scholarly journals EFMlrs: a Python package for elementary flux mode enumeration via lexicographic reverse search

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Bianca A Buchner ◽  
Jürgen Zanghellini
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Metabolic Networks ◽  
Stoichiometric Matrix ◽  
Elementary Flux Mode ◽  
Source Program ◽  
Metabolic Models ◽  
Performance Computing ◽  
Python Package ◽  
Flux Mode

Abstract Background Elementary flux mode (EFM) analysis is a well-established, yet computationally challenging approach to characterize metabolic networks. Standard algorithms require huge amounts of memory and lack scalability which limits their application to single servers and consequently limits a comprehensive analysis to medium-scale networks. Recently, Avis et al. developed —a parallel version of the lexicographic reverse search (lrs) algorithm, which, in principle, enables an EFM analysis on high-performance computing environments (Avis and Jordan. mplrs: a scalable parallel vertex/facet enumeration code. arXiv:1511.06487, 2017). Here we test its applicability for EFM enumeration. Results We developed , a Python package that gives users access to the enumeration capabilities of . uses COBRApy to process metabolic models from sbml files, performs loss-free compressions of the stoichiometric matrix, and generates suitable inputs for as well as , providing support not only for our proposed new method for EFM enumeration but also for already established tools. By leveraging COBRApy, also allows the application of additional reaction boundaries and seamlessly integrates into existing workflows. Conclusion We show that due to ’s properties, the algorithm is perfectly suited for high-performance computing (HPC) and thus offers new possibilities for the unbiased analysis of substantially larger metabolic models via EFM analyses. is an open-source program that comes together with a designated workflow and can be easily installed via pip.

scholarly journals COBREXA.jl: constraint-based reconstruction and exascale analysis

2021 ◽  
Author(s):  
Miroslav Kratochvil ◽  
Laurent Heirendt ◽  
St. Elmo Wilken ◽  
Taneli Pusa ◽  
Sylvain Arreckx ◽  
...  
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Large Scale ◽  
Biological Models ◽  
Task Distribution ◽  
Computing Environments ◽  
Metabolic Models ◽  
Performance Computing ◽  
Performance Constraint

COBREXA.jl is a Julia package for scalable, high-performance constraint-based reconstruction and analysis of very large-scale biological models. Its primary purpose is to facilitate the integration of modern high performance computing environments with the processing and analysis of large-scale metabolic models of challenging complexity. We report the architecture of the package, and demonstrate its scalability by benchmarking the task distribution overhead incurred when performing analyses of many variants of multi-organism community models simultaneously.

scholarly journals CGAT-core: a python framework for building scalable, reproducible computational biology workflows

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 377 ◽  
Author(s):  
Adam P. Cribbs ◽  
Sebastian Luna-Valero ◽  
Charlotte George ◽  
Ian M. Sudbery ◽  
Antonio J. Berlanga-Taylor ◽  
...  
Keyword(s):  
Computational Biology ◽  
High Performance Computing ◽  
High Performance ◽  
Large Data ◽  
Database Integration ◽  
Scientific Rigour ◽  
Rapid Construction ◽  
Rnaseq Data ◽  
Performance Computing ◽  
Python Package

In the genomics era computational biologists regularly need to process, analyse and integrate large and complex biomedical datasets. Analysis inevitably involves multiple dependent steps, resulting in complex pipelines or workflows, often with several branches. Large data volumes mean that processing needs to be quick and efficient and scientific rigour requires that analysis be consistent and fully reproducible. We have developed CGAT-core, a python package for the rapid construction of complex computational workflows. CGAT-core seamlessly handles parallelisation across high performance computing clusters, integration of Conda environments, full parameterisation, database integration and logging. To illustrate our workflow framework, we present a pipeline for the analysis of RNAseq data using pseudo-alignment.

scholarly journals CGAT-core: a python framework for building scalable, reproducible computational biology workflows

2019 ◽  
Author(s):  
Adam Cribbs ◽  
Sebastian Luna-Valero ◽  
Charlotte George ◽  
Ian M Sudbery ◽  
Antonio J Berlanga-Taylor ◽  
...  
Keyword(s):  
Computational Biology ◽  
High Performance Computing ◽  
High Performance ◽  
Large Data ◽  
Database Integration ◽  
Scientific Rigour ◽  
Rapid Construction ◽  
Rnaseq Data ◽  
Performance Computing ◽  
Python Package

