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Pierre-Luc Germain

@plger.bsky.social

Computational biologist with a background in history&philosophy of science, working on (single-cell) RNA-seq and ATAC-seq especially in the context of stress- and activity-dependent regulation in the brain.

Posts Replies Media

Pierre-Luc Germain @plger.bsky.social · 26/08/2026

We introduce 3 R packages: weightedMotifAccess implements fragment weighing strategies, betterChromVAR an analytical version of chromVAR (eliminates stochasticity, orders of magnitude faster, etc.), and epiwraps to facilitate especially visualization

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Pierre-Luc Germain @plger.bsky.social · 26/08/2026

Sharing a new preprint about technical variation in (bulk/ #SingleCell ) #ATACseq and motif accessibility analyses, and methods to correct for them www.biorxiv.org/content/10.6...

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Pierre-Luc Germain @plger.bsky.social · 23/06/2026

Going beyond the method, we generated a massive compendium of binding predictions for 1108 TFs across 43 cell types representing all major lineages. Interpretable features yield insights into the TFs, and the predictions can be used in downstream tasks such as reverse-engineering perturbed TFs.

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Pierre-Luc Germain @plger.bsky.social · 23/06/2026

Cell-type specific DNA accessibility profiles are instead easy to get, so we developed a scalable platform to predict ChIP peaks from ATACseq data based on >12k paired datasets. TFBlearner achieves competitive accuracy in unseen cell types in a pre-registered benchmark.

Pre-registered benchmark on unseen cell-types

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Pierre-Luc Germain @plger.bsky.social · 23/06/2026

How do >1600 transcription factors (TFs) bind across hundreds of cell types? Experimentally profiling every combination isn't practically doable. But relying on DNA motifs isn't a great alternative -- they lack both sensitivity and specificity. #Genomics #GeneRegulation 👇

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