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Tang H, Krishnakumar V, Zeng X, Xu Z, Taranto A, Lomas JS, Zhang Y, Huang Y, Wang Y, Yim WC, Zhang J, Zhang X. JCVI: A versatile toolkit for comparative genomics analysis. IMETA 2024; 3:e211. [PMID: 39135687 PMCID: PMC11316928 DOI: 10.1002/imt2.211] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 05/25/2024] [Accepted: 05/27/2024] [Indexed: 08/15/2024]
Abstract
The life cycle of genome builds spans interlocking pillars of assembly, annotation, and comparative genomics to drive biological insights. While tools exist to address each pillar separately, there is a growing need for tools to integrate different pillars of a genome project holistically. For example, comparative approaches can provide quality control of assembly or annotation; genome assembly, in turn, can help to identify artifacts that may complicate the interpretation of genome comparisons. The JCVI library is a versatile Python-based library that offers a suite of tools that excel across these pillars. Featuring a modular design, the JCVI library provides high-level utilities for tasks such as format parsing, graphics generation, and manipulation of genome assemblies and annotations. Supporting genomics algorithms like MCscan and ALLMAPS are widely employed in building genome releases, producing publication-ready figures for quality assessment and evolutionary inference. Developed and maintained collaboratively, the JCVI library emphasizes quality and reusability.
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Affiliation(s)
- Haibao Tang
- Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology, Haixia Institute of Science and Technology and College of Life SciencesFujian Agriculture and Forestry UniversityFuzhouFujianChina
| | | | - Xiaofei Zeng
- National Key Laboratory for Tropical Crop Breeding, Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at ShenzhenChinese Academy of Agricultural SciencesShenzhenGuangdongChina
| | - Zhougeng Xu
- National Key Laboratory of Plant Molecular Genetics (NKLPMG), CAS Center for Excellence in Molecular Plant Sciences (CEMPS), Institute of Plant Physiology and Ecology (SIPPE)Chinese Academy of Sciences (CAS)ShanghaiChina
| | - Adam Taranto
- School of BioSciencesThe University of MelbourneMelbourneVictoriaAustralia
| | - Johnathan S. Lomas
- Department of Biochemistry and Molecular BiologyUniversity of NevadaRenoNevadaUSA
| | - Yixing Zhang
- Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology, Haixia Institute of Science and Technology and College of Life SciencesFujian Agriculture and Forestry UniversityFuzhouFujianChina
| | - Yumin Huang
- Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology, Haixia Institute of Science and Technology and College of Life SciencesFujian Agriculture and Forestry UniversityFuzhouFujianChina
| | - Yibin Wang
- National Key Laboratory for Tropical Crop Breeding, Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at ShenzhenChinese Academy of Agricultural SciencesShenzhenGuangdongChina
| | - Won Cheol Yim
- Department of Biochemistry and Molecular BiologyUniversity of NevadaRenoNevadaUSA
| | - Jisen Zhang
- State Key Lab for Conservation and Utilization of Subtropical Agro‐Biological Resources, Guangxi Key Lab for Sugarcane BiologyGuangxi UniversityNanningGuangxiChina
| | - Xingtan Zhang
- National Key Laboratory for Tropical Crop Breeding, Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at ShenzhenChinese Academy of Agricultural SciencesShenzhenGuangdongChina
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Cai P, Liu S, Zhang D, Xing H, Han M, Liu D, Gong L, Hu QN. SynBioTools: a one-stop facility for searching and selecting synthetic biology tools. BMC Bioinformatics 2023; 24:152. [PMID: 37069545 PMCID: PMC10111727 DOI: 10.1186/s12859-023-05281-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 04/11/2023] [Indexed: 04/19/2023] Open
Abstract
BACKGROUND The rapid development of synthetic biology relies heavily on the use of databases and computational tools, which are also developing rapidly. While many tool registries have been created to facilitate tool retrieval, sharing, and reuse, no relatively comprehensive tool registry or catalog addresses all aspects of synthetic biology. RESULTS We constructed SynBioTools, a comprehensive collection of synthetic biology databases, computational tools, and experimental methods, as a one-stop facility for searching and selecting synthetic biology tools. SynBioTools includes databases, computational tools, and methods extracted from reviews via SCIentific Table Extraction, a scientific table-extraction tool that we built. Approximately 57% of the resources that we located and included in SynBioTools are not mentioned in bio.tools, the dominant tool registry. To improve users' understanding of the tools and to enable them to make better choices, the tools are grouped into nine modules (each with subdivisions) based on their potential biosynthetic applications. Detailed comparisons of similar tools in every classification are included. The URLs, descriptions, source references, and the number of citations of the tools are also integrated into the system. CONCLUSIONS SynBioTools is freely available at https://synbiotools.lifesynther.com/ . It provides end-users and developers with a useful resource of categorized synthetic biology databases, tools, and methods to facilitate tool retrieval and selection.
