Toil enables reproducible, open source, big biomedical data analyses
By John Vivian, A. Rao, Frank A. Nothaft, Christopher Ketchum, J. Armstrong, Adam M. Novak, Jacob Pfeil, Jake Narkizian, A. Deran, Audrey Musselman-Brown and 17 more. Nature Biotechnology, 2017
1,154 citations recorded by the source corpus.
1. Baker, M. Nature 533, 452–454 (2016). 2. Yachie, N. et al. Nat. Biotechnol. 35, 310–312 (2017). 3. Hadimioglu, B., Stearns, R. & Ellson, R. J. Lab. Autom. 21, 4–18 (2016). 4. ANSI SLAS 1–2004: Footprint dimensions; ANSI SLAS 2–2004: Height dimensions; ANSI SLAS 3–2004: Bottom outside flange dimensions; ANSI SLAS 4–2004: Well positions; (ANSI SLAS, 2004). 5. Mckernan, K. & Gustafson, E. in DNA Sequencing II: Optimizing Preparation and Cleanup (ed. Kieleczawa, J.) 9.128 (Jones and Bartlett Publishers, 2006). 6. Storch, M. et al. BASIC: a new biopart assembly standard for idempotent cloning provides accurate, singletier DNA assembly for synthetic biology. ACS Synth. Biol. 4, 781–787 (2015). open sharing of protocols. With a precise ontology to describe standardized protocols, it may be possible to share methods widely and create community standards. We envisage that in future individual research laboratories, or clusters of colocated laboratories, will have in-house, low-cost automation work cells but will access DNA foundries via the cloud to carry out complex experimental workflows. Technologies enabling this from companies such as Emerald Cloud Lab (S. San Francisco, CA, USA), Synthace (London) and Transcriptic (Menlo Park, CA, USA) could, for example, send experimental designs to foundries and return output data to a researcher. This ‘mixed economy’ should accelerate the development and sharing of standardized protocols and metrology standards and shift a growing proportion of molecular, cellular and synthetic biology into a fully quantitative and reproducible era.
Subfields: Biophysics, Information Systems and Management, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/d58eed018f81eeb023bad9d08680a4251fa50618
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biophysics
In Information Systems and Management
In Molecular Biology
- David Haussler
- Robert M. Kuhn
- W. James Kent
- Mark Diekhans
- Kate R. Rosenbloom
- A. Hinrichs
- Brian J. Raney
- Hiram Clawson
Citations in the corpus
- Massive mining of publicly available RNA-seq data from human and mouse (2017, methodology): “Multiple efforts attempted to uniformly reprocess large collections of RNA-seq data 16 – 20 .”
- GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis (2019, methodology): “We downloaded the TCGA and GTEx isoform expression data that are re-computed from raw RNA-Seq data by the UCSC Xena project (11) based on a uniform pipeline and collected different cancer subtypes information from TCGA papers.”
- Interrogation of the Microenvironmental Landscape in Brain Tumors Reveals Disease-Specific Alterations of Immune Cells (2020, methodology): “These gene sets also allowed us to utilize a publicly available integrated dataset (Vivian et al., 2017) containing bulk expression data of healthy cortical brain tissue from the Genotype-Tissue Expression project (GTEx; GTEx Consortium, 2013) and low- and high-grade glioma samples from TheCancer Ge”
- xCell: digitally portraying the tissue cellular heterogeneity landscape (2017, methodology): “We next applied our methodology to 9947 primary tumor samples across 37 cancer types from the TCGA and TARGET projects [30] (Additional file 2: Figure S10).”
- Sustainable data analysis with Snakemake (2021, neutral): “These define a declarative syntax for specifying workflows, which can be parsed and executed by arbitrary executors, e.g. Cromwell ( https://cromwell.readthedocs.io ), Toil [31], and Tibanna [20].”
- MGnify: the microbiome analysis resource in 2020 (2019, methodology): “The new pipeline is a major departure from this practice, where we have defined the CWL workflow first, and now utilize toil-cwl-runner (a CWL compatibility layer that is a part of the Toil workflow execution engine (26)) to orchestrate the execution on our compute infrastructure.”
- The UCSC Genome Browser database: 2019 update (2018, methodology): “Our track is based on a recent re-alignment to hg38 for 33 types of cancer (20).”