Biology subjects

Ochoa, I.

Publications and source records attributed to Ochoa, I..

At least 19 recordsLinked to original sources

bioRxiv2026

SNMF: Ultrafast, Spatially-Aware Deconvolution for Spatial Transcriptomics

Sequencing-based spatial transcriptomics has revolutionized the study of tissue architecture, but its spots often contain multiple cells, creating a key computational challenge, termed deconvolution, to decipher each spots cell-type composition. Reference-free deconvolution methods avoid the need for a matched single-cell RNA-seq dataset, but typically neglect the spatial correlation between neighboring spots and do not leverage modern hardware for efficient computation. Here, we propose SNMF (Spatial Non-negative Matrix Factorization): a rapid, accurate, and reference-free deconvolution method. SNMF extends the standard NMF framework with a spatial mixing matrix that models neighborhood influences, guiding the factorization toward spatially coherent solutions. Our R package is, to our knowledge, the first spatial transcriptomics deconvolution tool to natively support GPU execution, completing benchmark analyses in under one minute--over two orders of magnitude faster than the slowest competing methods-- with moderate memory requirements. On synthetic and real benchmark datasets, SNMF significantly outperforms state-of-the-art methods in deconvolution accuracy, and on a human melanoma dataset it recovers biologically meaningful cell-type signatures--including a tumor-boundary transition zone-- without any reference input. The proposed mehtod is publicly available at https://github.com/ML4BM-Lab/SNMF.

bioinformatics↗

bioRxiv2025

An open CAR-T single-cell atlas to enable in-depth characterization and rational engineering of CAR-T products

BRIEF ABSTRACTWe built a CAR-T cell functional atlas from over one million cells across 13 studies, integrating data from patients and healthy donors. The atlas captures 11 phenotypes, links infusion product composition with clinical response, and reveals sex- and age-dependent effects, metabolic signatures, and rare ICANS-associated cells. This open-access resource provides a foundation to understand CAR-T cell function and guide the design of next-generation therapies.

bioinformatics↗

bioRxiv2025

Patient-derived liver biopsy organoids enable precision alcohol-associated liver disease modeling

Background & AimsAlcohol-associated liver disease (ALD) is a major cause of liver disease worldwide with scarce therapeutic options. Animal models poorly recapitulate advanced ALD precluding the development of new treatments. Organoids have emerged as a powerful human-based preclinical tool. However, current patient-derived liver organoids fail to recapitulate the epithelial heterogeneity and its generation requires liver surgical resections, thus limiting personalized disease modeling. Here, we report the development of organoids from liver needle biopsies (b-Orgs) from patients with ALD. Methodsb-Orgs were generated from tru-cut biopsies from patients at early (n=28) and advanced (n=34) stages of ALD. b-Orgs were characterized by immunofluorescence, bulk and single cell RNA-sequencing and compared to parental tissues. b-Orgs were used to model ALD progression, identify pathogenic drivers, induce alcohol-associated hepatitis (AH) and evaluate response to prednisolone. ResultsPhenotypic and functional analysis of b-Orgs showed hepatocyte- enriched features. Single-cell RNA-sequencing revealed a heterogeneous cell composition comprising hepatocyte, biliary and progenitor populations, mirroring the epithelial landscape found in patients with advanced ALD. Moreover, b-Orgs preserved disease-stage features and allowed to identify the association of ELF3 with cell plasticity and disease progression. Finally, stimulation of b-Orgs with drivers of ALD induced pathophysiological features of alcohol-associated hepatitis, including ROS production, lipid accumulation, inflammation and decreased cell proliferation, which were mitigated in response to prednisolone. Conclusions Overall, we provide a human-based model that recapitulates epithelial complexity and patient specific features, allowing to identify drivers of cell plasticity and expanding organoid-based liver disease modeling for personalized medicine. Impact and implications Here, we describe the generation of biopsy-derived organoids (b-Orgs) from patients with liver disease. b-Orgs reproduce the liver epithelial cell composition found in patients liver tissue and are efficiently generated from different stages of the disease, providing a platform for patient- tailored disease modeling and drug testing.

