Juan Carlos Rivera‐Mulia

REPLAY automates DNA replication timing analysis from Repli-seq data, providing a reproducible and user-friendly tool without requiring programming expertise.

The paper re-analyzes replication timing data from early mouse embryos and shows that the relationship between replication timing, gene expression, and chromatin accessibility is maintained, contradicting previous conflicting conclusions.

PARTAGE jointly profiles replication timing, gene expression, and copy number variation from the same sample to better understand their relationships.

The paper manipulates transcription to show it can advance replication timing but is not required for the switch to early replication.

This paper develops an optimized Repli-seq method for high-resolution DNA replication timing analysis with low input samples.

The paper develops a custom ENCODE annotation for cancer genomics, highlighting TF-RBP interactions and network rewiring during oncogenesis, and validates findings using experimental assays.

The paper constructs replication timing networks from human cell types and identifies master regulators and novel trans interactions between transcription factors and RT changes.

The paper identifies a fragility signature linked to replication stress and transcription in chromosomal instability by analyzing replication timing and gene expression.

The paper identifies cis-regulatory elements (ERCEs) that control domain-wide replication timing, chromatin compartmentalization, and transcription in mouse embryonic stem cells.

The paper defines RT states as cell type-specific replication timing profiles using a Hidden Markov Model and finds they provide new functional insights into the human genome.

This paper presents an integrative framework to detect structural variants in cancer genomes using multiple methods and finds that non-coding structural variation significantly impacts genome function in cancer cells.

This paper introduces a protocol and computational tools for genome-wide replication timing analysis using next-generation sequencing.