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Affiliation(s)
- Pengli Cai
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Sheng Liu
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Dachuan Zhang
- Ecological Systems Design, Institute of Environmental Engineering, ETH Zurich, 8093, Zurich, Switzerland
| | - Huadong Xing
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Mengying Han
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Dongliang Liu
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Linlin Gong
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Qian-Nan Hu
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
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Tourasse NJ, Darfeuille F. T1TAdb: the database of type I toxin-antitoxin systems. RNA (NEW YORK, N.Y.) 2021; 27:1471-1481. [PMID: 34531327 PMCID: PMC8594479 DOI: 10.1261/rna.078802.121] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 09/03/2021] [Indexed: 05/11/2023]
Abstract
Type I toxin-antitoxin (T1TA) systems constitute a large class of genetic modules with antisense RNA (asRNA)-mediated regulation of gene expression. They are widespread in bacteria and consist of an mRNA coding for a toxic protein and a noncoding asRNA that acts as an antitoxin preventing the synthesis of the toxin by directly base-pairing to its cognate mRNA. The co- and post-transcriptional regulation of T1TA systems is intimately linked to RNA sequence and structure, therefore it is essential to have an accurate annotation of the mRNA and asRNA molecules to understand this regulation. However, most T1TA systems have been identified by means of bioinformatic analyses solely based on the toxin protein sequences, and there is no central repository of information on their specific RNA features. Here we present the first database dedicated to type I TA systems, named T1TAdb. It is an open-access web database (https://d-lab.arna.cnrs.fr/t1tadb) with a collection of ∼1900 loci in ∼500 bacterial strains in which a toxin-coding sequence has been previously identified. RNA molecules were annotated with a bioinformatic procedure based on key determinants of the mRNA structure and the genetic organization of the T1TA loci. Besides RNA and protein secondary structure predictions, T1TAdb also identifies promoter, ribosome-binding, and mRNA-asRNA interaction sites. It also includes tools for comparative analysis, such as sequence similarity search and computation of structural multiple alignments, which are annotated with covariation information. To our knowledge, T1TAdb represents the largest collection of features, sequences, and structural annotations on this class of genetic modules.
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Affiliation(s)
- Nicolas J Tourasse
- University of Bordeaux, CNRS, INSERM, ARNA, UMR 5320, U1212, F-33000 Bordeaux, France
| | - Fabien Darfeuille
- University of Bordeaux, CNRS, INSERM, ARNA, UMR 5320, U1212, F-33000 Bordeaux, France
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Schwengers O, Jelonek L, Dieckmann MA, Beyvers S, Blom J, Goesmann A. Bakta: rapid and standardized annotation of bacterial genomes via alignment-free sequence identification. Microb Genom 2021; 7:000685. [PMID: 34739369 PMCID: PMC8743544 DOI: 10.1099/mgen.0.000685] [Citation(s) in RCA: 198] [Impact Index Per Article: 66.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 09/08/2021] [Indexed: 12/21/2022] Open
Abstract
Command-line annotation software tools have continuously gained popularity compared to centralized online services due to the worldwide increase of sequenced bacterial genomes. However, results of existing command-line software pipelines heavily depend on taxon-specific databases or sufficiently well annotated reference genomes. Here, we introduce Bakta, a new command-line software tool for the robust, taxon-independent, thorough and, nonetheless, fast annotation of bacterial genomes. Bakta conducts a comprehensive annotation workflow including the detection of small proteins taking into account replicon metadata. The annotation of coding sequences is accelerated via an alignment-free sequence identification approach that in addition facilitates the precise assignment of public database cross-references. Annotation results are exported in GFF3 and International Nucleotide Sequence Database Collaboration (INSDC)-compliant flat files, as well as comprehensive JSON files, facilitating automated downstream analysis. We compared Bakta to other rapid contemporary command-line annotation software tools in both targeted and taxonomically broad benchmarks including isolates and metagenomic-assembled genomes. We demonstrated that Bakta outperforms other tools in terms of functional annotations, the assignment of functional categories and database cross-references, whilst providing comparable wall-clock runtimes. Bakta is implemented in Python 3 and runs on MacOS and Linux systems. It is freely available under a GPLv3 license at https://github.com/oschwengers/bakta. An accompanying web version is available at https://bakta.computational.bio.
