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A Practical Guide to Metabolomics Software Development
8 references·3 citing·41 second hop
A Practical Guide to Metabolomics Software Development (2021)A Practical Guide to Metabolomics Softwar…Improving the usability and archival stability of bioinformatics software (2019)Improving the usability and a…Challenges and recommendations to improve the installability and archival stability of omics computational tools (2019)Challenges and recommendation…The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update (2018)The Galaxy platform for acces…PhenoMeNal: processing and analysis of metabolomics data in the cloud (2018)PhenoMeNal: processing and an…Ten simple rules for making research software more robust (2017)Ten simple rules for making r…Ten Simple Rules for Developing Usable Software in Computational Biology (2017)Ten Simple Rules for Developi…On best practices in the development of bioinformatics software (2014)On best practices in the deve…Git can facilitate greater reproducibility and increased transparency in science (2013)Git can facilitate greater re…Implementation of FAIR Practices in Computational Metabolomics Workflows—A Case Study (2024)Implementation of FAIR Practi…Best practices to evaluate the impact of biomedical research software—metric collection beyond citations (2024)Best practices to evaluate th…MSCAT: A Machine Learning Assisted Catalog of Metabolomics Software Tools (2021)MSCAT: A Machine Learning Ass…Galaxy: A platform for interactive large-scale genome analysis (2005)Ten Simple Rules for Reproducible Computational Research (2013)Best Practices for Scientific Computing (2014)Recommendations for the packaging and containerizing of bioinformatics software (2018)The State of Software for Evolutionary Biology (2018)Recommendations for the packaging and containerizing of bioinformatics software (2019)Reproducible Research in Computational Science (2011)The case for open computer programs (2012)The promise of preschool in Africa: A randomized impact evaluation of early childhood development in rural Mozambique (2013)A Quick Introduction to Version Control with Git and GitHub (2016)Ten Simple Rules for Taking Advantage of Git and GitHub (2016)Bioconductor: open software development for computational biology and bioinformatics (2004)1,500 scientists lift the lid on reproducibility (2016)The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update (2016)Learning from reproducing computational results: introducing three principles and the Reproduction Package (2021)Ten simple rules on writing clean and reliable open-source scientific software (2021)Eleven quick tips for data cleaning and feature engineering (2022)Snakemake—a scalable bioinformatics workflow engine (2012)Ten simple rules for documenting scientific software (2018)Singularity: Scientific containers for mobility of compute (2017)Sustainable data analysis with Snakemake (2021)Sustainable data analysis with Snakemake (2021)Reproducibility of computational workflows is automated using continuous analysis (2017)Bioconda: sustainable and comprehensive software distribution for the life sciences (2018)iMicrobe: Tools and data-driven discovery platform for the microbiome sciences (2019)Principles of resilient coding for plant ecophysiologists (2021)Packaging and containerization of computational methods (2024)Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences (2010)Jupyter Notebooks – a publishing format for reproducible computational workflows (2016)Scalable Data Analysis in Proteomics and Metabolomics Using BioContainers and Workflows Engines (2019)Toward collaborative open data science in metabolomics using Jupyter Notebooks and cloud computing (2019)Noninvasive Technologies for Primate Conservation in the 21st Century (2021)Big Data: Astronomical or Genomical? (2015)Successes and Struggles with Computational Reproducibility: Lessons from the Fragile Families Challenge (2019)The five pillars of computational reproducibility: bioinformatics and beyond (2023)Genomic reproducibility in the bioinformatics era (2024)Time to Organize the Bioinformatics Resourceome (2005)Taking a fresh look at FAIR for research software (2021)Measuring reproducibility of high-throughput experiments (2010)Toil enables reproducible, open source, big biomedical data analyses (2017)The nf-core framework for community-curated bioinformatics pipelines (2020)
this work references cited by second hop

References

  • 2018The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update3,659 cited
  • 2013Git can facilitate greater reproducibility and increased transparency in science230 cited
  • 2018PhenoMeNal: processing and analysis of metabolomics data in the cloud150 cited
  • 2017Ten simple rules for making research software more robust111 cited
  • 2019Challenges and recommendations to improve the installability and archival stability of omics computational tools90 cited
  • 2017Ten Simple Rules for Developing Usable Software in Computational Biology84 cited
  • 2019Improving the usability and archival stability of bioinformatics software71 cited
  • 2014On best practices in the development of bioinformatics software62 cited

Cited by

  • 2021MSCAT: A Machine Learning Assisted Catalog of Metabolomics Software Tools18 cited
  • 2024Implementation of FAIR Practices in Computational Metabolomics Workflows—A Case Study17 cited
  • 2024Best practices to evaluate the impact of biomedical research software—metric collection beyond citations7 cited

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