Multimodel Pathway Enrichment Methods for Functional Evaluation of Expression Regulation

Abstract

Functional analysis of quantitative expression data is becoming common practice within the proteomics and transcriptomics fields; however, a gold standard for this type of analysis has yet not emerged. To grasp the systemic changes in biological systems, efficient and robust methods are needed for data analysis following expression regulation experiments. We discuss several conceptual and practical challenges potentially hindering the emergence of such methods and present a novel method, called FEvER, that utilizes two enrichment models in parallel. We also present analysis of three disparate differential expression data sets using our method and compare our results to other established methods. With many useful features such as pathway hierarchy overview, we believe the FEvER method and its software implementation will provide a useful tool for peers in the field of proteomics. Furthermore, we show that the method is also applicable to other types of expression data.

Molecular Portrait of Breast-Cancer-Derived Cell Lines Reveals Poor Similarity with Tumors

“…Pathway analysis was conducted using FEvER 25 and MetaCore version 6.13 (Thomson Reuters, NY, USA). IPA Ingenuity (Ingenuity Systems Inc., Redwood, CA, USA) was employed to translate UniProt and transcriptomic IDs into gene symbols.…”

“…The intensity of proteins identified in MDA-MB-231 was defined as the average of the four normalized intensities in each pool, and the corresponding coefficient of variability (standard deviation and average) was computed. Pathway analysis was conducted using FEvER 25 and MetaCore version 6.13 (Thomson Reuters, NY, USA). IPA Ingenuity (Ingenuity Systems Inc., Redwood, CA, USA) was employed to translate UniProt and transcriptomic IDs into gene symbols.…”

Analysis of differentially expressed genes in cold-exposed mice to investigate the potential causes of cold-induced hypertension

“…Multiple models for pathway enrichment evaluation were utilized (23,24). Blood pressure-associated DEGs were defined by blood pressure-associated GO function terms, while hypertension-associated DEGs were selected from hypertension-associated pathways identified by known hypertension genes in the Online Mendelian Inheritance in Man (OMIM) database and the Mammalian Phenotype Ontology in the Mouse Genome Informatics (MGI) database (http://www.omim.org and http://www.informatics.jax.org/).…”

ProteoMill: efficient network-based functional analysis portal for proteomics data

“…Analysis of proteomic data faces additional challenges ( Kirik et al , 2012 ). Different gene- and protein level identifier types are utilized in the various omics tools, which often require the researcher to convert between identifier types before proceeding to the next step of the analysis.…”