ReviewNature methods2021
Reproducible, scalable, and shareable analysis pipelines with bioinformatics workflow managers.
Review in Nature methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 110 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
110 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Assessment of the functionality and usability of open-source rare variant analysis pipelines.Briefings in bioinformatics · 2025Pooled it
- Packaging and containerization of computational methods.Nature protocols · 2024Pooled it
- metaFun: An analysis pipeline for metagenomic big data with fast and unified functional searches.Gut microbes · 2026Article
- Introducing GalaxyR: an easy-to-use R implementation of the Galaxy API.BMC bioinformatics · 2026Article
- Mapping the path to clinical implementation of multi-omics.Nature genetics · 2026Review
- A Compound-Centric Framework for Mapping Plant Chemical Space through Structural Scaffolds and Bioactivity Evidence.ACS omega · 2026Article
- A scoping review of approaches to evaluating workflow management systems for bioinformatics users.Briefings in bioinformatics · 2026Article
- Radiation Oncology-Biology Integration Network: Bridging the Gap between Biological Research and Clinical Practice.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Review
- GIPSy2: high-performance and scalable genomic island prediction software.Scientific reports · 2026Article
- Ten simple rules for executing an inherited research plan in computational biology.PLoS computational biology · 2026Article
- LCR-modules: a collection of workflows for cancer genome analysis.Bioinformatics (Oxford, England) · 2026Article
- systemPipeR: a multipurpose workflow management system for reproducible data analysis.NAR genomics and bioinformatics · 2026Article
- A generic, rapid and robust platform for personalized functional circuit-guided neuromodulation targeting.Nature protocols · 2026Review
- Progress and new challenges in image-based profiling.Molecular systems biology · 2026Review
- Empowering AI data scientists using a multi-agent LLM framework with self-evolving capabilities for autonomous, tool-aware biomedical data analyses.Nature biomedical engineering · 2026Article
- Complete Data Analysis Workflow for Quantitative DIA Mass Spectrometry Using Nextflow.Journal of proteome research · 2026Article
- 2Pipe starts with a question: matching you with the correct pipeline for MAG reconstruction.mSystems · 2026Review
- SnakeBITE: A SNAKEmake-Based Interface for Third-Generation Sequencing Data Analysis.Biomolecules · 2026Article
- Tutorial for variant interrogation in tumor samples.PLoS computational biology · 2026Article
- Breaking the boundaries of affinity selection-mass spectrometry: From ligand screening to target-ligand interaction insights.Journal of pharmaceutical analysis · 2026Review
50 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rapid growth of high-throughput technologies has transformed biomedical research. With the increasing amount and complexity of data, scalability and reproducibility have become essential not just for experiments, but also for computational analysis. However, transforming data into information involves running a large number of tools, optimizing parameters, and integrating dynamically changing reference data. Workflow managers were developed in response to such challenges. They simplify pipeline development, optimize resource usage, handle software installation and versions, and run on different compute platforms, enabling workflow portability and sharing. In this Perspective, we highlight key features of workflow managers, compare commonly used approaches for bioinformatics workflows, and provide a guide for computational and noncomputational users. We outline community-curated pipeline initiatives that enable novice and experienced users to perform complex, best-practice analyses without having to manually assemble workflows. In sum, we illustrate how workflow managers contribute to making computational analysis in biomedical research shareable, scalable, and reproducible.
Indexed as
Identifiers
34556866What Socratic holds
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 Socratic graph.