01428nas a2200205 4500008004100000022001400041245007300055210006900128260001300197300001400210490000700224520074300231100002700974700003001001700002301031700002401054700002001078700002001098856010401118 2024 eng d a2001-037000adrexml: A command line tool and Python package for drug repurposing.0 adrexml A command line tool and Python package for drug repurposi c2024 Dec a1129-11430 v233 a

We introduce drexml, a command line tool and Python package for rational data-driven drug repurposing. The package employs machine learning and mechanistic signal transduction modeling to identify drug targets capable of regulating a particular disease. In addition, it employs explainability tools to contextualize potential drug targets within the functional landscape of the disease. The methodology is validated in Fanconi Anemia and Familial Melanoma, two distinct rare diseases where there is a pressing need for solutions. In the Fanconi Anemia case, the model successfully predicts previously validated repurposed drugs, while in the Familial Melanoma case, it identifies a promising set of drugs for further investigation.

1 aEsteban-Medina, Marina1 aRoque, Víctor, Manuel de1 aHerráiz-Gil, Sara1 aPeña-Chilet, Maria1 aDopazo, Joaquin1 aLoucera, Carlos uhttps://www.clinbioinfosspa.es/content/drexml-command-line-tool-and-python-package-drug-repurposing03603nas a2200277 4500008004100000022001400041245013400055210006900189260001600258300000800274490000700282520262900289653001202918653000902930653002502939653002402964100002702988700002003015700001603035700002003051700003103071700002003102700002003122700002403142856015903166 2024 eng d a1479-587600aThe mechanistic functional landscape of retinitis pigmentosa: a machine learning-driven approach to therapeutic target discovery.0 amechanistic functional landscape of retinitis pigmentosa a machi c2024 Feb 06 a1390 v223 a

BACKGROUND: Retinitis pigmentosa is the prevailing genetic cause of blindness in developed nations with no effective treatments. In the pursuit of unraveling the intricate dynamics underlying this complex disease, mechanistic models emerge as a tool of proven efficiency rooted in systems biology, to elucidate the interplay between RP genes and their mechanisms. The integration of mechanistic models and drug-target interactions under the umbrella of machine learning methodologies provides a multifaceted approach that can boost the discovery of novel therapeutic targets, facilitating further drug repurposing in RP.

METHODS: By mapping Retinitis Pigmentosa-related genes (obtained from Orphanet, OMIM and HPO databases) onto KEGG signaling pathways, a collection of signaling functional circuits encompassing Retinitis Pigmentosa molecular mechanisms was defined. Next, a mechanistic model of the so-defined disease map, where the effects of interventions can be simulated, was built. Then, an explainable multi-output random forest regressor was trained using normal tissue transcriptomic data to learn causal connections between targets of approved drugs from DrugBank and the functional circuits of the mechanistic disease map. Selected target genes involvement were validated on rd10 mice, a murine model of Retinitis Pigmentosa.

RESULTS: A mechanistic functional map of Retinitis Pigmentosa was constructed resulting in 226 functional circuits belonging to 40 KEGG signaling pathways. The method predicted 109 targets of approved drugs in use with a potential effect over circuits corresponding to nine hallmarks identified. Five of those targets were selected and experimentally validated in rd10 mice: Gabre, Gabra1 (GABARα1 protein), Slc12a5 (KCC2 protein), Grin1 (NR1 protein) and Glr2a. As a result, we provide a resource to evaluate the potential impact of drug target genes in Retinitis Pigmentosa.

CONCLUSIONS: The possibility of building actionable disease models in combination with machine learning algorithms to learn causal drug-disease interactions opens new avenues for boosting drug discovery. Such mechanistically-based hypotheses can guide and accelerate the experimental validations prioritizing drug target candidates. In this work, a mechanistic model describing the functional disease map of Retinitis Pigmentosa was developed, identifying five promising therapeutic candidates targeted by approved drug. Further experimental validation will demonstrate the efficiency of this approach for a systematic application to other rare diseases.

