Biology subjects

Buendia, A.

Publications and source records attributed to Buendia, A..

3 recordsLinked to original sources

bioRxiv2024

The expression of integron arrays is shaped by the translation rate of cassettes

Integrons are key elements in the rise and spread of multidrug resistance in Gram-negative bacteria. These genetic platforms capture cassettes containing promoterless genes and stockpile them in arrays of variable length. In the current integron model, expression of cassettes is granted by the Pc promoter in the platform and is assumed to decrease as a function of its distance. Here we explored this model using a large collection of 136 antibiotic resistance cassettes and show that the effect of distance is in fact negligible. Instead, cassettes have a strong impact in the expression of downstream genes because their translation rate affects the stability of the whole polycistronic mRNA molecule. Hence, poorly translated cassettes decrease the expression and resistance phenotype of cassettes downstream. Our data puts forward a novel integron model in which expression is contingent on the translation of cassettes upstream, rather than on the distance to the Pc.

microbiology↗

bioRxiv2023

A Deep Dive into Single-Cell RNA Sequencing Foundation Models

Large-scale foundation models, which are pre-trained on massive, unlabeled datasets and subsequently fine-tuned on specific tasks, have recently achieved unparalleled success on a wide array of applications, including in healthcare and biology. In this paper, we explore two foundation models recently developed for single-cell RNA sequencing data, scBERT and scGPT. Focusing on the fine-tuning task of cell type annotation, we explore the relative performance of pre-trained models compared to a simple baseline, L1-regularized logistic regression, including in the few-shot setting. We perform ablation studies to understand whether pretraining improves model performance and to better understand the difficulty of the pre-training task in scBERT. Finally, using scBERT as an example, we demonstrate the potential sensitivity of fine-tuning to hyperparameter settings and parameter initializations. Taken together, our results highlight the importance of rigorously testing foundation models against well established baselines, establishing challenging fine-tuning tasks on which to benchmark foundation models, and performing deep introspection into the embeddings learned by the model in order to more effectively harness these models to transform single-cell data analysis. Code is available at https://github.com/clinicalml/sc-foundation-eval.

genomics↗

bioRxiv2023

GET: a foundation model of transcription across human cell types

Transcriptional regulation, involving the complex interplay between regulatory sequences and proteins, directs all biological processes. Computational models of transcription lack generalizability to accurately extrapolate in unseen cell types and conditions. Here, we introduce GET, an interpretable foundation model designed to uncover regulatory grammars across 213 human fetal and adult cell types. Relying exclusively on chromatin accessibility data and sequence information, GET achieves experimental-level accuracy in predicting gene expression even in previously unseen cell types. GET showcases remarkable adaptability across new sequencing platforms and assays, enabling regulatory inference across a broad range of cell types and conditions, and uncovering universal and cell type specific transcription factor interaction networks. We evaluated its performance on prediction of regulatory activity, inference of regulatory elements and regulators, and identification of physical interactions between transcription factors. Specifically, we show GET outperforms current models in predicting lentivirus-based massive parallel reporter assay readout with reduced input data. In fetal erythroblasts, we identify distal (>1Mbp) regulatory regions that were missed by previous models. In B cells, we identified a lymphocyte-specific transcription factor-transcription factor interaction that explains the functional significance of a leukemia-risk predisposing germline mutation. In sum, we provide a generalizable and accurate model for transcription together with catalogs of gene regulation and transcription factor interactions, all with cell type specificity.

bioinformatics↗