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Affiliation(s)
- Oliver Schwengers
- Bioinformatics and Systems Biology, Justus Liebig University Giessen, Giessen 35392, Germany
| | - Lukas Jelonek
- Bioinformatics and Systems Biology, Justus Liebig University Giessen, Giessen 35392, Germany
| | - Marius Alfred Dieckmann
- Bioinformatics and Systems Biology, Justus Liebig University Giessen, Giessen 35392, Germany
| | - Sebastian Beyvers
- Bioinformatics and Systems Biology, Justus Liebig University Giessen, Giessen 35392, Germany
| | - Jochen Blom
- Bioinformatics and Systems Biology, Justus Liebig University Giessen, Giessen 35392, Germany
| | - Alexander Goesmann
- Bioinformatics and Systems Biology, Justus Liebig University Giessen, Giessen 35392, Germany
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Kryś JD, Gront D. VisuaLife: Library for interactive visualization in rich web applications. Bioinformatics 2021; 37:3662-3663. [PMID: 33961033 PMCID: PMC8136008 DOI: 10.1093/bioinformatics/btab251] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 03/24/2021] [Accepted: 04/19/2021] [Indexed: 11/13/2022] Open
Abstract
Motivation Visualization is a powerful tool to analyze, understand and present big data. Computational biology, bioinformatics and molecular modeling require dedicated tools, tailored to very complex, highly multidimensional data. Over the recent years, numerous tools have been developed for online presentation, but new challenges like the COVID-19 pandemic require new libraries which will guarantee fast development of online tools for a better understanding of biomedical data/results. Results VisuaLife is a Python library that provides a new approach to visualization in a web browser. It offers 2D and 3D plotting capabilities as well as widgets designed to display the most common biological data types: nucleotide or protein sequences, 3D biomolecular structures and multiple sequence alignments. Components provided by the VisuaLife library can be assembled into a web application to create an analysis tool tailored to provide multidimensional analysis of a specific research problem. VisuaLife, to our best knowledge, is the most modern solution that allows one to implement such a client-side interactivity in Python. Availability and implementation The git repository of the library is hosted at BitBucket: https://bitbucket.org/dgront/visualife/. PyPI distribution is also provided for MacOS and Linux. While basic examples are provided in the supporting materials, the full documentation is available at ReadTheDocs website: https://visualife.readthedocs.io/. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Justyna D Kryś
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Pasteura 1, 02-093, Warsaw, Poland
| | - Dominik Gront
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Pasteura 1, 02-093, Warsaw, Poland
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6
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Seaman J, Buggs RJA. FluentDNA: Nucleotide Visualization of Whole Genomes, Annotations, and Alignments. Front Genet 2020; 11:292. [PMID: 32425967 PMCID: PMC7203487 DOI: 10.3389/fgene.2020.00292] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Accepted: 03/11/2020] [Indexed: 12/03/2022] Open
Abstract
Researchers seldom look at naked genome assemblies: instead the attributes of DNA sequences are mediated through statistics, annotations and high level summaries. Here we present software that visualizes the bare sequences of whole genome assemblies in a zoomable interface. This can assist in detection of chromosome architecture and contamination by the naked eye through changes in color patterns, in the absence of any other annotation. When available, annotations can be visualized alongside or on top of the naked sequence. Genome alignments can also be visualized, laying two genomes side by side in an alignment and highlighting their differences at nucleotide resolution. FluentDNA gives researchers direct visualization of whole genome assemblies, annotations and alignments, for quality control, hypothesis generation, and communicating results.
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Affiliation(s)
- Josiah Seaman
- Royal Botanic Gardens Kew, Jodrell Laboratory, Richmond, United Kingdom.,School of Biological and Chemical Sciences, Queen Mary University of London, London, United Kingdom
| | - Richard J A Buggs
- Royal Botanic Gardens Kew, Jodrell Laboratory, Richmond, United Kingdom.,School of Biological and Chemical Sciences, Queen Mary University of London, London, United Kingdom
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7
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Grüning BA, Lampa S, Vaudel M, Blankenberg D. Software engineering for scientific big data analysis. Gigascience 2019; 8:giz054. [PMID: 31121028 PMCID: PMC6532757 DOI: 10.1093/gigascience/giz054] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2018] [Revised: 01/20/2019] [Accepted: 04/18/2019] [Indexed: 11/14/2022] Open
Abstract
The increasing complexity of data and analysis methods has created an environment where scientists, who may not have formal training, are finding themselves playing the impromptu role of software engineer. While several resources are available for introducing scientists to the basics of programming, researchers have been left with little guidance on approaches needed to advance to the next level for the development of robust, large-scale data analysis tools that are amenable to integration into workflow management systems, tools, and frameworks. The integration into such workflow systems necessitates additional requirements on computational tools, such as adherence to standard conventions for robustness, data input, output, logging, and flow control. Here we provide a set of 10 guidelines to steer the creation of command-line computational tools that are usable, reliable, extensible, and in line with standards of modern coding practices.
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Affiliation(s)
- Björn A Grüning
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Koehler-Allee 106, D-79110 Freiburg, Germany
- Center for Biological Systems Analysis (ZBSA), University of Freiburg, Habsburgerstr. 49, D-79104 Freiburg, Germany
| | - Samuel Lampa
- Pharmaceutical Bioinformatics group, Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24, Uppsala, Sweden
- Department of Biochemistry and Biophysics, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Stockholm University, Svante Arrhenius vag 16C, 106 91, Solna, Sweden
| | - Marc Vaudel
- K.G. Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Postboks 7804, 5020, Bergen, Norway
- Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Postboks 7804, 5020, Bergen, Norway
| | - Daniel Blankenberg
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, 9500 Euclid Avenue / NE50, Cleveland, OH, USA
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8
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Tyzack JD, Furnham N, Sillitoe I, Orengo CM, Thornton JM. Exploring Enzyme Evolution from Changes in Sequence, Structure, and Function. Methods Mol Biol 2019; 1851:263-275. [PMID: 30298402 DOI: 10.1007/978-1-4939-8736-8_14] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The goal of our research is to increase our understanding of how biology works at the molecular level, with a particular focus on how enzymes evolve their functions through adaptations to generate new specificities and mechanisms. FunTree (Sillitoe and Furnham, Nucleic Acids Res 44:D317-D323, 2016) is a resource that brings together sequence, structure, phylogenetic, and chemical and mechanistic information for 2340 CATH superfamilies (Sillitoe et al., Nucleic Acids Res 43:D376-D381, 2015) (which all contain at least one enzyme) to allow evolution to be investigated within a structurally defined superfamily.We will give an overview of FunTree's use of sequence and structural alignments to cluster proteins within a superfamily into structurally similar groups (SSGs) and generate phylogenetic trees augmented by ancestral character estimations (ACE). This core information is supplemented with new measures of functional similarity (Rahman et al., Nat Methods 11:171-174, 2014) to compare enzyme reactions based on overall bond changes, reaction centers (the local environment atoms involved in the reaction), and the structural similarities of the metabolites involved in the reaction. These trees are also decorated with taxonomic and Enzyme Commission (EC) code and GO annotations, forming the basis of a comprehensive web interface that can be found at http://www.funtree.info . In this chapter, we will discuss the various analyses and supporting computational tools in more detail, describing the steps required to extract information.
