Bat 650 Université Paris-Sud
A deep learning approach in population genetics: inferring selection
M2 internship (or long M1) 3 to 6 months
Flora Jay (LRI, Paris saclay) email@example.com and Jean Cury
Our lab is designing and implementing deep learning approaches tailored to population genetics. In particular we are interested in inferring the demographic and adaptive history of populations using genomic data of human or bacterial samples.
This internship aims at testing and contributing to a deep learning method for inferring selection.
Selective pressures can act on a population for many generations in the past and leave patterns in genetic data of present-day individuals. Many methods have been developed to identify selection from these patterns. Recently, deep neural networks have been used to automatically detect selection from a “matrix” / ”image” of genetic markers sequenced in multiple individuals . One of them is currently in development in the lab , and this internship aims at testing its performances under various conditions and contributing to its improvement.
- Compare the method(s) to state-of-the-art approaches based on machine learning and expert features, such as SWIF(r) .
- Simulate ancient samples or low quality modern samples and test the robustness of the method(s) for inferring selection based on those data rather than high quality modern DNA.
- Contribute to the design of network architectures that are better calibrated to real datasets.
This work will be funded by the HFSP international collaboration with Emilia Huerta Sanchez (U Brown, USA) and Maria Avila Arcos (UNAM, Mexico).
M2 (ou M1) student, machine learning, biostatistics, bioinformatic, math/info, ...
Programmation Python, (R)
Machine Learning, Statistical analyses
Knowledgeable or eager to learn about biology and population genetics
Flagel, Lex, Yaniv Brandvain, and Daniel R. Schrider. "The unreasonable effectiveness of convolutional neural networks in population genetic inference." Molecular biology and evolution 36.2 (2018): 220-238.
Cury et al. (2019) Back to the future of bacterial population genomics. Oral presentation at ESEB 2019 https://app.oxfordabstracts.com/events/653/program-app/submission/123100
Poster at DS3 2018: http://2018.ds3-datascience-polytechnique.fr/wp-content/uploads/2018/06…
Sugden, Lauren Alpert, et al. "Localization of adaptive variants in human genomes using averaged one-dependence estimation." Nature communications 9.1 (2018): 703.