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Biology subjects

Badia, R. M.

Publications and source records attributed to Badia, R. M..

4 recordsLinked to original sources

Enabling the execution of large scale workflows for molecular dynamics simulations

The usage of workflows has led to progress in many fields of science, where the need to process large amounts of data is coupled with difficulty in accessing and efficiently using High Performance Computing platforms. On the one hand, scientists are focused on their problem and concerned with how to process their data. On top of that, the applications typically have different parts and use different tools for each part, thus complicating the distribution and the reproducibility of the simulations. On the other hand, computer scientists concentrate on how to develop frameworks for the deployment of workflows on HPC or HTC resources; often providing separate solutions for the computational aspects and the data analytic ones. In this paper we present an approach to support biomolecular researchers in the development of complex workflows that i) allow them to compose pipelines of individual simulations built from different tools and interconnected by data dependencies, ii) run them seamlessly on different computational platforms, and iii) scale them up to the large number of cores provided by modern supercomputing infrastructures. Our approach is based on the orchestration of computational building blocks for Molecular Dynamics simulations through an efficient workflow management system that has already been adopted in many scientific fields to run applications on multitudes of computing backends. Results demonstrate the validity of the proposed solution through the execution of massively parallel runs in a supercomputer facility.

molecular biology↗

The impact of non-additive genetic associations on age-related complex diseases.

Genome-wide association studies (GWAS) are not fully comprehensive as current strategies typically test only the additive model, exclude the X chromosome, and use only one reference panel for genotype imputation. We implemented an extensive GWAS strategy, GUIDANCE, which improves genotype imputation by using multiple reference panels, includes the analysis of the X chromosome and non-additive models to test for association. We applied this methodology to 62,281 subjects across 22 age-related diseases and identified 94 genome-wide associated loci, including 26 previously unreported. We observed that 27.6% of the 94 loci would be missed if we only used standard imputation strategies and only tested the additive model. Among the new findings, we identified three novel low-frequency recessive variants with odds ratios larger than 4, which would need at least a three-fold larger sample size to be detected under the additive model. This study highlights the benefits of applying innovative strategies to better uncover the genetic architecture of complex diseases.

genomics↗

MuGVRE. A virtual research environment for 3D/4D genomics

Multiscale Genomics (MuG) Virtual Research Environment (MuGVRE) is a cloud-based computational infrastructure created to support the deployment of software tools addressing the various levels of analysis in 3D/4D genomics. Integrated tools tackle needs ranging from high computationally demanding applications (e.g. molecular dynamics simulations) to high-throughput data analysis applications (like the processing of next generation sequencing). The MuG Infrastructure is based on openNebula cloud systems implemented at the Institute for research in Biomedicine, and the Barcelona Supercomputing Center, and has specific interfaces for users and developers. Interoperability of the tools included in MuGVRE is maintained through a rich set of metadata allowing the system to associate tools and data in a transparent manner. Execution scheduling is based in a traditional queueing system to handle demand peaks in applications of fixed needs, and an elastic and multi-scale programming model (pyCOMPSs, controlled by the PMES scheduler), for complex workflows requiring distributed or multi-scale executions schemes. MuGVRE is available at https://vre.multiscalegenomics.eu and documentation and general information at https://www.multiscalegenomics.eu. The infrastructure is open and freely accessible.

bioinformatics↗

A Comprehensive Reanalysis Of Publicly Available GWAS Datasets Reveals An X Chromosome Rare Regulatory Variant Associated With High Risk For Type 2 Diabetes.

The reanalysis of publicly available GWAS data represents a powerful and cost-effective opportunity to gain insights into the genetics and pathophysiology of complex diseases. We demonstrate this by gathering and reanalyzing public type 2 diabetes (T2D) GWAS data for 70,127 subjects, using an innovative imputation and association strategy based on multiple reference panels (1000G and UK10K). This approach led us replicate and fine map 50 known T2D loci, and identify seven novel associated regions: five driven by common variants in or near LYPLAL1, NEUROG3, CAMKK2, ABO and GIP genes; one by a low frequency variant near EHMT2; and one driven by a rare variant in chromosome Xq23, associated with a 2.7-fold increased risk for T2D in males, and located within an active enhancer associated with the expression of Angiotensin II Receptor type 2 gene (AGTR2), a known modulator of insulin sensitivity. We further show that the risk T allele reduces binding of a nuclear protein, resulting in increased enhancer activity in muscle cells. Beyond providing novel insights into the genetics and pathophysiology of T2D, these results also underscore the value of reanalyzing publicly available data using novel analytical approaches.

genetics↗