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Affiliation(s)
| | | | - Ian Sillitoe
- Institute of Structural and Molecular Biology, University College London, London, UK
| | - Christine M Orengo
- Institute of Structural and Molecular Biology, University College London, London, UK
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9
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Zimmermann L, Stephens A, Nam SZ, Rau D, Kübler J, Lozajic M, Gabler F, Söding J, Lupas AN, Alva V. A Completely Reimplemented MPI Bioinformatics Toolkit with a New HHpred Server at its Core. J Mol Biol 2017; 430:2237-2243. [PMID: 29258817 DOI: 10.1016/j.jmb.2017.12.007] [Citation(s) in RCA: 1563] [Impact Index Per Article: 223.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2017] [Revised: 12/10/2017] [Accepted: 12/11/2017] [Indexed: 12/12/2022]
Abstract
The MPI Bioinformatics Toolkit (https://toolkit.tuebingen.mpg.de) is a free, one-stop web service for protein bioinformatic analysis. It currently offers 34 interconnected external and in-house tools, whose functionality covers sequence similarity searching, alignment construction, detection of sequence features, structure prediction, and sequence classification. This breadth has made the Toolkit an important resource for experimental biology and for teaching bioinformatic inquiry. Recently, we replaced the first version of the Toolkit, which was released in 2005 and had served around 2.5 million queries, with an entirely new version, focusing on improved features for the comprehensive analysis of proteins, as well as on promoting teaching. For instance, our popular remote homology detection server, HHpred, now allows pairwise comparison of two sequences or alignments and offers additional profile HMMs for several model organisms and domain databases. Here, we introduce the new version of our Toolkit and its application to the analysis of proteins.
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Affiliation(s)
- Lukas Zimmermann
- Department of Protein Evolution, Max Planck Institute for Developmental Biology, Tübingen D-72076, Germany
| | - Andrew Stephens
- Department of Protein Evolution, Max Planck Institute for Developmental Biology, Tübingen D-72076, Germany
| | - Seung-Zin Nam
- Department of Protein Evolution, Max Planck Institute for Developmental Biology, Tübingen D-72076, Germany
| | - David Rau
- Department of Protein Evolution, Max Planck Institute for Developmental Biology, Tübingen D-72076, Germany
| | - Jonas Kübler
- Department of Protein Evolution, Max Planck Institute for Developmental Biology, Tübingen D-72076, Germany
| | - Marko Lozajic
- Department of Protein Evolution, Max Planck Institute for Developmental Biology, Tübingen D-72076, Germany
| | - Felix Gabler
- Department of Protein Evolution, Max Planck Institute for Developmental Biology, Tübingen D-72076, Germany
| | - Johannes Söding
- Group for Quantitative and Computational Biology, Max Planck Institute for Biophysical Chemistry, Göttingen D-37077, Germany
| | - Andrei N Lupas
- Department of Protein Evolution, Max Planck Institute for Developmental Biology, Tübingen D-72076, Germany.
| | - Vikram Alva
- Department of Protein Evolution, Max Planck Institute for Developmental Biology, Tübingen D-72076, Germany.
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Abriata LA, Rodrigues JPGLM, Salathé M, Patiny L. Augmenting Research, Education, and Outreach with Client-Side Web Programming. Trends Biotechnol 2017; 36:473-476. [PMID: 29254737 DOI: 10.1016/j.tibtech.2017.11.009] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2017] [Revised: 11/24/2017] [Accepted: 11/30/2017] [Indexed: 11/19/2022]
Abstract
The evolution of computing and web technologies over the past decade has enabled the development of fully fledged scientific applications that run directly on web browsers. Powered by JavaScript, the lingua franca of web programming, these 'web apps' are starting to revolutionize and democratize scientific research, education, and outreach.
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Affiliation(s)
- Luciano A Abriata
- Laboratory for Biomolecular Modeling, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL) and Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland.
| | - João P G L M Rodrigues
- Department of Structural Biology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Marcel Salathé
- School of Life Sciences, School of Computer and Communication Sciences, EPFL, CH-1015 Lausanne, Switzerland
| | - Luc Patiny
- Institute of Chemical Sciences and Engineering, EPFL, Lausanne CH-1015, Switzerland
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11
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Horro C, Cook M, Attwood TK, Brazas MD, Hancock JM, Palagi P, Corpas M, Jimenez R. BioCIDER: a Contextualisation InDEx for biological Resources discovery. Bioinformatics 2017; 33:2607-2608. [PMID: 28407033 PMCID: PMC5870719 DOI: 10.1093/bioinformatics/btx213] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2017] [Accepted: 04/11/2017] [Indexed: 11/13/2022] Open
Abstract
Summary The vast, uncoordinated proliferation of bioinformatics resources (databases, software tools, training materials etc.) makes it difficult for users to find them. To facilitate their discovery, various services are being developed to collect such resources into registries. We have developed BioCIDER, which, rather like online shopping ‘recommendations’, provides a contextualization index to help identify biological resources relevant to the content of the sites in which it is embedded. Availability and Implementation BioCIDER (www.biocider.org) is an open-source platform. Documentation is available online (https://goo.gl/Klc51G), and source code is freely available via GitHub (https://github.com/BioCIDER). The BioJS widget that enables websites to embed contextualization is available from the BioJS registry (http://biojs.io/). All code is released under an MIT licence.
