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Development of Compendium for Esophageal Squamous Cell Carcinoma

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Published on: April 12, 2024

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

Timothy DeFreitas^1,2, Hachem Saddiki³, Patrick Flaherty^4,5,6

¹Computer Science Department, Worcester Polytechnic Institute, 100 Institute Rd, Worcester, 01609, USA. tmdefreitas@wpi.edu.

BMC Bioinformatics

|February 26, 2016

Summary

This summary is machine-generated.

GEMINI is a novel search engine that enables users to query massive genomic databases using a genomic profile. This tool significantly speeds up the identification of similar genomic profiles, improving data accessibility.

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Pattern-based Search of Epigenomic Data Using GeNemo

Pattern-based Search of Epigenomic Data Using GeNemo

Published on: October 8, 2017

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Advancements in low-cost DNA sequencing generate vast genomic datasets.
Current genomic data retrieval relies on text-based queries, mismatched with genomic profile data.
Existing search methods limit efficient exploration of large-scale genomic resources.

Purpose of the Study:

To develop a fast and efficient search engine for large genomic databases.
To enable similarity searches using genomic profiles as queries.
To overcome the limitations of text-based metadata searches in genomics.

Main Methods:

Developed GEMINI, a search engine utilizing a genomic profile as a query.
Implemented a nearest-neighbor search algorithm with a vantage-point tree.
Tested GEMINI on gene expression data from The Cancer Genome Atlas (TCGA).

Main Results:

GEMINI achieves a query time that scales logarithmically with database size.
Demonstrated a significant speed improvement: 0.05 seconds for nearest neighbor search in a 10^5 sample database.
Outperformed brute-force search (0.6 seconds) in practical genomic data scenarios.

Conclusions:

GEMINI provides a fast search capability for massive genomic databases.
Enables identification of similar genomic profiles irrespective of sample labels or metadata.
Enhances accessibility and utility of large-scale genomic data for research.