cell biology↗

bioRxiv2025

JIND-Multi: Leveraging Multiple Labeled Datasets for Automated Annotation of Single-Cell RNA and ATAC Data

BackgroundThe creation of single-cell atlases is essential for understanding cellular diversity and heterogeneity. However, assembling these atlases is challenging due to batch effects and the need for accurate cell annotation. Current methods for single-cell RNA and ATAC sequencing, while effective for integration, are not optimized for cell annotation. Additionally, many annotation tools rely on external databases or reference scRNA-Seq datasets, which may limit their adaptability to specific study needs, especially for rare cell-types or scATAC-Seq data. ResultsWe introduce JIND-Multi, an extended version of the JIND framework, designed to transfer cell-type labels across multiple annotated datasets. JIND-Multi significantly reduces the proportion of unclassified cells in single-cell RNA sequencing (scRNA-Seq) data while maintaining the accuracy and performance of the original JIND model. Furthermore, JIND-Multi demonstrates robust and precise annotation results in its inaugural application to scATAC-Seq data, proving its versatility and effectiveness across different single-cell sequencing technologies. ConclusionsJIND-Multi represents an improvement in cell annotation, reducing unassigned cells and offering a reliable solution for both scRNA-Seq and scATAC-Seq data. Its ability to handle multiple labeled datasets enhances the precision of annotations, making it a valuable tool for the single-cell research community. JIND-Multi is publicly available at:https://github.com/ML4BM-Lab/JIND-Multi.git.

bioinformatics↗

bioRxiv2024

Pharming: Joint Clonal Tree Reconstruction of SNV and CNAEvolution from Single-cell DNA Sequencing of Tumors

Cancer arises through an evolutionary process in which somatic mutations, including single nucleotide variants (SNVs) and copy number aberrations (CNAs), drive the development of a malignant, heterogeneous tumor. Reconstructing this evolutionary history from sequencing data is critical for understanding the order in which mutations are acquired and the dynamic interplay between different types of alterations. Advances in modern whole genome single-cell sequencing now enable the accurate inference of copy number profiles in individual cells. However, the low sequencing coverage of these low pass sequencing technologies poses a challenge for reliably inferring the presence or absence of SNVs within tumor cells, limiting the ability to simultaneously study the evolutionary relationships between SNVs and CNAs. In this work, we introduce a novel tumor phylogeny inference method, PO_SCPLOWHARMINGC_SCPLOW, that jointly infers the evolutionary histories of SNVs and CNAs. Our key insight is to leverage the high accuracy of copy number inference methods and the fact that SNVs co-occur in regions with CNAs in order to enable more precise tumor phylogeny reconstruction for both alteration types. We demonstrate via simulations that PO_SCPLOWHARMINGC_SCPLOW outperforms state-of-the-art single-modality tumor phylogeny inference methods. Additionally, we apply PO_SCPLOWHARMINGC_SCPLOW to a triple-negative breast cancer case, achieving high-resolution, joint reconstruction of CNA and SNV evolution, including the de novo detection of a clonal whole-genome duplication event. Thus, PO_SCPLOWHARMINGC_SCPLOW offers the potential for more comprehensive and detailed tumor phylogeny inference for high-throughput, low-coverage single-cell DNA sequencing technologies compared to existing approaches. Availabilityhttps://github.com/elkebir-group/Pharming

bioinformatics↗

bioRxiv2024

DeepRBP: A novel deep neural network for inferring splicing regulation

MotivationAlternative splicing plays a pivotal role in various biological processes. In the context of cancer, aberrant splicing patterns can lead to disease progression and treatment resistance. Understanding the regulatory mechanisms underlying alternative splicing is crucial for elucidating disease mechanisms and identifying potential therapeutic targets. ResultsWe present DeepRBP, a deep learning (DL) based framework to identify potential RNA-binding proteins (RBP)-Gene regulation pairs for further in-vitro validation. DeepRBP is composed of a DL model that predicts transcript abundance given RBP and gene expression data coupled with an explainability module that computes informative RBP-Gene scores. We show that the proposed framework is able to identify known RBP-Gene regulations, demonstrating its applicability to identify new ones. Availability and ImplementationDeepRBP is implemented in PyTorch, and all the code and material used in this work is available at https://github.com/ML4BM-Lab/DeepRBP. Contactiochoal@unav.es Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2024

