Elena Zaslavsky

Single-nucleus multiomic analysis reveals modulation of the gene regulatory circuit landscape in pituitary cell types during mouse estrous cycle.

The paper uses single-nucleus multiomics to study pituitary cell changes during the mouse estrous cycle, finding stage-dependent gene regulatory circuits and validated ETS2-driven gene expression patterns.

Multi-omic identification of key transcriptional regulatory programs during endurance exercise training in rats

The paper identifies tissue-specific transcriptional regulatory programs in rats undergoing endurance exercise training by integrating chromatin accessibility, DNA methylation, and gene expression data.

Exercise modulation of the alternative splicing landscape in human tissues

This study examines how acute endurance and resistance exercise alter alternative splicing in human skeletal muscle, adipose tissue, and blood, identifying 5102 differential splicing events and their regulatory mechanisms.

Innate immune epigenomic landscape following controlled human influenza virus infection

The paper analyzes the epigenomic changes in innate immune cells after influenza virus infection and finds coordinated rewiring of the epigenetic landscape.

Spatial multi-omics defines a shared infiltrative signature in glioblastoma at the resection margin

This study identifies a shared infiltrative signature in glioblastoma at the resection margin using spatial multi-omics data and validates EGFR as a key marker.

Integrated single-cell multiome analysis reveals muscle fiber-type gene regulatory circuitry modulated by endurance exercise

The paper uses single-nucleus multiome sequencing to analyze gene regulatory changes in human skeletal muscle after endurance exercise, identifying fiber-type specific regulatory programs and transcription factor networks.

Comprehensive Guide of Epigenetics and Transcriptomics Data Quality Control

This paper provides a comprehensive guide for quality control metrics and mitigation strategies for 11 epigenetics and transcriptomics assays to improve exposure signature discovery.

Automated single-cell omics end-to-end framework with data-driven batch inference

SPEEDI automates single-cell multi-omics analysis by integrating data, inferring batches, and labeling cell types without user input, improving reproducibility and accessibility.

Comparing host module activation patterns and temporal dynamics in infection by influenza H1N1 viruses

This study compares host module activation patterns and temporal dynamics across four influenza H1N1 viruses to identify conserved and differentially regulated immune responses.

Cell type-specific isolation and transcriptomic profiling informs glial pathology in human temporal lobe epilepsy

The paper isolates and profiles cell types in human TLE samples, revealing dysregulated pathways and an immature glial phenotype.

Deciphering the combinatorial interaction landscape

The paper introduces SAIL, a machine learning tool to predict and analyze combinatorial stimulus interactions, revealing context-dependent functions and probabilistic algebra of cellular responses.

Pathway-Level Information ExtractoR (PLIER) for gene expression data

PLIER improves pathway inference and cell proportion analysis in gene expression data, enhancing replicability and biological insight in trans-eQTL studies.

High resolution annotation of Zebrafish transcriptome using long-read sequencing

This study improves zebrafish transcriptome annotation using long-read sequencing, identifying novel transcripts and isoforms, and validating them through computational and experimental methods.