Chloé Bessière

The paper identifies circZBTB46 as a driver of crizotinib resistance in ALK-positive lymphomas and shows its targeting restores drug sensitivity.

The paper identifies tumor microenvironment signatures linked to morphology and prognosis in ALK-positive anaplastic large cell lymphoma using computational analysis of gene expression data.

REINDEER2 provides a scalable, dynamic abundance index for RNA datasets, achieving faster construction and accurate abundance recovery.

KaMRaT reduces the dimensionality of k-mer count matrices from high-throughput sequencing data to identify condition-specific sequences.

The paper introduces a k-mer-based method to quantify RNA sequences from large RNA-seq datasets, demonstrating its accuracy for variant RNA detection in cancer cell lines.

The paper introduces Kmerator Suite, a set of tools for extracting k-mer signatures and inferring metadata from RNA-seq datasets, showing high accuracy in gene expression quantification and metadata discovery.

The paper analyzes public RNAseq data to identify lncRNAs specific to mesenchymal stem cells and proposes them as potential cell markers.

The paper identifies widespread transcription initiation at microsatellites across species and develops deep learning models to predict STR-associated transcription and its impact on disease variants.

The paper investigates transcription initiation at polyT short tandem repeats (STRs) using CAGE data and develops a deep learning model to predict STR-associated transcription with high accuracy.

The paper develops a sequence-based method to predict gene expression accurately, showing that nucleotide composition and regulatory region contributions are key factors.