KeyPathwayMiner 4.0: condition-specific pathway analysis by combining multiple omics studies and networks with Cytoscape

BMC Systems Biology

Jan Baumbach³  Henrik J Ditzel⁶  Vasco Azevedo²  Anne GL Christensen⁴  Alexander Junge⁵  Eudes Barbosa²  Richa Batra³  Josch Pauling¹  Nicolas Alcaraz⁴ 

[1] Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense, Denmark;Institute of Biological Sciences, Laboratory of Molecular and Cellular Genetic, Federal University of Minas Gerais, Belo Horizonte, Brazil;Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark;Department of Cancer and Inflammation Research, Institute of Molecular Medicine, University of Southern Denmark, Odense, Denmark;Center for non-coding RNA in Technology and Health, Section for Animal Genetics, Bioinformatics and Breeding, University of Copenhagen, Frederiksberg, Denmark;Department of Oncology, Odense University Hospital, Odense, Denmark

关键词: Key pathways;    Multi-omics;    Protein-protein interaction;    Network enrichment;   

Others  :  1127090
DOI  :  10.1186/s12918-014-0099-x

 received in 2014-05-20, accepted in 2014-08-13,  发布年份 2014

【 摘 要 】

Background

Over the last decade network enrichment analysis has become popular in computational systems biology to elucidate aberrant network modules. Traditionally, these approaches focus on combining gene expression data with protein-protein interaction (PPI) networks. Nowadays, the so-called omics technologies allow for inclusion of many more data sets, e.g. protein phosphorylation or epigenetic modifications. This creates a need for analysis methods that can combine these various sources of data to obtain a systems-level view on aberrant biological networks.

Results

We present a new release of KeyPathwayMiner (version 4.0) that is not limited to analyses of single omics data sets, e.g. gene expression, but is able to directly combine several different omics data types. Version 4.0 can further integrate existing knowledge by adding a search bias towards sub-networks that contain (avoid) genes provided in a positive (negative) list. Finally the new release now also provides a set of novel visualization features and has been implemented as an app for the standard bioinformatics network analysis tool: Cytoscape.

Conclusion

With KeyPathwayMiner 4.0, we publish a Cytoscape app for multi-omics based sub-network extraction. It is available in Cytoscape’s app store http://apps.cytoscape.org/apps/keypathwayminer webcite or via http://keypathwayminer.mpi-inf.mpg.de webcite.

【 授权许可】

   
2014 Alcaraz et al.; licensee BioMed Central

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

【 参考文献 】

• [1]Ulitsky I, Shamir R: Identification of functional modules using network topology and high-throughput data. BMC Syst Biol 2007, 1:8. BioMed Central Full Text
• [2]Ideker T, Ozier O, Schwikowski B, Siegel AF: Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics 2002, 18(Suppl 1):S233-S240.
• [3]Ulitsky I, Krishnamurthy A, Karp RM, Shamir R: DEGAS: de novo discovery of dysregulated pathways in human diseases. PLoS One 2010, 5:e13367.
• [4]Baumbach J, Friedrich T, Kotzing T, Krohmer A, Müller J, Pauling J: Efficient Algorithms for Extracting Biological key Pathways With Global Constraints Categories and Subject Descriptors. In GECCO. ACM, Philadelphia, Pennsylvania, USA; 2012:169-176.
• [5]Sun H, Wang H, Zhu R, Tang K, Gong Q, Cui J, Cao Z, Liu Q: iPEAP: integrating multiple omics and genetic data for pathway enrichment analysis. Bioinformatics 2014, 30:737-739.
• [6]Sass S, Buettner F, Mueller NS, Theis FJ: A modular framework for gene set analysis integrating multilevel omics data. Nucleic Acids Res 2013, 41:9622-9633.
• [7]Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T: Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2003, 13:2498-2504.
• [8]Alcaraz N, Kücük H, Weile J, Wipat A, Baumbach J: KeyPathwayMiner: detecting case-specific biological pathways using expression data. Internet Math 2011, 7:299-313.
• [9]Alcaraz N, Friedrich T, Kotzing T, Krohmer A, Muller J, Pauling J, Baumbach J: Efficient key pathway mining: combining networks and OMICS data. Integr Biol (Camb) 2012, 4:756-764.
• [10]Lotia S, Montojo J, Dong Y, Bader GD, Pico AR: Cytoscape app store. Bioinformatics 2013, 29:1350-1351.
• [11]Comprehensive molecular characterization of human colon and rectal cancer Nature 2012, 487:330-337.
• [12]Lao VV, Grady WM: Epigenetics and colorectal cancer. Nat Rev Gastroenterol Hepatol 2011, 8:686-700.
• [13]Keshava Prasad TS, Goel R, Kandasamy K, Keerthikumar S, Kumar S, Mathivanan S, Telikicherla D, Raju R, Shafreen B, Venugopal A, Balakrishnan L, Marimuthu A, Banerjee S, Somanathan DS, Sebastian A, Rani S, Ray S, Harrys Kishore CJ, Kanth S, Ahmed M, Kashyap MK, Mohmood R, Ramachandra YL, Krishna V, Rahiman BA, Mohan S, Ranganathan P, Ramabadran S, Chaerkady R, Pandey A: Human protein reference database–2009 update. Nucleic Acids Res 2009, 37:D767-D772.
• [14]Zhang J, Roberts TM, Shivdasani RA: Targeting PI3K signaling as a therapeutic approach for colorectal cancer. Gastroenterology 2011, 141:50-61.
• [15]Courtney KD, Corcoran RB, Engelman JA: The PI3K pathway as drug target in human cancer. J Clin Oncol 2010, 28:1075-1083.