🧬 PhD candidate @Caltech in the lab of @lpachter | Computational biology, bioinformatics, biophysics | 🧬

Joined June 2022
Photos and videos
Maria Carilli retweeted
biVI models the biophysical processes generating nascent and mature single-cell transcriptomes using variational autoencoders. @MariaCarilli @GorinGennady @funion10 @lpachter nature.com/articles/s41592-0…
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Maria Carilli retweeted
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/10.1101/…
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Maria Carilli retweeted
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
19 Jan 2023
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/10.1101/…
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Maria Carilli retweeted
19 Jan 2023
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/10.1101/…
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Maria Carilli retweeted
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:
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Maria Carilli retweeted
Neuron vs viruses vs bacteria. Microorganisms size comparison: #biology
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Maria Carilli retweeted
Spectral neural approximations for models of transcriptional dynamics biorxiv.org/cgi/content/shor… #biorxiv_bioinfo

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Maria Carilli retweeted
17 Jun 2022
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/10.1101/… 🧵1/
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