02570nas a2200457 4500008004100000022001400041245008300055210006900138260001600207490000700223520116500230653001301395653001801408653001101426653001301437653001401450653001401464653001501478100002001493700002601513700003301539700002501572700002101597700002301618700003201641700003101673700002101704700002901725700002601754700002001780700002501800700002701825700002801852700002301880700002301903700002001926700001801946700002201964700002001986856010602006 2022 eng d a1999-491500aAssessing the Impact of SARS-CoV-2 Lineages and Mutations on Patient Survival.0 aAssessing the Impact of SARSCoV2 Lineages and Mutations on Patie c2022 Aug 270 v143 a

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.

10aCOVID-1910aGenome, Viral10aHumans10amutation10aPandemics10aPhylogeny10aSARS-CoV-21 aLoucera, Carlos1 aPerez-Florido, Javier1 aCasimiro-Soriguer, Carlos, S1 aOrtuno, Francisco, M1 aCarmona, Rosario1 aBostelmann, Gerrit1 aMartínez-González, Javier1 aMuñoyerro-Muñiz, Dolores1 aVillegas, Román1 aRodríguez-Baño, Jesús1 aRomero-Gómez, Manuel1 aLorusso, Nicola1 aGarcia-León, Javier1 aNavarro-Marí, Jose, M1 aCamacho-Martinez, Pedro1 aMerino-Diaz, Laura1 ade Salazar, Adolfo1 aViñuela, Laura1 aLepe, Jose, A1 aGarcía, Federico1 aDopazo, Joaquin uhttp://clinbioinfosspa.es/content/assessing-impact-sars-cov-2-lineages-and-mutations-patient-survival03503nas a2200709 4500008004100000022001400041245008200055210006900137260001500206300001600221490000700237520139500244653001201639653002301651653001801674653002301692653001001715653001901725653002201744653002501766653001801791653001301809653001101822653001301833653002301846653001301869653001001882100002401892700001801916700002601934700002501960700002101985700002102006700002602027700002002053700002902073700002302102700002902125700002602154700002002180700002702200700002702227700001802254700001902272700003002291700001802321700003402339700001902373700002902392700002702421700001902448700002502467700001702492700002802509700002802537700001902565700002202584700001902606700002002625710004302645856010502688 2021 eng d a1362-496200aCSVS, a crowdsourcing database of the Spanish population genetic variability.0 aCSVS a crowdsourcing database of the Spanish population genetic  c2021 01 08 aD1130-D11370 v493 a

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/.

10aAlleles10aChromosome Mapping10aCrowdsourcing10aDatabases, Genetic10aExome10aGene Frequency10aGenetic Variation10aGenetics, Population10aGenome, Human10aGenomics10aHumans10aInternet10aPrecision Medicine10aSoftware10aSpain1 aPeña-Chilet, Maria1 aRoldán, Gema1 aPerez-Florido, Javier1 aOrtuno, Francisco, M1 aCarmona, Rosario1 aAquino, Virginia1 aLópez-López, Daniel1 aLoucera, Carlos1 aFernandez-Rueda, Jose, L1 aGallego, Asunción1 aGarcia-Garcia, Francisco1 aGonzález-Neira, Anna1 aPita, Guillermo1 aNúñez-Torres, Rocío1 aSantoyo-López, Javier1 aAyuso, Carmen1 aMinguez, Pablo1 aAvila-Fernandez, Almudena1 aCorton, Marta1 aMoreno-Pelayo, Miguel, Ángel1 aMorin, Matías1 aGallego-Martinez, Alvaro1 aLopez-Escamez, Jose, A1 aBorrego, Salud1 aAntiňolo, Guillermo1 aAmigo, Jorge1 aSalgado-Garrido, Josefa1 aPasalodos-Sanchez, Sara1 aMorte, Beatriz1 aCarracedo, Ángel1 aAlonso, Ángel1 aDopazo, Joaquin1 aSpanish Exome Crowdsourcing Consortium uhttp://clinbioinfosspa.es/content/csvs-crowdsourcing-database-spanish-population-genetic-variability02756nas a2200385 4500008004100000022001400041245015900055210006900214260001500283300001000298490000700308520150900315653001601824653001301840653001101853653001101864653002601875653000901901653002601910653001001936653002201946653001401968100002001982700002402002700002702026700003102053700002102084700002602105700002902131700001802160700002002178700002002198700002802218856012402246 2021 eng d a2045-232200aReal world evidence of calcifediol or vitamin D prescription and mortality rate of COVID-19 in a retrospective cohort of hospitalized Andalusian patients.0 aReal world evidence of calcifediol or vitamin D prescription and c2021 12 03 a233800 v113 a

COVID-19 is a major worldwide health problem because of acute respiratory distress syndrome, and mortality. Several lines of evidence have suggested a relationship between the vitamin D endocrine system and severity of COVID-19. We present a survival study on a retrospective cohort of 15,968 patients, comprising all COVID-19 patients hospitalized in Andalusia between January and November 2020. Based on a central registry of electronic health records (the Andalusian Population Health Database, BPS), prescription of vitamin D or its metabolites within 15-30 days before hospitalization were recorded. The effect of prescription of vitamin D (metabolites) for other indication previous to the hospitalization was studied with respect to patient survival. Kaplan-Meier survival curves and hazard ratios support an association between prescription of these metabolites and patient survival. Such association was stronger for calcifediol (Hazard Ratio, HR = 0.67, with 95% confidence interval, CI, of [0.50-0.91]) than for cholecalciferol (HR = 0.75, with 95% CI of [0.61-0.91]), when prescribed 15 days prior hospitalization. Although the relation is maintained, there is a general decrease of this effect when a longer period of 30 days prior hospitalization is considered (calcifediol HR = 0.73, with 95% CI [0.57-0.95] and cholecalciferol HR = 0.88, with 95% CI [0.75, 1.03]), suggesting that association was stronger when the prescription was closer to the hospitalization.

