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Schafferhans A.,Bioinformatics I12 | Rost B.,Bioinformatics I12

Structure comparisons are now the first step when a new experimental high-resolution protein structure has been determined. In this issue of Structure, Wiederstein and colleagues describe their latest tool for comparing structures, which gives us the unprecedented power to discover crucial structural connections between whole complexes of proteins in the full structural database in real time. © 2014 Elsevier Ltd. Source

Goldberg T.,Bioinformatics I12 | Hecht M.,Bioinformatics I12 | Hamp T.,Bioinformatics I12 | Karl T.,Bioinformatics I12 | And 35 more authors.
Nucleic Acids Research

The prediction of protein sub-cellular localization is an important step toward elucidating protein function. For each query protein sequence, LocTree2 applies machine learning (profile kernel SVM) to predict the native sub-cellular localization in 18 classes for eukaryotes, in six for bacteria and in three for archaea. The method outputs a score that reflects the reliability of each prediction. LocTree2 has performed on par with or better than any other state-of-the-art method. Here, we report the availability of LocTree3 as a public web server. The server includes the machine learning-based LocTree2 and improves over it through the addition of homology-based inference. Assessed on sequence-unique data, LocTree3 reached an 18-state accuracy Q18 = 80 ± 3% for eukaryotes and a six-state accuracy Q6 = 89 ± 4% for bacteria. The server accepts submissions ranging from single protein sequences to entire proteomes. Response time of the unloaded server is about 90 s for a 300-residue eukaryotic protein and a few hours for an entire eukaryotic proteome not considering the generation of the alignments. For over 1000 entirely sequenced organisms, the predictions are directly available as downloads. The web server is available at http://www.rostlab.org/services/loctree3. © 2014 The Author(s). Source

Ison J.,Technical University of Denmark | Rapacki K.,Technical University of Denmark | Menager H.,Institute Pasteur Paris | Kalas M.,University of Bergen | And 70 more authors.
Nucleic Acids Research

Life sciences are yielding huge data sets that underpin scientific discoveries fundamental to improvement in human health, agriculture and the environment. In support of these discoveries, a plethora of databases and tools are deployed, in technically complex and diverse implementations, across a spectrum of scientific disciplines. The corpus of documentation of these resources is fragmented across the Web, with much redundancy, and has lacked a common standard of information. The outcome is that scientists must often struggle to find, understand, compare and use the best resources for the task at hand. Here we present a community-driven curation effort, supported by ELIXIR-the European infrastructure for biological information-that aspires to a comprehensive and consistent registry of information about bioinformatics resources. The sustainable upkeep of this Tools and Data Services Registry is assured by a curation effort driven by and tailored to local needs, and shared amongst a network of engaged partners. As of November 2015, the registry includes 1785 resources, with depositions from 126 individual registrations including 52 institutional providers and 74 individuals. With community support, the registry can become a standard for dissemination of information about bioinformatics resources: we welcome everyone to join us in this common endeavour. The registry is freely available at https://bio.tools. © The Author(s) 2015. Source

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