mspecLINE: bridging knowledge of human disease with the proteome View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2010-03-10

AUTHORS

Jeremy Handcock, Eric W Deutsch, John Boyle

ABSTRACT

BackgroundPublic proteomics databases such as PeptideAtlas contain peptides and proteins identified in mass spectrometry experiments. However, these databases lack information about human disease for researchers studying disease-related proteins. We have developed mspecLINE, a tool that combines knowledge about human disease in MEDLINE with empirical data about the detectable human proteome in PeptideAtlas. mspecLINE associates diseases with proteins by calculating the semantic distance between annotated terms from a controlled biomedical vocabulary. We used an established semantic distance measure that is based on the co-occurrence of disease and protein terms in the MEDLINE bibliographic database.ResultsThe mspecLINE web application allows researchers to explore relationships between human diseases and parts of the proteome that are detectable using a mass spectrometer. Given a disease, the tool will display proteins and peptides from PeptideAtlas that may be associated with the disease. It will also display relevant literature from MEDLINE. Furthermore, mspecLINE allows researchers to select proteotypic peptides for specific protein targets in a mass spectrometry assay.ConclusionsAlthough mspecLINE applies an information retrieval technique to the MEDLINE database, it is distinct from previous MEDLINE query tools in that it combines the knowledge expressed in scientific literature with empirical proteomics data. The tool provides valuable information about candidate protein targets to researchers studying human disease and is freely available on a public web server. More... »

PAGES

7

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1755-8794-3-7

DOI

http://dx.doi.org/10.1186/1755-8794-3-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1049012532

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/20219133


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