10aAnimals10aMice10aRetinitis pigmentosa10aSignal Transduction1 aEsteban-Medina, Marina1 aLoucera, Carlos1 aRian, Kinza1 aVelasco, Sheyla1 aOlivares-González, Lorena1 aRodrigo, Regina1 aDopazo, Joaquin1 aPeña-Chilet, Maria uhttps://www.clinbioinfosspa.es/content/mechanistic-functional-landscape-retinitis-pigmentosa-machine-learning-driven-approach-therapeutic-target-discovery02759nas a2200265 4500008004100000022001400041245011700055210006900172260001600241520176800257100003002025700002102055700002002076700002302096700002302119700001902142700002602161700002602187700003302213700002302246700002002269700003602289700002202325856014602347 2024 eng d a1476-557800aMutational landscape of risk variants in comorbid depression and obesity: a next-generation sequencing approach.0 aMutational landscape of risk variants in comorbid depression and c2024 May 283 a

Major depression (MD) and obesity are complex genetic disorders that are frequently comorbid. However, the study of both diseases concurrently remains poorly addressed and therefore the underlying genetic mechanisms involved in this comorbidity remain largely unknown. Here we examine the contribution of common and rare variants to this comorbidity through a next-generation sequencing (NGS) approach. Specific genomic regions of interest in MD and obesity were sequenced in a group of 654 individuals from the PISMA-ep epidemiological study. We obtained variants across the entire frequency spectrum and assessed their association with comorbid MD and obesity, both at variant and gene levels. We identified 55 independent common variants and a burden of rare variants in 4 genes (PARK2, FGF21, HIST1H3D and RSRC1) associated with the comorbid phenotype. Follow-up analyses revealed significantly enriched gene-sets associated with biological processes and pathways involved in metabolic dysregulation, hormone signaling and cell cycle regulation. Our results suggest that, while risk variants specific to the comorbid phenotype have been identified, the genes functionally impacted by the risk variants share cell biological processes and signaling pathways with MD and obesity phenotypes separately. To the best of our knowledge, this is the first study involving a targeted sequencing approach toward the study of the comorbid MD and obesity. The framework presented here allowed a deep characterization of the genetics of the co-occurring MD and obesity, revealing insights into the mutational and functional profile that underlies this comorbidity and contributing to a better understanding of the relationship between these two disabling disorders.

1 aPérez-Gutiérrez, Ana, M1 aCarmona, Rosario1 aLoucera, Carlos1 aCervilla, Jorge, A1 aGutiérrez, Blanca1 aMolina, Esther1 aLópez-López, Daniel1 aPerez-Florido, Javier1 aZarza-Rebollo, Juan, Antonio1 aLópez-Isac, Elena1 aDopazo, Joaquin1 aMartinez-Gonzalez, Luis, Javier1 aRivera, Margarita uhttps://www.clinbioinfosspa.es/content/mutational-landscape-risk-variants-comorbid-depression-and-obesity-next-generation-sequencing-approach00729nam a2200205 4500008004100000020002200041022001400063245010000077210006900177260003800246300001200284490001000296100002000306700002500326700003000351700003600381700002000417700002000437856006600457 2023 eng d a978-3-031-42696-4 a0302-974300aCell-Level Pathway Scoring Comparison with a Biologically Constrained Variational Autoencoder0 aCellLevel Pathway Scoring Comparison with a Biologically Constra aChambSpringer Nature Switzerland a62 - 770 v141371 aGundogdu, Pelin1 aPayá-Milans, Miriam1 aAlamo-Alvarez, Inmaculada1 aNepomuceno-Chamorro, Isabel, A.1 aDopazo, Joaquin1 aLoucera, Carlos uhttps://link.springer.com/chapter/10.1007/978-3-031-42697-1_501712nas a2200169 4500008004100000022001400041245008500055210006900140260001600209490000700225520110800232100001801340700002001358700002401378700002001402856012001422 2023 eng d a1422-006700aCrosstalk between Metabolite Production and Signaling Activity in Breast Cancer.0 aCrosstalk between Metabolite Production and Signaling Activity i c2023 Apr 180 v243 a

The reprogramming of metabolism is a recognized cancer hallmark. It is well known that different signaling pathways regulate and orchestrate this reprogramming that contributes to cancer initiation and development. However, recent evidence is accumulating, suggesting that several metabolites could play a relevant role in regulating signaling pathways. To assess the potential role of metabolites in the regulation of signaling pathways, both metabolic and signaling pathway activities of Breast invasive Carcinoma (BRCA) have been modeled using mechanistic models. Gaussian Processes, powerful machine learning methods, were used in combination with SHapley Additive exPlanations (SHAP), a recent methodology that conveys causality, to obtain potential causal relationships between the production of metabolites and the regulation of signaling pathways. A total of 317 metabolites were found to have a strong impact on signaling circuits. The results presented here point to the existence of a complex crosstalk between signaling and metabolic pathways more complex than previously was thought.

