software dependencies
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GigaScience ◽  
2020 ◽  
Vol 9 (11) ◽  
Author(s):  
Justin Bedő ◽  
Leon Di Stefano ◽  
Anthony T Papenfuss

Abstract Motivation A challenge for computational biologists is to make our analyses reproducible—i.e. to rerun, combine, and share, with the assurance that equivalent runs will generate identical results. Current best practice aims at this using a combination of package managers, workflow engines, and containers. Results We present BioNix, a lightweight library built on the Nix deployment system. BioNix manages software dependencies, computational environments, and workflow stages together using a single abstraction: pure functions. This lets users specify workflows in a clean, uniform way, with strong reproducibility guarantees. Availability and Implementation BioNix is implemented in the Nix expression language and is released on GitHub under the 3-clause BSD license: https://github.com/PapenfussLab/bionix (biotools:BioNix) (BioNix, RRID:SCR_017662).


Author(s):  
Jingwen Bai ◽  
Chakradhar Bandla ◽  
Jiaxin Guo ◽  
Roberto Vera Alvarez ◽  
Juan Antonio Vizcaíno ◽  
...  

1AbstractBioContainers is an open-source project that aims to create, store, and distribute bioinformatics software containers and packages. The BioContainers community has developed a set of guidelines to standardize the software containers including the metadata, versions, licenses, and/or software dependencies. BioContainers supports multiple packaging and containers technologies such as Conda, Docker, and Singularity. Here, we introduce the BioContainers Registry and Restful API to make containerized bioinformatics tools more findable, accessible, interoperable, and reusable (FAIR). BioContainers registry provides a fast and convenient way to find and retrieve bioinformatics tools packages and containers. By doing so, it will increase the use of bioinformatics packages and containers while promoting replicability and reproducibility in research.


2019 ◽  
Vol 62 (9) ◽  
pp. 36-43 ◽  
Author(s):  
Russ Cox

2019 ◽  
Author(s):  
Philip A Ewels ◽  
Alexander Peltzer ◽  
Sven Fillinger ◽  
Johannes Alneberg ◽  
Harshil Patel ◽  
...  

AbstractThe standardization, portability, and reproducibility of analysis pipelines is a renowned problem within the bioinformatics community. Most pipelines are designed for execution on-premise, and the associated software dependencies are tightly coupled with the local compute environment. This leads to poor pipeline portability and reproducibility of the ensuing results - both of which are fundamental requirements for the validation of scientific findings. Here, we introduce nf-core: a framework that provides a community-driven, peer-reviewed platform for the development of best practice analysis pipelines written in Nextflow. Key obstacles in pipeline development such as portability, reproducibility, scalability and unified parallelism are inherently addressed by all nf-core pipelines. We are also continually developing a suite of tools that assist in the creation and development of both new and existing pipelines. Our primary goal is to provide a platform for high-quality, reproducible bioinformatics pipelines that can be utilized across various institutions and research facilities.


Queue ◽  
2019 ◽  
Vol 17 (2) ◽  
pp. 24-47
Author(s):  
Russ Cox

2017 ◽  
Vol 898 (10) ◽  
pp. 102010
Author(s):  
Ana Trisovic ◽  
Ben Couturier ◽  
Val Gibson ◽  
Chris Jones

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 1442 ◽  
Author(s):  
Upendra Kumar Devisetty ◽  
Kathleen Kennedy ◽  
Paul Sarando ◽  
Nirav Merchant ◽  
Eric Lyons

Docker has become a very popular container-based virtualization platform for software distribution that has revolutionized the way in which scientific software and software dependencies (software stacks) can be packaged, distributed, and deployed. Docker makes the complex and time-consuming installation procedures needed for scientific software a one-time process. Because it enables platform-independent installation, versioning of software environments, and easy redeployment and reproducibility, Docker is an ideal candidate for the deployment of identical software stacks on different compute environments such as XSEDE and Amazon AWS. Cyverse's Discovery Environment also uses Docker for integrating its powerful, community-recommended software tools into CyVerse's production environment for public use. This paper will help users bring their tools into CyVerse DE which will not only allows users to integrate their tools with relative ease compared to the earlier method of tool deployment in DE but also help users to share their apps with collaborators and also release them for public use.


2016 ◽  
Vol 64 ◽  
pp. 232-254 ◽  
Author(s):  
Tomasz Miksa ◽  
Andreas Rauber ◽  
Eleni Mina

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