Aviv Regev

The paper classifies and compares cell types in primate fovea and peripheral retina using single-cell RNA-seq, finding conserved and divergent features across species and regions.

The paper identifies IL-33 as a driver of tumor immunosuppression by analyzing longitudinal single-cell RNA-Seq data of regulatory T cells in a mouse model of lung adenocarcinoma.

scSVA provides a lightweight R package for interactive visualization and exploration of large single-cell omics datasets using efficient 3D embedding methods.

The paper uses an OU process to model gene expression evolution across mammals and identifies expression pathways under different selection pressures.

The paper identifies genetic variants influencing isoform usage in the anti-viral response, revealing ERAP2's role in balancing selection and influenza-specific regulation.

The paper identifies genetic variants affecting chromatin accessibility in T cells and links them to gene expression and autoimmune disease.

BROCKMAN identifies transcription factor activity variation in single-cell ATAC-seq data by analyzing k-mer frequencies associated with epigenomic marks.

The paper identifies a transcriptional regulatory network involving STAT3, NF-κB, and AP-1 that drives tumor-promoting inflammation in cancers, linking inflammation to immune infiltration and genome instability.

GeNets provides a platform to compare genomic networks using machine learning and allows analysis of genetic and RNA sequencing data.

Molecular network analysis of RNA sequencing data from the aging human cortex identifies new Alzheimer's and cognitive decline genes.

This study compares gene modules in human and mouse immune cells, finding conserved transcriptional organization but divergent regulatory mechanisms.

This study shows that T helper cells interact with intestinal stem cells via MHCII to regulate their renewal and differentiation, with distinct effects based on Th subtype and cytokine signaling.

This paper introduces composite measurements and molecular compressed sensing to efficiently recover gene expression profiles with fewer measurements than genes.

Optimal transport converts a 65,781-cell reprogramming time course into probabilistic fates and trajectories, exposing alternative cell fates and a gene, Obox6, that improves reprogramming efficiency.

DroNc-Seq enables high-throughput single nucleus RNA-seq analysis of archived brain tissues, demonstrating efficient and unbiased cell type classification in mouse and human samples.

The paper proposes the Human Cell Atlas project to catalog all human cell types through molecular profiling and connects this data with classical cellular descriptions.

Filtering STAR's chimeric alignments against gene annotations outscored 15 other fusion callers on 60 cancer cell lines and ran fastest, averaging 2.3 single-core hours per sample.

This paper uses a CRISPR screen to identify noncoding genomic elements that influence drug resistance in melanoma cells.

This paper systematically surveys X chromosome inactivation across human tissues using transcriptomic and genomic data, finding that XCI is generally uniform but shows heterogeneity and identifies new escape genes with sex-biased expression.

This study characterizes C2c2, a CRISPR-Cas effector that uses an RNA guide to target and cleave RNA, showing its potential as a programmable RNA-targeting tool.

Div-Seq tracks transcriptional dynamics of rare adult newborn neurons using single nucleus RNA-Seq and EdU labeling.

The paper introduces a computational tool for lncRNA discovery and analysis, identifying two distinct classes of lncRNAs based on evolutionary patterns.