1 aCubuk, Cankut1 aLoucera, Carlos1 aPeña-Chilet, Maria1 aDopazo, Joaquin uhttps://www.clinbioinfosspa.es/content/crosstalk-between-metabolite-production-and-signaling-activity-breast-cancer02629nas a2200205 4500008004100000022001400041245020600055210006900261260001600330520167600346100002002022700002102042700002302063700003102086700002102117700003202138700002002170700002002190856021302210 2023 eng d a1578-898900aEvidence of the association between increased use of direct oral anticoagulants and a reduction in the rate of atrial fibrillation-related stroke and major bleeding at the population level (2012-2019).0 aEvidence of the association between increased use of direct oral c2023 Nov 203 a

BACKGROUND: The introduction of direct-acting oral anticoagulants (DOACs) has shown to decrease atrial fibrillation (AF)-related stroke and bleeding rates in clinical studies, but there is no certain evidence about their effects at the population level. Our aim was to assess changes in AF-related stroke and major bleeding rates between 2012 and 2019 in Andalusia (Spain), and the association between DOACs use and events rates at the population level.

METHODS: All patients with an AF diagnosis from 2012 to 2019 were identified using the Andalusian Health Population Base, that provides clinical information on all Andalusian people. Annual ischemic and hemorrhagic stroke, major bleeding rates, and used antithrombotic treatments were determined. Marginal hazard ratios (HR) were calculated for each treatment.

RESULTS: A total of 95,085 patients with an AF diagnosis were identified. Mean age was 76.1±10.2 years (49.7% women). An increase in the use of DOACs was observed throughout the study period in both males and females (p<0.001). The annual rate of ischemic stroke decreased by one third, while that of hemorrhagic stroke and major bleeding decreased 2-3-fold from 2012 to 2019. Marginal HR was lower than 0.50 for DOACs compared to VKA for all ischemic or hemorrhagic events.

CONCLUSIONS: In this contemporary population-based study using clinical and administrative databases in Andalusia, a significant reduction in the incidence of AF-related ischemic and hemorrhagic stroke and major bleeding was observed between 2012 and 2019. The increased use of DOACs seems to be associated with this reduction.

1 aLoucera, Carlos1 aCarmona, Rosario1 aBostelmann, Gerrit1 aMuñoyerro-Muñiz, Dolores1 aVillegas, Román1 aGonzalez-Manzanares, Rafael1 aDopazo, Joaquin1 aAnguita, Manuel uhttps://www.clinbioinfosspa.es/content/evidence-association-between-increased-use-direct-oral-anticoagulants-and-reduction-rate-atrial-fibrillation-related-stroke-and-major-bleeding-population-level-2012-201902339nas a2200229 4500008004100000022001400041245013100055210006900186260001600255300000800271490000700279520147800286100002001764700002101784700002701805700002301832700003101855700002101886700002401907700002001931856015801951 2023 eng d a1743-422X00aReal-world evidence with a retrospective cohort of 15,968 COVID-19 hospitalized patients suggests 21 new effective treatments.0 aRealworld evidence with a retrospective cohort of 15968 COVID19  c2023 Oct 06 a2260 v203 a

PURPOSE: Despite the extensive vaccination campaigns in many countries, COVID-19 is still a major worldwide health problem because of its associated morbidity and mortality. Therefore, finding efficient treatments as fast as possible is a pressing need. Drug repurposing constitutes a convenient alternative when the need for new drugs in an unexpected medical scenario is urgent, as is the case with COVID-19.

METHODS: Using data from a central registry of electronic health records (the Andalusian Population Health Database), the effect of prior consumption of drugs for other indications previous to the hospitalization with respect to patient outcomes, including survival and lymphocyte progression, was studied on a retrospective cohort of 15,968 individuals, comprising all COVID-19 patients hospitalized in Andalusia between January and November 2020.

