Biology subjects

Sanchez-Garcia, R.

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

9 recordsLinked to original sources

bioRxiv2026

Genomic footprints of selfing, introduction history, and long-distance dispersal in an invasive alien plant

Biological invasions are natural experiments for studying the evolutionary and ecological processes underlying colonization success and range expansion. Using genome-wide data -- generated via genotyping-by-sequencing (GBS) from 30 populations spanning Europe and South Africa-- we investigated the colonization history and successful spread of the invasive buttonweed Cotula coronopifolia, an annual plant introduced into Europe from South Africa about 300 years ago. Our analyses identified three major lineages in Europe distributed across the continent, often co-occurring without evidence of admixture. Phylogenomic dating revealed that these lineages diverged > 2,000 years ago --well before the earliest European records-- suggesting divergence within the native range and either multiple introductions or a single introduction with multiple lineages. Mating-system inference shows that reproduction occurs primarily via self-fertilization ([~]70% on average), although outcrossing predominates in some populations, revealing a facultative mating system. This high selfing rate has led to extremely low heterozygosity in most populations and a strong genetic structure. Genetic clustering also revealed admixed individuals resulting from rare inter-lineage outcrossing; comparisons of empirical and simulated data indicate that increased diversity after sporadic admixture events decays rapidly under subsequent selfing. Both the introduction history and long-distance dispersal facilitated by waterbirds likely explain the scattered distribution of lineages across Europe. Altogether, these results provide an empirical demonstration of Bakers "ideal weed" concept, highlighting the role of a flexible mating system in providing reproductive assurance during colonization and showing how predominant selfing shapes the genomic landscape of an invasive species. SIGNIFICANCE STATEMENTUnderstanding how reproductive strategies influence colonization and spread of alien species is central to invasion biology. By combining population genomics and phylogenomic inference, this study provides key insights into the colonization history and successful invasion of the self-fertilizing plant C. coronopifolia, introduced from South Africa to Europe in the 18th century. We show that the species invasion success relies on the introduction of multiple lineages and the predominance of selfing, which has drastically reduced genetic diversity yet contributed to reproductive assurance and spread across diverse habitats. Occasional outcrossing and long-distance dispersal by waterbirds or through horticultural transport have further shaped the species genetic landscape. These findings illustrate how self-compatibility and ecological generalism can overcome genetic constraints during range expansion and provide the basis for understanding the evolutionary dynamics of selfing plant invasions.

genomics↗

bioRxiv2025

Supervised Deep Learning for Efficient Cryo-EM Image Alignment in Drug Discovery with cryoPARES

Cryo-Electron Microscopy (cryo-EM) is a pivotal tool for determining 3D structures of biological macromolecules. Current workflows are computationally demanding and require manual intervention, creating bottlenecks for high-throughput applications like structure-based drug discovery. In such contexts, where all protein samples can be assumed to be equivalent at resolutions relevant for image alignment, information about particle poses from previous refinements could be reused. Existing methods, however, ignore this prior knowledge, aligning each dataset from scratch. We present cryoPARES, a deep learning pose estimation method trained on pre-aligned datasets. Our method not only provides accurate angular predictions significantly faster than traditional approaches but also introduces automated particle pruning capabilities that eliminate manual intervention. Together with its single-pass operation, these features enable near real-time reconstructions that provide feedback during data acquisition. We demonstrate cryoPARESs effectiveness through rapid structural determination of seven ligand-bound complexes across four distinct protein targets. We also release three fragment-bound cryo-EM datasets.

bioinformatics↗

bioRxiv2024

Expanding the scope of a catalogue search to bioisosteric fragment merges using a graph database approach

The efficiency of fragment-to-lead optimization could be improved by automated workflows for the design of follow-up compounds. Pipelines that are able to fully exploit the interaction opportunities identified from the crystal structures of bound fragments would greatly aid this goal. To do so, these pipelines need to require minimal intervention from the user and be computationally efficient. In this work, we describe an updated version of our fragment merging methodology, which provides several feature enhancements, primarily by expanding the chemical space searched, allowing the identification of more diverse follow-up compounds, thus maximizing the chances of finding successful hits. While the original method focused on finding perfect merges, meaning compounds that directly incorporate substructures from the original fragments, here we expand the search to what we term bioisosteric merges, involving the incorporation of substructures that replicate the pharmacophoric features of the original fragments but may not be exactly identical. Unlike existing pharmacophore and shape-based descriptors used for virtual screening, this approach combines the search for these properties with the incorporation of novelty, which is necessary when searching for ways to link together distinct substructures. Compared with perfect merging, our new approach is able to find compounds that are directly informed by structures within the original fragments but are more chemically diverse. We contrast our approach with the use of a pharmacophore-constrained docking pipeline, run in parallel for select fragment pairs, and show that our method requires between 1.1-45.9-fold less computational time for conformer generation per merging hit identified, referring to compounds that show a favourable degree of shape and colour overlap and recapitulation of original fragment interactions. Overall, our results show that our method has potential to be used to generate designs inspired by all fragments within a given pocket.

bioinformatics↗

bioRxiv2023

Unraveling the Power of NAP-CNB's Machine Learning-enhanced Tumor Neoantigen Prediction

