02202nas 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-cell02128nas 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 aSpinal 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-data02961nas a2200505 4500008004100000022001400041245008800055210006900143260001300212300001000225490000900235520151700244653001501761653001701776653001001793653002101803653002101824653001001845653001101855653004201866653001101908653001101919653002801930653000901958653001301967653001301980653001401993653001402007653002302021100002002044700002102064700001902085700002902104700002302133700002202156700002202178700001902200700001902219700002002238700001702258700002302275700002102298700002102319856011502340 2016 eng d a1552-483300aScreening of CD96 and ASXL1 in 11 patients with Opitz C or Bohring-Opitz syndromes.0 aScreening of CD96 and ASXL1 in 11 patients with Opitz C or Bohri c2016 Jan a24-310 v170A3 aOpitz C trigonocephaly (or Opitz C syndrome, OTCS) and Bohring-Opitz syndrome (BOS or C-like syndrome) are two rare genetic disorders with phenotypic overlap. The genetic causes of these diseases are not understood. However, two genes have been associated with OTCS or BOS with dominantly inherited de novo mutations. Whereas CD96 has been related to OTCS (one case) and to BOS (one case), ASXL1 has been related to BOS only (several cases). In this study we analyze CD96 and ASXL1 in a group of 11 affected individuals, including 2 sibs, 10 of them were diagnosed with OTCS, and one had a BOS phenotype. Exome sequences were available on six patients with OTCS and three parent pairs. Thus, we could analyze the CD96 and ASXL1 sequences in these patients bioinformatically. Sanger sequencing of all exons of CD96 and ASXL1 was carried out in the remaining patients. Detailed scrutiny of the sequences and assessment of variants allowed us to exclude putative pathogenic and private mutations in all but one of the patients. In this patient (with BOS) we identified a de novo mutation in ASXL1 (c.2100dupT). By nature and location within the gene, this mutation resembles those previously described in other BOS patients and we conclude that it may be responsible for the condition. Our results indicate that in 10 of 11, the disease (OTCS or BOS) cannot be explained by small changes in CD96 or ASXL1. However, the cohort is too small to make generalizations about the genetic etiology of these diseases.
10aAdolescent10aAntigens, CD10aChild10aChild, Preschool10aCraniosynostoses10aExome10aFemale10aHigh-Throughput Nucleotide Sequencing10aHumans10aInfant10aIntellectual Disability10aMale10amutation10aPedigree10aPhenotype10aPrognosis10aRepressor Proteins1 aUrreizti, Roser1 aRoca-Ayats, Neus1 aTrepat, Judith1 aGarcia-Garcia, Francisco1 aAlemán, Alejandro1 aOrteschi, Daniela1 aMarangi, Giuseppe1 aNeri, Giovanni1 aOpitz, John, M1 aDopazo, Joaquin1 aCormand, Bru1 aVilageliu, Lluïsa1 aBalcells, Susana1 aGrinberg, Daniel uhttps://www.clinbioinfosspa.es/content/screening-cd96-and-asxl1-11-patients-opitz-c-or-bohring-opitz-syndromes02610nas a2200397 4500008004100000022001400041245017300055210006900228260001600297300001300313490000600326520129300332653001001625653000901635653002201644653003501666653002401701653001101725653001101736653001901747653000901766653001701775653001601792653004301808100003001851700002801881700002501909700002901934700001501963700002101978700001601999700002002015700001802035700002702053856013202080 2016 eng d a1949-255300aSerum metabolomic profiling facilitates the non-invasive identification of metabolic biomarkers associated with the onset and progression of non-small cell lung cancer.0 aSerum metabolomic profiling facilitates the noninvasive identifi c2016 Mar 15 a12904-160 v73 aLung cancer (LC) is responsible for most cancer deaths. One of the main factors contributing to the lethality of this disease is the fact that a large proportion of patients are diagnosed at advanced stages when a clinical intervention is unlikely to succeed. In this study, we evaluated the potential of metabolomics by 1H-NMR to facilitate the identification of accurate and reliable biomarkers to support the early diagnosis and prognosis of non-small cell lung cancer (NSCLC).We found that the metabolic profile of NSCLC patients, compared with healthy individuals, is characterized by statistically significant changes in the concentration of 18 metabolites representing different amino acids, organic acids and alcohols, as well as different lipids and molecules involved in lipid metabolism. Furthermore, the analysis of the differences between the metabolic profiles of NSCLC patients at different stages of the disease revealed the existence of 17 metabolites involved in metabolic changes associated with disease progression.Our results underscore the potential of metabolomics profiling to uncover pathophysiological mechanisms that could be useful to objectively discriminate NSCLC patients from healthy individuals, as well as between different stages of the disease.