RESULTS: Covariate-adjusted hazard ratios and analysis of lymphocyte progression curves support a significant association between consumption of 21 different drugs and better patient survival. Contrarily, one drug, furosemide, displayed a significant increase in patient mortality.

CONCLUSIONS: In this study we have taken advantage of the availability of a regional clinical database to study the effect of drugs, which patients were taking for other indications, on their survival. The large size of the database allowed us to control covariates effectively.

1 aLoucera, Carlos1 aCarmona, Rosario1 aEsteban-Medina, Marina1 aBostelmann, Gerrit1 aMuñoyerro-Muñiz, Dolores1 aVillegas, Román1 aPeña-Chilet, Maria1 aDopazo, Joaquin uhttps://www.clinbioinfosspa.es/content/real-world-evidence-retrospective-cohort-15968-covid-19-hospitalized-patients-suggests-21-new-effective-treatments02202nas a2200181 4500008004100000022001400041245011200055210006900167260001600236490000700252520151000259100002001769700002201789700003501811700002001846700002001866856013401886 2023 eng d a2079-773700aSigPrimedNet: A Signaling-Informed Neural Network for scRNA-seq Annotation of Known and Unknown Cell Types.0 aSigPrimedNet A SignalingInformed Neural Network for scRNAseq Ann c2023 Apr 100 v123 a

Single-cell RNA sequencing is increasing our understanding of the behavior of complex tissues or organs, by providing unprecedented details on the complex cell type landscape at the level of individual cells. Cell type definition and functional annotation are key steps to understanding the molecular processes behind the underlying cellular communication machinery. However, the exponential growth of scRNA-seq data has made the task of manually annotating cells unfeasible, due not only to an unparalleled resolution of the technology but to an ever-increasing heterogeneity of the data. Many supervised and unsupervised methods have been proposed to automatically annotate cells. Supervised approaches for cell-type annotation outperform unsupervised methods except when new (unknown) cell types are present. Here, we introduce SigPrimedNet an artificial neural network approach that leverages (i) efficient training by means of a sparsity-inducing signaling circuits-informed layer, (ii) feature representation learning through supervised training, and (iii) unknown cell-type identification by fitting an anomaly detection method on the learned representation. We show that SigPrimedNet can efficiently annotate known cell types while keeping a low false-positive rate for unseen cells across a set of publicly available datasets. In addition, the learned representation acts as a proxy for signaling circuit activity measurements, which provide useful estimations of the cell functionalities.

1 aGundogdu, Pelin1 aAlamo, Inmaculada1 aNepomuceno-Chamorro, Isabel, A1 aDopazo, Joaquin1 aLoucera, Carlos uhttps://www.clinbioinfosspa.es/content/sigprimednet-signaling-informed-neural-network-scrna-seq-annotation-known-and-unknown-cell02575nas a2200457 4500008004100000022001400041245008300055210006900138260001600207490000700223520116500230653001301395653001801408653001101426653001301437653001401450653001401464653001501478100002001493700002601513700003301539700002501572700002101597700002301618700003201641700003101673700002101704700002901725700002601754700002001780700002501800700002701825700002801852700002301880700002301903700002001926700001801946700002201964700002001986856011102006 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 uhttps://www.clinbioinfosspa.es/content/assessing-impact-sars-cov-2-lineages-and-mutations-patient-survival01970nas a2200157 4500008004100000022001400041245015200055210006900207260001600276520129100292100003001583700002001613700002401633700002001657856013501677 2022 eng d a1460-208300aDiscovering potential interactions between rare diseases and COVID-19 by combining mechanistic models of viral infection with statistical modeling.0 aDiscovering potential interactions between rare diseases and COV c2022 Jan 123 a

Recent studies have demonstrated a relevant role of the host genetics in the COVID-19 prognosis. Most of the 7000 rare diseases described to date have a genetic component, typically highly penetrant. However, this vast spectrum of genetic variability remains yet unexplored with respect to possible interactions with COVID-19. Here, a mathematical mechanistic model of the COVID-19 molecular disease mechanism has been used to detect potential interactions between rare disease genes and the COVID-19 infection process and downstream consequences. Out of the 2518 disease genes analyzed, causative of 3854 rare diseases, a total of 254 genes have a direct effect on the COVID-19 molecular disease mechanism and 207 have an indirect effect revealed by a significant strong correlation. This remarkable potential of interaction occurs for more than 300 rare diseases. Mechanistic modeling of COVID-19 disease map has allowed a holistic systematic analysis of the potential interactions between the loss of function in known rare disease genes and the pathological consequences of COVID-19 infection. The results identify links between disease genes and COVID-19 hallmarks and demonstrate the usefulness of the proposed approach for future preventive measures in some rare diseases.

