𝔾TEx V8 expression and splicing predictors […] PsychENCODE expression predictors […] BrainXcan brain features predictors […] More Read more →

𝕊ee description in CITE scPredixcan PAPER HERE See github reo in scPrediXcan github repo Download models here from Box folder or from Zenodo (for islet cells from T2D dataset) and Zenodo (for immune cells from OneK1K dataset). […] As scPrediXcan is a newly-developed tool, there may be some … Read more →

ℙrediction models of five brain region gene expression in rats. See description in Santhanam, Sanchez Roige, et al (2024). Download link To query the database, checkout this post […] Natasha Santhanam, Sandra Sanchez-Roige, Yanyu Liang, Apurva S. Chitre, Daniel Munro, Denghui Chen, Riyan … Read more →

𝔸RIC protein prediction models in predictdb format can be downloaded from here https://uchicago.box.com/s/3sf4y4gv6c7zam0l5fxicpcd3zji5wzc See more details here https://lab-notes.hakyimlab.org/post/2021-09-08-generating-metaxcan-prediction-model-from-aric-pwas/ Read more →

ℙrediXcan ready databases and covariance files of the paper “Protein prediction for trait mapping in diverse populations” can be downloaded from here https://doi.org/10.5281/zenodo.4837327 Read more →

𝕎e have produced different families of prediction models for sQTL and eQTL, using several prediction strategies, on GTEx v8 release data. We recommend MASHR-based models below. Elastic Net-based are a safe, robust alternative with decreased power. […] Expression and splicing prediction models … Read more →

ℙrediction models trained with GTEx V8 data using the cross tissue imputation approach (see UTMOST paper). Trained by Alvaro Barbeira for the paper Fine‐mapping and QTL tissue‐sharing information improves the reliability of causal gene identification Download in Zenodo here Read more →

𝔾andal et al analyzed autism spectrum disorder, schizophrenia, and bipolar disorder across multiple levels of transcriptomic organization—gene expression, local splicing, transcript isoform expression, and coexpression networks for both protein-coding and noncoding genes to produce a quantitative, … Read more →

𝔼xpression prediction models with LD reference data are available in this website. The models were trained on Common Mind Consortium, GTEx, and STARNET consortiums. The underlying algorithm is Elastic Net, informed by epigenetic data. These models were presented in “Integrative transcriptome … Read more →

𝕊ingle-tissue expression prediction models with LD reference data are available in this GitHub repository. The underlying algorithm is Elastic Net. These models were presented in “Gene expression imputation across multiple brain regions provides insights into schizophrenia risk, Huckins et al, … Read more →