GEMINI: a computationally-efficient search engine for large gene expression datasets

Abstract

BackgroundLow-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.ResultsTo 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 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 105 samples, GEMINI identifies the nearest neighbor in 0.05 sec compared to a brute force search time of 0.6 sec.ConclusionsGEMINI 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-0934-8) contains supplementary material, which is available to authorized users.

Both rare and common genetic variants contribute to autism in the Faroe Islands

“…VEP (using RefSeq and Ensembl 91) was used to annotate the variants. We used the GEMINI 43 framework that automatically integrates the VCF file into a database for exploring genetic variant for disease and population genetics. Genetic variants were analyzed using GRAVITY, a Cytoscape plugin that we designed for visualizing WES results using Protein-Protein Interaction networks (http://gravity.pasteur.fr/).…”

A New Anonymization Model for Privacy Preserving Data Publishing: CANON

“…• Vantage-point: a point which is selected from dataset, • Radius: a distance defining the range of vantage-point, • Left-hand side: the left subtree including the data points which are smaller than or equal to the radius of a vantagepoint and, • Right-hand side: the right subtree including the data points that are greater than the radius of a vantage-point. The main steps of a VP-tree construction are presented as below [46], [51][52][53][54][55]; 1) choose a vantage-point, 2) calculate the distances between the vantage-point and the others, 3) find the median of these distances, 4) accept the median as a splitting value, 5) according to the splitting value, partition data space into two subspaces, 6) go to step 1 until no data point is left.…”

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“…Annotating and filtering large numbers of variant alleles require specialty software. Existing annotators, such as ANNOVAR [ 1 ], SeqAnt [ 2 ], VEP [ 3 ], and GEMINI [ 4 ] have played an important research role, and are sufficient for small to medium experiments (e.g., read 10s to 100s of WES samples). However, they require significant computer science training to use in offline, distributed computing environments and have substantial restrictions in terms of performance and the maximum size of the data they will annotate online.…”