PathVisio and WikiPathways
- Introductory lectures: what are biological pathways and why is it useful to analyse data in the context of pathways (Canadian Bioinformatics workshops)
- Hands on tutorial how to make pathways using PathVisio Software for the pathway repository Wikipathways: Wikipathways Academy
- Installation guide
- Install PathVisio: PathVisio is open source free software available at www.pathvisio.org/downloads/. Download it and follow the installation instructions. PathVisio requires a Java environment pre-installed on the computer.
- Add WikiPathways plugin: For many applications PathVisio needs a plugin for WikiPathways which can be automatically downloaded and installed.
- Open PathVisio – PlugIns – PlugIn Manager –
- Choose WikiPathways plugin and click install.
- Add identifier mapping databases: On the PathVisio download page there is also a link to the “identifier mapping databases”. These are needed to link the different genes and metabolites to their database identifiers.
- Click “find the correct identifier mapping database”
- Download from the Metabolite database “Metabolites (all species)” and from Gene product/protein database the species you need e.g. “Homo sapiens”.
- Unpack and save them in a folder.
- Open PathVisio and go to Data
- Select Gene Database
- Browse to the folder where the gene product database file is stored and load it
- Check the status bar at the bottom to see if the gene database has been loaded correctly.
- YouTube channel of Alexander Pico containing several tutorial videos about making a pathway using PathVisio and several plugins
- PathVisio help pages
- How to make a pathway tutorial from eNanoMapper project
- Links to supporting material
- WikiPathways papers How to cite WikiPathways
- Slenter DN, Kutmon M, Hanspers K, Riutta A, Windsor J, Nunes N, Mélius J, Cirillo E, Coort SL, Digles D, Ehrhart F, Giesbertz P, Kalafati M, Martens M, Miller R, Nishida K, Rieswijk L, Waagmeester A, Eijssen LMT, Evelo CT, Pico AR, Willighagen EL. WikiPathways: a multifaceted pathway database bridging metabolomics to other omics research Nucleic Acids Research, (2017)doi.org/10.1093/nar/gkx1064
- Kutmon M, Riutta A, Nunes N, Hanspers K, Willighagen EL, Bohler A, Mélius J, Waagmeester A, Sinha SR, Miller R, Coort SL, Cirillo E, Smeets B, Evelo CT, Pico AR. WikiPathways: capturing the full diversity of pathway knowledge Nucl. Acids Res., 44, D488-D494 (2016) doi:10.1093/nar/gkv1024
- Kelder T, van Iersel MP, Hanspers K, Kutmon M, Conklin BR, Evelo C, Pico AR. WikiPathways: building research communities on biological pathways. Nucleic Acids Res. 2012 Jan;40(Database issue):D1301-7 (link to article)
- PathVisio paper
- Kutmon M, van Iersel MP, Bohler A, Kelder T, Nunes N, Pico AR, Evelo CT. PathVisio 3: an extendable pathway analysis toolbox. PLoS Comput Biol. 2015 Feb 23;11(2):e1004085. doi: 10.1371/journal.pcbi.1004085
- Example workflow and applications
- Martina Kutmon, Chris T Evelo, Susan L Coort. A network biology workflow to study transcriptomics data of the diabetic liver, BMC Genomics. 2014; 15(1): 971. doi: 10.1186/1471-2164-15-971
- F. Ehrhart, S.L.M. Coort, E. Cirillo, E. Smeets, C.T. Evelo, L.M. Curfs. “Rett syndrome – biological pathways leading from MECP2 to disorder phenotypes.” Orphanet J Rare Dis 11(1): 158. doi: 10.1186/s13023-016-0545-5
- Nymark, Penny, Rieswijk, Linda; Ehrhart, Friederike; Jeliazkova, Nina; Tsiliki, Georgia; Sarimveis, Haralambos; Evelo, Chris; Hongisto, Vesa; Kohonen, Pekka; Willighagen, Egon; Grafström, Roland: A data fusion pipeline for generating and enriching Adverse Outcome Pathway descriptions. Toxicological Sciences, 2017 https://doi.org/10.1093/toxsci/kfx252
- WikiPathways papers How to cite WikiPathways
Data analysis with Pathvisio
- Extensive tutorial on analyzing biological data sets with Pathvisio.
