%0 Journal Article %J Viruses %D 2022 %T Assessing the Impact of SARS-CoV-2 Lineages and Mutations on Patient Survival. %A Loucera, Carlos %A Perez-Florido, Javier %A Casimiro-Soriguer, Carlos S %A Ortuno, Francisco M %A Carmona, Rosario %A Bostelmann, Gerrit %A Martínez-González, L Javier %A Muñoyerro-Muñiz, Dolores %A Villegas, Román %A Rodríguez-Baño, Jesús %A Romero-Gómez, Manuel %A Lorusso, Nicola %A Garcia-León, Javier %A Navarro-Marí, Jose M %A Camacho-Martinez, Pedro %A Merino-Diaz, Laura %A Salazar, Adolfo de %A Viñuela, Laura %A Lepe, Jose A %A García, Federico %A Dopazo, Joaquin %K COVID-19 %K Genome, Viral %K Humans %K mutation %K Pandemics %K Phylogeny %K SARS-CoV-2 %X

OBJECTIVES: More than two years into the COVID-19 pandemic, SARS-CoV-2 still remains a global public health problem. Successive waves of infection have produced new SARS-CoV-2 variants with new mutations for which the impact on COVID-19 severity and patient survival is uncertain.

METHODS: A total of 764 SARS-CoV-2 genomes, sequenced from COVID-19 patients, hospitalized from 19th February 2020 to 30 April 2021, along with their clinical data, were used for survival analysis.

RESULTS: A significant association of B.1.1.7, the alpha lineage, with patient mortality (log hazard ratio (LHR) = 0.51, C.I. = [0.14,0.88]) was found upon adjustment by all the covariates known to affect COVID-19 prognosis. Moreover, survival analysis of mutations in the SARS-CoV-2 genome revealed 27 of them were significantly associated with higher mortality of patients. Most of these mutations were located in the genes coding for the S, ORF8, and N proteins.

CONCLUSIONS: This study illustrates how a combination of genomic and clinical data can provide solid evidence for the impact of viral lineage on patient survival.

%B Viruses %V 14 %8 2022 Aug 27 %G eng %N 9 %R 10.3390/v14091893 %0 Journal Article %J Nucleic Acids Res %D 2021 %T CSVS, a crowdsourcing database of the Spanish population genetic variability. %A Peña-Chilet, Maria %A Roldán, Gema %A Perez-Florido, Javier %A Ortuno, Francisco M %A Carmona, Rosario %A Aquino, Virginia %A López-López, Daniel %A Loucera, Carlos %A Fernandez-Rueda, Jose L %A Gallego, Asunción %A Garcia-Garcia, Francisco %A González-Neira, Anna %A Pita, Guillermo %A Núñez-Torres, Rocío %A Santoyo-López, Javier %A Ayuso, Carmen %A Minguez, Pablo %A Avila-Fernandez, Almudena %A Corton, Marta %A Moreno-Pelayo, Miguel Ángel %A Morin, Matías %A Gallego-Martinez, Alvaro %A Lopez-Escamez, Jose A %A Borrego, Salud %A Antiňolo, Guillermo %A Amigo, Jorge %A Salgado-Garrido, Josefa %A Pasalodos-Sanchez, Sara %A Morte, Beatriz %A Carracedo, Ángel %A Alonso, Ángel %A Dopazo, Joaquin %K Alleles %K Chromosome Mapping %K Crowdsourcing %K Databases, Genetic %K Exome %K Gene Frequency %K Genetic Variation %K Genetics, Population %K Genome, Human %K Genomics %K Humans %K Internet %K Precision Medicine %K Software %K Spain %X

The knowledge of the genetic variability of the local population is of utmost importance in personalized medicine and has been revealed as a critical factor for the discovery of new disease variants. Here, we present the Collaborative Spanish Variability Server (CSVS), which currently contains more than 2000 genomes and exomes of unrelated Spanish individuals. This database has been generated in a collaborative crowdsourcing effort collecting sequencing data produced by local genomic projects and for other purposes. Sequences have been grouped by ICD10 upper categories. A web interface allows querying the database removing one or more ICD10 categories. In this way, aggregated counts of allele frequencies of the pseudo-control Spanish population can be obtained for diseases belonging to the category removed. Interestingly, in addition to pseudo-control studies, some population studies can be made, as, for example, prevalence of pharmacogenomic variants, etc. In addition, this genomic data has been used to define the first Spanish Genome Reference Panel (SGRP1.0) for imputation. This is the first local repository of variability entirely produced by a crowdsourcing effort and constitutes an example for future initiatives to characterize local variability worldwide. CSVS is also part of the GA4GH Beacon network. CSVS can be accessed at: http://csvs.babelomics.org/.