10aAdult10aAged10aBiomarkers, Tumor10aCarcinoma, Non-Small-Cell Lung10aDisease Progression10aFemale10aHumans10aLung Neoplasms10aMale10ametabolomics10aMiddle Aged10aProton Magnetic Resonance Spectroscopy1 aPuchades-Carrasco, Leonor1 aJantus-Lewintre, Eloisa1 aPérez-Rambla, Clara1 aGarcia-Garcia, Francisco1 aLucas, Rut1 aCalabuig, Silvia1 aBlasco, Ana1 aDopazo, Joaquin1 aCamps, Carlos1 aPineda-Lucena, Antonio uhttps://www.clinbioinfosspa.es/content/serum-metabolomic-profiling-facilitates-non-invasive-identification-metabolic-biomarkers03446nas a2200277 4500008004100000022001400041245011600055210006900171260001300240300001000253490000600263520251800269100001902787700002102806700002002827700002202847700001802869700001902887700001702906700002202923700002002945700002402965700001902989700002903008856013103037 2016 eng d a2212-877800aStress-induced activation of brown adipose tissue prevents obesity in conditions of low adaptive thermogenesis.0 aStressinduced activation of brown adipose tissue prevents obesit c2016 Jan a19-330 v53 aBACKGROUND: Stress-associated conditions such as psychoemotional reactivity and depression have been paradoxically linked to either weight gain or weight loss. This bi-directional effect of stress is not understood at the functional level. Here we tested the hypothesis that pre-stress level of adaptive thermogenesis and brown adipose tissue (BAT) functions explain the vulnerability or resilience to stress-induced obesity.
METHODS: We used wt and triple β1,β2,β3-Adrenergic Receptors knockout (β-less) mice exposed to a model of chronic subordination stress (CSS) at either room temperature (22 °C) or murine thermoneutrality (30 °C). A combined behavioral, physiological, molecular, and immunohistochemical analysis was conducted to determine stress-induced modulation of energy balance and BAT structure and function. Immortalized brown adipocytes were used for in vitro assays.
RESULTS: Departing from our initial observation that βARs are dispensable for cold-induced BAT browning, we demonstrated that under physiological conditions promoting low adaptive thermogenesis and BAT activity (e.g. thermoneutrality or genetic deletion of the βARs), exposure to CSS acted as a stimulus for BAT activation and thermogenesis, resulting in resistance to diet-induced obesity despite the presence of hyperphagia. Conversely, in wt mice acclimatized to room temperature, and therefore characterized by sustained BAT function, exposure to CSS increased vulnerability to obesity. Exposure to CSS enhanced the sympathetic innervation of BAT in wt acclimatized to thermoneutrality and in β-less mice. Despite increased sympathetic innervation suggesting adrenergic-mediated browning, norepinephrine did not promote browning in βARs knockout brown adipocytes, which led us to identify an alternative sympathetic/brown adipocytes purinergic pathway in the BAT. This pathway is downregulated under conditions of low adaptive thermogenesis requirements, is induced by stress, and elicits activation of UCP1 in wt and β-less brown adipocytes. Importantly, this purinergic pathway is conserved in human BAT.
CONCLUSION: Our findings demonstrate that thermogenesis and BAT function are determinant of the resilience or vulnerability to stress-induced obesity. Our data support a model in which adrenergic and purinergic pathways exert complementary/synergistic functions in BAT, thus suggesting an alternative to βARs agonists for the activation of human BAT.
