Biology subjects

Covarrubias, M.

Publications and source records attributed to Covarrubias, M..

3 recordsLinked to original sources

bioRxiv2025

Accelerating scRNA-seq analysis: automated cell type annotation using representation learning and vector search

Cell type annotation in single-cell RNA sequencing (scRNA-seq) experiments is the fundamental step of assigning cell types to individual cells or clusters of cells based on their gene expression profiles. This process is crucial for developing biological insights from scRNA-seq experiments. We present a service that automates cell type annotation for 10x Genomics single-cell gene expression samples, enabling researchers to rapidly and accurately categorize cells within a sample. This service operates on the basis of reverse search: it compares each cells gene expression profile against the Chan Zuckerberg CELL by GENE (CZ CELLxGENE) Census, a comprehensive repository of published scRNA-seq datasets enriched with community-annotated cell types, and yields cell type annotations through summarizing the labels associated with similar cells. The annotation algorithm employed in this service avoids reliance on predefined marker genes or tissue-specific references, providing both fine-grained and coarse annotations. These initial annotations can be further refined by investigators to suit their specific research needs.

bioinformatics↗

bioRxiv2022

A genome-wide mutational constraint map quantified from variation in 76,156 human genomes

The depletion of disruptive variation caused by purifying natural selection (constraint) has been widely used to investigate protein-coding genes underlying human disorders, but attempts to assess constraint for non-protein-coding regions have proven more difficult. Here we aggregate, process, and release a dataset of 76,156 human genomes from the Genome Aggregation Database (gnomAD), the largest public open-access human genome reference dataset, and use this dataset to build a mutational constraint map for the whole genome. We present a refined mutational model that incorporates local sequence context and regional genomic features to detect depletions of variation across the genome. As expected, proteincoding sequences overall are under stronger constraint than non-coding regions. Within the non-coding genome, constrained regions are enriched for known regulatory elements and variants implicated in complex human diseases and traits, facilitating the triangulation of biological annotation, disease association, and natural selection to non-coding DNA analysis. More constrained regulatory elements tend to regulate more constrained protein-coding genes, while non-coding constraint captures additional functional information underrecognized by gene constraint metrics. We demonstrate that this genome-wide constraint map provides an effective approach for characterizing the non-coding genome and improving the identification and interpretation of functional human genetic variation.

genetics↗

bioRxiv2020

The propofol binding sites of prokaryotic voltage-gated sodium channels

Propofol, one of the most commonly used intravenous general anesthetics, modulates neuronal function by interacting with ion channels. The mechanisms that link propofol binding to the modulation of distinct ion channel states, however, are not understood. To tackle this problem, we investigated prokaryotic ancestors of eukaryotic voltage-gated Na+ channels (Navs) using unbiased photoaffinity labeling with a photoacitivatable propofol analog (AziPm), electrophysiological methods and mutagenesis. The results directly demonstrate conserved propofol binding sites involving the S4 voltage sensors and the S4-S5 linkers in NaChBac and NavMs, and also suggest state-dependent changes at these sites. Then, using molecular dynamics simulations to elucidate the structural basis of propofol modulation, we show that the S4 voltage sensors and the S4-S5 linkers shape two distinct propofol binding sites in a conformation-dependent manner. These interactions help explain how propofol binding promotes activation-coupled inactivation to inhibit Nav channel function.

biophysics↗