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Which genes lie in my copy number variation regions? Are these genes enriched for any biological functions or pathways?

This recipe provides a method for identifying biological functions for genes lying in copy number variation (CNV) regions. An example use of this recipe is a case where an investigator may want to examine their list of CNVs to see which genetic regions are amplified or deleted, and determine the biological functions or pathways associated with these amplified or deleted regions.


Copy number variations (CNVs) are large alterations to genomes, such as amplification or deletion of large segments of a chromosome. They can range in size from a focal aberration in a single gene to aberrations covering entire chromosome arms. These variations in the genome have been associated with different conditions, such as cancer. Many genomic analyses produce a set of genes which are assumed to be relevant to an underlying biological mechanism or phenotype. Thus, an investigator often has additional questions about the function or relatedness of these genes: Are they part of the same pathway? Do the gene products interact physically? Do the gene products localize to a specific part of the cell? Are the genes associated with certain stages of development? These questions, and others like them, can be answered by performing functional annotation of gene lists to better understand the underlying connections between genes.

In this particular example, we imagine a scenario in which an investigator identifies CNV regions that are amplified or deleted in glioblastoma multiforme (GBM) tumor samples, using a method called Genomic Identification of Significant Targets in Cancer (GISTIC, Mermel et al. (2011) Genome Biol.). Given a set of CNV regions, the goal is to infer which biological functions (e.g., metabolic and regulatory pathways, chemical perturbation signatures, etc.) are overrepresented in the set of reference genes that overlap with these regions. In particular, this recipe uses several Galaxy tools to find the overlap between CNV regions and reference genes obtained from the UCSC Table Browser. Then it uses the Molecular Signatures Database (MSigDB) to identify biological functions of the overlapped genes.

How can I use this recipe? This recipe may be modified to analyze CNV regions derived from any organism for which an annotated reference genome exists. Nor does the recipe depend on the algorithm used to identify these regions (GISTIC); any source of CNV data can be used. Once an investigator has pinpointed the CNV regions believed to be influencing their phenotype (disease state, cell type, etc.) of study he/she can use this recipe to identify functional pathways that may be affected by these copy number changes and draw closer to an understanding of the mechanisms behind these CNV effects.

How are SNP-related genes regulated in an expression dataset? Are these genes enriched for particular biological functions?

This recipe provides one method for identifying enriched biological functions in single-nucleotide polymorphisms (SNPs). An example use of this recipe is a case where an investigator may complete a genome-wide association study (GWAS) and wants to know the SNPs that are associated with certain genomic coordinates, in order to determine which genes have particular biological functions.

In this particular example, we imagine a scenario in which an investigator completes a GWAS study, obtaining a list of genomic coordinates that are associated with SNPs. However, simply knowing these genomic coordinates is not always informative; the investigator is also interested in knowing which genes lie in these regions, and what kinds of biological functions these genes may have. In this particular example, we are interested in answering two questions:

  1. Are the genes near these SNPs enriched with particular biological functions?
  2. Are the genes near these SNPs significantly up- or down-regulated in some other biological conditions, such as cancer?

To answer the first question, we will find Gene Ontology functional annotations using Genomica. To answer the second question, we will use the Gene Set Enrichment Analysis (GSEA) module in GenePattern, comparing the SNP-associated genes to a gene expression dataset from a study of epithelial cancer stem cells. This study evaluated the ability of oncogenes to activate an embryonic stem cell program in differentiated adult tumor cells, by transforming human keratinocytes into squamous cell carcinomas using oncogenic Ras and IκBα, plus one of three genes: c-Myc, E2F3, and GFP (Wong et al. 2009. Cell Stem Cell). Comparisons between these three genes showed that c-Myc could re-activate the embroynic stem cell program. Comparing the SNPs to this gene expression dataset can determine whether this set of SNP-associated genes are differentially regulated in c-Myc samples, when compared to other genes such as GFP and E2F3.

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