Standardized benchmarking in the quest for orthologs

Adrian M Altenhoff; Brigitte Boeckmann; Salvador Capella-Gutierrez; Daniel A Dalquen; Todd DeLuca; Kristoffer Forslund; Jaime Huerta-Cepas; Benjamin Linard; Cécile Pereira; Leszek P Pryszcz; Fabian Schreiber; Alan Sousa da Silva; Damian Szklarczyk; Clément-Marie Train; Peer Bork; Odile Lecompte; Christian von Mering; Ioannis Xenarios; Kimmen Sjölander; Lars Juhl Jensen; Maria J Martin; Matthieu Muffato; Toni Gabaldón; Suzanna E Lewis; Paul D Thomas; Erik Sonnhammer; Christophe Dessimoz; Quest for Orthologs consortium

doi:10.1038/nmeth.3830

Standardized benchmarking in the quest for orthologs

Adrian M Altenhoff, Brigitte Boeckmann, Salvador Capella-Gutierrez, Daniel A Dalquen, Todd DeLuca, Kristoffer Forslund, Jaime Huerta-Cepas, Benjamin Linard, Cécile Pereira, Leszek P Pryszcz, Fabian Schreiber, Alan Sousa da Silva, Damian Szklarczyk, Clément-Marie Train, Peer Bork, Odile Lecompte, Christian von Mering, Ioannis Xenarios, Kimmen Sjölander, Lars Juhl JensenMaria J Martin, Matthieu Muffato, Toni Gabaldón, Suzanna E Lewis, Paul D Thomas, Erik Sonnhammer, Christophe Dessimoz, Quest for Orthologs consortium

Disease Systems Biology Program

100 Citationer (Scopus)

51 Downloads (Pure)

Abstract

Achieving high accuracy in orthology inference is essential for many comparative, evolutionary and functional genomic analyses, yet the true evolutionary history of genes is generally unknown and orthologs are used for very different applications across phyla, requiring different precision-recall trade-offs. As a result, it is difficult to assess the performance of orthology inference methods. Here, we present a community effort to establish standards and an automated web-based service to facilitate orthology benchmarking. Using this service, we characterize 15 well-established inference methods and resources on a battery of 20 different benchmarks. Standardized benchmarking provides a way for users to identify the most effective methods for the problem at hand, sets a minimum requirement for new tools and resources, and guides the development of more accurate orthology inference methods.

Originalsprog	Engelsk
Tidsskrift	Nature Methods
Vol/bind	13
Sider (fra-til)	425-30
Antal sider	6
ISSN	1548-7091
DOI	https://doi.org/10.1038/nmeth.3830
Status	Udgivet - 1 maj 2016

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10.1038/nmeth.3830Licens: CC BY-NC-SA

Standardized benchmarking in the quest for orthologsForlagets udgivne version, 673 KBLicens: CC BY-NC-SA

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Altenhoff, A. M., Boeckmann, B., Capella-Gutierrez, S., Dalquen, D. A., DeLuca, T., Forslund, K., Huerta-Cepas, J., Linard, B., Pereira, C., Pryszcz, L. P., Schreiber, F., da Silva, A. S., Szklarczyk, D., Train, C-M., Bork, P., Lecompte, O., von Mering, C., Xenarios, I., Sjölander, K., ... Quest for Orthologs consortium (2016). Standardized benchmarking in the quest for orthologs. Nature Methods, 13, 425-30. https://doi.org/10.1038/nmeth.3830

Altenhoff, AM, Boeckmann, B, Capella-Gutierrez, S, Dalquen, DA, DeLuca, T, Forslund, K, Huerta-Cepas, J, Linard, B, Pereira, C, Pryszcz, LP, Schreiber, F, da Silva, AS, Szklarczyk, D, Train, C-M, Bork, P, Lecompte, O, von Mering, C, Xenarios, I, Sjölander, K, Jensen, LJ, Martin, MJ, Muffato, M, Gabaldón, T, Lewis, SE, Thomas, PD, Sonnhammer, E, Dessimoz, C & Quest for Orthologs consortium 2016, 'Standardized benchmarking in the quest for orthologs', Nature Methods, bind 13, s. 425-30. https://doi.org/10.1038/nmeth.3830

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abstract = "Achieving high accuracy in orthology inference is essential for many comparative, evolutionary and functional genomic analyses, yet the true evolutionary history of genes is generally unknown and orthologs are used for very different applications across phyla, requiring different precision-recall trade-offs. As a result, it is difficult to assess the performance of orthology inference methods. Here, we present a community effort to establish standards and an automated web-based service to facilitate orthology benchmarking. Using this service, we characterize 15 well-established inference methods and resources on a battery of 20 different benchmarks. Standardized benchmarking provides a way for users to identify the most effective methods for the problem at hand, sets a minimum requirement for new tools and resources, and guides the development of more accurate orthology inference methods.",

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AU - Altenhoff, Adrian M

AU - Boeckmann, Brigitte

AU - Capella-Gutierrez, Salvador

AU - Dalquen, Daniel A

AU - DeLuca, Todd

AU - Forslund, Kristoffer

AU - Huerta-Cepas, Jaime

AU - Linard, Benjamin

AU - Pereira, Cécile

AU - Pryszcz, Leszek P

AU - Schreiber, Fabian

AU - da Silva, Alan Sousa

AU - Szklarczyk, Damian

AU - Train, Clément-Marie

AU - Bork, Peer

AU - Lecompte, Odile

AU - von Mering, Christian

AU - Xenarios, Ioannis

AU - Sjölander, Kimmen

AU - Jensen, Lars Juhl

AU - Martin, Maria J

AU - Muffato, Matthieu

AU - Gabaldón, Toni

AU - Lewis, Suzanna E

AU - Thomas, Paul D

AU - Sonnhammer, Erik

AU - Dessimoz, Christophe

AU - Quest for Orthologs consortium

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N2 - Achieving high accuracy in orthology inference is essential for many comparative, evolutionary and functional genomic analyses, yet the true evolutionary history of genes is generally unknown and orthologs are used for very different applications across phyla, requiring different precision-recall trade-offs. As a result, it is difficult to assess the performance of orthology inference methods. Here, we present a community effort to establish standards and an automated web-based service to facilitate orthology benchmarking. Using this service, we characterize 15 well-established inference methods and resources on a battery of 20 different benchmarks. Standardized benchmarking provides a way for users to identify the most effective methods for the problem at hand, sets a minimum requirement for new tools and resources, and guides the development of more accurate orthology inference methods.

AB - Achieving high accuracy in orthology inference is essential for many comparative, evolutionary and functional genomic analyses, yet the true evolutionary history of genes is generally unknown and orthologs are used for very different applications across phyla, requiring different precision-recall trade-offs. As a result, it is difficult to assess the performance of orthology inference methods. Here, we present a community effort to establish standards and an automated web-based service to facilitate orthology benchmarking. Using this service, we characterize 15 well-established inference methods and resources on a battery of 20 different benchmarks. Standardized benchmarking provides a way for users to identify the most effective methods for the problem at hand, sets a minimum requirement for new tools and resources, and guides the development of more accurate orthology inference methods.

U2 - 10.1038/nmeth.3830

DO - 10.1038/nmeth.3830

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JO - Nature Methods

JF - Nature Methods

ER -

Standardized benchmarking in the quest for orthologs

Abstract

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