%B Nucleic Acids Res %V 49 %P D1130-D1137 %8 2021 01 08 %G eng %N D1 %1 https://www.ncbi.nlm.nih.gov/pubmed/32990755?dopt=Abstract %R 10.1093/nar/gkaa794 %0 Journal Article %J Gigascience %D 2021 %T Highly accurate whole-genome imputation of SARS-CoV-2 from partial or low-quality sequences. %A Ortuno, Francisco M %A Loucera, Carlos %A Casimiro-Soriguer, Carlos S %A Lepe, Jose A %A Camacho Martinez, Pedro %A Merino Diaz, Laura %A de Salazar, Adolfo %A Chueca, Natalia %A García, Federico %A Perez-Florido, Javier %A Dopazo, Joaquin %K Genome, Viral %K Phylogeny %K SARS-CoV-2 %K Whole Genome Sequencing %X

BACKGROUND: The current SARS-CoV-2 pandemic has emphasized the utility of viral whole-genome sequencing in the surveillance and control of the pathogen. An unprecedented ongoing global initiative is producing hundreds of thousands of sequences worldwide. However, the complex circumstances in which viruses are sequenced, along with the demand of urgent results, causes a high rate of incomplete and, therefore, useless sequences. Viral sequences evolve in the context of a complex phylogeny and different positions along the genome are in linkage disequilibrium. Therefore, an imputation method would be able to predict missing positions from the available sequencing data.

RESULTS: We have developed the impuSARS application, which takes advantage of the enormous number of SARS-CoV-2 genomes available, using a reference panel containing 239,301 sequences, to produce missing data imputation in viral genomes. ImpuSARS was tested in a wide range of conditions (continuous fragments, amplicons or sparse individual positions missing), showing great fidelity when reconstructing the original sequences, recovering the lineage with a 100% precision for almost all the lineages, even in very poorly covered genomes (<20%).

CONCLUSIONS: Imputation can improve the pace of SARS-CoV-2 sequencing production by recovering many incomplete or low-quality sequences that would be otherwise discarded. ImpuSARS can be incorporated in any primary data processing pipeline for SARS-CoV-2 whole-genome sequencing.

%B Gigascience %V 10 %8 2021 12 02 %G eng %N 12 %1 https://www.ncbi.nlm.nih.gov/pubmed/34865008?dopt=Abstract %R 10.1093/gigascience/giab078 %0 Journal Article %J PLoS Comput Biol %D 2021 %T A versatile workflow to integrate RNA-seq genomic and transcriptomic data into mechanistic models of signaling pathways. %A Garrido-Rodriguez, Martín %A López-López, Daniel %A Ortuno, Francisco M %A Peña-Chilet, Maria %A Muñoz, Eduardo %A Calzado, Marco A %A Dopazo, Joaquin %K Algorithms %K Cell Line, Tumor %K Computational Biology %K Databases, Factual %K Gene Expression Profiling %K Genomics %K High-Throughput Nucleotide Sequencing %K Humans %K Models, Theoretical %K mutation %K RNA-seq %K Signal Transduction %K Software %K Transcriptome %K whole exome sequencing %K Workflow %X

MIGNON is a workflow for the analysis of RNA-Seq experiments, which not only efficiently manages the estimation of gene expression levels from raw sequencing reads, but also calls genomic variants present in the transcripts analyzed. Moreover, this is the first workflow that provides a framework for the integration of transcriptomic and genomic data based on a mechanistic model of signaling pathway activities that allows a detailed biological interpretation of the results, including a comprehensive functional profiling of cell activity. MIGNON covers the whole process, from reads to signaling circuit activity estimations, using state-of-the-art tools, it is easy to use and it is deployable in different computational environments, allowing an optimized use of the resources available.

%B PLoS Comput Biol %V 17 %P e1008748 %8 2021 02 %G eng %N 2 %1 https://www.ncbi.nlm.nih.gov/pubmed/33571195?dopt=Abstract %R 10.1371/journal.pcbi.1008748 %0 Journal Article %J IEEE J Biomed Health Inform %D 2020 %T Towards Improving Skin Cancer Diagnosis by Integrating Microarray and RNA-Seq Datasets. %A Galvez, Juan M %A Castillo-Secilla, Daniel %A Herrera, Luis J %A Valenzuela, Olga %A Caba, Octavio %A Prados, Jose C %A Ortuno, Francisco M %A Rojas, Ignacio %K Biomarkers, Tumor %K Computational Biology %K Diagnosis, Computer-Assisted %K Gene Expression Profiling %K Humans %K Machine Learning %K RNA-seq %K Skin Neoplasms %X

Many clinical studies have revealed the high biological similarities existing among different skin pathological states. These similarities create difficulties in the efficient diagnosis of skin cancer, and encourage to study and design new intelligent clinical decision support systems. In this sense, gene expression analysis can help find differentially expressed genes (DEGs) simultaneously discerning multiple skin pathological states in a single test. The integration of multiple heterogeneous transcriptomic datasets requires different pipeline stages to be properly designed: from suitable batch merging and efficient biomarker selection to automated classification assessment. This article presents a novel approach addressing all these technical issues, with the intention of providing new sights about skin cancer diagnosis. Although new future efforts will have to be made in the search for better biomarkers recognizing specific skin pathological states, our study found a panel of 8 highly relevant multiclass DEGs for discerning up to 10 skin pathological states: 2 healthy skin conditions a priori, 2 cataloged precancerous skin diseases and 6 cancerous skin states. Their power of diagnosis over new samples was widely tested by previously well-trained classification models. Robust performance metrics such as overall and mean multiclass F1-score outperformed recognition rates of 94% and 80%, respectively. Clinicians should give special attention to highlighted multiclass DEGs that have high gene expression changes present among them, and understand their biological relationship to different skin pathological states.

%B IEEE J Biomed Health Inform %V 24 %P 2119-2130 %8 2020 07 %G eng %N 7 %1 https://www.ncbi.nlm.nih.gov/pubmed/31871000?dopt=Abstract %R 10.1109/JBHI.2019.2953978