The hitchhikers' guide to RNA sequencing and functional analysis

Area of Science:

• Genomics and Molecular Biology
• Bioinformatics and Computational Biology

Background:

• High-throughput sequencing technologies like RNA sequencing (RNA-Seq) are crucial for transcript identification and quantification.
• Researchers often face challenges in analyzing raw RNA-Seq data, including differential expression, pathway analysis, and interpretation.
• Existing pipelines offer various approaches, but comprehensive guidance on deriving actionable knowledge is limited.

Purpose of the Study:

• To provide a detailed explanation of RNA sequencing analysis steps.
• To compare popular RNA-Seq analysis options, highlighting their advantages and disadvantages.
• To illustrate the impact of algorithmic decisions on RNA-Seq results and interpretation.

Main Methods:

• Detailed explanation of standard RNA-Seq analysis workflow: quality control, read alignment, summarization, differential expression analysis, and gene set analysis.
• Discussion of advanced topics including non-coding RNA, multi-omics, meta-transcriptomics, and artificial intelligence in RNA-Seq.
• Practical demonstration of a complete RNA-Seq analysis from raw reads to functional enrichment.

Main Results:

• RNA-Seq analysis involves sequential decision-making points, with diverse options impacting outcomes.
• Algorithmic choices significantly influence the final results and their biological interpretation.
• Results from RNA-Seq analyses are not absolute and require careful consideration of analytical methods.

Conclusions:

• This paper offers a comprehensive guide to RNA sequencing data analysis, empowering researchers to navigate complex workflows.
• Understanding the nuances of different analytical choices is critical for accurate interpretation of transcriptomic data.
• The study emphasizes the importance of critical evaluation of RNA-Seq results, considering the influence of analytical methodologies.