Single-cell gene expression prediction from DNA sequence at . . . - bioRxiv Abstract Human genetic variants impacting traits such as disease susceptibility frequently act through modulation of gene expression in a highly cell-type-specific manner Computational models capable of predicting gene expression directly from DNA sequence can assist in the interpretation of expression-modulating variants, and machine learning models now operate at the large sequence contexts
Mapping genetic effects on cell type-specific chromatin . . . - PLOS Author summary In this study we profiled regulatory elements in specific immune cell types and sub-types in peripheral blood using single cell experiments in 13 individuals We then identified genetic variation between individuals associated with the activity of regulatory elements in each cell type and sub-type We finally used these results to identify genetic variants associated with blood
Modeling combinatorial regulation from single-cell multi . . . - Springer Advances in single-cell technology enable large-scale generation of omics data, promising for clarifying gene regulatory networks governing different cell type states Nonetheless, prevailing methods fail to account for universal and reusable regulatory modules in GRNs, which are fundamental underpinnings of cell type landscape We introduce cRegulon to infer regulatory modules by modeling
seq2cells: single-cell gene expression prediction from DNA sequences In a recent preprint, we introduce seq2cells – a machine learning framework that captures cell-specific gene expression beyond the resolution of pseudo-bulked data and allows variant effect prediction at single-cell level Our framework leverages transfer learning, using models pretrained on bulk-level epigenomic tasks to overcome the context limitations posed by the finite number of genes
Discovering mechanisms of human genetic variation and controlling cell . . . As a result, most genetic variation is not clinically actionable Single-cell sequencing provides a high-throughput, disease- and pathway-agnostic means to profile variant effects with mechanistic detail across the entire regulatory cascade
Single Cell Resolution Machine Learning Predicts Non-Coding Cis . . . Conclusions : The ML models generated in this study demonstrate the capacity for single nucleus epigenomic data to be used for the prediction of non-coding sequence variant impacts Derived models demonstrate cell class specificity, and can predict class-specific sequence motifs and impact scores for variants in crucial sequences
HumanBase ExPecto - Variant Effects - Simons Foundation Cell-type-specific expression ExPecto models make highly accurate cell-type-specific predictions of expression solely from DNA sequence With ExPecto, the tissue-specific impact of human gene transcriptional dysregulation can be systematically probed 'in silico' - at a scale not yet possible experimentally