1 aRazzoli, Maria1 aFrontini, Andrea1 aGurney, Allison1 aMondini, Eleonora1 aCubuk, Cankut1 aKatz, Liora, S1 aCero, Cheryl1 aBolan, Patrick, J1 aDopazo, Joaquin1 aVidal-Puig, Antonio1 aCinti, Saverio1 aBartolomucci, Alessandro uhttps://www.clinbioinfosspa.es/content/stress-induced-activation-brown-adipose-tissue-prevents-obesity-conditions-low-adaptive02925nas a2200409 4500008004100000022001400041245011400055210006900169260001300238300001000251490000700261520165700268653001201925653001501937653001101952653003901963653001802002653001902020653001202039653003102051653001302082653000902095653001402104653001602118653002702134653002402161653001802185100002102203700002502224700003102249700001602280700002002296700001802316700002502334700003202359856012402391 2014 eng d a1096-093700aSequencing and functional analysis of the genome of a nematode egg-parasitic fungus, Pochonia chlamydosporia.0 aSequencing and functional analysis of the genome of a nematode e c2014 Apr a69-800 v653 aPochonia chlamydosporia is a worldwide-distributed soil fungus with a great capacity to infect and destroy the eggs and kill females of plant-parasitic nematodes. Additionally, it has the ability to colonize endophytically roots of economically-important crop plants, thereby promoting their growth and eliciting plant defenses. This multitrophic behavior makes P. chlamydosporia a potentially useful tool for sustainable agriculture approaches. We sequenced and assembled ∼41 Mb of P. chlamydosporia genomic DNA and predicted 12,122 gene models, of which many were homologous to genes of fungal pathogens of invertebrates and fungal plant pathogens. Predicted genes (65%) were functionally annotated according to Gene Ontology, and 16% of them found to share homology with genes in the Pathogen Host Interactions (PHI) database. The genome of this fungus is highly enriched in genes encoding hydrolytic enzymes, such as proteases, glycoside hydrolases and carbohydrate esterases. We used RNA-Seq technology in order to identify the genes expressed during endophytic behavior of P. chlamydosporia when colonizing barley roots. Functional annotation of these genes showed that hydrolytic enzymes and transporters are expressed during endophytism. This structural and functional analysis of the P. chlamydosporia genome provides a starting point for understanding the molecular mechanisms involved in the multitrophic lifestyle of this fungus. The genomic information provided here should also prove useful for enhancing the capabilities of this fungus as a biocontrol agent of plant-parasitic nematodes and as a plant growth-promoting organism.
10aAnimals10aAscomycota10aFemale10aGene Expression Regulation, Fungal10aGene ontology10aGenome, Fungal10aHordeum10aHost-Pathogen Interactions10aNematoda10aOvum10aPhylogeny10aPlant Roots10aSequence Analysis, DNA10aSignal Transduction10aTranscriptome1 aLarriba, Eduardo1 aJaime, María, D L A1 aCarbonell-Caballero, José1 aConesa, Ana1 aDopazo, Joaquin1 aNislow, Corey1 aMartín-Nieto, José1 aLopez-Llorca, Luis, Vicente uhttps://www.clinbioinfosspa.es/content/sequencing-and-functional-analysis-genome-nematode-egg-parasitic-fungus-pochonia02236nas a2200325 4500008004100000022001400041245010200055210006900157260001300226300001100239490000700250520119100257653002301448653001101471653001301482653002701495653001401522653003601536653002501572653001301597100002001610700002101630700001801651700002801669700001801697700002001715700002301735700002301758856012901781 2012 eng d a1362-496200aSNPeffect 4.0: on-line prediction of molecular and structural effects of protein-coding variants.0 aSNPeffect 40 online prediction of molecular and structural effec c2012 Jan aD935-90 v403 aSingle nucleotide variants (SNVs) are, together with copy number variation, the primary source of variation in the human genome and are associated with phenotypic variation such as altered response to drug treatment and susceptibility to disease. Linking structural effects of non-synonymous SNVs to functional outcomes is a major issue in structural bioinformatics. The SNPeffect database (http://snpeffect.switchlab.org) uses sequence- and structure-based bioinformatics tools to predict the effect of protein-coding SNVs on the structural phenotype of proteins. It integrates aggregation prediction (TANGO), amyloid prediction (WALTZ), chaperone-binding prediction (LIMBO) and protein stability analysis (FoldX) for structural phenotyping. Additionally, SNPeffect holds information on affected catalytic sites and a number of post-translational modifications. The database contains all known human protein variants from UniProt, but users can now also submit custom protein variants for a SNPeffect analysis, including automated structure modeling. The new meta-analysis application allows plotting correlations between phenotypic features for a user-selected set of variants.
