Stage M2 - Investigating transcription factor binding and regulation in cancer

Stage M2 - Investigating transcription factor binding and regulation in cancerCancer Deep learning Genomics Transcription factorsDescriptionMulticellular 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.Studying transcription factor binding and regulation in cancer patient samples remains limited experimentally, and we are currently developing deep learning models of transcription factor binding to better understand how their misregulation can be implicated in cancer.During this internship, you will integrate genomic data such as TF binding by ChIP-seq, chromatin accessibility by ATAC-seq, DNA methylation by WGBS, and use deep learning models to predict transcription factor binding in many different cancer types. You will then investigate how changes in transcription factor binding, due to DNA methylation, changes in the factor’s or cooperative factors’ expression, or single nucleotide polymorphisms, affect gene expression and contribute to the cancer state. Your predictions will then be validated experimentally in our group.You will work in the computational group 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 EnglishCandidatureProcédure: Please send a cover letter, CV, grades and ranking to [email protected]. The recruitment process is open until one candidate is selected. Deadline: November 30th, 2024 #J-18808-Ljbffr