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| = Pathway and Network Analysis Service = {{attachment:Software/EnrichmentMap/UserManual/enrichmentmap_logo3.png|Enrichment Map Logo|align="right"}}<<BR>> == Cancer Stem Cell program == ---- ----- == Introduction to the service: == |
= OICR Cancer Stem Cell program - Pathway and Network Analysis Service = |
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| * High-throughput genomic experiments (e.g. gene expression, protein expression, molecular interactions, large-scale genetic screens and other omics data) lead to the identification of large gene lists. <<BR>> The interpretation of results and the formulation of consistent biological hypotheses from these gene lists are challenging.<<BR>> Computational 'pathway and network analysis' approaches can aid interpretation by relating the gene lists to knowledge about the biological system. | * High-throughput genomic experiments (e.g. gene expression, large-scale genetic screens) often lead to the identification of large gene lists. The interpretation of results and the formulation of consistent biological hypotheses from these gene lists can be challenging. Computational 'pathway and network analysis' approaches can aid interpretation by relating the gene list to knowledge about the biological system, such as pathways. |
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| * Goal: help researchers interpret results of genomics experiments. Analysis is conducted in close collaboration with researchers on each project to ensure correct input data and effective interpretation of results. | * '''Our goal''' is to help researchers interpret results of genomics experiments. Analysis is conducted in close collaboration with researchers on each project to ensure correct input data and effective interpretation of results. Ideally researchers do as much of the analysis and interpretation as they can. |
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| * Standard types of pathway analysis offered * Find pathways enriched in a list of genes * Gene-set enrichment analysis helps characterize large gene lists by finding functionally coherent gene-sets, such as pathways, that are statistically over-represented in a given gene list. Input: gene list from genomics experiment. Output: enriched pathways * Enrichment Map is a visualization method to organize the results of enrichment analysis making it easier to quickly identify the major enriched functional themes and to interpret the results. Input: enriched pathways. Output: network of pathway relationships and major functional themes. * Predict the function of an unknown gene * GeneMANIA finds other genes that are related to a set of input genes, using a very large set of functional association data. Input: a gene or set of genes. Output: connections between input genes and suggestions for additional related genes. |
* '''Standard types of pathway analysis offered''' * '''Find pathways enriched in a list of genes (e.g. differentially expressed genes)''' Gene-set enrichment analysis helps characterize large gene lists by finding functionally coherent gene-sets, such as pathways, that are statistically over-represented in a given gene list. We have also developed a method to visualize the results of this analysis, called Enrichment Map. '''Input''': gene list from genomics experiment (statistically analyzed). '''Output''': enriched pathways visually displayed. * Related publications: [[http://www.ncbi.nlm.nih.gov/pubmed/16199517|Gene-set enrichment analysis (GSEA)]] [[http://www.ncbi.nlm.nih.gov/pubmed/21085593|Enrichment Map]] * '''Predict the function of an unknown gene''' GeneMANIA finds other genes that are related to a set of input genes, using a very large set of functional association data. '''Input''': a gene or set of genes. '''Output''': connections between input genes and suggestions for additional related genes. * Related publications: [[http://www.ncbi.nlm.nih.gov/pubmed/20576703|GeneMANIA]] |
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== More Information about Pathway and Network Analysis == * Enrichment Map: Enrichment map: a network-based method for gene-set enrichment visualization and interpretation. Merico D, Isserlin R, Stueker O, Emili A, Bader GD. PLoS One. 2010 Nov 15;5(11):e13984. http://www.ncbi.nlm.nih.gov/pubmed/21085593 * GSEA Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP. Proc Natl Acad Sci U S A. 2005 Oct 25;102(43):15545-50. http://www.ncbi.nlm.nih.gov/pubmed/16199517 * [[CancerStemCellProject/VeroniqueVoisin/PathwayAnalysisService/Information | Link to more information ]] ---- ----- {{attachment:flowchart.png|flowchart|align="right"}} |
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| * You are planning to generate 'omics' (e.g. genomics) data * You already have large gene lists coming from large-scale omics projects that are ready to be analyzed. |
* You are planning to generate 'omics' (e.g. gene expression) data * You have a large gene list derived from a large-scale omics project that is ready to be analyzed |
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OICR Cancer Stem Cell program - Pathway and Network Analysis Service
The Pathway and Network Analysis Service is freely available to all Cancer Stem Cell program members.
- High-throughput genomic experiments (e.g. gene expression, large-scale genetic screens) often lead to the identification of large gene lists. The interpretation of results and the formulation of consistent biological hypotheses from these gene lists can be challenging. Computational 'pathway and network analysis' approaches can aid interpretation by relating the gene list to knowledge about the biological system, such as pathways.
Our goal is to help researchers interpret results of genomics experiments. Analysis is conducted in close collaboration with researchers on each project to ensure correct input data and effective interpretation of results. Ideally researchers do as much of the analysis and interpretation as they can.
Standard types of pathway analysis offered
Find pathways enriched in a list of genes (e.g. differentially expressed genes) Gene-set enrichment analysis helps characterize large gene lists by finding functionally coherent gene-sets, such as pathways, that are statistically over-represented in a given gene list. We have also developed a method to visualize the results of this analysis, called Enrichment Map. Input: gene list from genomics experiment (statistically analyzed). Output: enriched pathways visually displayed.
Related publications: Gene-set enrichment analysis (GSEA) Enrichment Map
Predict the function of an unknown gene GeneMANIA finds other genes that are related to a set of input genes, using a very large set of functional association data. Input: a gene or set of genes. Output: connections between input genes and suggestions for additional related genes.
Related publications: GeneMANIA
- We can discuss custom analysis
- What can you expect from the service:
- We run the analysis for you and help interpret the data.
- We can help you at different stages:
- at the experimental design stage
- during the analysis: we offer training in data analysis and exploration
- after an initial analysis is complete and any validation experiments have been performed, we can book a follow-up meeting to see if you need additional analyses or to help plan subsequent genomics experiments.
How to use the service
Who can use the service
- Members of the OICR Cancer Stem Cell program may use the service if
- You are planning to generate 'omics' (e.g. gene expression) data
- You have a large gene list derived from a large-scale omics project that is ready to be analyzed
- You require training in pathway and network analysis
Please schedule an appointment with us:Consulting meeting If you are planning a genomics experiment and you need some advice concerning the experiment design. Typical time: 30-60 minutes
Analysis planning meeting If you have data ready to analyze and they have been already statistically analyzed. Typical time: 60 minutes
Training session If you want to do your own pathway and network analyses, we can explain how various state of the are software tools and methods work, such as GSEA, Enrichment Map and GeneMANIA. Typical time: Regular training schedule is currently being planned. Individual or group sessions can be arranged.
How to book an appointment
Normal in person meetings are on Tuesdays at TMDT 8th floor. Let us know if this doesn't work for you. Check our meeting calendar to see available times (30 min to 1 h meeting)
Send an e-mail to veronique.voisin@gmail.com with your preferred meeting time and the purpose of the meeting and wait for e-mail confirmation.
- For first-time meetings, please send a paper that best describes your work prior to the meeting.
If you booked an initial meeting, please read our standard operating procedure to know what to expect
If you must cancel a meeting, please give 24 hours notice to veronique.voisin@gmail.com.
Initial meeting and Data input requirement
Tutorial : How to explore an interactive Enrichment Map on your computer