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Bioinformatics approaches for the functional interpretation of protein lists: From ontology term enrichment to network analysis
Author(s) -
Laukens Kris,
Naulaerts Stefan,
Berghe Wim Vanden
Publication year - 2015
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.201400296
Subject(s) - proteomics , proteome , ontology , computer science , interpretation (philosophy) , field (mathematics) , relation (database) , task (project management) , data science , computational biology , gene ontology , term (time) , bioinformatics , information retrieval , data mining , biology , biochemistry , philosophy , gene expression , physics , mathematics , epistemology , quantum mechanics , gene , pure mathematics , programming language , management , economics
The main result of a great deal of the published proteomics studies is a list of identified proteins, which then needs to be interpreted in relation to the research question and existing knowledge. In the early days of proteomics this interpretation was only based on expert insights, acquired by digesting a large amount of relevant literature. With the growing size and complexity of the experimental datasets, many computational techniques, databases, and tools have claimed a central role in this task. In this review we discuss commonly and less commonly used methods to functionally interpret experimental proteome lists and compare them with available knowledge. We first address several functional analysis and enrichment techniques based on ontologies and literature. Then we outline how various types of network and pathway information can be used. While the problem of functional interpretation of proteome data is to an extent equivalent to the interpretation of transcriptome or other ‘‘omics’’ data, this paper addresses some of the specific challenges and solutions of the proteomics field.