1 aLópez-Sánchez, Macarena1 aLoucera, Carlos1 aPeña-Chilet, Maria1 aDopazo, Joaquin uhttps://www.clinbioinfosspa.es/content/discovering-potential-interactions-between-rare-diseases-and-covid-19-combining-mechanistic03236nas a2200193 4500008004100000022001400041245011800055210006900173260001600242300000600258490000700264520252200271100002002793700002002813700003002833700002002863700002302883856013602906 2022 eng d a1756-038100aIntegrating pathway knowledge with deep neural networks to reduce the dimensionality in single-cell RNA-seq data.0 aIntegrating pathway knowledge with deep neural networks to reduc c2022 Jan 03 a10 v153 a

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) data provide valuable insights into cellular heterogeneity which is significantly improving the current knowledge on biology and human disease. One of the main applications of scRNA-seq data analysis is the identification of new cell types and cell states. Deep neural networks (DNNs) are among the best methods to address this problem. However, this performance comes with the trade-off for a lack of interpretability in the results. In this work we propose an intelligible pathway-driven neural network to correctly solve cell-type related problems at single-cell resolution while providing a biologically meaningful representation of the data.

RESULTS: In this study, we explored the deep neural networks constrained by several types of prior biological information, e.g. signaling pathway information, as a way to reduce the dimensionality of the scRNA-seq data. We have tested the proposed biologically-based architectures on thousands of cells of human and mouse origin across a collection of public datasets in order to check the performance of the model. Specifically, we tested the architecture across different validation scenarios that try to mimic how unknown cell types are clustered by the DNN and how it correctly annotates cell types by querying a database in a retrieval problem. Moreover, our approach demonstrated to be comparable to other less interpretable DNN approaches constrained by using protein-protein interactions gene regulation data. Finally, we show how the latent structure learned by the network could be used to visualize and to interpret the composition of human single cell datasets.

CONCLUSIONS: Here we demonstrate how the integration of pathways, which convey fundamental information on functional relationships between genes, with DNNs, that provide an excellent classification framework, results in an excellent alternative to learn a biologically meaningful representation of scRNA-seq data. In addition, the introduction of prior biological knowledge in the DNN reduces the size of the network architecture. Comparative results demonstrate a superior performance of this approach with respect to other similar approaches. As an additional advantage, the use of pathways within the DNN structure enables easy interpretability of the results by connecting features to cell functionalities by means of the pathway nodes, as demonstrated with an example with human melanoma tumor cells.

1 aGundogdu, Pelin1 aLoucera, Carlos1 aAlamo-Alvarez, Inmaculada1 aDopazo, Joaquin1 aNepomuceno, Isabel uhttps://www.clinbioinfosspa.es/content/integrating-pathway-knowledge-deep-neural-networks-reduce-dimensionality-single-cell-rna-seq02443nas a2200241 4500008004100000022001400041245012800055210006900183260001600252490000700268520156100275100003101836700002001867700001901887700002701906700001901933700002001952700002901972700002402001700002402025700002002049856013202069 2022 eng d a2076-392100aAn SPM-Enriched Marine Oil Supplement Shifted Microglia Polarization toward M2, Ameliorating Retinal Degeneration in Mice.0 aSPMEnriched Marine Oil Supplement Shifted Microglia Polarization c2022 Dec 300 v123 a

