dc.contributor.author
Robinson, Peter N. [u.v.m.]
dc.date.accessioned
2018-06-08T03:47:37Z
dc.date.available
2016-10-20T08:00:25.932Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/15946
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-20132
dc.description.abstract
Background A major bottleneck in our understanding of the molecular
underpinnings of life is the assignment of function to proteins. While
molecular experiments provide the most reliable annotation of proteins, their
relatively low throughput and restricted purview have led to an increasing
role for computational function prediction. However, assessing methods for
protein function prediction and tracking progress in the field remain
challenging. Results We conducted the second critical assessment of functional
annotation (CAFA), a timed challenge to assess computational methods that
automatically assign protein function. We evaluated 126 methods from 56
research groups for their ability to predict biological functions using Gene
Ontology and gene-disease associations using Human Phenotype Ontology on a set
of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared
with CAFA1, with regards to data set size, variety, and assessment metrics. To
review progress in the field, the analysis compared the best methods from
CAFA1 to those of CAFA2. Conclusions The top-performing methods in CAFA2
outperformed those from CAFA1. This increased accuracy can be attributed to a
combination of the growing number of experimental annotations and improved
methods for function prediction. The assessment also revealed that the
definition of top-performing algorithms is ontology specific, that different
performance metrics can be used to probe the nature of accurate predictions,
and the relative diversity of predictions in the biological process and human
phenotype ontologies. While there was methodological improvement between CAFA1
and CAFA2, the interpretation of results and usefulness of individual methods
remain context-dependent.
en
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
Protein function prediction
dc.subject
Disease gene prioritization
dc.subject.ddc
600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit
dc.title
An expanded evaluation of protein function prediction methods shows an
improvement in accuracy
dc.type
Wissenschaftlicher Artikel
dcterms.bibliographicCitation
Genome Biology. - 17 (2016), Artikel Nr. 184
dcterms.bibliographicCitation.doi
10.1186/s13059-016-1037-6
dcterms.bibliographicCitation.url
http://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1037-6#Sec1
refubium.affiliation
Charité - Universitätsmedizin Berlin
de
refubium.mycore.fudocsId
FUDOCS_document_000000025587
refubium.note.author
Der Artikel wurde in einer reinen Open-Access-Zeitschrift publiziert.
refubium.resourceType.isindependentpub
no
refubium.mycore.derivateId
FUDOCS_derivate_000000007247
dcterms.accessRights.openaire
open access