title: Classification of fish samples via an integrated proteomics and bioinformatics approach.
abstract: There is an increasing demand to develop cost-effective and accurate approaches to analyzing biological tissue samples. This is especially relevant in the fishing industry where closely related fish samples can be mislabeled, and the high market value of certain fish leads to the use of alternative species as substitutes, for example, Barramundi and Nile Perch (belonging to the same genus, Lates). There is a need to combine selective proteomic datasets with sophisticated computational analysis to devise a robust classification approach. This paper describes an integrated MS-based proteomics and bioinformatics approach to classifying a range of fish samples. A classifier is developed using training data that successfully discriminates between Barramundi and Nile Perch samples using a selected protein subset of the proteome. Additionally, the classifier is shown to successfully discriminate between test samples not used to develop the classifier, including samples that have been cooked, and to classify other fish species as neither Barramundi nor Nile Perch. This approach has applications to truth in labeling for fishmongers and restaurants, monitoring fish catches, and for scientific research into distances between species.
authors.affiliation: Centre for Comparative Genomics, Murdoch University, Western Australia, Australia.
authors.forename: Matthew
authors.initials: M
authors.lastname: Bellgard
authors.forename: Ross
authors.initials: R
authors.lastname: Taplin
authors.forename: Brett
authors.initials: B
authors.lastname: Chapman
authors.forename: Andreja
authors.initials: A
authors.lastname: Livk
authors.forename: Crispin
authors.initials: C
authors.lastname: Wellington
authors.forename: Adam
authors.initials: A
authors.lastname: Hunter
authors.forename: Richard
authors.initials: R
authors.lastname: Lipscombe
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D015415:Biomarkers / Q000032:analysis* / Q000737:chemistry; D019295:Computational Biology / Q000379:methods*; D029941:Fish Proteins / Q000032:analysis* / Q000737:chemistry / Q000145:classification; D010473:Perciformes; D017747:Seafood / Q000032:analysis / Q000145:classification
chemical_list: D015415:Biomarkers; D029941:Fish Proteins
keywords: Bioinformatics; Biomarkers; Fish classification; Naïve Bayes classifier