Stage M2 - Predicting transcription factor binding from single cell data using deep learning

Stage M2 - Predicting transcription factor binding from single cell data using deep learning

Single cell Deep learning Genomics Transcription factors

Description

Multicellular organisms establish and maintain different transcriptional states in disparate cell types through complex and specific regulation of gene expression. This regulation is mediated by the cooperative binding of transcription factors (TFs) to regulatory elements through the recognition of specific DNA sequence motifs. Additionally, the physical access of transcription factors to DNA can be modulated by epigenetic regulation, such as nucleosome positioning and DNA methylation. Failure to maintain this tight regulation of gene expression results in developmental defects and various diseases including cancer.

Recent genomics approaches have enabled the study of gene regulation in heterogeneous samples at the single cell level (gene expression by scRNA-seq, chromatin accessibility by scATAC-seq, DNA methylation by scWGBS). However, it still remains impossible to profile transcription factor binding in single cells. As an alternative, we are currently developing deep learning models of transcription factor binding.

During this internship, you will analyze single cell multiome data (scATAC-seq and scRNA-seq) and apply a deep learning architecture developed in our group to predict transcription factor binding sites to better understand how they regulate gene expression.

You will work in the computational team of Anaïs Bardet in the department of Functional Genomics and Cancer in an international environment at the IGBMC, a leading European institute. You will benefit from our expertise in computational biology for the success of the project.

The student recruited should be motivated to continue this work as a PhD project by applying to PhD fellowships.

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 and machine learning
• 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

Candidature

Procedure: Please send a cover letter, CV, grades and ranking to . The recruitment process is open until one candidate is selected. Deadline: November 30th 2024