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Related Experiment Video

Updated: Aug 28, 2025

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

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

Published on: March 7, 2018

Transcriptomic Harmonization as the Way for Suppressing Cross-Platform Bias and Batch Effect.

Nicolas Borisov^1,2, Anton Buzdin^1,2,3,4

¹World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, 119435 Moscow, Russia.

|September 23, 2022

Summary

This summary is machine-generated.

Harmonizing gene expression data is crucial for comparing profiles from different experiments. New methods transform data into a universal format, enabling broader analysis of transcriptomic datasets.

Keywords:

Big Data RNA sequencing batch effect data normalization and harmonization gene expression machine learning microarray hybridization transcriptional profiles universal data indexing

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Area of Science:

* Genomics
* Bioinformatics
* Computational Biology

Background:

* High-throughput quantitative transcriptomics generates vast gene expression data.
* Comparing transcriptomic profiles across different experiments, protocols, and platforms is challenging due to inherent biases.
* Millions of transcriptomic profiles in public databases are underutilized due to inter-comparison issues.

Purpose of the Study:

* To provide an overview of modern approaches for transcriptomic data harmonization.
* To focus on the practical applications of these harmonization methods.
* To address the challenge of comparing gene expression profiles from diverse sources.

Main Methods:

* Review of dozens of existing methods and software packages for transcriptomic data harmonization.
* Classification of harmonizers into flexible or predefined format categories.
* Discussion of recent developments in transforming gene expression profiles into a universal format.

Main Results:

* Platform, protocol, and batch biases can be significantly reduced in transcriptomic data.
* Instruments exist for transforming gene expression profiles into a universally applicable format.
* This harmonization enables efficient inter-comparisons and universal indexing of RNA sequencing and microarray data.

Conclusions:

* Transcriptomic harmonization is essential for maximizing the utility of public gene expression data.
* Emerging methods offer efficient solutions for reducing bias and enabling large-scale data integration.
* Practical application of these methods can unlock the full potential of transcriptomic Big Data.