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The Peltz lab at the Stanford University School of Medicine has received NIH funding for two projects, which will develop an AI pipeline for mouse genetic discovery. These projects will perform long range sequencing (LRS) of 40 mouse strains to develop a complete map of the pattern of genetic variation in their genomes, which covers single nucleotide polymorphisms, structural variants, and TA-repeat elements. The sequence data will be used to analyze the 18,000 phenotypic datasets in a publicly available database, which measures biomedical trait responses in panels of inbred strains.
We have openings for two bioinformatic post-doctoral fellows. The project will require: (i) A large amount of genomic sequence data to be analyzed for characterization of genetic variants in inbred strains, which is performed in collaboration with the company that is the leader in analyzing LRS data. (ii) A large phenotypic dataset will be genetically analyzed by to identify candidate genes for many biomedical phenotypes. (iii) An existing AI pipeline (Bioinformatics 38:3385, 2022) for analysis of genetic candidates will be optimized by testing whether: recently developed large language models can be used for candidate gene assessment and for broadening the type of queries analyzed, and if new types of information (mouse gene knockout or human GWAS databases) can be incorporated into the pipeline used for candidate gene assessment.
- A qualified applicant will have a PhD in bioinformatics, computer science, or genetics; and demonstrated experience with machine learning and/or AI-methods.
- Applications must include a cover letter indicating your past experience and should state why you are interested in this project, a CV with publication list, and name at least two individuals that can provide a reference.