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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 Integrated Approach for RNA-seq Data Normalization.

Shengping Yang¹, Donald E Mercante², Kun Zhang³

¹Department of Pathology, School of Medicine, Texas Tech University Health Sciences Center, Lubbock, TX, USA.; Biostatistics Program, School of Public Health, LSU Health Sciences Center, New Orleans, LA, USA.

Cancer Informatics

|July 8, 2016

Summary

This summary is machine-generated.

This study introduces a new RNA-seq data normalization method that accounts for DNA copy number alterations. This approach improves gene expression analysis accuracy and identifies more reliable internal controls.

Keywords:

DNA copy number alterations RNA-seq normalization

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Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

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Area of Science:

Genomics
Bioinformatics
Cancer Research

Background:

DNA copy number alterations are prevalent in cancer and impact gene expression.
RNA-sequencing (RNA-seq) data normalization is crucial for accurate gene expression analysis.
Existing normalization methods do not account for DNA copy number variations.

Purpose of the Study:

To develop an integrated approach for RNA-seq data normalization that incorporates DNA copy number information.
To improve the accuracy and biological relevance of gene expression profiling.

Main Methods:

Proposed an integrated normalization method for RNA-seq data.
Evaluated the method's performance in downstream analyses.

Main Results:

The integrated normalization approach enhances the power of differential gene expression detection.
Results indicate improved biological interpretability of gene profiling.
Demonstrated that copy number changes can affect the suitability of housekeeping genes as internal controls.

Conclusions:

The proposed method effectively reduces noise from biological and non-biological sources in RNA-seq data by utilizing DNA copy number information.
This integrated approach increases the accuracy of gene profiling in cancer research.