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Biobank for Translational Medicine: Standard Operating Procedures for Optimal Sample Management

Biobank for Translational Medicine: Standard Operating Procedures for Optimal Sample Management

Published on: November 30, 2022

Biobank informatics: connecting genotypes and phenotypes.

Jan-Eric Litton¹

¹Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. jan-eric.litton@ki.se

Methods in Molecular Biology (Clifton, N.J.)

|October 16, 2010

Summary

This summary is machine-generated.

Integrating human genome data with demographic and lifestyle information is now possible, revolutionizing genetic risk factor research. Biobank informatics is crucial for managing this complex data for epidemiology and clinical studies.

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Infinium Assay for Large-scale SNP Genotyping Applications

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Published on: November 19, 2013

Area of Science:

Genomics
Bioinformatics
Epidemiology

Background:

The human genome sequencing enables integration of genetic risk data with modern demographic and lifestyle information.
Technical advancements facilitate merging molecular genetic data with national health registers, epidemiological, and clinical data.
Long-term storage of biological samples and data is essential for epidemiological and clinical research.

Purpose of the Study:

To highlight the critical role of informatics in designing and managing biobanks.
To discuss the integration of diverse data sources for comprehensive research.

Main Methods:

Utilizing informatics for sample and data handling in biobanks.
Standardizing information from population registries, biobanks, patient records, and molecular measurements.

Main Results:

Informatics is vital for efficient biobank operations.
Standardized data management enables robust integration of multi-source information.

Conclusions:

Biobank informatics is essential for managing complex, integrated datasets.
This integration supports advanced epidemiological and clinical research by leveraging genomic and lifestyle data.