Joaquín Dopazo

Sma3s v3 improves functional annotation of proteomes, pangenomes, and metagenomes by scaling annotation workflows and outperforming existing tools.

AFQuery computes capture-aware allele frequencies for clinical genomics cohorts, reducing errors and bias in variant classification.

This study analyzes the genotypic characteristics, antimicrobial resistance, and virulence factors of 114 Campylobacter jejuni isolates from southern Spain, revealing high resistance to ciprofloxacin and tetracycline.

SIEGA is a genomic surveillance system that integrates data from humans, animals, and the environment to monitor infectious diseases and antimicrobial resistance.

The paper analyzes genetic secondary findings in the Spanish population using the Collaborative Spanish Variant Server database, finding 5% of individuals have reportable variants in 32 ACMG-listed genes.

The paper characterizes common genetic variants in the Navarre population, identifying 1,069 SNVs common in Navarre but rare in other populations.

The paper creates a database of genetic variants specific to Iberian Roma individuals to aid in genetic diagnosis and understand evolutionary adaptations.

The paper presents a computational framework for analyzing SARS-CoV-2-host interactions to identify drug targets and model disease mechanisms.

The paper introduces a cloud-based platform called ORCESTRA for reproducible processing and sharing of multimodal biomedical data in a FAIR manner.

The paper organizes a DREAM challenge to benchmark predictive models of single-cell signaling using mass cytometry data from breast cancer cell lines.

ADEx is a database integrating omics data from autoimmune diseases, offering analysis tools for exploring transcriptomics and methylation studies.

The paper develops impuSARS, a method to impute missing SARS-CoV-2 genome sequences, improving the utility of incomplete data for epidemiological surveillance.

The paper presents a web interface for a mechanistic model of the SARS-CoV-2 disease map to infer gene expression-related activities and study intervention effects.

SMAca is a tool to detect SMA carriers and estimate SMN1 copy-number from NGS data using variant-specific knowledge, validated on 326 samples.

This paper uses mechanistic signaling pathway models to analyze single-cell RNA-seq data from glioblastoma, revealing functional heterogeneity and drug resistance mechanisms at the single-cell level.

This study uses machine learning and mechanistic models to predict therapeutic targets related to Fanconi anemia, identifying over 20 potential targets.

The paper presents a large-scale cancer pharmacogenomic screen with 11,576 experiments on 910 drug combinations across 85 cancer cell lines, enabling crowd-sourced prediction of synergistic drug pairs and biomarkers.

The paper introduces Metabolizer, a web tool that uses gene expression data to analyze metabolic pathway activities and predict therapeutic targets for disease treatment.

This study shows that signaling pathway activities are better predictors of breast cancer prognosis than gene-level features and identify additional genes contributing to prognosis beyond pathway analysis.