Mickaël Mendez

Segzoo simplifies genome annotation analysis by automating data retrieval and analysis, providing a single visualization of results.

This paper introduces CLA, a method to identify cell-type-specific genes and alternative promoters by integrating Cell Ontology and random forest classification with stratified bootstrapping.

The paper identifies cis-interacting lncRNAs and their targets using RNA-chromatin and Hi-C data across 18 human cell types.

SegRNA identifies unannotated transcripts by analyzing multi-experiment transcriptomic data without relying on pre-existing annotations.

The paper functionally annotates human long non-coding RNAs by systematically knocking down 285 lncRNAs and analyzing molecular phenotypes using CAGE sequencing.

This paper introduces a semi-supervised method for genome annotation that improves accuracy by incorporating expert annotations and evaluates it using a new precision and recall metric.

C1 CAGE detects transcription start sites and enhancer activity in lung cancer cells, revealing subpopulations with distinct responses to TGF-β and enhancer RNA dynamics.

The paper enhances the FANTOM5 expression atlas by associating gene-orphan promoter regions with their transcripts using CAGEscan.

The paper describes using Segway software to annotate genomes with chromatin data from ChIP-seq experiments, providing a semi-automated workflow for interpretation.