@article{JiangOronClarketal.2016,
  author    = {Jiang, Yuxiang and Oron, Tal Ronnen and Clark, Wyatt T. and Bankapur, Asma R. and D'Andrea, Daniel and Lepore, Rosalba and Funk, Christopher S. and Kahanda, Indika and Verspoor, Karin M. and Ben-Hur, Asa and Koo, Da Chen Emily and Penfold-Brown, Duncan and Shasha, Dennis and Youngs, Noah and Bonneau, Richard and Lin, Alexandra and Sahraeian, Sayed M. E. and Martelli, Pier Luigi and Profiti, Giuseppe and Casadio, Rita and Cao, Renzhi and Zhong, Zhaolong and Cheng, Jianlin and Altenhoff, Adrian and Skunca, Nives and Dessimoz, Christophe and Dogan, Tunca and Hakala, Kai and Kaewphan, Suwisa and Mehryary, Farrokh and Salakoski, Tapio and Ginter, Filip and Fang, Hai and Smithers, Ben and Oates, Matt and Gough, Julian and T{\"o}r{\"o}nen, Petri and Koskinen, Patrik and Holm, Liisa and Chen, Ching-Tai and Hsu, Wen-Lian and Bryson, Kevin and Cozzetto, Domenico and Minneci, Federico and Jones, David T. and Chapman, Samuel and BKC, Dukka and Khan, Ishita K. and Kihara, Daisuke and Ofer, Dan and Rappoport, Nadav and Stern, Amos and Cibrian-Uhalte, Elena and Denny, Paul and Foulger, Rebecca E. and Hieta, Reija and Legge, Duncan and Lovering, Ruth C. and Magrane, Michele and Melidoni, Anna N. and Mutowo-Meullenet, Prudence and Pichler, Klemens and Shypitsyna, Aleksandra and Li, Biao and Zakeri, Pooya and ElShal, Sarah and Tranchevent, L{\´e}on-Charles and Das, Sayoni and Dawson, Natalie L. and Lee, David and Lees, Jonathan G. and Sillitoe, Ian and Bhat, Prajwal and Nepusz, Tam{\´a}s and Romero, Alfonso E. and Sasidharan, Rajkumar and Yang, Haixuan and Paccanaro, Alberto and Gillis, Jesse and Sede{\~n}o-Cort{\´e}s, Adriana E. and Pavlidis, Paul and Feng, Shou and Cejuela, Juan M. and Goldberg, Tatyana and Hamp, Tobias and Richter, Lothar and Salamov, Asaf and Gabaldon, Toni and Marcet-Houben, Marina and Supek, Fran and Gong, Qingtian and Ning, Wei and Zhou, Yuanpeng and Tian, Weidong and Falda, Marco and Fontana, Paolo and Lavezzo, Enrico and Toppo, Stefano and Ferrari, Carlo and Giollo, Manuel and Piovesan, Damiano and Tosatto, Silvio C. E. and del Pozo, Angela and Fern{\´a}ndez, Jos{\´e} M. and Maietta, Paolo and Valencia, Alfonso and Tress, Michael L. and Benso, Alfredo and Di Carlo, Stefano and Politano, Gianfranco and Savino, Alessandro and Rehman, Hafeez Ur and Re, Matteo and Mesiti, Marco and Valentini, Giorgio and Bargsten, Joachim W. and van Dijk, Aalt D. J. and Gemovic, Branislava and Glisic, Sanja and Perovic, Vladmir and Veljkovic, Veljko and Almeida-e-Silva, Danillo C. and Vencio, Ricardo Z. N. and Sharan, Malvika and Vogel, J{\"o}rg and Kansakar, Lakesh and Zhang, Shanshan and Vucetic, Slobodan and Wang, Zheng and Sternberg, Michael J. E. and Wass, Mark N. and Huntley, Rachael P. and Martin, Maria J. and O'Donovan, Claire and Robinson, Peter N. and Moreau, Yves and Tramontano, Anna and Babbitt, Patricia C. and Brenner, Steven E. and Linial, Michal and Orengo, Christine A. and Rost, Burkhard and Greene, Casey S. and Mooney, Sean D. and Friedberg, Iddo and Radivojac, Predrag and Veljkovic, Nevena},
  title     = {An expanded evaluation of protein function prediction methods shows an improvement in accuracy},
  series = {Genome Biology},
  volume    = {17},
  journal   = {Genome Biology},
  number    = {184},
  doi       = {10.1186/s13059-016-1037-6},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-166293},
  year      = {2016},
  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.},
  language  = {en}
}
@article{ViljurAbellaAdameketal.2022,
  author    = {Viljur, Mari-Liis and Abella, Scott R. and Ad{\´a}mek, Martin and Alencar, Janderson Batista Rodrigues and Barber, Nicholas A. and Beudert, Burkhard and Burkle, Laura A. and Cagnolo, Luciano and Campos, Brent R. and Chao, Anne and Chergui, Brahim and Choi, Chang-Yong and Cleary, Daniel F. R. and Davis, Thomas Seth and Dechnik-V{\´a}zquez, Yanus A. and Downing, William M. and Fuentes-Ramirez, Andr{\´e}s and Gandhi, Kamal J. K. and Gehring, Catherine and Georgiev, Kostadin B. and Gimbutas, Mark and Gongalsky, Konstantin B. and Gorbunova, Anastasiya Y. and Greenberg, Cathryn H. and Hylander, Kristoffer and Jules, Erik S. and Korobushkin, Daniil I. and K{\"o}ster, Kajar and Kurth, Valerie and Lanham, Joseph Drew and Lazarina, Maria and Leverkus, Alexandro B. and Lindenmayer, David and Marra, Daniel Magnabosco and Mart{\´i}n-Pinto, Pablo and Meave, Jorge A. and Moretti, Marco and Nam, Hyun-Young and Obrist, Martin K. and Petanidou, Theodora and Pons, Pere and Potts, Simon G. and Rapoport, Irina B. and Rhoades, Paul R. and Richter, Clark and Saifutdinov, Ruslan A. and Sanders, Nathan J. and Santos, Xavier and Steel, Zachary and Tavella, Julia and Wendenburg, Clara and Wermelinger, Beat and Zaitsev, Andrey S. and Thorn, Simon},
  title     = {The effect of natural disturbances on forest biodiversity: an ecological synthesis},
  series = {Biological Reviews},
  volume    = {97},
  journal   = {Biological Reviews},
  number    = {5},
  doi       = {10.1111/brv.12876},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-287168},
  pages     = {1930 -- 1947},
  year      = {2022},
  abstract  = {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.},
  language  = {en}
}