10 research outputs found

    Designing a cloud and HPC based M&S platform to Investigate the IVD diseases mechanisms

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    The main objective of the PhD proposal consists of the creation of a platform for IVD Models & Simulations (M&S) tools and their integration into automated workflows, within the HORIZON MSCA Disc4All project. Based on the European Open Science Cloud (EOSC) vision, the expected platform will be a Cloud-based one, furnished with a front-end, to guarantee reproducibility, accessibility and easy use for experts and non-experts. The development of an automated and specialized platform can represent the best hybrid technology with perks on both healthcare data management and computational environments exploitation, given the use of Cloud infrastructures on healthcare software and databases. Though rendering automatic not only the database, but prediction and simulation models in a user-friendly integrated system, may facilitate a difficult diagnosis and forward therapy, especially considering the various forces at play in a multi-omics data analysis of its kind

    jmpegg 1.0 – the pure java implementation of the MPEG-G ISO/IEC 23092 standard

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    jmpegg library provides MPEG-G files encoding/decoding features. The library is written in a pure java language without external dependencies. The library also supports conversion between MPEG-G and well-known genomic formats.High-throughput sequencing technologies consistently produce overwhelming amount of genomic data. This data is extremely important for the biomedical research and has an enormous impact on the progress in healthcare system. Considering the amount of generated sequencing data, data storage capacity became the principal concern for organizations like European Genome-phenome Archive (EGA) or Database of Genotypes and Phenotypes (dbGaP). The efficient and standard storage format is an important part of forging interoperability between different organizations. Several global initiatives such as ELIXIR and GA4GH already established the strategic partnership in standardization of genomic data formats andAPIs. On the other hand, other initiatives to provide a secure and efficient compressed format have appeared, such as MPEG-G (ISO/IEC 23092), developed by the MPEG working group of ISO (ISO/IEC JTC1 SC29/WG11). MPEG-G, also supported by ISO/TC 276 on Biotechnology, is making efforts for a better alignment and integration with approaches taken by other initiatives such as GA4GH.Postprint (published version

    jmpegg 1.0 – the pure java implementation of the MPEG-G ISO/IEC 23092 standard

    No full text
    jmpegg library provides MPEG-G files encoding/decoding features. The library is written in a pure java language without external dependencies. The library also supports conversion between MPEG-G and well-known genomic formats.High-throughput sequencing technologies consistently produce overwhelming amount of genomic data. This data is extremely important for the biomedical research and has an enormous impact on the progress in healthcare system. Considering the amount of generated sequencing data, data storage capacity became the principal concern for organizations like European Genome-phenome Archive (EGA) or Database of Genotypes and Phenotypes (dbGaP). The efficient and standard storage format is an important part of forging interoperability between different organizations. Several global initiatives such as ELIXIR and GA4GH already established the strategic partnership in standardization of genomic data formats andAPIs. On the other hand, other initiatives to provide a secure and efficient compressed format have appeared, such as MPEG-G (ISO/IEC 23092), developed by the MPEG working group of ISO (ISO/IEC JTC1 SC29/WG11). MPEG-G, also supported by ISO/TC 276 on Biotechnology, is making efforts for a better alignment and integration with approaches taken by other initiatives such as GA4GH

    MoDEL (Molecular Dynamics Extended Library): A Database of Atomistic Molecular Dynamics Trajectories

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    More than 1700 trajectories of proteins representative of monomeric soluble structures in the protein data bank (PDB) have been obtained by means of state-of-the-art atomistic molecular dynamics simulations in near-physiological conditions. The trajectories and analyses are stored in a large data warehouse, which can be queried for dynamic information on proteins, including interactions. Here, we describe the project and the structure and contents of our database, and provide examples of how it can be used to describe the global flexibility properties of proteins. Basic analyses and trajectories stripped of solvent molecules at a reduced resolution level are available from our web server

    A fast method for the determination of fractional contributions to solvation in proteins

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    A fast method for the calculation of residue contributions to protein solvation is presented. The approach uses the exposed polar and apolar surface of protein residues and has been parametrized from the fractional contributions to solvation determined from linear response theory coupled to molecular dynamics simulations. Application of the method to a large subset of proteins taken from the Protein Data Bank allowed us to compute the expected fractional solvation of residues. This information is used to discuss when a residue or a group of residues presents an uncommon solvation profile

    Identification of IRX1 as a Risk Locus for Rheumatoid Factor Positivity in Rheumatoid Arthritis in a Genome-Wide Association Study.

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    Rheumatoid factor (RF) is a well-established diagnostic and prognostic biomarker in rheumatoid arthritis (RA). However, ∼20% of RA patients are negative for this anti-IgG antibody. To date, only variation at the HLA-DRB1 gene has been associated with the presence of RF. This study was undertaken to identify additional genetic variants associated with RF positivity. A genome-wide association study (GWAS) for RF positivity was performed using an Illumina Quad610 genotyping platform. A total of 937 RF-positive and 323 RF-negative RA patients were genotyped for >550,000 single-nucleotide polymorphisms (SNPs). Association testing was performed using an allelic chi-square test implemented in Plink software. An independent cohort of 472 RF-positive and 190 RF-negative RA patients was used to validate the most significant findings. In the discovery stage, a SNP in the IRX1 locus on chromosome 5p15.3 (SNP rs1502644) showed a genome-wide significant association with RF positivity (P = 4.13 × 10(-8) , odds ratio [OR] 0.37 [95% confidence interval (95% CI) 0.26-0.53]). In the validation stage, the association of IRX1 with RF was replicated in an independent group of RA patients (P = 0.034, OR 0.58 [95% CI 0.35-0.97] and combined P = 1.14 × 10(-8) , OR 0.43 [95% CI 0.32-0.58]). To our knowledge, this is the first GWAS of RF positivity in RA. Variation at the IRX1 locus on chromosome 5p15.3 is associated with the presence of RF. Our findings indicate that IRX1 and HLA-DRB1 are the strongest genetic factors for RF production in RA

    A genome-wide association study identifies SLC8A3 as a susceptibility locus for ACPA-positive rheumatoid arthritis.

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    RA patients with serum ACPA have a strong and specific genetic background. The objective of the study was to identify new susceptibility genes for ACPA-positive RA using a genome-wide association approach. A total of 924 ACPA-positive RA patients with joint damage in hands and/or feet, and 1524 healthy controls were genotyped in 582 591 single-nucleotide polymorphisms (SNPs) in the discovery phase. In the validation phase, the most significant SNPs in the genome-wide association study representing new candidate loci for RA were tested in an independent cohort of 863 ACPA-positive patients with joint damage and 1152 healthy controls. All individuals from the discovery and validation cohorts were Caucasian and of Southern European ancestry. In the discovery phase, 60 loci not previously associated with RA risk showed evidence for association at P SLC8A3 was identified as a new risk locus for ACPA-positive RA. This study demonstrates the advantage of analysing relevant subsets of RA patients to identify new genetic risk variants
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