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 implement an extensive GWAS …
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 …
In the recent joint venture between High-Performance Computing (HPC) and Big-Data (BD) Ecosystems towards the Exascale Computing, the scientific community has realized that powerful programming models and high-level abstraction tools are a must. …
In the past years, e-Science applications have evolved from large-scale simulations executed in a single cluster to more complex workflows where these simulations are combined with Artificial Intelligence (AI) and High-Performance Data Analytics …
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 …
The last improvements in programming languages and models have focused on simplicity and abstraction; leading Python to the top of the list of the programming languages. However, there is still room for improvement when preventing users from dealing …
Python is a popular programming language due to the simplicity of its syntax, while still achieving a good performance even being an interpreted language. The adoption from multiple scientific communities has evolved in the emergence of a large …
This paper presents a framework to easily build and execute parallel applications in container-based distributed computing platforms in a user-transparent way. The proposed framework is a combination of the COMP Superscalar (COMPSs) programming model …