biVI models the biophysical processes generating nascent and mature single-cell transcriptomes using variational autoencoders.
@MariaCarilli @GorinGennady @funion10 @lpachter

nature.com/articles/s4159…

We expanded kallisto to translated alignment (nucleotides <-> amino acids), and used it to detect novel viruses in RNA seq data. The single-cell resolution allowed us to determine whether the presence of these viruses affected host gene expression. 🧵

biorxiv.org/content/10.110…

We have another preprint out! Please see @lpachter's thread for an overview from the viewpoint of integration, or read on for
1/ biVI: THE CHEMICAL ENGINEER’S PERSPECTIVE

Interested in "integrating" multimodal #scRNAseq data? W/ @MariaCarilli, @GorinGennady, @funion10 & Tara Chari we introduce biVI, which combines the scVI variational autoencoder with biophysically motivated bivariate models for RNA distributions. 🧵 1/
biorxiv.org/content/10.110…

gget alphafold: Predict the 3D structure of a protein from its amino acid sequence using @DeepMind’s AlphaFold v2.0 from a Python or command-line environment in 3 lines of code. Runs on any laptop and requires only ~4 GB of disk space. Simply ‘pip install gget’ and:

Neuron vs viruses vs bacteria. Microorganisms size comparison:
#biology

Spectral neural approximations for models of transcriptional dynamics biorxiv.org/cgi/content/sh… #biorxiv_bioinfo

In a new preprint with @GorinGennady and Maria Carilli (co-first) along with Tara Chari, we show how neural networks can be used to efficiently approximate steady state solutions of two species (nascent and mature RNA) bursty transcription models.
biorxiv.org/content/10.110… 🧵1/