Biology subjects

Sanchez Garcia, R.

Publications and source records attributed to Sanchez Garcia, R..

5 recordsLinked to original sources

bioRxiv2026

All for one or one for all? Disentangling the Juncus bufonius complex through morphometrics, cytometry and genomics

Juncus bufonius L. s.l. is a species complex with several ploidy levels, for which species delimitation remains unclear due to a lack of reliable morphological characters and the paucity of molecular studies. To clarify taxonomic and geographic relationships in the complex, we combined genomic, cytometric and morphological data from a broad latitudinal range from England down to Spain. We collected morphometric and cytometric data from 31 populations, and genomic data were obtained through Hyb-Seq using the Angiosperm353 kit for a subset of individuals. These three datasets were combined to explore phylogenetic relationships, population structure, and the validity of four previously proposed morphospecies (J. bufonius s.str., a hexaploid; J. minutulus, a tetraploid; and J. ranarius and J. hybridus, both diploids). Sequencing supported the separation of diploids and polyploids as two distinct taxa, but morphometric characters used previously to describe morphospecies showed continuous variation with no diagnostic value, and were not congruent with genomic and cytometric data. Polyploids likely originated through allopolyploidisation from diploids and tetraploids. Phylogenetic lineages were extensively mixed geographically, both for diploid and polyploid taxa, which suggests repeated long-distance dispersal events for both diploids and polyploids, and no separation of taxa by geography. Splitting of diploids into J. ranarius and J. hybridus was not supported. We recommend J. ranarius be treated as a synonym of J. hybridus, and that tetraploids and hexaploids be grouped under J. bufonius. The observed geographical patterns are consistent with high rates of seed dispersal by migratory waterbirds.

plant biology↗

bioRxiv2025

Multi-scale structural similarity embedding search across entire proteomes

The rapid expansion of three-dimensional (3D) biomolecular structure information, driven by breakthroughs in artificial intelligence/deep learning (AI/DL)-based structure predictions, has created an urgent need for scalable and efficient structure similarity search methods. Traditional alignment-based approaches, such as structural superposition tools, are computationally expensive and challenging to scale with the vast number of available macromolecular structures. Herein, we present a scalable structure similarity search strategy designed to navigate extensive repositories of experimentally determined structures and computed structure models predicted using AI/DL methods. Our approach leverages protein language models and a deep neural network architecture to transform 3D structures into fixed-length vectors, enabling efficient large-scale comparisons. Although trained to predict TM-scores between single-domain structures, our model generalizes beyond the domain level, accurately identifying 3D similarity for full-length polypeptide chains and multimeric assemblies. By integrating vector databases, our method facilitates efficient large-scale structure retrieval, addressing the growing challenges posed by the expanding volume of 3D biostructure information.

bioinformatics↗

bioRxiv2022

Cryo-EM map anisotropy can be attenuated by map post-processing and a new method for its estimation

One of the most important challenges in cryogenic electron microscopy (cryo-EM) is the substantial number of samples that exhibit preferred orientations, which leads to an uneven coverage of the projection sphere. As a result, the overall quality of the reconstructed maps can be severely affected, as manifested by the presence of anisotropy in the map resolution. Several methods have been proposed to measure the directional resolution of maps in tandem with experimental protocols to address the problem of preferential orientations in cryo-EM. Following these works, in this manuscript we identified one potential limitation that may affect most of the existing methods and we proposed an alternative approach to evaluate the presence of preferential orientations in cryo-EM reconstructions. In addition, we also showed that some of the most recently proposed cryo-EM map post-processing algorithms can attenuate map anisotropy, thus offering alternative visualization opportunities for cases affected by moderate levels of preferential orientations.

bioinformatics↗

bioRxiv2020

DeepEMhacer: a deep learning solution for cryo-EM volume post-processing

Cryo-EM maps are valuable sources of information for protein structure modeling. However, due to the loss of contrast at high frequencies, they generally need to be post-processed to improve their interpretability. Most popular approaches, based on B-factor correction, suffer from limitations. For instance, they ignore the heterogeneity in the map local quality that reconstructions tend to exhibit. Aiming to overcome these problems, we present DeepEMhancer, a deep learning approach designed to perform automatic post-processing of cryo-EM maps. Trained on a dataset of pairs of experimental maps and maps sharpened using their respective atomic models, DeepEMhancer has learned how to post-process experimental maps performing masking-like and sharpening-like operations in a single step. DeepEMhancer was evaluated on a testing set of 20 different experimental maps, showing its ability to obtain much cleaner and more detailed versions of the experimental maps. Additionally, we illustrated the benefits of DeepEMhancer on the structure of the SARS-CoV-2 RNA polymerase.

bioinformatics↗

bioRxiv2019

A Topological Data Analysis Network Model of Asthma Based on Blood Gene Expression Profiles

Stratified medicine requires discretisation of disease populations for targeted treatments. We have developed and applied a discrete Morse theory clustering algorithm to a Topological Data Analysis (TDA) network model of 498 gene expression profiles of peripheral blood from asthma and healthy participants. The Morse clustering algorithm defined nine clusters, BC1-9, representing molecular phenotypes with discrete phenotypes including Type-1, 2 & 17 cytokine inflammatory pathways. The TDA network model and clusters were also characterised by activity of glucocorticoid receptor signalling associated with different expression profiles of glucocorticoid receptor (GR), according to microarray probesets targeted to the start or end of the GR mRNAs 3 UTR; suggesting differential GR mRNA processing as a possible driver of asthma phenotypes including steroid insensitivity.

molecular biology↗