gMCSpy: Efficient and accurate computation of Genetic Minimal Cut Sets in Python

MotivationThe identification of minimal genetic interventions that modulate metabolic processes constitutes one of the most relevant applications of genome-scale metabolic models (GEMs). The concept of Minimal Cut Sets (MCSs) and its extension at the gene level, genetic Minimal Cut Sets (gMCSs), have attracted increasing interest in the field of Systems Biology to address this task. Different computational tools have been developed to calculate MCSs and gMCSs using both commercial and open-source software. ResultsHere, we present gMCSpy, an efficient Python package to calculate gMCSs in GEMs using both commercial and non-commercial optimization solvers. We show that gMCSpy substantially overperforms our previous computational tool GMCS, which exclusively relied on commercial software. Moreover, we compared gMCSpy with recently published competing algorithms in the literature, finding significant improvements in both accuracy and computation time. All these advances make gMCSpy an attractive tool for researchers in the field of Systems Biology for different applications in health and biotechnology. Availability and ImplementationThe Python package gMCSpy can be accessed at: https://github.com/PlanesLab/gMCSpy Contactfplanes@tecnun.es Supplementary Information

systems biology↗

bioRxiv2023

Modelling glioblastoma resistance to temozolomide. Combination of spheroid and mathematical models to simulate cellular adaptation in vitro.

AO_SCPLOWBSTRACTC_SCPLOWDrug resistance is one of the biggest challenges in the fight against cancer. In particular, in the case of glioblastoma, the most lethal brain tumour, resistance to temozolomide (the standard of care drug for chemotherapy in this tumour), is one of the main reasons behind treatment failure and hence responsible for the poor prognosis of patients diagnosed with this disease. In this paper, we combine the power of three-dimensional in vitro experiments of treated glioblastoma spheroids with mathematical models of tumour evolution and adaptation. We use a novel approach based on internal variables for modelling the acquisition of resistance to temozolomide that is observed in a group of treated spheroids in the experiments. These internal variables describe the cells phenotypic state, which depends on the history of drug exposure and affects cell behaviour. We use model selection to determine the most parsimonious model and calibrate it to reproduce the experimental data, obtaining a high level of agreement between the in vitro and in silico outcomes. A sensitivity analysis is carried out to investigate the impact of each model parameter in the predictions. More importantly, we show the utility of our model for answering biological questions, such as what is the intrinsic adaptation mechanism, or for separating the sensitive and resistant populations. We conclude that the proposed in silico framework, in combination with experiments, can be useful to improve our understanding of the mechanisms behind drug resistance in glioblastoma and to eventually set some guidelines for the design of new treatment schemes.

cancer biology↗

bioRxiv2023

NetActivity enhances transcriptional signals by combining gene expression into robust gene set activity scores through interpretable autoencoders

Grouping gene expression into gene set activity scores (GSAS) provides better biological insights than studying individual genes. However, existing gene set projection methods cannot return representative, robust, and interpretable GSAS. We developed NetActivity, a framework based on a sparsely-connected autoencoder and a three-tier training that yields robust and interpretable GSAS. NetActivity was trained with 1,518 well-known gene sets and all GTEx samples, returning GSAS representative of the original transcriptome and assigning higher importance to more biologically relevant genes. Moreover, NetActivity returns GSAS with a more consistent definition than GSVA and hipathia, state-of-the-art gene set projection methods. Finally, NetActivity enables combining bulk RNA-seq and microarray datasets in a meta-analysis of prostate cancer progression, highlighting gene sets related to cell division. When applied to metastatic prostate cancer, gene sets associated with cancer progression were also altered due to drug resistance, while a classical enrichment analysis identified gene sets irrelevant to the phenotype.

bioinformatics↗

bioRxiv2023

GeNNius: An ultrafast drug-target interaction inference method based on graph neural networks

