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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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An approach for normalization and quality control for NanoString RNA expression data.

Arjun Bhattacharya¹, Alina M Hamilton¹, Helena Furberg²

¹University of North Carolina at Chapel Hill.

Briefings in Bioinformatics

|August 14, 2020

Summary

A new normalization method improves NanoString RNA counting assay data by reducing technical variation in clinical samples. This robust approach enhances analysis of formalin-fixed paraffin-embedded samples, ensuring reliable results for breast cancer studies.

Keywords:

NanoString nCounter expression data visualization gene expression normalization quality control

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Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

Area of Science:

Molecular Biology
Bioinformatics
Genomics

Background:

The NanoString nCounter assay offers sensitive RNA quantification for formalin-fixed paraffin-embedded (FFPE) samples.
Existing commercial normalization methods may be suboptimal for samples with significant technical or biological variability.
Housekeeping gene performance can vary across cohorts, impacting data accuracy.

Purpose of the Study:

To develop and evaluate a comprehensive normalization procedure for NanoString nCounter data.
To improve the analysis of clinical and archival FFPE samples, especially those with high variability.
To establish a systematic approach for quality control, normalization, and data visualization.

Main Methods:

A multi-step normalization procedure including quality control, housekeeping target selection, and iterative data visualization.
Evaluation across multiple cohorts of varying sizes (N=12 to 1649) from the Carolina Breast Cancer Study and other published datasets.
Comparison with existing normalization methods, including the commercial NanoString package.

Main Results:

The developed iterative normalization process effectively eliminates technical variation across different study phases and sites.
Biological variation is preserved, crucial for analyzing complex cohorts.
Validated probe sets, like the PAM50 gene signature, demonstrated resilience to batch effects.

Conclusions:

Systematic quality control, normalization, and visualization are essential for reliable NanoString nCounter data analysis.
The proposed comprehensive normalization procedure offers a more robust alternative for diverse sample sets.
This methodology enhances the accuracy and reproducibility of RNA counting assays in clinical and archival research.