A survey of best practices for RNA-seq data analysis

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• RNA-sequencing (RNA-seq) is a powerful tool for transcriptome analysis.
• No single RNA-seq analysis pipeline is universally applicable.
• Diverse applications necessitate customized analytical approaches.

Purpose of the Study:

• To provide a comprehensive review of RNA-seq data analysis.
• To highlight challenges and solutions at each analytical stage.
• To discuss the integration of RNA-seq with other omics and future technologies.

Main Methods:

• Review of major RNA-seq analysis steps: experimental design, quality control, read alignment, quantification, visualization.
• Exploration of advanced analyses: differential gene expression, alternative splicing, functional analysis, gene fusion detection, eQTL mapping.
• Discussion of small RNA analysis and integration with functional genomics.

Main Results:

• Identified key challenges in each step of RNA-seq data processing.
• Outlined strategies for analyzing small RNAs and integrating multi-omics data.
• Presented an outlook on novel transcriptomic technologies.

Conclusions:

• Effective RNA-seq analysis depends on careful consideration of experimental goals and data characteristics.
• Addressing analytical challenges is crucial for reliable transcriptomic insights.
• Emerging technologies promise to advance the field of transcriptomics.