Welcome to the "Computational Methods for Paleogenomics and Comparative Genomics" group of Cedric Chauve, Department of Mathematics at Simon Fraser University.

The research of our group focuses currently on various problems in computational biology, with a strong focus on comparative genomics and cancer genomics. We are working on various problems related to phylogenomics (the construction and analysis of gene families and gene trees), genome rearrangements and the reconstruction of ancestral gene orders, pathogen genomics and cancer genomics. We are also working on machine-learning algorithms for the analysis of large-scale flow cytometry data.

See our Research page for more details.

News and announcements

• October 2022: our paper "Freddie: Annotation-independent Detection and Discovery of Transcriptomic Alternative Splicing Isoforms Using Long-read Sequencing" has been accepted in Nucleic Acids Research.
• June 2022: our paper " Fast and accurate matching of cellular barcodes across short- and long-reads of single-cell RNA-seq experiments" has been published in iScience.
• May 2022: our paper "plASgraph - using graph neural networks to detect plasmid contigs from an assembly graph" is available on biorXiv.
• May 2022: our paper "A Mixed Integer Linear Programming Algorithm for Plasmid Binning" has been presented at RECOMB – Comparative Genomics 2022 and published in Lecture Notes in Bioinformatics.
• May 2022: our paper " Fast and accurate matching of cellular barcodes across short- and long-reads of single-cell RNA-seq experiments" has been presented at RECOMB-Seq 2022.
• March 2022: Tomas Vinar and Brona Brejova, from Comenius University, will visit the lab from 6 months, funded by the PANGAIA project.
• February 2022: Katharina and Janik Sielemann, from Bielefeld University, will visit the lab for three months.
• December 2021: the paper "Genion, an accurate tool to detect gene fusion from long transcriptomics reads" has been published in BMC Genomics.
• November 2021: the paper "Automated identification of maximal differential cell populations in flow cytometry data" has been published in Cytometry A.