Anna-Sophie Fiston-Lavier

The paper introduces GBZ-base and GAF-base, indexed pangenome file formats for efficient subgraph and alignment extraction.

The paper analyzes chromosome 22q11.2 haplotypes to show how structural variations in specific regions correlate with microdeletion and inversion risks across populations.

The paper uses long-read sequencing to identify pathogenic variants in autism cases, finding three pathogenic mutations and nine candidate structural variants missed by short-read sequencing.

This paper introduces a synthetic biology framework that enables tunable differentiation ratios in microbial consortia, validated in a mouse tumor model.

This study uses genomic data from a Southern African rodent to show how aridity influences genetic divergence and population connectivity, with genes related to water conservation and neurotransmission identified.

This study compares manual and automated methods for transposable element annotation in Drosophila melanogaster and Aedes albopictus, finding that automated methods identify more TEs but manual curation provides more detailed classifications.

The paper examines the relationship between effective population size and genome size variation in animals, finding no evidence that genetic drift drives TE accumulation across species.

This study investigates structural variation (SV) diversity and dynamics in CyHV-3 genome during serial passages, finding SV turnovers and defective genomes drive in vitro evolution.

TrEMOLO estimates transposable element allele frequencies using long-read sequencing data by combining assembly and mapping approaches, outperforming existing tools in benchmark tests.

RF4Del is a random forest method for accurate deletion detection that outperforms existing tools and is robust to sequencing coverage and deletion size variations.

BREC identifies heterochromatin boundaries and estimates local recombination rates using statistical methods, validated on Drosophila melanogaster data.

PlasForest detects plasmids in unassembled bacterial genomes with high accuracy and outperforms existing tools.

The paper introduces a machine learning method to detect somatic L1 insertions in human brain tissue, revealing their widespread distribution in neurons and glia and potential role in neuropsychiatric diseases.