Retinitis pigmentosa (RP) is the most common inherited retinal dystrophy causing progressive vision loss. It is accompanied by chronic and sustained inflammation, including M1 microglia activation. This study evaluated the effect of an essential fatty acid (EFA) supplement containing specialized pro-resolving mediators (SPMs), on retinal degeneration and microglia activation in mice, a model of RP, as well as on LPS-stimulated BV2 cells. The EFA supplement was orally administered to mice from postnatal day (P)9 to P18. At P18, the electrical activity of the retina was examined by electroretinography (ERG) and innate behavior in response to light were measured. Retinal degeneration was studied via histology including the TUNEL assay and microglia immunolabeling. Microglia polarization (M1/M2) was assessed by flow cytometry, qPCR, ELISA and histology. Redox status was analyzed by measuring antioxidant enzymes and markers of oxidative damage. Interestingly, the EFA supplement ameliorated retinal dysfunction and degeneration by improving ERG recording and sensitivity to light, and reducing photoreceptor cell loss. The EFA supplement reduced inflammation and microglia activation attenuating M1 markers as well as inducing a shift to the M2 phenotype in mouse retinas and LPS-stimulated BV2 cells. It also reduced oxidative stress markers of lipid peroxidation and carbonylation. These findings could open up new therapeutic opportunities based on resolving inflammation with oral supplementation with SPMs such as the EFA supplement.

1 aOlivares-González, Lorena1 aVelasco, Sheyla1 aGallego, Idoia1 aEsteban-Medina, Marina1 aPuras, Gustavo1 aLoucera, Carlos1 aMartínez-Romero, Alicia1 aPeña-Chilet, Maria1 aPedraz, José, Luis1 aRodrigo, Regina uhttps://www.clinbioinfosspa.es/content/spm-enriched-marine-oil-supplement-shifted-microglia-polarization-toward-m2-ameliorating02053nas a2200181 4500008004100000022001400041245013200055210006900187260001600256300000800272490000700280520135300287100003301640700002001673700002401693700002001717856013401737 2022 eng d a2045-232200aTowards a metagenomics machine learning interpretable model for understanding the transition from adenoma to colorectal cancer.0 aTowards a metagenomics machine learning interpretable model for  c2022 Jan 10 a4500 v123 a

Gut microbiome is gaining interest because of its links with several diseases, including colorectal cancer (CRC), as well as the possibility of being used to obtain non-intrusive predictive disease biomarkers. Here we performed a meta-analysis of 1042 fecal metagenomic samples from seven publicly available studies. We used an interpretable machine learning approach based on functional profiles, instead of the conventional taxonomic profiles, to produce a highly accurate predictor of CRC with better precision than those of previous proposals. Moreover, this approach is also able to discriminate samples with adenoma, which makes this approach very promising for CRC prevention by detecting early stages in which intervention is easier and more effective. In addition, interpretable machine learning methods allow extracting features relevant for the classification, which reveals basic molecular mechanisms accounting for the changes undergone by the microbiome functional landscape in the transition from healthy gut to adenoma and CRC conditions. Functional profiles have demonstrated superior accuracy in predicting CRC and adenoma conditions than taxonomic profiles and additionally, in a context of explainable machine learning, provide useful hints on the molecular mechanisms operating in the microbiota behind these conditions.

1 aCasimiro-Soriguer, Carlos, S1 aLoucera, Carlos1 aPeña-Chilet, Maria1 aDopazo, Joaquin uhttps://www.clinbioinfosspa.es/content/towards-metagenomics-machine-learning-interpretable-model-understanding-transition-adenoma03508nas a2200709 4500008004100000022001400041245008200055210006900137260001500206300001600221490000700237520139500244653001201639653002301651653001801674653002301692653001001715653001901725653002201744653002501766653001801791653001301809653001101822653001301833653002301846653001301869653001001882100002401892700001801916700002601934700002501960700002101985700002102006700002602027700002002053700002902073700002302102700002902125700002602154700002002180700002702200700002702227700001802254700001902272700003002291700001802321700003402339700001902373700002902392700002702421700001902448700002502467700001702492700002802509700002802537700001902565700002202584700001902606700002002625710004302645856011002688 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 uhttps://www.clinbioinfosspa.es/content/csvs-crowdsourcing-database-spanish-population-genetic-variability00806nas a2200229 4500008004100000022001300041245014700054210006900201260001600270300001600286490000700302100001600309700002300325700001800348700002200366700002000388700002700408700003000435700002400465700002000489856006700509 2021 eng d a2001037000aGenome-scale mechanistic modeling of signaling pathways made easy: A bioconductor/cytoscape/web server framework for the analysis of omic data0 aGenomescale mechanistic modeling of signaling pathways made easy cJan-01-2021 a2968 - 29780 v191 aRian, Kinza1 aHidalgo, Marta, R.1 aCubuk, Cankut1 aFalco, Matias, M.1 aLoucera, Carlos1 aEsteban-Medina, Marina1 aAlamo-Alvarez, Inmaculada1 aPeña-Chilet, Maria1 aDopazo, Joaquin uhttps://linkinghub.elsevier.com/retrieve/pii/S200103702100203802553nas a2200301 4500008004100000022001400041245009700055210006900152260001500221490000700236520154600243653001801789653001401807653001501821653002801836100002501864700002001889700003301909700001801942700002901960700002401989700002302013700002002036700002202056700002602078700002002104856012702124 2021 eng d a2047-217X00aHighly accurate whole-genome imputation of SARS-CoV-2 from partial or low-quality sequences.0 aHighly accurate wholegenome imputation of SARSCoV2 from partial  c2021 12 020 v103 a

