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Genomics

Genomics

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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Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

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Published on: June 28, 2018

Named Data Networking for Genomics Data Management and Integrated Workflows.

Cameron Ogle¹, David Reddick², Coleman McKnight³

¹School of Computing, Clemson University, Clemson, SC, United States.

Frontiers in Big Data

|March 22, 2021

Summary

This summary is machine-generated.

Named Data Networking (NDN) offers a solution to manage large genomics datasets, improving data discovery and retrieval speed. This novel internet architecture simplifies workflows and accelerates data insertion, addressing key cyberinfrastructure challenges for the biology community.

Keywords:

cloud computing genomics data genomics workflows large science data named data networking

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Area of Science:

Genomics
Computer Science
Bioinformatics

Background:

The biology community faces challenges managing massive, geographically distributed genomics datasets.
Data repositories like NCBI SRA are growing exponentially, nearing the exabyte scale.
Current data management struggles with size, distribution, and efficient retrieval.

Purpose of the Study:

To identify data-management challenges in the genomics community.
To introduce Named Data Networking (NDN) as a solution to these challenges.
To demonstrate NDN's application in a contemporary genomics workflow.

Main Methods:

Systematic enumeration of genomics cyberinfrastructure challenges.
Introduction and application of Named Data Networking (NDN) architecture.
Integration of NDN with the GEMmaker genomics workflow.
Pilot publication of data from NCBI SRA using an NDN naming scheme.

Main Results:

NDN simplifies data discovery and retrieval through content-based naming.
In-network caching in NDN speeds up access to popular datasets.
Preliminary evaluation shows a sixfold speed-up in data insertion for the GEMmaker workflow.
NDN facilitates federation of repositories and remote data subsetting.

Conclusions:

NDN can effectively address the data-management challenges faced by the genomics community.
NDN streamlines genomics workflows and accelerates data access.
Further integration with cloud platforms like PRP is underway to enhance usability.