6. Proteomic Data Analysis:
Functional Enrichment
Hsin-Yi Chang and Hsueh-Fen Juan∗
Institute of Molecular and Cellular Biology, National Taiwan University,
Taipei, Taiwan
∗[email protected]- Introduction
High-throughput protein identification and quantification analysis based
on mass spectrometry are evolving at a rapid pace. The state-of-art mass
spectrometry provides platform to identify complicated proteome with high
sensitivity at a relatively low cost and high reproducibility.1,2Data acquired
from mass spectrometry need to be processed, managed, visualized, and
analyzed in advance. It can be achieved by a broad diversity of commercially
available and free softwares.3–6Protein identification, hence, is presented as
routine pipelines due to development of good statistical algorithms.7–9
However, interpretation of the shotgun proteomics data is relatively
considered as a challenge. Biological systems execute reactions and bio-
logical processes in functionality aspects, which rely on coordination of
a bunch of proteins with related functions. Therefore, interpretation of
large-scale data often includes looking for the biological functions that are
enriched in lists of genes. Functional enrichment analysis uses statistical
methods to discover significantly associated functional annotations, such
as gene ontology terms, metabolic pathways, signaling pathways, protein-
protein interactions, transcriptional regulations, post-transcriptional/post-
translational modifications, and related diseases.10–13
In this chapter, we will demonstrate how to use two commonly used
bioinformatics tools for functional enrichment analysis: (1) Gene Set Enrich-
ment Analysis (GSEA)^12 and (2) the Database for Annotation, Visualization
and Integrated Discovery (DAVID).^10 Moreover, we will use EnrichmentMap
for network-based visualization of enriched functions.^14
63