Genome Architecture

The long-term goal of our research group is to understand how the architecture of the genome impacts gene expression. We develop technologies that molecular biology and bioinformatics to study how the genomic context influences gene regulation. This question is particularly relevant for virus such as HIV that need to integrate in the genome of their host. Our results suggest that the virus is somewhat sensitive to the chromatin context, but viral expression fluctuates over several order of magnitude even for viruses inserted at the same location. We are developing split-barcoding technologies to assay the expression of HIV in millions of single cells and understand what generates this variability. We also develop new statistical approaches based on the expanding fields of analytic combinatorics and artificial intelligence. More specifically, we are designing algorithms to robustly assign any DNA read to its species of origin, and to model gene regulatory networks for engineering purposes.

• Assay HIV expression in millions of single cells
• Improve and calibrate read mapping algorithms
• Use artificial intelligence to model gene expression

Cortini R, Filion GJ.
“Theoretical principles of transcription factor traffic on folded chromatin.”
Nature Communications, 9:1740, doi:10.1038/s41467-018-04130-x, 2018.

Chen HC, Zorita E, Filion GJ.
“Using Barcoded HIV Ensembles (B-HIVE) for Single Provirus Transcriptomics.”
Current Protocols in Molecular Biology, 122 e56, doi:10.1002/cpmb.56, 2018.

Vidal E, le Dily F, Quilez J, Stadhouders R, Cuartero Y, Graf T, Marti-Renom MA, Beato M, Filion GJ.
“OneD: increasing reproducibility of Hi-C Samples with abnormal karyotypes.”
Nucleic Acids Research, gky064, doi:10.1093/nar/gky064, 2018.

Stadhouders R, Vidal E, Serra F, Di Stefano B, Le Dily F, Quilez J, Gomez A, Collombet S, Berenguer C, Cuartero Y, Hecht J, Filion GJ, Beato M, Marti-Renom MA, Graf T.
“Transcription factors orchestrate dynamic interplay between genome topology and gene regulation during cell reprogramming.”
Nature Genetics, 50:238–249, doi:10.1038/s41588-017- 0030-7, 2018.

Filion GJ.
“Analytic combinatorics for computing seeding probabilities.”
Algorithms, 11, doi:10.3390/a11010003, 2018.