10aDatabases, Protein10aHumans10aInternet10aMeta-Analysis as Topic10aPhenotype10aPolymorphism, Single Nucleotide10aProtein Conformation10aProteins1 aDe Baets, Greet1 aVan Durme, Joost1 aReumers, Joke1 aMaurer-Stroh, Sebastian1 aVanhee, Peter1 aDopazo, Joaquin1 aSchymkowitz, Joost1 aRousseau, Frederic uhttps://www.clinbioinfosspa.es/content/snpeffect-40-line-prediction-molecular-and-structural-effects-protein-coding-variants00608nas a2200169 4500008004100000245010100041210006900142300001300211490000600224100002500230700001900255700002700274700001900301700002000320700002000340856007800360 2010 eng d00aSelection upon Genome Architecture: Conservation of Functional Neighborhoods with Changing Genes0 aSelection upon Genome Architecture Conservation of Functional Ne ae10009530 v61 aAl-Shahrour, Fátima1 aMinguez, Pablo1 aMarqués-Bonet, Tomás1 aGazave, Elodie1 aNavarro, Arcadi1 aDopazo, Joaquin uhttp://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.100095301786nas a2200277 4500008004100000022001400041245009200055210006900147260001300216300001200229490000700241520089700248653001501145653003001160653001301190653001301203653001801216653004401234653001301278100002401291700002101315700002001336700002001356700001601376856011601392 2010 eng d a1362-496200aSerial Expression Analysis: a web tool for the analysis of serial gene expression data.0 aSerial Expression Analysis a web tool for the analysis of serial c2010 Jul aW239-450 v383 aSerial transcriptomics experiments investigate the dynamics of gene expression changes associated with a quantitative variable such as time or dosage. The statistical analysis of these data implies the study of global and gene-specific expression trends, the identification of significant serial changes, the comparison of expression profiles and the assessment of transcriptional changes in terms of cellular processes. We have created the SEA (Serial Expression Analysis) suite to provide a complete web-based resource for the analysis of serial transcriptomics data. SEA offers five different algorithms based on univariate, multivariate and functional profiling strategies framed within a user-friendly interface and a project-oriented architecture to facilitate the analysis of serial gene expression data sets from different perspectives. SEA is available at sea.bioinfo.cipf.es.
10aAlgorithms10aGene Expression Profiling10aInternet10aKinetics10aLinear Models10aOligonucleotide Array Sequence Analysis10aSoftware1 aNueda, Maria, José1 aCarbonell, José1 aMedina, Ignacio1 aDopazo, Joaquin1 aConesa, Ana uhttps://www.clinbioinfosspa.es/content/serial-expression-analysis-web-tool-analysis-serial-gene-expression-data01472nas a2200205 4500008004100000245009600041210006900137300001300206490000700219520083200226653001601058653001201074653000901086100001901095700001301114700002001127700002401147700002001171856007501191 2009 eng d00aSNOW, a web-based tool for the statistical analysis of protein-protein interaction networks0 aSNOW a webbased tool for the statistical analysis of proteinprot aW109-1140 v373 aUnderstanding the structure and the dynamics of the complex intercellular network of interactions that contributes to the structure and function of a living cell is one of the main challenges of today’s biology. SNOW inputs a collection of protein (or gene) identifiers and, by using the interactome as scaffold, draws the connections among them, calculates several relevant network parameters and, as a novelty among the rest of tools of its class, it estimates their statistical significance. The parameters calculated for each node are: connectivity, betweenness and clustering coefficient. It also calculates the number of components, number of bicomponents and articulation points. An interactive network viewer is also available to explore the resulting network. SNOW is available at http://snow.bioinfo.cipf.es.
10ainteractome10anetwork10asnow1 aMinguez, Pablo1 aGotz, S.1 aMontaner, David1 aAl-Shahrour, Fatima1 aDopazo, Joaquin uhttp://nar.oxfordjournals.org/content/early/2009/05/19/nar.gkp402.full01737nas a2200277 4500008004100000022001400041245009700055210006900152260001300221300001200234490000700246520083000253653002201083653003701105653002301142653001101165653001301176653003201189653001301221100001901234700001801253700002001271700002501291700002001316856012301336 2009 eng d a1362-496200aSNOW, a web-based tool for the statistical analysis of protein-protein interaction networks.0 aSNOW a webbased tool for the statistical analysis of proteinprot c2009 Jul aW109-140 v373 aUnderstanding the structure and the dynamics of the complex intercellular network of interactions that contributes to the structure and function of a living cell is one of the main challenges of today's biology. SNOW inputs a collection of protein (or gene) identifiers and, by using the interactome as scaffold, draws the connections among them, calculates several relevant network parameters and, as a novelty among the rest of tools of its class, it estimates their statistical significance. The parameters calculated for each node are: connectivity, betweenness and clustering coefficient. It also calculates the number of components, number of bicomponents and articulation points. An interactive network viewer is also available to explore the resulting network. SNOW is available at http://snow.bioinfo.cipf.es.
