Pathway analysis

In bioinformatics research, Pathway Analysis Software is used to identify related proteins within a pathway. This is helpful when studying differential expression of a gene in a disease. By examining the changes in gene expression in a pathway, the biological causes of a phenotype can be explored.

Uses

By using pathway analysis software, researchers can determine up and down regulated proteins and expression changes in a network overall. They can also infer upstream regulators, downstream molecules, and associated diseases. High throughput sequencing data can be used for pathway analysis. This includes data from RNA-seq and microarray data. However, the gene expression data must be compared to a standard. This calls for a large, comprehensive database to draw on.

Historical uses

Over the last decade, pathway analysis software has undergone changes.[1] The different methods include:

Over-Representation Analysis (ORA)

This method assesses the percentage of genes in a pathway that have differential expression.

Functional Class Scoring (FCS)

This method analyzes the change in expression of a pathway overall.

Pathway Topology (PT)

Pathway topology is essentially the same as FCS, except PT uses gene-level statistics.

Notable companies

Several companies have licensed software to perform a number of analytic methods on a gene or gene set. Ingenuity, for example, charges a fee for use of their software. Some software, like STRING, is open-source. However, Ingenuity maintains a knowledge base to compare gene expression data to.[2]

Limits

Missing annotations on cell types and conditions

Many current methods for pathway analysis depend on already existing databases. The data used, however, is not always completely annotated. Many of the literature values for gene expression are pulled from a specific cell type or disease. This affects gene expression, and so complete annotation of literature data is important.[3]

References

  1. ↑ Khatri P, Sirota M, Butte AJ. Ten years of pathway analysis: current approaches and outstanding challenges. Plos Comput Biol. 2012;8(2)
  2. ↑ "Ingenuity IPA - Integrate and Understand Complex 'omics Data." Ingenuity. Web. 8 Apr. 2015. <http://www.ingenuity.com/products/ipa#/?tab=features>.
  3. ↑ Henderson-Maclennan, Nicole K., Jeanette C. Papp, C. Conover Talbot, Edward R.b. Mccabe, and Angela P. Presson. "Pathway Analysis Software: Annotation Errors and Solutions."Molecular Genetics and Metabolism (2010): 134-40. PMC. Web. 8 Apr. 2015.
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