Difference between revisions of "The facebook of genes: community detection in biological networks"

 
(5 intermediate revisions by the same user not shown)
Line 1: Line 1:
'''Background''': Various genomic data have been used to construct biological networks. It is well known that such networks have a high degree of modularity, and that the corresponding modules often comprise genes or proteins that are involved in the same biological functions. Network module identification methods — also known as community detection and graph clustering methods — attempt to reveal these functional units, which is key to derive biological insights from genomic networks.
+
'''Background:''' In biology, many processes involve a group of genes that are expressed at the same time or a group of proteins that interact with each other. These interactions can be represented as networks and allow the identification of modules, which are groups of densely interconnected nodes (i.eg. genes or proteins). A module should regroup nodes that work in the same pathway or have a related function, thus allowing to understand better different biological processes, pathways and causes of disease.  
 
 
'''Goal''': The aim of this project is to explore methods that leverage biological networks to predict groups of functionally related genes (modules). We will explore different module identification and visualization methods. We will then analyze whether the identified modules are novel or correspond to known groups of functionally related genes using information from the gene ontology and pathway databases.
 
  
'''Computational tools''': This project has a computational flavor and is best suited for students with interest in programming. We will apply standard tools used in many diverse problems in computational biology (e.g., clustering, gene ontology enrichment analysis, permutation tests, etc). This project will be implemented using R.
+
'''Goal:''' To identify overlapping and non-overlapping modules in different biological networks.  
  
'''Keywords''': Network biology, pathway analysis
+
'''Computational tools:''' For this project, we used the ''linkcomm'' R package to identify overlapping modules. We also created an algorithm to transform the results into a list of non-overlapping modules.
 +
 
 +
'''Keywords:''' community identification, biological networks, pathways
  
 
'''Supervisor''':  [[User:Daniel|Daniel Marbach]]
 
'''Supervisor''':  [[User:Daniel|Daniel Marbach]]
Line 11: Line 11:
 
'''[http://www2.unil.ch/cbg/images/e/ec/Facebook_of_genes_slides.pdf Download project introduction slides]'''
 
'''[http://www2.unil.ch/cbg/images/e/ec/Facebook_of_genes_slides.pdf Download project introduction slides]'''
  
Our wiki : [[https://www2.unil.ch/cbg/images/9/99/The-facebook-of-genes.pdf]]
+
Students wiki: [[Media:The-facebook-of-genes.pdf|The facebook of genes]]
 
 
[[Media:The-facebook-of-genes.pdf]]
 

Latest revision as of 20:14, 7 June 2017

Background: In biology, many processes involve a group of genes that are expressed at the same time or a group of proteins that interact with each other. These interactions can be represented as networks and allow the identification of modules, which are groups of densely interconnected nodes (i.eg. genes or proteins). A module should regroup nodes that work in the same pathway or have a related function, thus allowing to understand better different biological processes, pathways and causes of disease.

Goal: To identify overlapping and non-overlapping modules in different biological networks.

Computational tools: For this project, we used the linkcomm R package to identify overlapping modules. We also created an algorithm to transform the results into a list of non-overlapping modules.

Keywords: community identification, biological networks, pathways

Supervisor: Daniel Marbach

Download project introduction slides

Students wiki: The facebook of genes