Drug-target interaction (DTI) prediction is a relevant but challenging task in the drug repurposing field. In-silico approaches have drawn particular attention as they can reduce associated costs and time commitment of traditional methodologies. Yet, current state-of-the-art methods present several limitations: existing DTI prediction approaches are computationally expensive, thereby hindering the ability to use large networks and exploit available datasets and, the generalization to unseen datasets of DTI prediction methods remains unexplored, which could potentially improve the development processes of DTI inferring approaches in terms of accuracy and robustness. In this work, we introduce GO_SCPLOWEC_SCPLOWnnO_SCPLOWIUSC_SCPLOW (Graph Embedding Neural Network Interaction Uncovering System), a Graph Neural Network (GNN)-based method that outperforms state-of-the-art models in terms of both accuracy and time efficiency across a variety of datasets. We also demonstrated its prediction power to uncover new interactions by evaluating not previously known DTIs for each dataset. We further assessed the generalization capability of GO_SCPLOWEC_SCPLOWnnO_SCPLOWIUSC_SCPLOW by training and testing it on different datasets, showing that this framework can potentially improve the DTI prediction task by training on large datasets and testing on smaller ones. Finally, we investigated qualitatively the embeddings generated by GO_SCPLOWEC_SCPLOWnnO_SCPLOWIUSC_SCPLOW, revealing that the GNN encoder maintains biological information after the graph convolutions while diffusing this information through nodes, eventually distinguishing protein families in the node embedding space. Code Availabilityhttps://github.com/ubioinformat/GeNNius

bioinformatics↗

bioRxiv2022

Phertilizer: growing a clonal tree from ultra-low coverage single-cell DNA sequencing data of tumors

Emerging ultra-low coverage single-cell DNA sequencing (scDNA-seq) technologies have enabled high resolution evolutionary studies of copy number aberrations (CNAs) within tumors. While these sequencing technologies are well suited for identifying CNAs due to the uniformity of sequencing coverage, the sparsity of coverage poses challenges for the study of single-nucleotide variants (SNVs). In order to maximize the utility of increasingly available ultra-low coverage scDNA-seq data and obtain a comprehensive understanding of tumor evolution, it is important to also analyze the evolution of SNVs from the same set of tumor cells. We present PO_SCPLOWHERTILIZERC_SCPLOW, a method to infer a clonal tree from ultra-low coverage scDNA-seq data of a tumor. Based on a probabilistic model, our method recursively partitions the data by identifying key evolutionary events in the history of the tumor. We demonstrate the performance of PO_SCPLOWHERTILIZERC_SCPLOW on simulated data as well as on two real datasets, finding that PO_SCPLOWHERTILIZERC_SCPLOW effectively utilizes the copynumber signal inherent in the data to more accurately uncover clonal structure and genotypes compared to previous methods. Availabilityhttps://github.com/elkebir-group/phertilizer

bioinformatics↗

bioRxiv2022

Nanopore quality score resolution can be reduced with little effect on downstream analysis

We investigate the effect of quality score information loss on downstream analysis from nanopore sequencing FASTQ files. We polished denovo assemblies for a mock microbial community and a human genome, and we called variants on a human genome. We repeated these experiments using various pipelines, under various coverage level scenarios, and various quality score quantizers. In all cases we found that the quantization of quality scores cause little difference on (or even improves) the results obtained with the original (non-quantized) data. This suggests that the precision that is currently used for nanopore quality scores is unnecessarily high, and motivates the use of lossy compression algorithms for this kind of data. Moreover, we show that even a non-specialized compressor, like gzip, yields large storage space savings after quantization of quality scores.

bioinformatics↗

bioRxiv2021

A mechanobiological model for tumor spheroids evolution: application to glioblastoma