10aCalcifediol10aCOVID-1910aFemale10aHumans10aKaplan-Meier Estimate10aMale10aRetrospective Studies10aSpain10aSurvival Analysis10aVitamin D1 aLoucera, Carlos1 aPeña-Chilet, Maria1 aEsteban-Medina, Marina1 aMuñoyerro-Muñiz, Dolores1 aVillegas, Román1 aLópez-Miranda, José1 aRodríguez-Baño, Jesús1 aTúnez, Isaac1 aBouillon, Roger1 aDopazo, Joaquin1 aGomez, Jose, Manuel Que uhttp://clinbioinfosspa.es/content/real-world-evidence-calcifediol-or-vitamin-d-prescription-and-mortality-rate-covid-1901082nas a2200313 4500008004100000022001400041245014500055210006900200260001500269300000800284490000600292653002800298653001300326653002300339653001100362653002100373653003300394653003100427653001300458653001500471653002400486100002000510700002700530700001600557700002100573700002000594700002400614856013000638 2020 eng d a2059-363500aDrug repurposing for COVID-19 using machine learning and mechanistic models of signal transduction circuits related to SARS-CoV-2 infection.0 aDrug repurposing for COVID19 using machine learning and mechanis c2020 12 11 a2900 v510aComputational Chemistry10aCOVID-1910adrug repositioning10aHumans10aMachine Learning10aMolecular Docking Simulation10aMolecular Targeted Therapy10aProteins10aSARS-CoV-210aSignal Transduction1 aLoucera, Carlos1 aEsteban-Medina, Marina1 aRian, Kinza1 aFalco, Matias, M1 aDopazo, Joaquin1 aPeña-Chilet, Maria uhttp://clinbioinfosspa.es/content/drug-repurposing-covid-19-using-machine-learning-and-mechanistic-models-signal-transduction02123nas a2200325 4500008004100000022001400041245008900055210006900144260001200213300001400225490000700239520106200246653001801308653003101326653004201357653001101399653003101410653001301441653003901454100002601493700002001519700002101539700002101560700002001581700002001601700002001621700001901641700002001660856011701680 2020 eng d a1098-100400aSMN1 copy-number and sequence variant analysis from next-generation sequencing data.0 aSMN1 copynumber and sequence variant analysis from nextgeneratio c2020 12 a2073-20770 v413 a

Spinal muscular atrophy (SMA) is a severe neuromuscular autosomal recessive disorder affecting 1/10,000 live births. Most SMA patients present homozygous deletion of SMN1, while the vast majority of SMA carriers present only a single SMN1 copy. The sequence similarity between SMN1 and SMN2, and the complexity of the SMN locus makes the estimation of the SMN1 copy-number by next-generation sequencing (NGS) very difficult. Here, we present SMAca, the first python tool to detect SMA carriers and estimate the absolute SMN1 copy-number using NGS data. Moreover, SMAca takes advantage of the knowledge of certain variants specific to SMN1 duplication to also identify silent carriers. This tool has been validated with a cohort of 326 samples from the Navarra 1000 Genomes Project (NAGEN1000). SMAca was developed with a focus on execution speed and easy installation. This combination makes it especially suitable to be integrated into production NGS pipelines. Source code and documentation are available at https://www.github.com/babelomics/SMAca.

10aBase Sequence10aDNA Copy Number Variations10aHigh-Throughput Nucleotide Sequencing10aHumans10aReproducibility of Results10aSoftware10aSurvival of Motor Neuron 1 Protein1 aLópez-López, Daniel1 aLoucera, Carlos1 aCarmona, Rosario1 aAquino, Virginia1 aSalgado, Josefa1 aPasalodos, Sara1 aMiranda, María1 aAlonso, Ángel1 aDopazo, Joaquin uhttp://clinbioinfosspa.es/content/smn1-copy-number-and-sequence-variant-analysis-next-generation-sequencing-data01957nas a2200277 4500008004100000022001400041245011500055210006900170260001600239300000800255490000700263520105900270653002301329653001901352653001301371653001101384653002101395653001401416653001301430653002401443100002701467700002401494700002001518700002001538856012101558 2019 eng d a1471-210500aExploring the druggable space around the Fanconi anemia pathway using machine learning and mechanistic models.0 aExploring the druggable space around the Fanconi anemia pathway  c2019 Jul 02 a3700 v203 a

BACKGROUND: In spite of the abundance of genomic data, predictive models that describe phenotypes as a function of gene expression or mutations are difficult to obtain because they are affected by the curse of dimensionality, given the disbalance between samples and candidate genes. And this is especially dramatic in scenarios in which the availability of samples is difficult, such as the case of rare diseases.

RESULTS: The application of multi-output regression machine learning methodologies to predict the potential effect of external proteins over the signaling circuits that trigger Fanconi anemia related cell functionalities, inferred with a mechanistic model, allowed us to detect over 20 potential therapeutic targets.

CONCLUSIONS: The use of artificial intelligence methods for the prediction of potentially causal relationships between proteins of interest and cell activities related with disease-related phenotypes opens promising avenues for the systematic search of new targets in rare diseases.

10aDatabases, Factual10aFanconi Anemia10aGenomics10aHumans10aMachine Learning10aPhenotype10aProteins10aSignal Transduction1 aEsteban-Medina, Marina1 aPeña-Chilet, Maria1 aLoucera, Carlos1 aDopazo, Joaquin uhttp://clinbioinfosspa.es/content/exploring-druggable-space-around-fanconi-anemia-pathway-using-machine-learning-and