#atacseq — Public Fediverse posts

🧬 Ever wondered how the genome actually looks when it folds inside the nucleus — and how fast we can simulate it?

🔗 Multiscale molecular modeling of chromatin with MultiMM: From nucleosomes to the whole genome. Computational and Structural Biotechnology Journal, DOI: https://doi.org/10.1016/j.csbj.2024.09.025

📚 CSBJ: https://www.csbj.org/

#ChromatinModeling #3DGenome #ComputationalBiology #Genomics #MolecularModeling #GenomeArchitecture #ChromatinStructure #HiC #ATACSeq #Biophysics #StructuralBiology

#ucsc We are pleased to announce the release of a new Public Hub, UniversalEPI ENCODE for hg38. This track hub displays normalized #atacseq and #HiC interaction predictions based on ENCODE data and generated using UniversalEPI. UniversalEPI is an attention-based deep ensemble model that predicts enhancer–promoter interactions (EPIs) up to 2 Mb apart using only DNA sequence and chromatin accessibility (ATAC-seq) data.

https://genome.ucsc.edu/cgi-bin/hgTracks?hubUrl=https://boevalab.inf.ethz.ch/resources/universalepi_pred_encode/ucsc_track_hub/hub.txt&genome=hg38&position=lastDbPos

#bioinformatics

For scRNA-seq and scATAC-seq, have people been using "reads" as the important measurement instead of "fragments" for the past few years? Because this paper seems to suggest people have been unintentionally over-inflating the variance in their data by counting "reads"

https://www.nature.com/articles/s41592-023-02112-6/figures/1

I thought we we figured this out a long time ago with the whole "RPKM vs FPKM vs TPM" debate in bulk RNA-seq years ago.

#single_cell #rnaseq #atacseq #compBio #bioinformatics

Anyone know a good review paper / learning resource for building gene regulatory networks #GRN from #scATACseq #ATACseq and/or #scRNAseq data?
Thanks!