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Affiliation(s)
- Carlos Horro
- Elixir Department, Earlham Institute, Norwich Research Park Innovation Centre, Norwich NR4 7UH, UK
| | - Martin Cook
- ELIXIR Hub, The European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton CB10 1SD, UK
| | - Teresa K Attwood
- School of Computer Science, The University of Manchester, Manchester M13 9PL, UK
| | - Michelle D Brazas
- Informatics and Bio-computing, Ontario Institute for Cancer Research, Toronto M5G 0A3, Canada
| | - John M Hancock
- Elixir Department, Earlham Institute, Norwich Research Park Innovation Centre, Norwich NR4 7UH, UK
| | - Patricia Palagi
- SIB Training Group, SIB Swiss Institute of Bioinformatics, Lausanne 1005, Switzerland
| | - Manuel Corpas
- Repositive, Future Business Centre, Kings' Hedges Road, Cambridge CB4 2HY, UK
| | - Rafael Jimenez
- ELIXIR Hub, The European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton CB10 1SD, UK
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12
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Allot A, Chennen K, Nevers Y, Poidevin L, Kress A, Ripp R, Thompson JD, Poch O, Lecompte O. MyGeneFriends: A Social Network Linking Genes, Genetic Diseases, and Researchers. J Med Internet Res 2017. [PMID: 28623182 PMCID: PMC5493784 DOI: 10.2196/jmir.6676] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Background The constant and massive increase of biological data offers unprecedented opportunities to decipher the function and evolution of genes and their roles in human diseases. However, the multiplicity of sources and flow of data mean that efficient access to useful information and knowledge production has become a major challenge. This challenge can be addressed by taking inspiration from Web 2.0 and particularly social networks, which are at the forefront of big data exploration and human-data interaction. Objective MyGeneFriends is a Web platform inspired by social networks, devoted to genetic disease analysis, and organized around three types of proactive agents: genes, humans, and genetic diseases. The aim of this study was to improve exploration and exploitation of biological, postgenomic era big data. Methods MyGeneFriends leverages conventions popularized by top social networks (Facebook, LinkedIn, etc), such as networks of friends, profile pages, friendship recommendations, affinity scores, news feeds, content recommendation, and data visualization. Results MyGeneFriends provides simple and intuitive interactions with data through evaluation and visualization of connections (friendships) between genes, humans, and diseases. The platform suggests new friends and publications and allows agents to follow the activity of their friends. It dynamically personalizes information depending on the user’s specific interests and provides an efficient way to share information with collaborators. Furthermore, the user’s behavior itself generates new information that constitutes an added value integrated in the network, which can be used to discover new connections between biological agents. Conclusions We have developed MyGeneFriends, a Web platform leveraging conventions from popular social networks to redefine the relationship between humans and biological big data and improve human processing of biomedical data. MyGeneFriends is available at lbgi.fr/mygenefriends.
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Affiliation(s)
- Alexis Allot
- ICUBE UMR 7357, Complex Systems and Translational Bioinformatics, Université de Strasbourg - CNRS - FMTS, Strasbourg, France
| | - Kirsley Chennen
- ICUBE UMR 7357, Complex Systems and Translational Bioinformatics, Université de Strasbourg - CNRS - FMTS, Strasbourg, France
| | - Yannis Nevers
- ICUBE UMR 7357, Complex Systems and Translational Bioinformatics, Université de Strasbourg - CNRS - FMTS, Strasbourg, France
| | - Laetitia Poidevin
- ICUBE UMR 7357, Complex Systems and Translational Bioinformatics, Université de Strasbourg - CNRS - FMTS, Strasbourg, France
| | - Arnaud Kress
- ICUBE UMR 7357, Complex Systems and Translational Bioinformatics, Université de Strasbourg - CNRS - FMTS, Strasbourg, France
| | - Raymond Ripp
- ICUBE UMR 7357, Complex Systems and Translational Bioinformatics, Université de Strasbourg - CNRS - FMTS, Strasbourg, France
| | - Julie Dawn Thompson
- ICUBE UMR 7357, Complex Systems and Translational Bioinformatics, Université de Strasbourg - CNRS - FMTS, Strasbourg, France
| | - Olivier Poch
- ICUBE UMR 7357, Complex Systems and Translational Bioinformatics, Université de Strasbourg - CNRS - FMTS, Strasbourg, France
| | - Odile Lecompte
- ICUBE UMR 7357, Complex Systems and Translational Bioinformatics, Université de Strasbourg - CNRS - FMTS, Strasbourg, France
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13
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O’Halloran DM. phylo-node: A molecular phylogenetic toolkit using Node.js. PLoS One 2017; 12:e0175480. [PMID: 28410421 PMCID: PMC5391935 DOI: 10.1371/journal.pone.0175480] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2016] [Accepted: 03/27/2017] [Indexed: 12/05/2022] Open
Abstract
Background Node.js is an open-source and cross-platform environment that provides a JavaScript codebase for back-end server-side applications. JavaScript has been used to develop very fast and user-friendly front-end tools for bioinformatic and phylogenetic analyses. However, no such toolkits are available using Node.js to conduct comprehensive molecular phylogenetic analysis. Results To address this problem, I have developed, phylo-node, which was developed using Node.js and provides a stable and scalable toolkit that allows the user to perform diverse molecular and phylogenetic tasks. phylo-node can execute the analysis and process the resulting outputs from a suite of software options that provides tools for read processing and genome alignment, sequence retrieval, multiple sequence alignment, primer design, evolutionary modeling, and phylogeny reconstruction. Furthermore, phylo-node enables the user to deploy server dependent applications, and also provides simple integration and interoperation with other Node modules and languages using Node inheritance patterns, and a customized piping module to support the production of diverse pipelines. Conclusions phylo-node is open-source and freely available to all users without sign-up or login requirements. All source code and user guidelines are openly available at the GitHub repository: https://github.com/dohalloran/phylo-node.
