03508nas 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-variability00926nas a2200229 4500008004100000022001400041245021100055210006900266260001600335490000700351100002900358700002600387700002000413700003400433700002300467700003100490700003300521700002500554700002000579700001900599856007800618 2021 eng d a1868-707500aGenome-wide analysis of DNA methylation in Hirschsprung enteric precursor cells: unraveling the epigenetic landscape of enteric nervous system developmentAbstractBackgroundResultsConclusionsGraphic abstract0 aGenomewide analysis of DNA methylation in Hirschsprung enteric p cJan-12-20210 v131 aVillalba-Benito, Leticia1 aLópez-López, Daniel1 aTorroglosa, Ana1 aCasimiro-Soriguer, Carlos, S.1 aLuzón-Toro, Berta1 aFernández, Raquel, María1 aMoya-Jiménez, María, José1 aAntiňolo, Guillermo1 aDopazo, Joaquin1 aBorrego, Salud uhttp://link.springer.com/article/10.1186/s13148-021-01040-6/fulltext.html02120nas a2200409 4500008004100000022001400041245012500055210006900180260001200249300001300261490000700274520080500281653001501086653002101101653002601122653002301148653003001171653001301201653004201214653001101256653002401267653001301291653001201304653002401316653001301340653001801353653002701371653001301398100003101411700002601442700002501468700002401493700002001517700002201537700002001559856013101579 2021 eng d a1553-735800aA versatile workflow to integrate RNA-seq genomic and transcriptomic data into mechanistic models of signaling pathways.0 aversatile workflow to integrate RNAseq genomic and transcriptomi c2021 02 ae10087480 v173 a

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.

10aAlgorithms10aCell Line, Tumor10aComputational Biology10aDatabases, Factual10aGene Expression Profiling10aGenomics10aHigh-Throughput Nucleotide Sequencing10aHumans10aModels, Theoretical10amutation10aRNA-seq10aSignal Transduction10aSoftware10aTranscriptome10awhole exome sequencing10aWorkflow1 aGarrido-Rodriguez, Martín1 aLópez-López, Daniel1 aOrtuno, Francisco, M1 aPeña-Chilet, Maria1 aMuñoz, Eduardo1 aCalzado, Marco, A1 aDopazo, Joaquin uhttps://www.clinbioinfosspa.es/content/versatile-workflow-integrate-rna-seq-genomic-and-transcriptomic-data-mechanistic-models02128nas 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-predict