics or a related field


• Good programming skills (e.g. bash, python, R) in a linux environment


• Good Knowledge of statistics


• Good knowledge of biology and interest in genomics and gene regulation


• Previous experience analyzing sequencing data is a plus


• Ability to work in a team with both computational and experimental biologists


• Good level in spoken and written english

Relevant publications

1. Quante, T., and Bird, A. (2016). Do short, frequent DNA sequence motifs mould the epigenome? Nat Rev Mol Cell Biol 17, 257–262. https://doi.org/10.1038/nrm.2015.31


2. Pantier, R., Chhatbar, K., Quante, T., Skourti-Stathaki, K., Cholewa-Waclaw, J., Alston, G., Alexander-Howden, B., Lee, H.Y., Cook, A.G., Spruijt, C.G., et al. (2021). SALL4 controls cell fate in response to DNA base composition. Molecular Cell 81, 845–858. https://doi.org/10.1016/j.molcel.2020.11.046


3. Chhatbar, K., Giuliani, S., Quante, T., Alexander-Howden, B., Selfridge, J., Guy, J., Auchynnikava, T., Spanos, C., Mathieson, T., Sanguinetti, G., et al. (2025). Pervasive binding of the stem cell transcription factor SALL4 shapes the chromatin landscape. Preprint at bioRxiv, https://doi.org/10.1101/2025.11.14.688441


4. Dixit, A., Parnas, O., Li, B., Chen, J., Fulco, C.P., Jerby-Arnon, L., Marjanovic, N.D., Dionne, D., Burks, T., Raychowdhury, R., et al. (2016). Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens. Cell 167, 1853-1866.e17. https://doi.org/10.1016/j.cell.2016.11.038