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Related Concept Videos

Genomics02:02

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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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Related Experiment Video

Updated: Dec 23, 2025

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Advancing clinical cohort selection with genomics analysis on a distributed platform.

Jaclyn M Smith1,2, Melvin Lathara2, Hollis Wright2

  • 1Department of Computer Science, University of Oxford, Oxford, United Kingdom.

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Summary

The ODA framework integrates genomic data and distributed computing for scalable precision medicine. It enables efficient analysis of clinical and genomic data, supporting personalized diagnoses and treatments in a real-world setting.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Advancements in genomic sequencing and medical data management drive the evolution of biological analysis.
  • Precision medicine requires analyzing large-scale, multi-modal biomedical datasets for personalized diagnosis and treatment.
  • Existing platforms need to optimize data storage, distributed querying, and scalability for precision medicine.

Purpose of the Study:

  • To extend GenomicsDB into a distributed analytics platform (ODA framework) for clinical and genomic data integration.
  • To support precision medicine analysis through seamless interaction with i2b2 plugins and notebook environments.
  • To demonstrate the scalability and efficiency of the ODA framework for real-world precision medicine applications.

Main Methods:

  • Extended GenomicsDB, a genomics-based columnar data store, into the ODA framework.
  • Integrated distributed storage and query mechanisms for large-scale biomedical datasets.
  • Evaluated the framework's performance for data import, storage, and query execution times with increasing sample sizes.

Main Results:

  • The ODA framework demonstrated worst-case linear scaling for array size, import time, and query time as the number of samples increased.
  • Import of clinical data and aggregate query execution also showed worst-case linear time performance in a distributed environment.
  • The framework successfully integrates distributed genomic databases with distributed compute environments for efficient querying.

Conclusions:

  • The ODA framework provides a scalable and efficient solution for precision medicine queries.
  • It supports HIPAA-compliant cohort systems, enabling real-world analysis for clinicians and researchers.
  • The framework is deployed in production at UCLA David Geffen School of Medicine for precision medicine exploration.