In this study, we present a proof-of-concept classical vaccination experiment that validates the in silico identification of tumor neoantigens (TNAs) using a machine learning-based platform called NAP-CNB. Unlike other TNA predictors, NAP-CNB leverages RNAseq data to consider the relative expression of neoantigens in tumors. Our experiments show the efficacy of NAP-CNB. Predicted TNAs elicited potent antitumor responses in vivo following classical vaccination protocols. Notably, optimal antitumor activity was observed when targeting the antigen with higher expression in the tumor, which was not the most immunogenic. Additionally, the vaccination combining different neoantigens resulted in vastly improved responses compared to each one individually, showing the worth of multiantigen-based approaches. These findings validate NAP-CNB as an innovative TNA-identification platform and make a substantial contribution to advancing the next generation of personalized immunotherapies

immunology↗

bioRxiv2022

The use of a graph database is a complementary approach to a classical similarity search for identifying commercially available fragment merges

AO_SCPLOWBSTRACTC_SCPLOWFragment screening using X-ray crystallography can yield rich structural data to help guide the optimization of low-molecular-weight compounds into more potent binders. Fragment merging, whereby substructural motifs from partially overlapping fragments are incorporated into a single larger compound, represents a potentially powerful and efficient approach for increasing potency. Searching commercial catalogues provides one useful way to quickly and cheaply identify follow-up compounds for purchase and further screening, and circumvents the challenge of synthetic accessibility. The Fragment Network is a graph database that provides a novel way to explore the chemical space surrounding fragment hits. We use an iteration of the database containing >120 million catalogue compounds to find fragment merges for four XChem fragment screening campaigns. Retrieved molecules were filtered using a pipeline of 2D and 3D filters and contrasted against a traditional fingerprint-based similarity search. The two search techniques were found to have complementary results, identifying merges in different regions of chemical space. Both techniques were able to identify merges that are predicted to replicate the interactions made by the parent fragments. This work demonstrates the use of the Fragment Network to increase the yield of fragment merges beyond that of a classical catalogue search, thus increasing the likelihood of finding promising follow-up compounds. We present a pipeline that is able to systematically exploit all known fragment hits by performing large-scale enumeration of all possible fragment pairs for merging.

bioinformatics↗

bioRxiv2021

MetaFun: Unveiling sex differences in multiple omics studies through comprehensive functional meta-analysis

While sex-based differences in various health scenarios have been thoroughly acknowledged in the literature, we lack a deep analysis of sex as a variable in this context. To fill this knowledge gap, we created MetaFun as an easy-to-use web-based tool to meta-analyze multiple transcriptomic datasets with a sex-based perspective to gain major statistical power and biological soundness. Furthermore, MetaFun can be used to perform case-control meta-analyses, allowing researchers with basic programming skills to access this methodology. Availability and implementationMetaFun is freely available at http://bioinfo.cipf.es/metafun The back end was implemented in R and Java, and the front end was developed using Angular Contactfgarcia@cipf.es, mhidalgo@cipf.es Supplementary informationR code available at https://gitlab.com/ubb-cipf/metafunr

bioinformatics↗

bioRxiv2020

NAP-CNB: Bioinformatic pipeline to predict MHC-I-restricted T cell epitopes in mice

Lack of a dedicated integrated pipeline for neoantigen discovery in mice hinders cancer immunotherapy research. Novel sequential approaches through recurrent neural networks can improve the accuracy of T-cell epitope immunogenicity predictions in mice, and a simplified variant selection process can reduce operational requirements. We have developed a web server tool (NAP-CNB) for a full and automatic pipeline based on recurrent neural networks, to predict putative neoantigens from tumoral RNA sequencing reads. The developed software can estimate H-2 peptide ligands, with an AUC of 0.95, directly from tumor samples. As a proof-of-concept, we used the B16 melanoma model to test the systems predictive capabilities, and we report its putative neoantigens. NAP-CNB web server is freely available at http://biocomp.cnb.csic.es/NeoantigensApp/ with scripts and datasets accessible through the download section.

bioinformatics↗

bioRxiv2020

Local computational methods to improve the interpretability and analysis of cryo-EM maps

Cryo-electron microscopy (cryo-EM) maps usually show heterogeneous distributions of B-factors and electron density occupancies and are typically B-factor sharpened to improve their contrast and interpretability at high-resolutions. However, over-sharpening due to the application of a single global B-factor can distort processed maps causing connected densities to appear broken and disconnected. This issue limits the interpretability of cryo-EM maps, i.e. ab initio modelling. In this work, we propose 1) approaches to enhance high-resolution features of cryo-EM maps, while preventing map distortions and 2) methods to obtain local B-factors and electron density occupancy maps. These algorithms have as common link the use of the spiral phase transformation and are called LocSpiral, LocBSharpen, LocBFactor and LocOccupancy. Our results, which include improved maps of recent SARS-CoV-2 structures, show that our methods can improve the interpretability and analysis of obtained reconstructions.

biophysics↗

bioRxiv2019

MicrographCleaner: a python package for cryo-EM micrograph segmentation using deep learning

Cryo-EM Single Particle Analysis workflows require from tens of thousands of high-quality particle projections to unveil the three-dimensional structure of macromolecules. Conventional methods for automatic particle picking tend to suffer from high false-positive rates, hurdling the reconstruction process. One common cause of this problem is the presence of carbon and different types of high-contrast contaminations. In order to overcome this limitation, we have developed MicrographCleaner, a deep learning package designed to discriminate which regions of micrographs are suitable for particle picking and which are not in an automatic fashion. MicrographCleaner implements a U-net-like deep learning model trained on a manually curated dataset compiled from over five hundred micrographs. The benchmarking, carried out on about one hundred independent micrographs, shows that MicrographCleaner is a very efficient approach for micrograph preprocessing. MicrographCleaner (micrograph_cleaner_em) package is available at PyPI and Anaconda Cloud and also as a Scipion/Xmipp protocol. Source code is available at https://github.com/rsanchezgarc/micrograph_cleaner_em.

bioinformatics↗