Spheroids are in vitro spherical structures of cell aggregates, eventually cultured within a hydrogel matrix, that are used, among other applications, as a technological platform to investigate tumor formation and evolution. Several interesting features can be replicated using this methodology, such as cell communication mechanisms, the effect of gradients of nutrients, or the creation of realistic 3D biological structures. In this paper, we propose a continuum mechanobiological model which accounts for the most relevant phenomena that take place in tumor spheroids evolution under in vitro suspension, namely, nutrients diffusion in the spheroid, kinetics of cellular growth and death, and mechanical interactions among the cells. The model is qualitatively validated, after calibration of the model parameters, versus in vitro experiments of spheroids of different glioblastoma cell lines. This preliminary validation allowed us to conclude that glioblastoma tumor spheroids evolution is mainly driven by mechanical interactions of the cell aggregate and the dynamical evolution of the cell population. In particular, it is concluded that our model is able to explain quite different setups, such as spheroids growth (up to six times the initial configuration for U-87 MG cell line) or shrinking (almost half of the initial configuration for U-251 MG cell line); as the result of the mechanical interplay of cells driven by cellular evolution. Indeed, the main contribution of this work is to link the spheroid evolution with the mechanical activity of cells, coupled with nutrient consumption and the subsequent cell dynamics. All this information can be used to further investigate mechanistic effects in the evolution of tumors and their role in cancer disease. Author summarySpheroids structures of cell aggregates are an available experimental platform to analyze the evolution and drug response of solid tumors. In particular, the dynamics of different glioblastoma cell lines have been studied in this work using spheroids. Interestingly, very different behaviors were observed, from a half of the initial configuration shrinking for U-251 MG cell line to six times the initial configuration growth for U-87 MG cell line. These results were replicated by means of a coupled mathematical model which accounts for nutrients diffusion in the spheroid, kinetics of cellular growth and death, and mechanical interactions among the cells. Tumor growth or shrinkage can be explained from a continuum mechanics view driven by cell activity and nutrients availability. This modeling put the focus on mechanistic effects and is aligned with novel experimental techniques to quantify the mechanical microenvironment in tumors. These techniques may be combined with the approach presented in this work to further investigate the role of mechanics in cancer disease.

biophysics↗

bioRxiv2021

RENANO: a REference-based compressor for NANOpore FASTQ files

Nanopore sequencing technologies are rapidly gaining popularity, in part, due to the massive amounts of genomic data they produce in short periods of time (up to 8.5 TB of data in less than 72 hours). In order to reduce the costs of transmission and storage, efficient compression methods for this type of data are needed. Unlike short-read technologies, nanopore sequencing generates long noisy reads of variable length. In this note we introduce RENANO, a reference-based lossless FASTQ data compressor, specifically tailored to compress FASTQ files generated with nanopore sequencing technologies. RENANO builds on the recent compressor ENANO, which is the current state of the art. RENANO focuses on improving the compression of the base call sequence portion of the FASTQ file, leaving the other parts of ENANO intact. Two novel reference-based compression algorithms are introduced, contemplating different scenarios: in the first scenario, a reference genome is available without cost to both the compressor and the decompressor; in the second, the reference genome is available only on the compressor side, and a compacted version of the reference is transmitted to the decompressor as part of the compressed file. To evaluate the proposed algorithms, we compare RENANO against ENANO on several publicly available nanopore datasets. In the first scenario considered, RENANO improves the base call sequences compression of ENANO by 39.8%, on average, over all the datasets. As for total compression (including the other parts of the FASTQ file), the average improvement is 12.7%. In the second scenario considered, the base call compression improvements of RENANO over ENANO range from 15.2% to 49.0%, depending on the coverage of the compressed dataset, while in terms of total size, the improvements range from 5.1% to 16.5%. Implementations of the proposed algorithms are freely available for download at https://github.com/guilledufort/RENANO.

