Tal Ashuach

The paper constructs enhancer-promoter interaction networks during neural differentiation and identifies their temporal dynamics and disease-associated variant enrichment using computational and experimental methods.

This paper evaluates sequence design strategies for perturbation MPRA using a computational framework and finds that random nucleotide shuffling outperforms other methods.

MultiVI integrates multi-modal single-cell data by creating a joint representation and imputing missing values while correcting batch effects.

The paper uses perturbation assays to study how regulatory elements guide neural differentiation over time, identifying sequence-driven activity and environmental effects on transcription.

PeakVI is a deep generative model for scATAC-seq data analysis that corrects batch effects, preserves biological heterogeneity, and identifies differential accessibility.

scvi-tools provides a Python package for probabilistic analysis of single-cell omics data, offering standardized interfaces and extensible tools for various analysis tasks.

VISION annotates variation sources in single-cell RNA-seq data without preconceived group stratification, linking findings to clinical phenotypes in lupus B cells.

MPRAnalyze provides a statistical framework for analyzing MPRA data to estimate regulatory sequence effects and compare differential activity across conditions.

The paper characterizes regulatory dynamics during neural induction using genomic assays and computational tools, identifying key transcription factors and functional regulatory elements.