TY  - JOUR
A1  - Jiang, Yuxiang
A1  - Oron, Tal Ronnen
A1  - Clark, Wyatt T.
A1  - Bankapur, Asma R.
A1  - D'Andrea, Daniel
A1  - Lepore, Rosalba
A1  - Funk, Christopher S.
A1  - Kahanda, Indika
A1  - Verspoor, Karin M.
A1  - Ben-Hur, Asa
A1  - Koo, Da Chen Emily
A1  - Penfold-Brown, Duncan
A1  - Shasha, Dennis
A1  - Youngs, Noah
A1  - Bonneau, Richard
A1  - Lin, Alexandra
A1  - Sahraeian, Sayed M. E.
A1  - Martelli, Pier Luigi
A1  - Profiti, Giuseppe
A1  - Casadio, Rita
A1  - Cao, Renzhi
A1  - Zhong, Zhaolong
A1  - Cheng, Jianlin
A1  - Altenhoff, Adrian
A1  - Skunca, Nives
A1  - Dessimoz, Christophe
A1  - Dogan, Tunca
A1  - Hakala, Kai
A1  - Kaewphan, Suwisa
A1  - Mehryary, Farrokh
A1  - Salakoski, Tapio
A1  - Ginter, Filip
A1  - Fang, Hai
A1  - Smithers, Ben
A1  - Oates, Matt
A1  - Gough, Julian
A1  - Törönen, Petri
A1  - Koskinen, Patrik
A1  - Holm, Liisa
A1  - Chen, Ching-Tai
A1  - Hsu, Wen-Lian
A1  - Bryson, Kevin
A1  - Cozzetto, Domenico
A1  - Minneci, Federico
A1  - Jones, David T.
A1  - Chapman, Samuel
A1  - BKC, Dukka
A1  - Khan, Ishita K.
A1  - Kihara, Daisuke
A1  - Ofer, Dan
A1  - Rappoport, Nadav
A1  - Stern, Amos
A1  - Cibrian-Uhalte, Elena
A1  - Denny, Paul
A1  - Foulger, Rebecca E.
A1  - Hieta, Reija
A1  - Legge, Duncan
A1  - Lovering, Ruth C.
A1  - Magrane, Michele
A1  - Melidoni, Anna N.
A1  - Mutowo-Meullenet, Prudence
A1  - Pichler, Klemens
A1  - Shypitsyna, Aleksandra
A1  - Li, Biao
A1  - Zakeri, Pooya
A1  - ElShal, Sarah
A1  - Tranchevent, Léon-Charles
A1  - Das, Sayoni
A1  - Dawson, Natalie L.
A1  - Lee, David
A1  - Lees, Jonathan G.
A1  - Sillitoe, Ian
A1  - Bhat, Prajwal
A1  - Nepusz, Tamás
A1  - Romero, Alfonso E.
A1  - Sasidharan, Rajkumar
A1  - Yang, Haixuan
A1  - Paccanaro, Alberto
A1  - Gillis, Jesse
A1  - Sedeño-Cortés, Adriana E.
A1  - Pavlidis, Paul
A1  - Feng, Shou
A1  - Cejuela, Juan M.
A1  - Goldberg, Tatyana
A1  - Hamp, Tobias
A1  - Richter, Lothar
A1  - Salamov, Asaf
A1  - Gabaldon, Toni
A1  - Marcet-Houben, Marina
A1  - Supek, Fran
A1  - Gong, Qingtian
A1  - Ning, Wei
A1  - Zhou, Yuanpeng
A1  - Tian, Weidong
A1  - Falda, Marco
A1  - Fontana, Paolo
A1  - Lavezzo, Enrico
A1  - Toppo, Stefano
A1  - Ferrari, Carlo
A1  - Giollo, Manuel
A1  - Piovesan, Damiano
A1  - Tosatto, Silvio C. E.
A1  - del Pozo, Angela
A1  - Fernández, José M.
A1  - Maietta, Paolo
A1  - Valencia, Alfonso
A1  - Tress, Michael L.
A1  - Benso, Alfredo
A1  - Di Carlo, Stefano
A1  - Politano, Gianfranco
A1  - Savino, Alessandro
A1  - Rehman, Hafeez Ur
A1  - Re, Matteo
A1  - Mesiti, Marco
A1  - Valentini, Giorgio
A1  - Bargsten, Joachim W.
A1  - van Dijk, Aalt D. J.
A1  - Gemovic, Branislava
A1  - Glisic, Sanja
A1  - Perovic, Vladmir
A1  - Veljkovic, Veljko
A1  - Almeida-e-Silva, Danillo C.
A1  - Vencio, Ricardo Z. N.
A1  - Sharan, Malvika
A1  - Vogel, Jörg
A1  - Kansakar, Lakesh
A1  - Zhang, Shanshan
A1  - Vucetic, Slobodan
A1  - Wang, Zheng
A1  - Sternberg, Michael J. E.
A1  - Wass, Mark N.
A1  - Huntley, Rachael P.
A1  - Martin, Maria J.
A1  - O'Donovan, Claire
A1  - Robinson, Peter N.
A1  - Moreau, Yves
A1  - Tramontano, Anna
A1  - Babbitt, Patricia C.
A1  - Brenner, Steven E.
A1  - Linial, Michal
A1  - Orengo, Christine A.
A1  - Rost, Burkhard
A1  - Greene, Casey S.
A1  - Mooney, Sean D.
A1  - Friedberg, Iddo
A1  - Radivojac, Predrag
A1  - Veljkovic, Nevena
T1  - An expanded evaluation of protein function prediction methods shows an improvement in accuracy
JF  - Genome Biology
N2  - 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.
