ArticlePLoS biology2015
Computing Workflows for Biologists: A Roadmap.
Article in PLoS biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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
14 citing papers in PubMed.
- Cryptic species conservation: a review.Biological reviews of the Cambridge Philosophical Society · 2025Review
- Ten simple rules for building and maintaining a responsible data science workflow.PLoS computational biology · 2024Article
- Beyond blast: enabling microbiologists to better extract literature, taxonomic distributions and gene neighbourhood information for protein families.Microbial genomics · 2024Article
- Beyond Blast: Enabling Microbiologists to Better Extract Literature, Taxonomic Distributions and Gene Neighborhood Information for Protein Families.bioRxiv : the preprint server for biology · 2024Article
- Beyond the horizon: immersive developments for animal ecology research.Visual computing for industry, biomedicine, and art · 2023Article
- Pre-exascale HPC approaches for molecular dynamics simulations. Covid-19 research: A use case.Wiley interdisciplinary reviews. Computational molecular science · 2022Article
- Using prototyping to choose a bioinformatics workflow management system.PLoS computational biology · 2021Article
- Streamlining data-intensive biology with workflow systems.GigaScience · 2021Article
- Internal replication of computational workflows in scientific research.Gates open research · 2020Article
- Strategies for Building Computing Skills To Support Microbiome Analysis: a Five-Year Perspective from the EDAMAME Workshop.mSystems · 2019Article
- A town on fire! Integrating 16S rRNA gene amplicon analyses into an undergraduate microbiology lecture class.FEMS microbiology letters · 2018Article
- Orthoscape: a cytoscape application for grouping and visualization KEGG based gene networks by taxonomy and homology principles.BMC bioinformatics · 2017Article
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Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Extremely large datasets have become routine in biology. However, performing a computational analysis of a large dataset can be overwhelming, especially for novices. Here, we present a step-by-step guide to computing workflows with the biologist end-user in mind. Starting from a foundation of sound data management practices, we make specific recommendations on how to approach and perform computational analyses of large datasets, with a view to enabling sound, reproducible biological research.
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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.