Literature DB >> 18635566

ProCope--protein complex prediction and evaluation.

Jan Krumsiek1, Caroline C Friedel, Ralf Zimmer.   

Abstract

SUMMARY: Recent advances in high-throughput technology have increased the quantity of available data on protein complexes and stimulated the development of many new prediction methods. In this article, we present ProCope, a Java software suite for the prediction and evaluation of protein complexes from affinity purification experiments which integrates the major methods for calculating interaction scores and predicting protein complexes published over the last years. Methods can be accessed via a graphical user interface, command line tools and a Java API. Using ProCope, existing algorithms can be applied quickly and reproducibly on new experimental results, individual steps of the different algorithms can be combined in new and innovative ways and new methods can be implemented and integrated in the existing prediction framework. AVAILABILITY: Source code and executables are available at http://www.bio.ifi.lmu.de/Complexes/ProCope/.

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Year:  2008        PMID: 18635566     DOI: 10.1093/bioinformatics/btn376

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  10 in total

1.  Detecting overlapping protein complexes in protein-protein interaction networks.

Authors:  Tamás Nepusz; Haiyuan Yu; Alberto Paccanaro
Journal:  Nat Methods       Date:  2012-03-18       Impact factor: 28.547

2.  Intronic microRNAs support their host genes by mediating synergistic and antagonistic regulatory effects.

Authors:  Dominik Lutter; Carsten Marr; Jan Krumsiek; Elmar W Lang; Fabian J Theis
Journal:  BMC Genomics       Date:  2010-04-06       Impact factor: 3.969

3.  An efficient protein complex mining algorithm based on Multistage Kernel Extension.

Authors:  Xianjun Shen; Yanli Zhao; Yanan Li; Tingting He; Jincai Yang; Xiaohua Hu
Journal:  BMC Bioinformatics       Date:  2014-11-06       Impact factor: 3.169

4.  Dynamic identifying protein functional modules based on adaptive density modularity in protein-protein interaction networks.

Authors:  Xianjun Shen; Li Yi; Yang Yi; Jincai Yang; Tingting He; Xiaohua Hu
Journal:  BMC Bioinformatics       Date:  2015-08-25       Impact factor: 3.169

5.  Protein complex prediction via dense subgraphs and false positive analysis.

Authors:  Cecilia Hernandez; Carlos Mella; Gonzalo Navarro; Alvaro Olivera-Nappa; Jaime Araya
Journal:  PLoS One       Date:  2017-09-22       Impact factor: 3.240

6.  A two-step framework for inferring direct protein-protein interaction network from AP-MS data.

Authors:  Bo Tian; Can Zhao; Feiyang Gu; Zengyou He
Journal:  BMC Syst Biol       Date:  2017-09-21

7.  An Improved Memetic Algorithm for Detecting Protein Complexes in Protein Interaction Networks.

Authors:  Rongquan Wang; Huimin Ma; Caixia Wang
Journal:  Front Genet       Date:  2021-12-14       Impact factor: 4.599

8.  Identifying the topology of protein complexes from affinity purification assays.

Authors:  Caroline C Friedel; Ralf Zimmer
Journal:  Bioinformatics       Date:  2009-06-08       Impact factor: 6.937

Review 9.  Visualizing genome and systems biology: technologies, tools, implementation techniques and trends, past, present and future.

Authors:  Georgios A Pavlopoulos; Dimitris Malliarakis; Nikolas Papanikolaou; Theodosis Theodosiou; Anton J Enright; Ioannis Iliopoulos
Journal:  Gigascience       Date:  2015-08-25       Impact factor: 6.524

10.  A comparative analysis of computational approaches and algorithms for protein subcomplex identification.

Authors:  Nazar Zaki; Antonio Mora
Journal:  Sci Rep       Date:  2014-03-03       Impact factor: 4.379

  10 in total

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