MR MAQ : algorisme de Read Mapping utilitzant la plataforma Hadoop

L'èxit del Projecte Genoma Humà (PGH) l'any 2000 va fer de la "medicina personalitzada" una realitat més propera. Els descobriments del PGH han simplificat les tècniques de seqüenciació de tal manera que actualment qualsevol persona pot aconseguir la seva seqüència d'ADN complerta. La tecnologia de Read Mapping destaca en aquest tipus de tècniques i es caracteritza per manegar una gran quantitat de dades. Hadoop, el framework d'Apache per aplicacions intensives de dades sota el paradigma Map Reduce, resulta un aliat perfecte per aquest tipus de tecnologia i ha sigut l'opció escollida per a realitzar aquest projecte. Durant tot el treball es realitza l'estudi, l'anàlisi i les experimentacions necessàries per aconseguir un Algorisme Genètic innovador que utilitzi tot el potencial de Hadoop.El éxito del Proyecto Genoma Humano (PGH) en el año 2.000 hizo de la "medicina personalizada" una relidad más cercana. Los descubrimientos del PGH han simplificado las técnicas de secuenciación de tal manera que actualmente cualquier persona puede conseguir su secuencia de ADN completa. La tecnología de Read Mapping destaca en este tipo de técnicas y se caracteriza por manejar una gran cantidad de datos. Hadoop, el Framework de Apache para aplicaciones intensivas de datos bajo el paradigma Map Reduce, resulta un aliado perfecto para este tipo de tecnología y ha sido la opción escogida para realizar este proyecto. A lo largo del trabajo se realiza el estudio, el análisis y las experimentaciones necesarias para conseguir un Algoritmo Genómico novedoso que utilice todo el potencial de Hadoop.In the 2000th the Human Genome Project (PGH) was accomplished successfully and it made "personalized medicine" a closer reality. The PGH has simplified the sequencing techniques in a high way so nowadays anyone can get his full ADN sequence. Read Mapping technology is one of most important sequencing techniques and it is characterized to work with lots of data. Hadoop is the Framework of Apache for data intensive applications under Map Reduce paradigm and it becomes a perfect tool for this kind of technology. For this reason it has been selected for this project. Along this entire project we will realize the study, the analysis and the experimentations to get a new Genetic Algorithm with all Hadoop potential

Analysis and improvement of map-reduce data distribution in read mapping applications

The map-reduce paradigm has shown to be a simple and feasible way of filtering and analyzing large data sets in cloud and cluster systems. Algorithms designed for the paradigm must implement regular data distribution patterns so that appropriate use of resources is ensured. Good scalability and performance on Map-Reduce applications greatly depend on the design of regular intermediate data generation-consumption patterns at the map and reduce phases. We describe the data distribution patterns found in current Map-Reduce read mapping bioinformatics applications and show some data decomposition principles to greatly improve their scalability and performanc

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

MR MAQ: algorisme de Read Mapping utilitzant la plataforma Hadoop

L’èxit del Projecte Genoma Humà (PGH) l’any 2000 va fer de la “medicina personalitzada” una realitat més propera. Els descobriments del PGH han simplificat les tècniques de seqüenciació de tal manera que actualment qualsevol persona pot aconseguir la seva seqüència d’ADN complerta. La tecnologia de Read Mapping destaca en aquest tipus de tècniques i es caracteritza per manegar una gran quantitat de dades. Hadoop, el framework d’Apache per aplicacions intensives de dades sota el paradigma Map Reduce, resulta un aliat perfecte per aquest tipus de tecnologia i ha sigut l’opció escollida per a realitzar aquest projecte. Durant tot el treball es realitza l’estudi, l’anàlisi i les experimentacions necessàries per aconseguir un Algorisme Genètic innovador que utilitzi tot el potencial de Hadoop.El éxito del Proyecto Genoma Humano (PGH) en el año 2.000 hizo de la “medicina personalizada” una relidad más cercana. Los descubrimientos del PGH han simplificado las técnicas de secuenciación de tal manera que actualmente cualquier persona puede conseguir su secuencia de ADN completa. La tecnología de Read Mapping destaca en este tipo de técnicas y se caracteriza por manejar una gran cantidad de datos. Hadoop, el Framework de Apache para aplicaciones intensivas de datos bajo el paradigma Map Reduce, resulta un aliado perfecto para este tipo de tecnología y ha sido la opción escogida para realizar este proyecto. A lo largo del trabajo se realiza el estudio, el análisis y las experimentaciones necesarias para conseguir un Algoritmo Genómico novedoso que utilice todo el potencial de Hadoop.In the 2000th the Human Genome Project (PGH) was accomplished successfully and it made “personalized medicine” a closer reality. The PGH has simplified the sequencing techniques in a high way so nowadays anyone can get his full ADN sequence. Read Mapping technology is one of most important sequencing techniques and it is characterized to work with lots of data. Hadoop is the Framework of Apache for data intensive applications under Map Reduce paradigm and it becomes a perfect tool for this kind of technology. For this reason it has been selected for this project. Along this entire project we will realize the study, the analysis and the experimentations to get a new Genetic Algorithm with all Hadoop potential

