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Archival Research

Archival Research

Some researchers gain access to large amounts of data without interacting with a single research participant. Instead, they use existing records to answer various research questions. This type of research approach is known as archival research. Archival research relies on looking at past records or data sets to look for interesting patterns or relationships. For example, a researcher might access the academic records of all individuals who enrolled in college within the past ten years and...

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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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Exploring the Effects of Spaceflight on Mouse Physiology using the Open Access NASA GeneLab Platform

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CNSA: a data repository for archiving omics data.

Xueqin Guo¹, Fengzhen Chen¹, Fei Gao¹

¹China National GeneBank, Shenzhen 518120, China.

Database : the Journal of Biological Databases and Curation

|July 25, 2020

Summary

This summary is machine-generated.

The China National GeneBank Sequence Archive (CNSA) database manages massive omics data from high-throughput sequencing. It ensures data traceability and accessibility for life science research and the bio-industry.

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Last Updated: Dec 14, 2025

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Area of Science:

Genomics
Bioinformatics
Data Science

Background:

High-throughput sequencing generates massive multi-omics data, necessitating efficient management and utilization.
Database development and biocuration are crucial for the reuse of large-scale biological data.

Purpose of the Study:

To present the China National GeneBank Sequence Archive (CNSA) for archiving and managing omics data.
To establish a correlation model for tracing data throughout its lifecycle, from living samples to analytical results.

Main Methods:

Archiving raw sequencing data and analyzed results within CNSA.
Organizing data into six objects: Project, Sample, Experiment, Run, Assembly, and Variation.
Implementing a correlation model linking living samples, sample information, and analytical data.

Main Results:

CNSA provides a structured repository for omics data, including raw and analyzed results.
A correlation model enables data traceability from living samples to analytical outcomes.
Data is organized according to established life science data standards.

Conclusions:

CNSA serves as a comprehensive, curated data repository for omics data storage, management, and sharing.
The database promotes data reuse and accessibility for the global scientific community.
CNSA supports academic research and the bio-industry through open-access data resources.