Bioconda: sustainable and comprehensive software distribution for the life sciences

Grüning, Björn; Dale, Ryan; Sjödin, Andreas; Chapman, Brad; Rowe, Jillian; Tomkins-Tinch, Christopher H.; Valieris, Renan; Köster, Johannes

doi:10.1038/s41592-018-0046-7

Cited by 1,105 publications

(555 citation statements)

References 10 publications

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“…Software with source Bioconda was installed with the environment management software conda (https://conda.io) and obtained from the Bioconda channel 13 .NameVersionSourceUsed forBedtools 26 2.21.0 https://github.com/arq5x/bedtools2 Vcf and Bed file processingBowtie 2 16 2.3.4.1BiocondaRead alignmentGATK 17 3.8BiocondaAlignment file processingHiLive22.0 https://gitlab.com/rki_bioinformatics/hilive2 Real-time read alignmentRTG Tools 24 3.9BiocondaBenchmark of variant callsSamtools 27 1.8BiocondaSAM/BAM file processingVCFLIB1.0.0_rc1BiocondaVcf file processingVCFtools 25 0.1.12 https://sourceforge.net/projects/vcftools/ Vcf file processingxAtlas 12 0.1BiocondaFast variant calling…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Reliable variant calling during runtime of Illumina sequencing

Loka

Tausch

Renard

2019

Sci Rep

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The sequential paradigm of data acquisition and analysis in next-generation sequencing leads to high turnaround times for the generation of interpretable results. We combined a novel real-time read mapping algorithm with fast variant calling to obtain reliable variant calls still during the sequencing process. Thereby, our new algorithm allows for accurate read mapping results for intermediate cycles and supports large reference genomes such as the complete human reference. This enables the combination of real-time read mapping results with complex follow-up analysis. In this study, we showed the accuracy and scalability of our approach by applying real-time read mapping and variant calling to seven publicly available human whole exome sequencing datasets. Thereby, up to 89% of all detected SNPs were already identified after 40 sequencing cycles while showing similar precision as at the end of sequencing. Final results showed similar accuracy to those of conventional post-hoc analysis methods. When compared to standard routines, our live approach enables considerably faster interventions in clinical applications and infectious disease outbreaks. Besides variant calling, our approach can be adapted for a plethora of other mapping-based analyses.

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Section: Methodsmentioning

confidence: 99%

“…Our new real-time read mapping software HiLive2 is publicly available under BSD-3-clause on https://gitlab.com/rki_bioinformatics/hilive2 and on Bioconda 13 for easy installation (conda install –c bioconda hilive2).…”

Section: Introductionmentioning

confidence: 99%

Reliable variant calling during runtime of Illumina sequencing

Loka

Tausch

Renard

2019

Sci Rep

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“…Leveraging Conda, Bioconda (https://bioconda.github.io) is a community project dedicated to data analysis in life sciences that contains over 4,000 tool packages with contributions by more than 400 authors (Grüning et al, 2017). Despite the fact that Bioconda is one of the most recent package managers dedicated to biomedical tools, it contains by far the largest number of software tools, underscoring its rapid uptake by the community (see Figure 2 in Grüning et al, 2017).…”

Section: A Technology Stack For Reproducibilitymentioning

confidence: 99%

“…Despite the fact that Bioconda is one of the most recent package managers dedicated to biomedical tools, it contains by far the largest number of software tools, underscoring its rapid uptake by the community (see Figure 2 in Grüning et al, 2017). Bioconda packages are well maintained and include a testing system to ensure their quality.…”

Section: A Technology Stack For Reproducibilitymentioning

confidence: 99%

Practical Computational Reproducibility in the Life Sciences

et al. 2018

Self Cite

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Many areas of research suffer from poor reproducibility, particularly in computationally intensive domains where results rely on a series of complex methodological decisions that are not well captured by traditional publication approaches. Various guidelines have emerged for achieving reproducibility, but implementation of these practices remains difficult due to the challenge of assembling software tools plus associated libraries, connecting tools together into pipelines, and specifying parameters. Here, we discuss a suite of cutting-edge technologies that make computational reproducibility not just possible, but practical in both time and effort. This suite combines three well-tested components—a system for building highly portable packages of bioinformatics software, containerization and virtualization technologies for isolating reusable execution environments for these packages, and workflow systems that automatically orchestrate the composition of these packages for entire pipelines—to achieve an unprecedented level of computational reproducibility. We also provide a practical implementation and five recommendations to help set a typical researcher on the path to performing data analyses reproducibly.

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“…• Conda (https://conda.io/docs/) is a package installation tool that, while popularised by the Anaconda Python distribution, can be used to manage software written in any language. Coupled with Bioconda [7] (https://bio conda.github.io/), its software catalogue provides immediate access to many common bioinformatics packages.…”

Section: Methodsmentioning

confidence: 99%

Robust Cross-Platform Workflows: How Technical and Scientific Communities Collaborate to Develop, Test and Share Best Practices for Data Analysis

et al. 2017

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Information integration and workflow technologies for data analysis have always been major fields of investigation in bioinformatics. A range of popular workflow suites are available to support analyses in computational biology. Commercial providers tend to offer prepared applications remote to their clients. However, for most academic environments with local expertise, novel data collection techniques or novel data analysis, it is essential to have all the flexibility of open-source tools and open-source workflow descriptions. Workflows in datadriven science such as computational biology have considerably gained in complexity. New tools or new releases with additional features arrive at an enormous pace, and new reference data or concepts for quality control are emerging. A well-abstracted workflow and the exchange of the same across work groups have an enormous impact on the efficiency of research and the further development of the field. High-throughput sequencing adds to the avalanche of data available in the field; efficient computation and, in particular, parallel execution motivate the transition from traditional scripts and Makefiles to workflows. We here review the extant software development and distribution model with a focus on the role of integration testing and discuss the effect of common workflow language on distributions of open-source scientific software to swiftly and reliably provide the tools demanded for the execution of such formally described workflows. It is contended that, alleviated from technical differences for the execution on local machines, clusters or the cloud, communities also gain the technical means to test workflow-driven interaction across several software packages.

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Bioconda: sustainable and comprehensive software distribution for the life sciences

Cited by 1,105 publications

References 10 publications

Reliable variant calling during runtime of Illumina sequencing

Reliable variant calling during runtime of Illumina sequencing

Practical Computational Reproducibility in the Life Sciences

Robust Cross-Platform Workflows: How Technical and Scientific Communities Collaborate to Develop, Test and Share Best Practices for Data Analysis

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