Claves de participación: errores frecuentes

Presentación de la primera jornada1- Propuestas NO alineadas con los objetivos de la convocatoria 2- Propuestas NO equilibradas (ambición versus realismo) 3- Propuestas SIN detalles en relación a la metodología o el posible impacto 4- Propuestas que NO desarrollan en profundidad algún aspecto RRI (Investigación e Innovación Responsable) 5- Propuestas que NO tienen en cuenta infraestructuras, plataformas o asociaciones europeas existentes 6- Propuestas NO estructuradasN

Multiple platform assessment of the EGF dependent transcriptome by microarray and deep tag sequencing analysis

<p>Abstract</p> <p>Background</p> <p>Epidermal Growth Factor (EGF) is a key regulatory growth factor activating many processes relevant to normal development and disease, affecting cell proliferation and survival. Here we use a combined approach to study the EGF dependent transcriptome of HeLa cells by using multiple long oligonucleotide based microarray platforms (from Agilent, Operon, and Illumina) in combination with digital gene expression profiling (DGE) with the Illumina Genome Analyzer.</p> <p>Results</p> <p>By applying a procedure for cross-platform data meta-analysis based on RankProd and GlobalAncova tests, we establish a well validated gene set with transcript levels altered after EGF treatment. We use this robust gene list to build higher order networks of gene interaction by interconnecting associated networks, supporting and extending the important role of the EGF signaling pathway in cancer. In addition, we find an entirely new set of genes previously unrelated to the currently accepted EGF associated cellular functions.</p> <p>Conclusions</p> <p>We propose that the use of global genomic cross-validation derived from high content technologies (microarrays or deep sequencing) can be used to generate more reliable datasets. This approach should help to improve the confidence of downstream <it>in silico </it>functional inference analyses based on high content data.</p

Microarray and deep sequencing cross-platform analysis of the mirRNome and isomiR variation in response to epidermal growth factor

BACKGROUND: Epidermal Growth Factor (EGF) plays an important function in the regulation of cell growth, proliferation, and differentiation by binding to its receptor (EGFR) and providing cancer cells with increased survival responsiveness. Signal transduction carried out by EGF has been extensively studied at both transcriptional and post-transcriptional levels. Little is known about the involvement of microRNAs (miRNAs) in the EGF signaling pathway. miRNAs have emerged as major players in the complex networks of gene regulation, and cancer miRNA expression studies have evidenced a direct involvement of miRNAs in cancer progression. RESULTS: In this study, we have used an integrative high content analysis approach to identify the specific miRNAs implicated in EGF signaling in HeLa cells as potential mediators of cancer mediated functions. We have used microarray and deep-sequencing technologies in order to obtain a global view of the EGF miRNA transcriptome with a robust experimental cross-validation. By applying a procedure based on Rankprod tests, we have delimited a solid set of EGF-regulated miRNAs. After validating regulated miRNAs by reverse transcription quantitative PCR, we have derived protein networks and biological functions from the predicted targets of the regulated miRNAs to gain insight into the potential role of miRNAs in EGF-treated cells. In addition, we have analyzed sequence heterogeneity due to editing relative to the reference sequence (isomiRs) among regulated miRNAs. CONCLUSIONS: We propose that the use of global genomic miRNA cross-validation derived from high throughput technologies can be used to generate more reliable datasets inferring more robust networks of co-regulated predicted miRNA target genes

The RD-Connect Genome-Phenome Analysis Platform: Accelerating diagnosis, research, and gene discovery for rare diseases.

Rare disease patients are more likely to receive a rapid molecular diagnosis nowadays thanks to the wide adoption of next-generation sequencing. However, many cases remain undiagnosed even after exome or genome analysis, because the methods used missed the molecular cause in a known gene, or a novel causative gene could not be identified and/or confirmed. To address these challenges, the RD-Connect Genome-Phenome Analysis Platform (GPAP) facilitates the collation, discovery, sharing, and analysis of standardized genome-phenome data within a collaborative environment. Authorized clinicians and researchers submit pseudonymised phenotypic profiles encoded using the Human Phenotype Ontology, and raw genomic data which is processed through a standardized pipeline. After an optional embargo period, the data are shared with other platform users, with the objective that similar cases in the system and queries from peers may help diagnose the case. Additionally, the platform enables bidirectional discovery of similar cases in other databases from the Matchmaker Exchange network. To facilitate genome-phenome analysis and interpretation by clinical researchers, the RD-Connect GPAP provides a powerful user-friendly interface and leverages tens of information sources. As a result, the resource has already helped diagnose hundreds of rare disease patients and discover new disease causing genes

Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs

Most psychiatric disorders are moderately to highly heritable. The degree to which genetic variation is unique to individual disorders or shared across disorders is unclear. To examine shared genetic etiology, we use genome-wide genotype data from the Psychiatric Genomics Consortium (PGC) for cases and controls in schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorders (ASD) and attention-deficit/hyperactivity disorder (ADHD). We apply univariate and bivariate methods for the estimation of genetic variation within and covariation between disorders. SNPs explained 17–29% of the variance in liability. The genetic correlation calculated using common SNPs was high between schizophrenia and bipolar disorder (0.68 ± 0.04 s.e.), moderate between schizophrenia and major depressive disorder (0.43 ± 0.06 s.e.), bipolar disorder and major depressive disorder (0.47 ± 0.06 s.e.), and ADHD and major depressive disorder (0.32 ± 0.07 s.e.), low between schizophrenia and ASD (0.16 ± 0.06 s.e.) and non-significant for other pairs of disorders as well as between psychiatric disorders and the negative control of Crohn’s disease. This empirical evidence of shared genetic etiology for psychiatric disorders can inform nosology and encourages the investigation of common pathophysiologies for related disorders

Systematic Collaborative Reanalysis of Genomic Data Improves Diagnostic Yield in Neurologic Rare Diseases

Altres ajuts: Generalitat de Catalunya, Departament de Salut; Generalitat de Catalunya, Departament d'Empresa i Coneixement i CERCA Program; Ministerio de Ciencia e Innovación; Instituto Nacional de Bioinformática; ELIXIR Implementation Studies (CNAG-CRG); Centro de Investigaciones Biomédicas en Red de Enfermedades Raras; Centro de Excelencia Severo Ochoa; European Regional Development Fund (FEDER).Many patients experiencing a rare disease remain undiagnosed even after genomic testing. Reanalysis of existing genomic data has shown to increase diagnostic yield, although there are few systematic and comprehensive reanalysis efforts that enable collaborative interpretation and future reinterpretation. The Undiagnosed Rare Disease Program of Catalonia project collated previously inconclusive good quality genomic data (panels, exomes, and genomes) and standardized phenotypic profiles from 323 families (543 individuals) with a neurologic rare disease. The data were reanalyzed systematically to identify relatedness, runs of homozygosity, consanguinity, single-nucleotide variants, insertions and deletions, and copy number variants. Data were shared and collaboratively interpreted within the consortium through a customized Genome-Phenome Analysis Platform, which also enables future data reinterpretation. Reanalysis of existing genomic data provided a diagnosis for 20.7% of the patients, including 1.8% diagnosed after the generation of additional genomic data to identify a second pathogenic heterozygous variant. Diagnostic rate was significantly higher for family-based exome/genome reanalysis compared with singleton panels. Most new diagnoses were attributable to recent gene-disease associations (50.8%), additional or improved bioinformatic analysis (19.7%), and standardized phenotyping data integrated within the Undiagnosed Rare Disease Program of Catalonia Genome-Phenome Analysis Platform functionalities (18%)

Joint Analysis Of Psychiatric Disorders Increases Accuracy Of Risk Prediction For Schizophrenia, Bipolar Disorder, And Major Depressive Disorder

Genetic risk prediction has several potential applications in medical research and clinical practice and could be used, for example, to stratify a heterogeneous population of patients by their predicted genetic risk. However, for polygenic traits, such as psychiatric disorders, the accuracy of risk prediction is low. Here we use a multivariate linear mixed model and apply multi-trait genomic best linear unbiased prediction for genetic risk prediction. This method exploits correlations between disorders and simultaneously evaluates individual risk for each disorder. We show that the multivariate approach significantly increases the prediction accuracy for schizophrenia, bipolar disorder, and major depressive disorder in the discovery as well as in independent validation datasets. By grouping SNPs based on genome annotation and fitting multiple random effects, we show that the prediction accuracy could be further improved. The gain in prediction accuracy of the multivariate approach is equivalent to an increase in sample size of 34% for schizophrenia, 68% for bipolar disorder, and 76% for major depressive disorders using single trait models. Because our approach can be readily applied to any number of GWAS datasets of correlated traits, it is a flexible and powerful tool to maximize prediction accuracy. With current sample size, risk predictors are not useful in a clinical setting but already are a valuable research tool, for example in experimental designs comparing cases with high and low polygenic risk

