Publication Date
2016
Journal or Book Title
BMC Bioinformatics
Abstract
Background
Low-cost DNA sequencing allows organizations to accumulate massive amounts of genomic data and use that data to answer a diverse range of research questions. Presently, users must search for relevant genomic data using a keyword, accession number of meta-data tag. However, in this search paradigm the form of the query – a text-based string – is mismatched with the form of the target – a genomic profile.
Results
To improve access to massive genomic data resources, we have developed a fast search engine, GEMINI, that uses a genomic profile as a query to search for similar genomic profiles. GEMINI implements a nearest-neighbor search algorithm using a vantage-point tree to store a database of n profiles and in certain circumstances achieves an O(log n) expected query time in the limit. We tested GEMINI on breast and ovarian cancer gene expression data from The Cancer Genome Atlas project and show that it achieves a query time that scales as the logarithm of the number of records in practice on genomic data. In a database with 105samples, GEMINI identifies the nearest neighbor in 0.05 sec compared to a brute force search time of 0.6 sec.
Conclusions
GEMINI is a fast search engine that uses a query genomic profile to search for similar profiles in a very large genomic database. It enables users to identify similar profiles independent of sample label, data origin or other meta-data information.
DOI
https://doi.org/10.1186/s12859-016-0934-8
Volume
17
Issue
102
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Funder
UMass SOAR Fund
Recommended Citation
DeFreitas, Timothy; Saddiki, Hachem; and Flaherty, Patrick, "GEMINI: a computationally-efficient search engine for large gene expression datasets" (2016). BMC Bioinformatics. 1207.
https://doi.org/10.1186/s12859-016-0934-8