Nicolas Robine

DECONVersation uses single-cell foundation model embeddings to improve bulk RNA-seq deconvolution by reducing batch effects and eliminating marker gene selection.

Songbird is a single cell whole genome sequencing copy number caller that detects whole genome duplication and outperforms existing tools in breakpoints identification and true copy number detection.

This paper constructs a single-cell atlas of uterine carcinosarcoma across diverse ancestries, revealing cancer cell plasticity and stromal-immune remodeling in primary and metastatic tumors.

This paper develops an ancestrally inclusive patient-derived organoid platform for UCS that captures cancer cell plasticity and identifies therapeutic vulnerabilities.

This study characterizes the molecular differences in high-grade endometrial cancer across an ancestrally diverse cohort, identifying ancestry-associated mutations and immune microenvironment differences.

This study shows that elevated intraocular pressure compromises the blood-retinal barrier, leading to vascular leakage and retinal ganglion cell death in glaucoma models.

Lancet2 improves somatic variant calling with faster runtime and better performance, especially for InDels, using enhanced methods and machine learning.

This paper introduces an explainable boosting machine model to identify and filter variant artifacts caused by formalin-fixed paraffin embedding in cancer genomics.

This study identifies TMEJ as the main repair pathway for APOBEC3A-induced DSBs and shows Polθ inhibition causes synthetic lethality in cancer cells with APOBEC3A activity.

DeepSomatic is a deep learning method for detecting somatic variants from short-read and long-read sequencing data, outperforming existing callers, especially for indels.

This paper develops SMART-PTA for single-cell whole-genome and transcriptome sequencing, revealing clonal evolution in aged human esophageal tissue over 70 years.

The paper identifies African Pan Genome contigs that are missing from current human reference genomes, showing they contain functionally relevant sequences and are enriched in African ancestry.

This paper introduces ppmSeq, a sequencing technology that improves accuracy and efficiency in detecting SNVs by sequencing both DNA strands, showing superior performance in clinical applications like ctDNA detection.

Severus detects and characterizes somatic structural variations in tumor genomes using long reads, outperforming existing methods in accuracy and revealing clinically relevant rearrangements.

This paper introduces MRD-EDGE, a machine learning platform for ultrasensitive ctDNA detection in plasma WGS, showing improved sensitivity and broader applicability for MRD monitoring.

This study examines how mutagenesis and extrachromosomal DNA contribute to urothelial cancer evolution, revealing clonal APOBEC3 mutations and chemotherapy-induced mutational bursts within ecDNA.

This study applies low-cost whole genome sequencing to detect circulating tumor DNA at high sensitivity in plasma samples, achieving error rates as low as 10^-7 and enabling cancer monitoring without matched tumor sequencing.

This study identifies HPV integration hotspots in oropharyngeal cancers and shows their role in disrupting host gene expression and genomic stability.

The paper benchmarks a new sequencing platform and develops a somatic calling pipeline using deep sequencing of cancer cell lines on two platforms.

The paper identifies three novel complex structural variation patterns in cancer genomes using genome graph analysis across 2,833 tumor WGS.

SCANVIS scores, annotates, and visualizes splice junctions across samples, showing separation of tissue/cancer types using a few thousand SJs.

taxMaps provides fast and accurate taxonomic classification of short-read metagenomic data with significantly reduced computational cost compared to BLASTn.

Lancet improves somatic variant calling accuracy, especially for indels, using colored DeBruijn graphs and local assembly across synthetic and real whole-genome sequencing data.