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