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Affiliation(s)
- Damien M. O’Halloran
- Department of Biological Sciences, The George Washington University, Washington, DC, United States of America
- Institute for Neuroscience, The George Washington University, Washington, DC, United States of America
- * E-mail:
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14
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Alva V, Nam SZ, Söding J, Lupas AN. The MPI bioinformatics Toolkit as an integrative platform for advanced protein sequence and structure analysis. Nucleic Acids Res 2016; 44:W410-5. [PMID: 27131380 PMCID: PMC4987908 DOI: 10.1093/nar/gkw348] [Citation(s) in RCA: 297] [Impact Index Per Article: 37.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2016] [Accepted: 04/19/2016] [Indexed: 12/21/2022] Open
Abstract
The MPI Bioinformatics Toolkit (http://toolkit.tuebingen.mpg.de) is an open, interactive web service for comprehensive and collaborative protein bioinformatic analysis. It offers a wide array of interconnected, state-of-the-art bioinformatics tools to experts and non-experts alike, developed both externally (e.g. BLAST+, HMMER3, MUSCLE) and internally (e.g. HHpred, HHblits, PCOILS). While a beta version of the Toolkit was released 10 years ago, the current production-level release has been available since 2008 and has serviced more than 1.6 million external user queries. The usage of the Toolkit has continued to increase linearly over the years, reaching more than 400 000 queries in 2015. In fact, through the breadth of its tools and their tight interconnection, the Toolkit has become an excellent platform for experimental scientists as well as a useful resource for teaching bioinformatic inquiry to students in the life sciences. In this article, we report on the evolution of the Toolkit over the last ten years, focusing on the expansion of the tool repertoire (e.g. CS-BLAST, HHblits) and on infrastructural work needed to remain operative in a changing web environment.
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Affiliation(s)
- Vikram Alva
- Department of Protein Evolution, Max Planck Institute for Developmental Biology, Tübingen D-72076, Germany
| | - Seung-Zin Nam
- Department of Protein Evolution, Max Planck Institute for Developmental Biology, Tübingen D-72076, Germany
| | - Johannes Söding
- Group for Quantitative and Computational Biology, Max Planck Institute for Biophysical Chemistry, Göttingen D-37077, Germany
| | - Andrei N Lupas
- Department of Protein Evolution, Max Planck Institute for Developmental Biology, Tübingen D-72076, Germany
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15
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Abstract
BACKGROUND To cope with the ever-increasing amount of sequence data generated in the field of genomics, the demand for efficient and fast database searches that drive functional and structural annotation in both large- and small-scale genome projects is on the rise. The tools of the BLAST+ suite are the most widely employed bioinformatic method for these database searches. Recent trends in bioinformatics application development show an increasing number of JavaScript apps that are based on modern frameworks such as Node.js. Until now, there is no way of using database searches with the BLAST+ suite from a Node.js codebase. RESULTS We developed blastjs, a Node.js library that wraps the search tools of the BLAST+ suite and thus allows to easily add significant functionality to any Node.js-based application. CONCLUSION blastjs is a library that allows the incorporation of BLAST+ functionality into bioinformatics applications based on JavaScript and Node.js. The library was designed to be as user-friendly as possible and therefore requires only a minimal amount of code in the client application. The library is freely available under the MIT license at https://github.com/teammaclean/blastjs.
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Affiliation(s)
- Martin Page
- Bioinformatics Group, The Sainsbury Laboratory, Norwich Research Park, Norwich, NR4 7UH, UK.
| | - Dan MacLean
- Bioinformatics Group, The Sainsbury Laboratory, Norwich Research Park, Norwich, NR4 7UH, UK.
| | - Christian Schudoma
- Bioinformatics Group, The Sainsbury Laboratory, Norwich Research Park, Norwich, NR4 7UH, UK.