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.

10aGenome, Viral10aPhylogeny10aSARS-CoV-210aWhole Genome Sequencing1 aOrtuno, Francisco, M1 aLoucera, Carlos1 aCasimiro-Soriguer, Carlos, S1 aLepe, Jose, A1 aMartinez, Pedro, Camacho1 aDiaz, Laura, Merino1 ade Salazar, Adolfo1 aChueca, Natalia1 aGarcía, Federico1 aPerez-Florido, Javier1 aDopazo, Joaquin uhttps://www.clinbioinfosspa.es/content/highly-accurate-whole-genome-imputation-sars-cov-2-partial-or-low-quality-sequences01577nas a2200253 4500008004100000022001400041245005600055210005300111260001600164300000600180490000700186520083300193100001601026700002701042700002201069700001801091700002101109700002001130700001901150700002301169700002401192700002001216856008701236 2021 eng d a1756-038100aMechanistic modeling of the SARS-CoV-2 disease map.0 aMechanistic modeling of the SARSCoV2 disease map c2021 Jan 21 a50 v143 a

Here we present a web interface that implements a comprehensive mechanistic model of the SARS-CoV-2 disease map. In this framework, the detailed activity of the human signaling circuits related to the viral infection, covering from the entry and replication mechanisms to the downstream consequences as inflammation and antigenic response, can be inferred from gene expression experiments. Moreover, the effect of potential interventions, such as knock-downs, or drug effects (currently the system models the effect of more than 8000 DrugBank drugs) can be studied. This freely available tool not only provides an unprecedentedly detailed view of the mechanisms of viral invasion and the consequences in the cell but has also the potential of becoming an invaluable asset in the search for efficient antiviral treatments.