In the genomics era computational biologists regularly need to process, analyse and integrate large and complex biomedical datasets. Analysis inevitably involves multiple dependent steps, resulting in complex pipelines or workflows, often with several branches. Large data volumes mean that processing needs to be quick and efficient and scientific rigour requires that analysis be consistent and fully reproducible. We have developed CGAT-core, a python package for the rapid construction of complex computational workflows. CGAT-core seamlessly handles parallelisation across high performance computing clusters, integration of Conda environments, full parameterisation, database integration and logging. To illustrate our workflow framework, we present a pipeline for the analysis of RNAseq data using pseudo-alignment.

scholarly journals CGAT-core: a python framework for building scalable, reproducible computational biology workflows

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 377 ◽  
Author(s):  
Adam P. Cribbs ◽  
Sebastian Luna-Valero ◽  
Charlotte George ◽  
Ian M. Sudbery ◽  
Antonio J. Berlanga-Taylor ◽  
...  
Keyword(s):  
Computational Biology ◽  
High Performance Computing ◽  
High Performance ◽  
Large Data ◽  
Database Integration ◽  
Scientific Rigour ◽  
Rapid Construction ◽  
Rnaseq Data ◽  
Performance Computing ◽  
Python Package

In the genomics era computational biologists regularly need to process, analyse and integrate large and complex biomedical datasets. Analysis inevitably involves multiple dependent steps, resulting in complex pipelines or workflows, often with several branches. Large data volumes mean that processing needs to be quick and efficient and scientific rigour requires that analysis be consistent and fully reproducible. We have developed CGAT-core, a python package for the rapid construction of complex computational workflows. CGAT-core seamlessly handles parallelisation across high performance computing clusters, integration of Conda environments, full parameterisation, database integration and logging. To illustrate our workflow framework, we present a pipeline for the analysis of RNAseq data using pseudo-alignment.

scholarly journals ipyrad: Interactive assembly and analysis of RADseq datasets

2020 ◽  
Vol 36 (8) ◽  
pp. 2592-2594 ◽  
Author(s):  
Deren A R Eaton ◽  
Isaac Overcast
Keyword(s):  
Open Source ◽  
High Performance ◽  
De Novo ◽  
Application Programming Interface ◽  
Analysis Tools ◽  
Source Program ◽  
Application Programming ◽  
Downstream Analysis ◽  
Performance Computing ◽  
Python Package

Abstract Summary ipyrad is a free and open source tool for assembling and analyzing restriction site-associated DNA sequence datasets using de novo and/or reference-based approaches. It is designed to be massively scalable to hundreds of taxa and thousands of samples, and can be efficiently parallelized on high performance computing clusters. It is available both as a command line interface and as a Python package with an application programming interface, the latter of which can be used interactively to write complex, reproducible scripts and implement a suite of downstream analysis tools. Availability and implementation ipyrad is a free and open source program written in Python. Source code is available from the GitHub repository (https://github.com/dereneaton/ipyrad/), and Linux and MacOS installs are distributed through the conda package manager. Complete documentation, including numerous tutorials, and Jupyter notebooks demonstrating example assemblies and applications of downstream analysis tools are available online: https://ipyrad.readthedocs.io/.

The Recent Revolution in High Performance Computing

MRS Bulletin ◽  
1997 ◽  
Vol 22 (10) ◽  
pp. 5-6
Author(s):  
Horst D. Simon
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
New Technologies ◽  
New Technology ◽  
Parallel Architecture ◽  
Time Frame ◽  
Good News ◽  
Computing Industry ◽  
Time And Energy ◽  
Performance Computing

Recent events in the high-performance computing industry have concerned scientists and the general public regarding a crisis or a lack of leadership in the field. That concern is understandable considering the industry's history from 1993 to 1996. Cray Research, the historic leader in supercomputing technology, was unable to survive financially as an independent company and was acquired by Silicon Graphics. Two ambitious new companies that introduced new technologies in the late 1980s and early 1990s—Thinking Machines and Kendall Square Research—were commercial failures and went out of business. And Intel, which introduced its Paragon supercomputer in 1994, discontinued production only two years later.During the same time frame, scientists who had finished the laborious task of writing scientific codes to run on vector parallel supercomputers learned that those codes would have to be rewritten if they were to run on the next-generation, highly parallel architecture. Scientists who are not yet involved in high-performance computing are understandably hesitant about committing their time and energy to such an apparently unstable enterprise.However, beneath the commercial chaos of the last several years, a technological revolution has been occurring. The good news is that the revolution is over, leading to five to ten years of predictable stability, steady improvements in system performance, and increased productivity for scientific applications. It is time for scientists who were sitting on the fence to jump in and reap the benefits of the new technology.

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