- Triticeae Genomics Group, The Genome Analysis Centre, Norwich Research Park, Norwich, NR4 7UH, UK.
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16
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Kuchinke W, Ohmann C, Stenzhorn H, Anguista A, Sfakianakis S, Graf N, Demotes J. Ensuring sustainability of software tools and services by cooperation with a research infrastructure. Per Med 2016; 13:43-55. [PMID: 29749867 DOI: 10.2217/pme.15.43] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Sustainability of project output and especially of the maintenance and further development of software is of growing concern for the research community. In the personalized medicine project p-medicine solutions that address this sustainability problem were developed and discussed in a workshop. They involve a number of interrelated and mutually supportive measures including the creation of a service center, building modular software, using common data standards, mutual service exchange with a research infrastructure, Open Source and fee-based software provision, joint promotion and deployment of tools in a regulated, clinical trial situation. These ideas join a nascent literature seeking to understand how project output can be put into a sustainable environment and to suggest solutions that may be useful for academic projects in general.
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Affiliation(s)
- Wolfgang Kuchinke
- Heinrich-Heine University, University Clinics, Moorenstr. 5, 40225 Düsseldorf, Germany
| | - Christian Ohmann
- ECRIN KKS Düsseldorf, Universitätsklinikum, 40225 Düsseldorf, Germany
| | - Holger Stenzhorn
- Universitaet des Saarlandes, Universitätsklinikum, 66421 Homburg, Germany
| | | | - Stelios Sfakianakis
- Foundation for Research & Technology - Hellas, 711 10 Heraklion, Crete, Greece
| | - Norbert Graf
- Universitaet des Saarlandes, Universitätsklinikum, 66421 Homburg, Germany
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17
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Sillitoe I, Furnham N. FunTree: advances in a resource for exploring and contextualising protein function evolution. Nucleic Acids Res 2015; 44:D317-23. [PMID: 26590404 PMCID: PMC4702901 DOI: 10.1093/nar/gkv1274] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2015] [Accepted: 11/03/2015] [Indexed: 11/13/2022] Open
Abstract
FunTree is a resource that brings together protein sequence, structure and functional information, including overall chemical reaction and mechanistic data, for structurally defined domain superfamilies. Developed in tandem with the CATH database, the original FunTree contained just 276 superfamilies focused on enzymes. Here, we present an update of FunTree that has expanded to include 2340 superfamilies including both enzymes and proteins with non-enzymatic functions annotated by Gene Ontology (GO) terms. This allows the investigation of how novel functions have evolved within a structurally defined superfamily and provides a means to analyse trends across many superfamilies. This is done not only within the context of a protein's sequence and structure but also the relationships of their functions. New measures of functional similarity have been integrated, including for enzymes comparisons of overall reactions based on overall bond changes, reaction centres (the local environment atoms involved in the reaction) and the sub-structure similarities of the metabolites involved in the reaction and for non-enzymes semantic similarities based on the GO. To identify and highlight changes in function through evolution, ancestral character estimations are made and presented. All this is accessible through a new re-designed web interface that can be found at http://www.funtree.info.
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Affiliation(s)
- Ian Sillitoe
- Institute of Structural and Molecular Biology, University College London, Darwin Building, Gower Street, London WC1E 6BT, UK
| | - Nicholas Furnham
- Department of Pathogen Molecular Biology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
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18
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Ison J, Rapacki K, Ménager H, Kalaš M, Rydza E, Chmura P, Anthon C, Beard N, Berka K, Bolser D, Booth T, Bretaudeau A, Brezovsky J, Casadio R, Cesareni G, Coppens F, Cornell M, Cuccuru G, Davidsen K, Vedova GD, Dogan T, Doppelt-Azeroual O, Emery L, Gasteiger E, Gatter T, Goldberg T, Grosjean M, Grüning B, Helmer-Citterich M, Ienasescu H, Ioannidis V, Jespersen MC, Jimenez R, Juty N, Juvan P, Koch M, Laibe C, Li JW, Licata L, Mareuil F, Mičetić I, Friborg RM, Moretti S, Morris C, Möller S, Nenadic A, Peterson H, Profiti G, Rice P, Romano P, Roncaglia P, Saidi R, Schafferhans A, Schwämmle V, Smith C, Sperotto MM, Stockinger H, Vařeková RS, Tosatto SCE, de la Torre V, Uva P, Via A, Yachdav G, Zambelli F, Vriend G, Rost B, Parkinson H, Løngreen P, Brunak S. Tools and data services registry: a community effort to document bioinformatics resources. Nucleic Acids Res 2015; 44:D38-47. [PMID: 26538599 PMCID: PMC4702812 DOI: 10.1093/nar/gkv1116] [Citation(s) in RCA: 94] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2015] [Accepted: 10/13/2015] [Indexed: 01/24/2023] Open
Abstract
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.
