ArticleGigaScience2021
Streamlining data-intensive biology with workflow systems.
Article in GigaScience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
38 citing papers in PubMed.
- Article
- Managing complexity in research data management for heterogeneous biotechnological data: a perspective on interacting as data steward.Bioprocess and biosystems engineering · 2026Review
- systemPipeR: a multipurpose workflow management system for reproducible data analysis.NAR genomics and bioinformatics · 2026Article
- Ten simple rules for executing an inherited research plan in computational biology.PLoS computational biology · 2026Article
- 2Pipe starts with a question: matching you with the correct pipeline for MAG reconstruction.mSystems · 2026Review
- GeneFamily: a comprehensive mammalian gene family database with extensive annotation and interactive visualization.Nucleic acids research · 2026Article
- A review for navigating the trade-offs: evaluating open-source and proprietary large language models for clinical and biomedical information extraction.Frontiers in digital health · 2026Review
- Next generation sequencing and beyond: a review of genomic sequencing methods.Functional & integrative genomics · 2025Article
- Autonomous Self-Evolving Research on Biomedical Data: The DREAM Paradigm.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- WorkflowHub: a registry for computational workflows.Scientific data · 2025Article
- A novel PolyAr87-based cell transfection protocol for nanobody expression optimized via a targeted design of transfection approach.Frontiers in bioengineering and biotechnology · 2025Article
- Cloud-based large-scale curation of medical imaging data using AI segmentation.Research square · 2024Article
- A practical guide to cross-cultural and multi-sited data collection in the biological and behavioural sciences.Proceedings. Biological sciences · 2024Article
- A multi-omics data analysis workflow packaged as a FAIR Digital Object.GigaScience · 2024Article
- ginmappeR: an unified approach for integrating gene and protein identifiers across biological sequence databases.Bioinformatics advances · 2024Article
- The five pillars of computational reproducibility: bioinformatics and beyond.Briefings in bioinformatics · 2023Article
- Leveraging History to Predict Infrequent Abnormal Transfers in Distributed Workflows.Sensors (Basel, Switzerland) · 2023Article
- Navigating bottlenecks and trade-offs in genomic data analysis.Nature reviews. Genetics · 2023Review
- Reverse engineering environmental metatranscriptomes clarifies best practices for eukaryotic assembly.BMC bioinformatics · 2023Article
- Scikick: A sidekick for workflow clarity and reproducibility during extensive data analysis.PloS one · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As the scale of biological data generation has increased, the bottleneck of research has shifted from data generation to analysis. Researchers commonly need to build computational workflows that include multiple analytic tools and require incremental development as experimental insights demand tool and parameter modifications. These workflows can produce hundreds to thousands of intermediate files and results that must be integrated for biological insight. Data-centric workflow systems that internally manage computational resources, software, and conditional execution of analysis steps are reshaping the landscape of biological data analysis and empowering researchers to conduct reproducible analyses at scale. Adoption of these tools can facilitate and expedite robust data analysis, but knowledge of these techniques is still lacking. Here, we provide a series of strategies for leveraging workflow systems with structured project, data, and resource management to streamline large-scale biological analysis. We present these practices in the context of high-throughput sequencing data analysis, but the principles are broadly applicable to biologists working beyond this field.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.