Next-generation sequencing technologies open a new era in genomics

Les tecnologies i aplicacions de la gen&ograve;mica han experimentat r&agrave;pids avan&ccedil;os en els darrers anys. La nova generaci&oacute; de tecnologies de seq&uuml;enciaci&oacute; (next generation sequencing, NGS) permeten recopilar informaci&oacute; a escala gen&ograve;mica d&rsquo;una mostra per identificar la seq&uuml;&egrave;ncia dels fragments de DNA, la variaci&oacute; dels nivells d&rsquo;expressi&oacute; dels gens o les modificacions de les bases, en un temps relativament curt i a un cost relativament baix. Generen una quantitat immensa de dades que s&rsquo;interpreten en el marc d&rsquo;una disciplina, la bioinform&agrave;tica, i mitjan&ccedil;ant l&rsquo;&uacute;s de potents recursos inform&agrave;tics. Les tecnologies NGS possibiliten avan&ccedil;os en camps molt diversos com ara la millora gen&egrave;tica dels conreus i del bestiar, la recerca relacionada amb la base molecular de les malalties humanes, el diagn&ograve;stic molecular, el desenvolupament de nous agents terap&egrave;utics i el descobriment de nous organismes infecciosos.Paraules clau: gen&ograve;mica, seq&uuml;enciaci&oacute; massiva, genoma, exoma, medicina personalitzada.DNA sequencing technologies and applications have undergone a remarkable evolution during the last few years. Next-generation sequencing (NGS) technologies enable the collection of genome-wide information from a sample and the identification of the DNA nucleotide sequence, the levels of gene expression and/or the nucleotide modification profiles, very rapidly and at relatively low costs. They produce an overwhelming amount of data that is interpreted within a bioinformatics framework that requires significant computational infrastructure. NGS advances open up new opportunities in genomics-assisted breeding of crops and livestock, in research about the molecular base of human diseases and clinical molecular diagnostics, and in the development of new therapies and the discovery of new infectious agents.Keywords: genomics, massive sequencing, genome, exome, personalized medicine

Advances in DNA sequencing technology

L&rsquo;any 1977 Frederick Sanger va desenvolupar un m&egrave;tode per a determinar l&rsquo;ordre de les bases dels fragments de DNA. Aquesta tecnologia encara s&rsquo;utilitza actualment i ha sigut clau per a aconseguir fites tan importants com la primera seq&uuml;enciaci&oacute; completa del genoma hum&agrave;. L&rsquo;aparici&oacute; d&rsquo;una nova generaci&oacute; de tecnologies de seq&uuml;enciaci&oacute; del DNA, tecnologies d&rsquo;NGS (de l&rsquo;angl&egrave;s next generation sequencing), m&eacute;s la gran explosi&oacute; d&rsquo;eines computacionals per a analitzar-lo, ha perm&egrave;s seq&uuml;enciar de manera r&agrave;pida, econ&ograve;mica i amb una elevada precisi&oacute; genomes de microbis, plantes i animals. Durant l&rsquo;&uacute;ltima d&egrave;cada, hi ha hagut una gran expansi&oacute; de plataformes d&rsquo;NGS: primer van sorgir les tecnologies d&rsquo;NGS de cadena curta i, m&eacute;s endavant, les de cadena llarga. Tot i que les tecnologies d&rsquo;NGS de lectures llargues prometien grans aven&ccedil;os per a resoldre genomes complexos, les freq&uuml;&egrave;ncies d&rsquo;error d&rsquo;aquestes tecnologies s&oacute;n elevades. Aix&ograve; ha fet que, en els &uacute;ltims anys, hagin aparegut una s&egrave;rie de m&egrave;todes de seq&uuml;enciaci&oacute; complementaris per a resoldre les defici&egrave;ncies de les tecnologies d&rsquo;NGS de lectures curtes i llargues.In 1977, Frederick Sanger developed a method for determining the order of the bases of DNA fragments. This technology still works today and has been crucial in achieving such important milestones as the first complete sequencing of the human genome. The emergence of the new generation of DNA sequencing technologies (NGS) plus the great explosion of computer tools for their analysis has become a matter of routine and allows the sequencing of the genomes of microbes, plants and animals in a way that is quick, relatively cheap and highly precise. There has been a great expansion of NGS sequencing platforms over the last decade, first involving short-read and later longread NGS sequencing technologies. Although long-read NGS sequencing promised great advances in solving complex genomes, the error rates of these technologies are high. This has led to the appearance in recent years of a number of complementary sequencing methods to address the shortcomings of NGS sequencing of short and long readings

Characterization of alternatively spliced products and tissue-specific isoforms of USP28 and USP25

Background: The ubiquitin-dependent protein degradation pathway is essential for the proteolysis of intracellular proteins and peptides. Deubiquitinating enzymes constitute a complex protein family involved in a multitude of cellular processes. The ubiquitin-specific proteases (UBP) are a group of enzymes whose predicted function is to reverse the ubiquitinating reaction by removing ubiquitin from a large variety of substrates. We have lately reported the characterization of human USP25, a specific-ubiquitin protease gene at 21q11.2, with a specific pattern of expression in murine fetal brains and adult testis. Results: Database homology searches at the DNA and protein levels and cDNA library screenings led to the identification of a new UBP member in the human genome, named USP28, at 11q23. This novel gene showed preferential expression in heart and muscle. Moreover, cDNA, expressed sequence tag and RT-PCR analyses provided evidence for alternatively spliced products and tissue-specific isoforms. Concerning function, USP25 overexpression in Down syndrome fetal brains was shown by real-time PCR. Conclusions: On the basis of the genomic and protein sequence as well as the functional data, USP28 and USP25 establish a new subfamily of deubiquitinating enzymes. Both genes have alternatively spliced exons that could generate protein isoforms with distinct tissue-specific activity. The overexpression of USP25 in Down syndrome fetal brains supports the gene-dosage effects suggested for other UBP members related to aneuploidy syndromes