12 papers · ranked by Valyu relevance
Alex M. Ascension, Marcos J. Araúzo-Bravo
Big Data analysis is a discipline with a growing number of areas where huge amounts of data is extracted and analyzed. Parallelization in Python integrates Message Passing Interface via mpi4py module. Since mpi4py does not support parallelization of objects greater than 2^31^ bytes, we developed BigMPI4py, a Python…
Jose L Figueroa, Richard Allen White
We now exist in the era of massive datasets from genomics, large language models, and all the known knowledge of humanity right at our fingertips. Much of this data is becoming more accessible; however, processing such data remains an ongoing issue across systems including high performance computing (HPC)…
Pau Andrio, Adam Hospital, Cristian Ramon-Cortes, Javier Conejero + 4 more
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.…
Gregor Mönke, Tim Schäfer, Mohsen Parto-Dezfouli, Diljit Singh Kajal + 3 more
We introduce an open-source Python package for the analysis of large-scale electrophysiological data called SyNCoPy, for Systems Neuroscience Computing in Python. The package includes signal processing analyses across time (e.g. time-lock analysis), frequency (e.g. power spectrum), and connectivity (e.g. coherence)…
Jinghan Yang, Yuyang Xu, Minhao Yao, Gao Wang + 1 more
Large-scale multi-ethnic DNA sequencing data is increasingly available owing to decreasing cost of modern sequencing technologies. Inference of the population structure with such sequencing data is fundamentally important. However, the ultra-dimensionality and complicated linkage disequilibrium patterns across the…
Omar G. Younis, Matteo Turchetta, Daniel Ariza Suarez, Steven Yates + 5 more
ChromaX is a Python library that enables the simulation of genetic recombination, genomic estimated breeding value calculations, and selection processes. By utilizing GPU processing, it can perform these simulations up to two orders of magnitude faster than existing tools with standard hardware. This offers breeders…
Erik De Schutter
The Neural Development Simulator, NeuroDevSim, is a Python module that simulates the most important aspects of development: growth, migration and pruning. It uses an agent-based modeling approach inherited from the NeuroMaC software. Each cycle, agents called fronts execute code. In the case of a growing dendritic or…
Ling-Hong Hung, Wes Lloyd, Radhika Agumbe Sridhar, Saranya Devi Athmalingam Ravishankar + 3 more
For many next-generation sequencing pipelines, the most computationally intensive step is the alignment of reads to a reference sequence. As a result, alignment software such as the Burrows-Wheeler Aligner (BWA) is optimized for speed and and is often executed in parallel on the cloud. However, there are other less…
Ge Zhang
bcftools is the standard toolkit for handling VCF and BCF variant files, but it processes records on a single core; its --threads option speeds up only compression of the output, not the work done on variant records. Processing large call sets is therefore slow, and users often divide the genome and reassemble the…
Sikao Guo, Nenad Korolija, Kent Milfeld, Adip Jhaveri + 3 more
Particle-based reaction-diffusion models offer a high-resolution alternative to the continuum reaction-diffusion approach, capturing the discrete and volume-excluding nature of molecules undergoing stochastic dynamics. These methods are thus uniquely capable of simulating explicit self-assembly of particles into…
J Kyle Medley, Shaik Asifullah, Joseph Hellerstein, Herbert M Sauro
Mechanistic kinetic models of biological pathways are an important tool for understanding biological systems. Constructing kinetic models requires fitting the parameters to experimental data. However, parameter fitting on these models is a non–convex, non–linear optimization problem. Many algorithms have been proposed…
Pierre Carrier, Bill Long, Richard Walsh, Jef Dawson + 4 more
High Performance Computing (HPC) Best Practice offers opportunities to implement lessons learned in areas such as computational chemistry and physics in genomics workflows, specifically Next-Generation Sequencing (NGS) workflows. In this study we will briefly describe how distributed-memory parallelism can be an…