Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks

Computational methods are becoming an increasingly important part of biological research. Using the Rosetta framework as an example, the authors demonstrate how community-driven development of computational methods can be done in a reproducible and reliable fashion.

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Detalles Bibliográficos
Autores principales:	Julia Koehler Leman, Sergey Lyskov, Steven M. Lewis, Jared Adolf-Bryfogle, Rebecca F. Alford, Kyle Barlow, Ziv Ben-Aharon, Daniel Farrell, Jason Fell, William A. Hansen, Ameya Harmalkar, Jeliazko Jeliazkov, Georg Kuenze, Justyna D. Krys, Ajasja Ljubetič, Amanda L. Loshbaugh, Jack Maguire, Rocco Moretti, Vikram Khipple Mulligan, Morgan L. Nance, Phuong T. Nguyen, Shane Ó Conchúir, Shourya S. Roy Burman, Rituparna Samanta, Shannon T. Smith, Frank Teets, Johanna K. S. Tiemann, Andrew Watkins, Hope Woods, Brahm J. Yachnin, Christopher D. Bahl, Chris Bailey-Kellogg, David Baker, Rhiju Das, Frank DiMaio, Sagar D. Khare, Tanja Kortemme, Jason W. Labonte, Kresten Lindorff-Larsen, Jens Meiler, William Schief, Ora Schueler-Furman, Justin B. Siegel, Amelie Stein, Vladimir Yarov-Yarovoy, Brian Kuhlman, Andrew Leaver-Fay, Dominik Gront, Jeffrey J. Gray, Richard Bonneau
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/7ccba8c202cd473d906c246ef7b2eb77
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Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks

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