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Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

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A scoping review on deep learning for next-generation RNA-Seq. data analysis.

Diksha Pandey¹, P Onkara Perumal²

¹Department of Biotechnology, National Institute of Technology, Warangal, Telanga na, 506004, India.

Functional & Integrative Genomics

|April 21, 2023

Summary

Deep learning shows promise for revolutionizing next-generation RNA-Sequencing (RNA-Seq.) data analysis in biomedical research. This review explores the current literature on deep learning applications for RNA-Seq. data, highlighting open-source tools.

Keywords:

Data analysis Deep learning Functional genomics Machine learning NGS Omics

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Next-generation sequencing (NGS) technologies have generated vast amounts of transcriptomic data.
RNA-Sequencing (RNA-Seq.) data analysis requires significant bioinformatics expertise and specialized software.
Challenges in RNA-Seq. data analysis include data quality, computational resources, tool selection, and machine learning implementation.

Purpose of the Study:

To review the existing literature on deep learning applications in next-generation RNA-Seq. data analysis.
To assess the current landscape and potential of deep learning in this field.
To critically analyze contemporary topics and emphasize open-source resources.

Main Methods:

Scoping review of scientific literature.
Analysis of contemporary topics in deep learning for RNA-Seq. data analysis.
Emphasis on open-source resources and tools.

Main Results:

The adoption of NGS technologies has led to an abundance of transcriptomic data.
Shallow learning approaches have been widely used, but deep learning is emerging as a powerful alternative.
Deep learning has the potential to revolutionize biomedical data analysis.

Conclusions:

Deep learning algorithms offer a promising avenue for advancing RNA-Seq. data analysis.
Further research and implementation of deep learning are crucial for maximizing the exploitation of transcriptomic data.
Open-source resources are key to facilitating the adoption and development of deep learning in this domain.