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Affiliation(s)
- Jon Ison
- Center for Biological Sequence Analysis Department of Systems Biology, Technical University of Denmark, Denmark
| | - Kristoffer Rapacki
- Center for Biological Sequence Analysis Department of Systems Biology, Technical University of Denmark, Denmark
| | - Hervé Ménager
- Centre d'Informatique pour la Biologie, C3BI, Institut Pasteur, France
| | - Matúš Kalaš
- Computational Biology Unit, Department of Informatics, University of Bergen, Norway
| | - Emil Rydza
- Center for Biological Sequence Analysis Department of Systems Biology, Technical University of Denmark, Denmark
| | - Piotr Chmura
- Center for Biological Sequence Analysis Department of Systems Biology, Technical University of Denmark, Denmark
| | - Christian Anthon
- Department of Veterinary Clinical and Animal Sciences, Faculty for Health and Medical Sciences, University of Copenhagen, Denmark
| | - Niall Beard
- School of Computer Science, University of Manchester, UK
| | - Karel Berka
- Department of Physical Chemistry, RCPTM, Faculty of Science, Palacky University, Czech Republic
| | - Dan Bolser
- The European Bioinformatics Institute (EMBL-EBI), UK
| | - Tim Booth
- NEBC Wallingford, Centre for Ecology and Hydrology, UK
| | - Anthony Bretaudeau
- INRA, UMR Institut de Génétique, Environnement et Protection des Plantes (IGEPP), BioInformatics Platform for Agroecosystems Arthropods (BIPAA), France INRIA, IRISA, GenOuest Core Facility, France
| | - Jan Brezovsky
- Loschmidt Laboratories, Department of Experimental Biology and Research Centre for Toxic Compounds in the Environment RECETOX, Masaryk University, Czech Republic
| | - Rita Casadio
- Bologna Biocomputing Group, University of Bologna, Italy
| | | | - Frederik Coppens
- Department of Plant Systems Biology, VIB, Belgium Department of Plant Biotechnology and Bioinformatics, Ghent University, Belgium
| | | | | | - Kristian Davidsen
- Center for Biological Sequence Analysis Department of Systems Biology, Technical University of Denmark, Denmark
| | | | - Tunca Dogan
- UniProt, European Bioinformatics Institute (EMBL-EBI), UK
| | | | - Laura Emery
- The European Bioinformatics Institute (EMBL-EBI), UK
| | | | - Thomas Gatter
- Faculty of Technology and Center for Biotechnology, Universität Bielefeld, Germany
| | | | - Marie Grosjean
- Institut Français de Bioinformatique (French Institute of Bioinformatics), CNRS, UMS3601, France
| | - Björn Grüning
- Albert-Ludwigs-Universität Freiburg, Fahnenbergplatz, 79085 Freiburg
| | | | - Hans Ienasescu
- Bioinformatics Centre, Department of Biology, University of Copenhagen, Denmark
| | | | - Martin Closter Jespersen
- Center for Biological Sequence Analysis Department of Systems Biology, Technical University of Denmark, Denmark
| | | | - Nick Juty
- The European Bioinformatics Institute (EMBL-EBI), UK
| | - Peter Juvan
- Centre for Functional Genomics and Biochips, Faculty of Medicine, University of Ljubljana, Slovenia
| | | | - Camille Laibe
- The European Bioinformatics Institute (EMBL-EBI), UK
| | - Jing-Woei Li
- Faculty of Medicine, The Chinese University of Hong Kong, China Hong Kong Bioinformatics Centre, School of Life Sciences,The Chinese University of Hong Kong, China
| | - Luana Licata
- Dept. of Biology, University of Rome Tor Vergata, Italy
| | - Fabien Mareuil
- Centre d'Informatique pour la Biologie, C3BI, Institut Pasteur, France
| | - Ivan Mičetić
- Department of Biomedical Sciences, University of Padua, Italy
| | | | - Sebastien Moretti
- SIB Swiss Institute of Bioinformatics, Switzerland Department of Ecology and Evolution, Biophore, Evolutionary Bioinformatics group, University of Lausanne, Switzerland
| | | | - Steffen Möller
- Department of Dermatology, University of Lübeck, Germany Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, Germany
| | | | - Hedi Peterson
- Institute of Computer Science, University of Tartu, Estonia
| | | | - Peter Rice
- Department of Computing, William Penney Laboratory, Imperial College London, UK
| | | | | | - Rabie Saidi
- UniProt, European Bioinformatics Institute (EMBL-EBI), UK
| | | | - Veit Schwämmle
- Protein Research Group, Department for Biochemistry and Molecular Biology, University of Southern Denmark, Denmark
| | | | - Maria Maddalena Sperotto
- Center for Biological Sequence Analysis Department of Systems Biology, Technical University of Denmark, Denmark
| | | | | | | | - Victor de la Torre
- National Bioinformatics Institute Unit (INB), Fundacion Centro Nacional de Investigaciones Oncologicas, Spain
| | | | - Allegra Via
- Dept. of Physics, Sapienza University, Italy
| | - Guy Yachdav
- Department of Informatics, Bioinformatics-I12, TUM, Germany
| | - Federico Zambelli
- Institute of Biomembranes and Bioenergetics, National Research Council (CNR), and Dept. of Biosciences, University of Milano, Italy
| | - Gert Vriend
- Radboud University Medical Centre, CMBI, Netherlands
| | - Burkhard Rost
- Department of Informatics, Bioinformatics-I12, TUM, Germany
| | | | - Peter Løngreen
- Center for Biological Sequence Analysis Department of Systems Biology, Technical University of Denmark, Denmark
| | - Søren Brunak
- Center for Biological Sequence Analysis Department of Systems Biology, Technical University of Denmark, Denmark Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
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