KW  - Protein function prediction
KW  - Disease gene prioritization
Y1  - 2016
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-166293
VL  - 17
IS  - 184
ER  - 
TY  - JOUR
A1  - Viljur, Mari‐Liis
A1  - Abella, Scott R.
A1  - Adámek, Martin
A1  - Alencar, Janderson Batista Rodrigues
A1  - Barber, Nicholas A.
A1  - Beudert, Burkhard
A1  - Burkle, Laura A.
A1  - Cagnolo, Luciano
A1  - Campos, Brent R.
A1  - Chao, Anne
A1  - Chergui, Brahim
A1  - Choi, Chang‐Yong
A1  - Cleary, Daniel F. R.
A1  - Davis, Thomas Seth
A1  - Dechnik‐Vázquez, Yanus A.
A1  - Downing, William M.
A1  - Fuentes‐Ramirez, Andrés
A1  - Gandhi, Kamal J. K.
A1  - Gehring, Catherine
A1  - Georgiev, Kostadin B.
A1  - Gimbutas, Mark
A1  - Gongalsky, Konstantin B.
A1  - Gorbunova, Anastasiya Y.
A1  - Greenberg, Cathryn H.
A1  - Hylander, Kristoffer
A1  - Jules, Erik S.
A1  - Korobushkin, Daniil I.
A1  - Köster, Kajar
A1  - Kurth, Valerie
A1  - Lanham, Joseph Drew
A1  - Lazarina, Maria
A1  - Leverkus, Alexandro B.
A1  - Lindenmayer, David
A1  - Marra, Daniel Magnabosco
A1  - Martín‐Pinto, Pablo
A1  - Meave, Jorge A.
A1  - Moretti, Marco
A1  - Nam, Hyun‐Young
A1  - Obrist, Martin K.
A1  - Petanidou, Theodora
A1  - Pons, Pere
A1  - Potts, Simon G.
A1  - Rapoport, Irina B.
A1  - Rhoades, Paul R.
A1  - Richter, Clark
A1  - Saifutdinov, Ruslan A.
A1  - Sanders, Nathan J.
A1  - Santos, Xavier
A1  - Steel, Zachary
A1  - Tavella, Julia
A1  - Wendenburg, Clara
A1  - Wermelinger, Beat
A1  - Zaitsev, Andrey S.
A1  - Thorn, Simon
T1  - The effect of natural disturbances on forest biodiversity: an ecological synthesis
JF  - Biological Reviews
N2  - Disturbances alter biodiversity via their specific characteristics, including severity and extent in the landscape, which act at different temporal and spatial scales. Biodiversity response to disturbance also depends on the community characteristics and habitat requirements of species. Untangling the mechanistic interplay of these factors has guided disturbance ecology for decades, generating mixed scientific evidence of biodiversity responses to disturbance. Understanding the impact of natural disturbances on biodiversity is increasingly important due to human‐induced changes in natural disturbance regimes. In many areas, major natural forest disturbances, such as wildfires, windstorms, and insect outbreaks, are becoming more frequent, intense, severe, and widespread due to climate change and land‐use change. Conversely, the suppression of natural disturbances threatens disturbance‐dependent biota. Using a meta‐analytic approach, we analysed a global data set (with most sampling concentrated in temperate and boreal secondary forests) of species assemblages of 26 taxonomic groups, including plants, animals, and fungi collected from forests affected by wildfires, windstorms, and insect outbreaks. The overall effect of natural disturbances on α‐diversity did not differ significantly from zero, but some taxonomic groups responded positively to disturbance, while others tended to respond negatively. Disturbance was beneficial for taxonomic groups preferring conditions associated with open canopies (e.g. hymenopterans and hoverflies), whereas ground‐dwelling groups and/or groups typically associated with shady conditions (e.g. epigeic lichens and mycorrhizal fungi) were more likely to be negatively impacted by disturbance. Across all taxonomic groups, the highest α‐diversity in disturbed forest patches occurred under moderate disturbance severity, i.e. with approximately 55% of trees killed by disturbance. We further extended our meta‐analysis by applying a unified diversity concept based on Hill numbers to estimate α‐diversity changes in different taxonomic groups across a gradient of disturbance severity measured at the stand scale and incorporating other disturbance features. We found that disturbance severity negatively affected diversity for Hill number q = 0 but not for q = 1 and q = 2, indicating that diversity–disturbance relationships are shaped by species relative abundances. Our synthesis of α‐diversity was extended by a synthesis of disturbance‐induced change in species assemblages, and revealed that disturbance changes the β‐diversity of multiple taxonomic groups, including some groups that were not affected at the α‐diversity level (birds and woody plants). Finally, we used mixed rarefaction/extrapolation to estimate biodiversity change as a function of the proportion of forests that were disturbed, i.e. the disturbance extent measured at the landscape scale. The comparison of intact and naturally disturbed forests revealed that both types of forests provide habitat for unique species assemblages, whereas species diversity in the mixture of disturbed and undisturbed forests peaked at intermediate values of disturbance extent in the simulated landscape. Hence, the relationship between α‐diversity and disturbance severity in disturbed forest stands was strikingly similar to the relationship between species richness and disturbance extent in a landscape consisting of both disturbed and undisturbed forest habitats. This result suggests that both moderate disturbance severity and moderate disturbance extent support the highest levels of biodiversity in contemporary forest landscapes.
KW  - natural disturbance
KW  - diversity–disturbance relationship
KW  - disturbance severity
KW  - disturbance extent
KW  - intermediate disturbance hypothesis
KW  - forest communities
KW  - α‐diversity
KW  - β‐diversity
Y1  - 2022
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-287168
VL  - 97
IS  - 5
SP  - 1930
EP  - 1947
ER  -