6ppd-q in Stormwater: the state of science and Washington Department of Ecology’s initial responses

Recent research using a novel laboratory non-targeted analysis approach has named 6ppd-quinone (6PPD-q) as the primary compound in stormwater runoff that is responsible for acute mortality of Coho salmon. This chemical is a transformation byproduct of 6PPD, an anti-oxidant and anti-ozonant that is commonly used in automobile tire rubber and is deposited on highways where it can be conveyed in stormwater runoff to streams and other receiving waters. This discovery has spurred expansive evaluation along several lines of inquiry at Ecology, the universities, and many others. This panel will start with the state of science and what we know about 6ppd-q, and its known water quality impacts and data gaps, followed by a discussion on how current research and data gaps affect management decisions. We will describe current activities to support management strategies: product substitution in tires; development of a lab analysis method for water samples; identifying available Best Management Practices (source control, treatment, and other management approaches) to control and/or treat stormwater to prevent 6ppd-q toxicity in receiving waters; and considerations to take into account when prioritizing the areas and streams affected by 6ppd-q toxicity so that treatment retrofits and other stormwater management actions can be efficiently planned and effectively implemented

Cerebrospinal fluid-based kinetic biomarkers of axonal transport in monitoring neurodegeneration.

Progress in neurodegenerative disease research is hampered by the lack of biomarkers of neuronal dysfunction. We here identified a class of cerebrospinal fluid-based (CSF-based) kinetic biomarkers that reflect altered neuronal transport of protein cargo, a common feature of neurodegeneration. After a pulse administration of heavy water (2H2O), distinct, newly synthesized 2H-labeled neuronal proteins were transported to nerve terminals and secreted, and then appeared in CSF. In 3 mouse models of neurodegeneration, distinct 2H-cargo proteins displayed delayed appearance and disappearance kinetics in the CSF, suggestive of aberrant transport kinetics. Microtubule-modulating pharmacotherapy normalized CSF-based kinetics of affected 2H-cargo proteins and ameliorated neurodegenerative symptoms in mice. After 2H2O labeling, similar neuronal transport deficits were observed in CSF of patients with Parkinson's disease (PD) compared with non-PD control subjects, which indicates that these biomarkers are translatable and relevant to human disease. Measurement of transport kinetics may provide a sensitive method to monitor progression of neurodegeneration and treatment effects

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.We acknowledge the support of the developers of PhenoTips, which was used in the past by RD-Connect and NeurOmics as the primary tool to collate phenotypic data. We would also like to thank the leaders and members of the Instituto Nacional de Bioinformática (INB) and ELIXIR for their support and collaboration throughout the years. RD-Connect (RD-Connect, an integrated platform connecting registries, biobanks, and clinical bioinformatics) received funding from the Seventh Framework (FP7) Programme of the European Union under grant agreement No 305444. Data were analyzed using the RD-Connect GPAP, which received funding from EU projects Solve-RD, EJP-RD (grant numbers H2020 779257, H2020 825575), Instituto de Salud Carlos III (Grant numbers PT13/0001/0044, PT17/0009/0019; Instituto Nacional de Bioinformática, INB), ELIXIR-EXCELERATE (Grant number EU H2020 #676559) and ELIXIR Implementation Studies (Remote real-time visualization of human rare disease genomics data (RD-Connect) stored at the EGA ELIXIR. 2017-2018; ELIXIR IT-2017-INTEGRATION, Rare Disease Infrastructure ELIXIR, 2019-2020 and the Beacon ELIXIR, 2019-2021). The RD-Connect GPAP has leveraged developments funded through project VEIS (001-P-001647 co-financed by the European Regional Development Fund of the European Union in the framework of the Operational Program FEDER of Catalonia 2014-2020 with the support of the Secretaria d'Universitats i Recerca del Departament d'Empresa i Coneixement de la Generalitat de Catalunya) and URD-Cat (PERIS SLT002/16/00174, Departament de Salut, Generalitat de Catalunya). The research leading to these results has received funding from Consequitur (Newton Fund UK/Turkey, MR/N027302/1), BBMRI-LPC (EU FP7 #313010), NeurOmics (EU FP7 #305121), the Economic Development Department of the Navarra Government (Grant number 001114112017), the European Reference Network for Rare Neurological Diseases (Project ID number 739510) and NIH, National Institute of Child Health and Human Development (1R01HD103805-01). We acknowledge the support of the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) to the EMBL partnership, the Centro de Excelencia Severo Ochoa, and the CERCA Program/Generalitat de Catalunya. We also acknowledge the support of the Generalitat de Catalunya through Departament de Salut and Departament d'Empresa i Coneixement and Co-financing by the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) with funds from the European Regional Development Fund (ERDF) corresponding to the 2014-2020 Smart Growth Operating Program. HL receives support from the Canadian Institutes of Health Research (Foundation Grant FDN-167281), the Canadian Institutes of Health Research and Muscular Dystrophy Canada (Network Catalyst Grant for NMD4C), the Canada Foundation for Innovation (CFI-JELF 38412), and the Canada Research Chairs program (Canada Research Chair in Neuromuscular Genomics and Health, 950-232279)

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