1 aRian, Kinza1 aEsteban-Medina, Marina1 aHidalgo, Marta, R1 aCubuk, Cankut1 aFalco, Matias, M1 aLoucera, Carlos1 aGunyel, Devrim1 aOstaszewski, Marek1 aPeña-Chilet, Maria1 aDopazo, Joaquin uhttps://www.clinbioinfosspa.es/content/mechanistic-modeling-sars-cov-2-disease-map02761nas a2200385 4500008004100000022001400041245015900055210006900214260001500283300001000298490000700308520150900315653001601824653001301840653001101853653001101864653002601875653000901901653002601910653001001936653002201946653001401968100002001982700002402002700002702026700003102053700002102084700002602105700002902131700001802160700002002178700002002198700002802218856012902246 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 uhttps://www.clinbioinfosspa.es/content/real-world-evidence-calcifediol-or-vitamin-d-prescription-and-mortality-rate-covid-1901087nas a2200313 4500008004100000022001400041245014500055210006900200260001500269300000800284490000600292653002800298653001300326653002300339653001100362653002100373653003300394653003100427653001300458653001500471653002400486100002000510700002700530700001600557700002100573700002000594700002400614856013500638 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 uhttps://www.clinbioinfosspa.es/content/drug-repurposing-covid-19-using-machine-learning-and-mechanistic-models-signal-transduction00750nas a2200157 4500008004100000245011500041210006900156260001600225490000600241100002100247700002400268700002000292700002200312700002000334856023800354 2020 eng d00aMechanistic models of signaling pathways deconvolute the glioblastoma single-cell functional landscapeAbstract0 aMechanistic models of signaling pathways deconvolute the gliobla cJan-06-20200 v21 aFalco, Matias, M1 aPeña-Chilet, Maria1 aLoucera, Carlos1 aHidalgo, Marta, R1 aDopazo, Joaquin uhttps://academic.oup.com/narcancer/article/doi/10.1093/narcan/zcaa011/5862620http://academic.oup.com/narcancer/article-pdf/2/2/zcaa011/33428092/zcaa011.pdfhttp://academic.oup.com/narcancer/article-pdf/2/2/zcaa011/33428092/zcaa011.pdf02128nas a2200325 4500008004100000022001400041245008900055210006900144260001200213300001400225490000700239520106200246653001801308653003101326653004201357653001101399653003101410653001301441653003901454100002601493700002001519700002101539700002101560700002001581700002001601700002001621700001901641700002001660856012201680 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 uhttps://www.clinbioinfosspa.es/content/smn1-copy-number-and-sequence-variant-analysis-next-generation-sequencing-data02126nas a2200289 4500008004100000022001400041245015000055210006900205260001500274300000700289490000700296520113800303653001501441653001101456653003101467653002101498653001501519653001501534653001701549653001501566100003301581700002001614700002701634700002601661700002001687856012901707 2019 eng d a1745-615000aAntibiotic resistance and metabolic profiles as functional biomarkers that accurately predict the geographic origin of city metagenomics samples.0 aAntibiotic resistance and metabolic profiles as functional bioma c2019 08 20 a150 v143 a

BACKGROUND: The availability of hundreds of city microbiome profiles allows the development of increasingly accurate predictors of the origin of a sample based on its microbiota composition. Typical microbiome studies involve the analysis of bacterial abundance profiles.

RESULTS: Here we use a transformation of the conventional bacterial strain or gene abundance profiles to functional profiles that account for bacterial metabolism and other cell functionalities. These profiles are used as features for city classification in a machine learning algorithm that allows the extraction of the most relevant features for the classification.

CONCLUSIONS: We demonstrate here that the use of functional profiles not only predict accurately the most likely origin of a sample but also to provide an interesting functional point of view of the biogeography of the microbiota. Interestingly, we show how cities can be classified based on the observed profile of antibiotic resistances.

REVIEWERS: Open peer review: Reviewed by Jin Zhuang Dou, Jing Zhou, Torsten Semmler and Eran Elhaik.

10abiomarkers10aCities10aDrug Resistance, Microbial10aMachine Learning10aMetabolome10aMetagenome10ametagenomics10aMicrobiota1 aCasimiro-Soriguer, Carlos, S1 aLoucera, Carlos1 aFlorido, Javier, Perez1 aLópez-López, Daniel1 aDopazo, Joaquin uhttps://www.clinbioinfosspa.es/content/antibiotic-resistance-and-metabolic-profiles-functional-biomarkers-accurately-predict01962nas a2200277 4500008004100000022001400041245011500055210006900170260001600239300000800255490000700263520105900270653002301329653001901352653001301371653001101384653002101395653001401416653001301430653002401443100002701467700002401494700002001518700002001538856012601558 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 uhttps://www.clinbioinfosspa.es/content/exploring-druggable-space-around-fanconi-anemia-pathway-using-machine-learning-and00765nas a2200181 4500008004100000245009000041210006900131260001600200490000600216100002400222700002700246700002200273700001600295700002300311700002000334700002000354856020900374 2019 eng d00aUsing mechanistic models for the clinical interpretation of complex genomic variation0 aUsing mechanistic models for the clinical interpretation of comp cJan-12-20190 v91 aPeña-Chilet, Maria1 aEsteban-Medina, Marina1 aFalco, Matias, M.1 aRian, Kinza1 aHidalgo, Marta, R.1 aLoucera, Carlos1 aDopazo, Joaquin uhttp://www.nature.com/articles/s41598-019-55454-7http://www.nature.com/articles/s41598-019-55454-7.pdfhttp://www.nature.com/articles/s41598-019-55454-7.pdfhttp://www.nature.com/articles/s41598-019-55454-7