- General website: http://www.bridgedb.org/
- Explanation on databases used for mapping.
- Mapping database for metabolites.
- Mapping database for genes.
- Issue tracker.
- R-package code and documentation.
- Tutorials on general use of Cytoscape.
- Developer tutorials on Cytoscape.
- Programmable access to Cytoscape via R.
- Links to supporting material
- Cytoscape paper: 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 Research 2003 Nov; 13(11):2498-504
- Youtube video about “Network Visualization and Analysis with Cytoscape” from the Canadian Bioinformatics Workshop
(Download here) provides a quick and extensive enrichment of biological networks in Cytoscape (Kutmon et al. 2013). The visualization options allow biological interpretation of complex regulatory networks in a graphical way. The current data repository includes i.a.
- microRNAs (predicted and validated) (mirTarBase, microcosm, targetscan)
- Drugs and drug targets (drugbank)
- Transcription factors (predicted and validated) (ENCODE, The Transcription Factor Encyclopedia (tfe)
In this tutorial a step-by-step explanation is given on how to extend a list of microRNAs with predicted and validated microRNA-target interactions (MTIs). (Note that in this tutorial the Cytoscape version 3.0 is used.)
ArrayAnalysis offers user-friendly solutions for gene expression data analysis, from raw data to biological pathways. It contains modules of three types that can be launched individually or successively as an integrated workflow.
The Pathway Analysis module allows to quickly and easily visualise your statistics results on a biological pathway basis and identify significantly changed processes using PathVisio technology. This module will be activated soon, for now a mock-up module is in place that shows the possibilities using an example data sets.
- User guide of the pathway analysis module: http://arrayanalysis.org/Path/doc_Path.php
- How to use the pathway module of arrayanalysis.org for pathway analysis of microarray data. You can download the tutorial >> here <<
This technical documentation has two main objectives:
- to guide you in the use of the Path module
- to give interpretative help on the outputs of the module
- [Statistical analysis] The Statistical analysis module models your gene expression data using a linear model applied at the probe set level. You are given the possibility to custom your analysis and computing several models on a run. For a quick interpretation of the output result, P-Value and Fold change histograms can be computed as well as custom summary tables.
[QC & pre-processing]
The QC & pre-processing module gathers a complete panel of QC plots and indicators: a variety output plots or tables help you determine sample quality, hybridisation and overall signal quality, signal comparability and bias diagnostic and array correlation. Pre-processing methods combine probe set re-annotation, background correction and normalisation. Currently, modules are available for Affymetrix and Illumina arrays.
- User guide of the Affymetrix gene expression QC & pre-processing web module: http://arrayanalysis.org/affyQC/doc_affyQC_web.php
- User guide of the Illumina gene expression QC & pre-processing web module: http://arrayanalysis.org/ilmnQC/ILLUMINA_USER_GUIDE.pdf
- User guide of the Affymetrix QC & pre-processing module for local use: http://arrayanalysis.org/affyQC/doc_affyQC_R.php
- Technical documentation of the Affymetrix QC & pre-processing module: http://arrayanalysis.org/affyQC/doc_affyQC_func.php
- How to use the AFFYQC web tool of arrayanalysis.org for quality control and pre-processing of affymetrix microarray data. You can download the tutorial >> here <<
OpenPHACTS Youtube channel, including webinars, tutorials and demos: https://www.youtube.com/channel/UCPTV85VaV2XfMfv1WXPS9EQ
Programming for the life sciences course material: https://github.com/egonw/mscpils