10aComputer Graphics10aData Interpretation, Statistical10aDatabases, Protein10aHumans10aInternet10aProtein Interaction Mapping10aSoftware1 aMinguez, Pablo1 aGötz, Stefan1 aMontaner, David1 aAl-Shahrour, Fátima1 aDopazo, Joaquin uhttps://www.clinbioinfosspa.es/content/snow-web-based-tool-statistical-analysis-protein-protein-interaction-networks-003454nas a2200877 4500008004100000022001400041245006300055210006200118260001300180300001000193490000700203520101900210653001201229653002301241653002301264653001101287653001501298653002701313653001401340653003601354653002801390653000901418653002501427653002701452110002001479700001801499700001701517700003001534700001701564700002401581700001501605700001801620700002401638700002001662700001701682700001801699700001901717700001601736700002401752700002001776700001901796700002101815700001901836700001601855700001701871700001901888700001801907700001701925700002201942700001701964700001901981700001802000700002302018700001802041700002002059700002002079700001802099700002102117700003402138700002202172700002102194700002302215700002202238700002302260700002002283700002002303700002202323700002202345700002002367700002002387700001802407700002002425700001902445700002002464856009202484 2008 eng d a1546-171800aSNP and haplotype mapping for genetic analysis in the rat.0 aSNP and haplotype mapping for genetic analysis in the rat c2008 May a560-60 v403 aThe laboratory rat is one of the most extensively studied model organisms. Inbred laboratory rat strains originated from limited Rattus norvegicus founder populations, and the inherited genetic variation provides an excellent resource for the correlation of genotype to phenotype. Here, we report a survey of genetic variation based on almost 3 million newly identified SNPs. We obtained accurate and complete genotypes for a subset of 20,238 SNPs across 167 distinct inbred rat strains, two rat recombinant inbred panels and an F2 intercross. Using 81% of these SNPs, we constructed high-density genetic maps, creating a large dataset of fully characterized SNPs for disease gene mapping. Our data characterize the population structure and illustrate the degree of linkage disequilibrium. We provide a detailed SNP map and demonstrate its utility for mapping of quantitative trait loci. This community resource is openly available and augments the genetic tools for this workhorse of physiological studies.
10aAnimals10aChromosome Mapping10aDatabases, Genetic10aGenome10aHaplotypes10aLinkage Disequilibrium10aPhylogeny10aPolymorphism, Single Nucleotide10aQuantitative Trait Loci10aRats10aRats, Inbred Strains10aRecombination, Genetic1 aSTAR Consortium1 aSaar, Kathrin1 aBeck, Alfred1 aBihoreau, Marie-Thérèse1 aBirney, Ewan1 aBrocklebank, Denise1 aChen, Yuan1 aCuppen, Edwin1 aDemonchy, Stephanie1 aDopazo, Joaquin1 aFlicek, Paul1 aFoglio, Mario1 aFujiyama, Asao1 aGut, Ivo, G1 aGauguier, Dominique1 aGuigó, Roderic1 aGuryev, Victor1 aHeinig, Matthias1 aHummel, Oliver1 aJahn, Niels1 aKlages, Sven1 aKren, Vladimir1 aKube, Michael1 aKuhl, Heiner1 aKuramoto, Takashi1 aKuroki, Yoko1 aLechner, Doris1 aLee, Young-Ae1 aLopez-Bigas, Nuria1 aLathrop, Mark1 aMashimo, Tomoji1 aMedina, Ignacio1 aMott, Richard1 aPatone, Giannino1 aPerrier-Cornet, Jeanne-Antide1 aPlatzer, Matthias1 aPravenec, Michal1 aReinhardt, Richard1 aSakaki, Yoshiyuki1 aSchilhabel, Markus1 aSchulz, Herbert1 aSerikawa, Tadao1 aShikhagaie, Medya1 aTatsumoto, Shouji1 aTaudien, Stefan1 aToyoda, Atsushi1 aVoigt, Birger1 aZelenika, Diana1 aZimdahl, Heike1 aHubner, Norbert uhttps://www.clinbioinfosspa.es/content/snp-and-haplotype-mapping-genetic-analysis-rat-0