What is Total RNA Sequencing (Total RNA-Seq)?

Total RNA Sequencing (Total RNA-Seq) is a comprehensive RNA sequencing method that profiles all types of RNA molecules in a sample, including coding RNAs (mRNA) and non-coding RNAs (ncRNAs) such as long non-coding RNAs (lncRNAs), ribosomal RNA (rRNA), small nucleolar RNAs (snoRNAs), and circular RNAs (circRNAs).

By capturing both polyadenylated and non-polyadenylated transcripts, Total RNA-Seq offers a global view of the transcriptome, making it ideal for gene expression analysis, transcript discovery, and studying non-coding RNA function.

Overview

• Purpose: Capture the full RNA transcriptome, both coding and non-coding

• Input: Total RNA extracted from cells or tissues

• RNA Types: mRNA, lncRNA, snoRNA, circRNA, rRNA (optional removal), etc.

• Output: Quantitative and qualitative data on all RNA transcripts

• Applications: Transcriptomics, biomarker discovery, disease research, RNA biotype analysis

How Total RNA-Seq Works

1. RNA Extraction
   High-quality total RNA is extracted from biological samples.

2. rRNA Depletion or Globin Removal
   Ribosomal RNAs (which constitute ~80–90% of RNA) are depleted using methods like Ribo-Zero to enrich meaningful transcripts.

3. Fragmentation & cDNA Synthesis
   RNA is fragmented and reverse-transcribed into complementary DNA (cDNA).

4. Library Preparation
   cDNA is prepared into sequencing libraries with adapters for NGS.

5. Sequencing
   Libraries are sequenced using platforms like Illumina, ONT, or PacBio, producing short or long reads.

6. Data Analysis
   Reads are aligned to the reference genome or transcriptome for quantification, differential expression, splicing analysis, and novel transcript discovery.

Applications of Total RNA-Seq

• Whole Transcriptome Profiling
  Capture both coding and non-coding RNAs to understand transcriptional complexity.

• Gene Expression Quantification
  Measure expression levels of genes across conditions, time points, or treatments.

• Non-Coding RNA Discovery
  Identify lncRNAs, snoRNAs, antisense RNAs, and circular RNAs.

• Alternative Splicing Analysis
  Detect isoforms, exon skipping, and other splicing events.

• Disease Biomarker Discovery
  Uncover RNA-based signatures for cancer, neurological disorders, infections, etc.

• Functional Genomics
  Explore gene regulation, RNA decay, and chromatin-associated RNA functions.

Advantages of Total RNA-Seq

• Unbiased Transcriptome View
  Captures both poly-A and non-poly-A RNAs, including pre-mRNA and lncRNAs.

• Comprehensive
  Includes coding, non-coding, spliced, and unspliced RNA species.

• Flexible rRNA Removal
  Allows targeting specific rRNA species or globin mRNA in blood-derived samples.

• Suitable for Degraded RNA
  Especially useful for FFPE samples or low-quality RNA inputs.

• Customizable Protocols
  Compatible with single-cell, strand-specific, or long-read platforms.

Key Features of Total RNA-Seq

Limitations and Challenges

• Data Complexity
  Requires more advanced bioinformatics due to the wide variety of RNA types.

• Cost and Depth
  Higher sequencing depth is often needed to cover less abundant transcripts.

• rRNA Contamination
  Incomplete depletion can reduce effective data output.

• Batch Effects
  Sensitive to library preparation and sample processing variations.

• Requires High-Quality RNA
  Degraded RNA can affect transcript detection unless specifically adapted for.

Popular Tools and Pipelines for Total RNA-Seq Analysis

• FastQC / MultiQC – Quality control

• STAR / HISAT2 – Read alignment

• featureCounts / HTSeq – Gene-level read counting

• DESeq2 / EdgeR / limma-voom – Differential expression analysis

• StringTie / Cufflinks – Transcript assembly and quantification

• GSEA / GO / KEGG – Functional enrichment and pathway analysis

• IGV / UCSC Genome Browser – Visualization of read alignments