Statistical machine learning for regulatory genomics, transcriptomics, and rare disease.
The PejLab develops probabilistic, mechanistic, and machine learning methods for understanding how genetic variation shapes gene regulation and human disease. This organization hosts software, data resources, and websites from the Mohammadi lab at Seattle Children's Research Institute and the University of Washington.
- Rare disease genomics
- Regulatory effect-size modeling
- Multimodal transcriptomics and complex trait genetics
- Integrative genomics for personalized medicine
- Pantry: Pan-transcriptomic phenotyping and downstream integrative analysis.
- ANEVAh_repo: Haplotype-aware allele-specific expression methods for rare variant discovery.
- ANEVA-DOT: Dosage outlier detection for transcriptome-guided rare disease analysis.
- aFCn: Multi-eQTL effect-size modeling with haplotype-aware allelic fold change.
- ANEVA: Analysis of expression variance from allelic expression data.
- pejlab.github.io: Source for the lab website at pejlab.org.
- Website: pejlab.org
- Publications: Google Scholar
- Tools and data: pejlab.org/tools-and-data
- Join us: pejlab.org/join-us
We are not currently running an open search, but we are always interested in exceptional candidates. Prospective PhD students should apply through a relevant University of Washington graduate program; if you are interested in working with us, you are welcome to mention Pejman Mohammadi in your application.
Most repositories here are research software associated with published or in-progress methods. Active maintenance varies by project, but we aim to keep core tools and public resources accessible and usable.