Reproducible biomedical benchmarking in the cloud: Lessons from crowd-sourced data challenges

Kyle Ellrott; Alex Buchanan; Allison Creason; Michael Mason; Thomas Schaffter; Bruce Hoff; James Eddy; John M. Chilton; Thomas Yu; Joshua M. Stuart; Julio Saez-Rodriguez; Gustavo Stolovitzky; Paul C. Boutros; Justin Guinney

doi:10.1186/s13059-019-1794-0

Reproducible biomedical benchmarking in the cloud: Lessons from crowd-sourced data challenges

Kyle Ellrott, Alex Buchanan, Allison Creason, Michael Mason, Thomas Schaffter, Bruce Hoff, James Eddy, John M. Chilton, Thomas Yu, Joshua M. Stuart, Julio Saez-Rodriguez, Gustavo Stolovitzky, Paul C. Boutros, Justin Guinney

Research output: Contribution to journal › Article › peer-review

15 Scopus citations

Abstract

Challenges are achieving broad acceptance for addressing many biomedical questions and enabling tool assessment. But ensuring that the methods evaluated are reproducible and reusable is complicated by the diversity of software architectures, input and output file formats, and computing environments. To mitigate these problems, some challenges have leveraged new virtualization and compute methods, requiring participants to submit cloud-ready software packages. We review recent data challenges with innovative approaches to model reproducibility and data sharing, and outline key lessons for improving quantitative biomedical data analysis through crowd-sourced benchmarking challenges.

Original language	English (US)
Article number	195
Journal	Genome biology
Volume	20
Issue number	1
DOIs	https://doi.org/10.1186/s13059-019-1794-0
State	Published - Sep 10 2019

ASJC Scopus subject areas

Ecology, Evolution, Behavior and Systematics
Genetics
Cell Biology

Access to Document

10.1186/s13059-019-1794-0

Cite this

Ellrott, K., Buchanan, A., Creason, A., Mason, M., Schaffter, T., Hoff, B., Eddy, J., Chilton, J. M., Yu, T., Stuart, J. M., Saez-Rodriguez, J., Stolovitzky, G., Boutros, P. C., & Guinney, J. (2019). Reproducible biomedical benchmarking in the cloud: Lessons from crowd-sourced data challenges. Genome biology, 20(1), Article 195. https://doi.org/10.1186/s13059-019-1794-0

@article{90e345326493416ca7ad5c439fbb93b1,

title = "Reproducible biomedical benchmarking in the cloud: Lessons from crowd-sourced data challenges",

abstract = "Challenges are achieving broad acceptance for addressing many biomedical questions and enabling tool assessment. But ensuring that the methods evaluated are reproducible and reusable is complicated by the diversity of software architectures, input and output file formats, and computing environments. To mitigate these problems, some challenges have leveraged new virtualization and compute methods, requiring participants to submit cloud-ready software packages. We review recent data challenges with innovative approaches to model reproducibility and data sharing, and outline key lessons for improving quantitative biomedical data analysis through crowd-sourced benchmarking challenges.",

author = "Kyle Ellrott and Alex Buchanan and Allison Creason and Michael Mason and Thomas Schaffter and Bruce Hoff and James Eddy and Chilton, {John M.} and Thomas Yu and Stuart, {Joshua M.} and Julio Saez-Rodriguez and Gustavo Stolovitzky and Boutros, {Paul C.} and Justin Guinney",

note = "Publisher Copyright: {\textcopyright} 2019 The Author(s).",

year = "2019",

month = sep,

day = "10",

doi = "10.1186/s13059-019-1794-0",

language = "English (US)",

volume = "20",

journal = "Genome biology",

issn = "1474-7596",

publisher = "BioMed Central",

number = "1",

}

TY - JOUR

T1 - Reproducible biomedical benchmarking in the cloud

T2 - Lessons from crowd-sourced data challenges

AU - Ellrott, Kyle

AU - Buchanan, Alex

AU - Creason, Allison

AU - Mason, Michael

AU - Schaffter, Thomas

AU - Hoff, Bruce

AU - Eddy, James

AU - Chilton, John M.

AU - Yu, Thomas

AU - Stuart, Joshua M.

AU - Saez-Rodriguez, Julio

AU - Stolovitzky, Gustavo

AU - Boutros, Paul C.

AU - Guinney, Justin

PY - 2019/9/10

Y1 - 2019/9/10

N2 - Challenges are achieving broad acceptance for addressing many biomedical questions and enabling tool assessment. But ensuring that the methods evaluated are reproducible and reusable is complicated by the diversity of software architectures, input and output file formats, and computing environments. To mitigate these problems, some challenges have leveraged new virtualization and compute methods, requiring participants to submit cloud-ready software packages. We review recent data challenges with innovative approaches to model reproducibility and data sharing, and outline key lessons for improving quantitative biomedical data analysis through crowd-sourced benchmarking challenges.

AB - Challenges are achieving broad acceptance for addressing many biomedical questions and enabling tool assessment. But ensuring that the methods evaluated are reproducible and reusable is complicated by the diversity of software architectures, input and output file formats, and computing environments. To mitigate these problems, some challenges have leveraged new virtualization and compute methods, requiring participants to submit cloud-ready software packages. We review recent data challenges with innovative approaches to model reproducibility and data sharing, and outline key lessons for improving quantitative biomedical data analysis through crowd-sourced benchmarking challenges.

UR - http://www.scopus.com/inward/record.url?scp=85072025943&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85072025943&partnerID=8YFLogxK

U2 - 10.1186/s13059-019-1794-0

DO - 10.1186/s13059-019-1794-0

M3 - Article

C2 - 31506093

AN - SCOPUS:85072025943

SN - 1474-7596

VL - 20

JO - Genome biology

JF - Genome biology

IS - 1

M1 - 195

ER -

Reproducible biomedical benchmarking in the cloud: Lessons from crowd-sourced data challenges

Abstract

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this