bioinformatics↗

bioRxiv2020

JIND: Joint Integration and Discrimination for Automated Single-Cell Annotation

Single-cell RNA-seq is a powerful tool in the study of the cellular composition of different tissues and organisms. A key step in the analysis pipeline is the annotation of cell-types based on the expression of specific marker genes. Since manual annotation is labor-intensive and does not scale to large datasets, several methods for automated cell-type annotation have been proposed based on supervised learning. However, these methods generally require feature extraction and batch alignment prior to classification, and their performance may become unreliable in the presence of cell-types with very similar transcriptomic profiles, such as differentiating cells. We propose JIND, a framework for automated cell-type identification based on neural networks that directly learns a low-dimensional representation (latent code) in which cell-types can be reliably determined. To account for batch effects, JIND performs a novel asymmetric alignment in which the transcriptomic profile of unseen cells is mapped onto the previously learned latent space, hence avoiding the need of retraining the model whenever a new dataset becomes available. JIND also learns cell-type-specific confidence thresholds to identify and reject cells that cannot be reliably classified. We show on datasets with and without batch effects that JIND classifies cells more accurately than previously proposed methods while rejecting only a small proportion of cells. Moreover, JIND batch alignment is parallelizable, being more than five or six times faster than Seurat integration. Availability: https://github.com/mohit1997/JIND.

bioinformatics↗

bioRxiv2020

SimiC: A Single Cell Gene Regulatory Network Inference method with Similarity Constraints

Single-cell RNA-Sequencing has made it possible to infer high-resolution gene regulatory networks (GRNs), providing deep biological insights by revealing regulatory interactions at single-cell resolution. However, current single-cell GRN analysis methods produce only a single GRN per input dataset, potentially missing relationships between cells from different phenotypes. To address this issue, we present SimiC, a single-cell GRN inference method that produces a GRN per phenotype while imposing a similarity constraint that forces a smooth transition between GRNs, allowing for a direct comparison between different states, treatments, or conditions. We show that jointly inferring GRNs can uncover variation in regulatory relationships across phenotypes that would have otherwise been missed. Moreover, SimiC can recapitulate complex regulatory dynamics across a range of systems, both model and non-model alike. Taken together, we establish a new approach to quantitating regulatory architectures between the GRNs of distinct cellular phenotypes, with far-reaching implications for systems biology.

bioinformatics↗

bioRxiv2019

\"GPress: a framework for querying General Feature Format (GFF) files and feature expression files in a compressed form\"

11.1 MotivationSequencing data are often summarized at different annotation levels for further analysis. The general feature format (GFF) and its descendants, the gene transfer format (GTF) and GFF3, are the most commonly used data formats for genomic annotations. These files are extensively updated, queried and shared, and hence as the number of generated GFF files increases, efficient data storage and retrieval are becoming increasingly important. Existing GFF utilities for accessing these files, like gffutils and gffread, do not focus on reducing the storage space, significantly increasing it in some cases. Hence, we propose GPress, a framework for querying GFF files in a compressed form. In addition, GPress can also incorporate and compress feature expression files, supporting simultaneous queries on both files.\n\n1.2 ResultsWe tested GPress on several GFF files of different organisms, and showed that it achieves on average a 98% reduction in size, while being able to retrieve all annotations for a given identifier or a range of coordinates in a few seconds. For example, on a Human GFF file, GPress can find all items with a unique identifier in 2.47 seconds and all items with coordinates within the range of 1,000 to 100,000 in 4.61 seconds. In contrast, gffutils provides faster retrieval but doubles the size of the GFF files. When additionally linking an expression file, we show that GPress can reduce the size of the expression file by more than 92%, while still retrieving the information within seconds. GPress is freely available at https://github.com/qm2/gpress.

bioinformatics↗

bioRxiv2019

Denoising of Aligned Genomic Data

Noise in genomic sequencing data is known to have effects on various stages of genomic data analysis pipelines. Variant identification is an important step of many of these pipelines, and is increasingly being used in clinical settings to aid medical practices. We propose a denoising method, dubbed SAMDUDE, which operates on aligned genomic data in order to improve variant calling performance. Denoising human data with SAMDUDE resulted in improved variant identification in both individual chromosome as well as whole genome sequencing (WGS) data sets. In the WGS data set, denoising led to identification of almost 2,000 additional true variants, and elimination of over 1,500 erroneously identified variants. In contrast, we found that denoising with other state-of-the-art denoisers significantly worsens variant calling performance. SAMDUDE is written in Python and is freely available at https://github.com/ihwang/SAMDUDE.

bioinformatics↗