What RNA types can OmniRNA-seq detect?

OmniRNA-seq simultaneously captures 8 RNA types: mRNA, lncRNA, miRNA, piRNA, tsRNA (tRNA-derived small RNA), ysRNA (Y RNA-derived small RNA), snoRNA (small nucleolar RNA), and rsRNA (rRNA-derived small RNA) — all from a single library preparation.

How does OmniRNA-seq differ from standard total RNA-seq?

Standard total RNA-seq (Ribo-depletion) primarily captures long RNAs (mRNA and lncRNA) and loses small RNAs (<200 nt) during size selection. OmniRNA-seq uses modified-independent library chemistry that retains both long and small RNA populations, enabling detection of miRNA, piRNA, tsRNA, ysRNA, snoRNA, and rsRNA in addition to mRNA and lncRNA — all from the same library.

What is the minimum RNA input requirement?

We recommend ≥200 ng of total RNA for standard samples (cells or tissues) and ≥50 ng for cell-free RNA (cfRNA) or exosomal RNA.

Can OmniRNA-seq detect circRNAs?

Yes. Since OmniRNA-seq uses Ribo-depletion (not polyA selection), it preserves circular RNAs. Our bioinformatics pipeline includes dedicated circRNA detection via back-splice junction analysis alongside linear RNA quantification.

OmniRNA-seq Service | All-in-One Whole Transcriptome Sequencing

OmniRNA-seq Overview

The transcriptome is not limited to mRNA. Non-coding RNAs — including lncRNAs, miRNAs, piRNAs, tsRNAs, and other regulatory species — comprise the majority of transcribed RNA in eukaryotic cells and play central roles in gene regulation, development, and disease. Standard RNA-seq methods systematically lose information: polyA selection captures only coding transcripts, size-selection protocols discard either small or long RNA fractions, and multiple separate libraries are required for comprehensive coverage.

Our OmniRNA-seq service solves this challenge with a modified-independent library preparation strategy that converts all RNA species to cDNA simultaneously, without bias toward specific terminal modifications or size ranges. This approach delivers:

A single comprehensive dataset — one library, one sequencing run, all eight RNA types: mRNA, lncRNA, miRNA, piRNA, tsRNA, ysRNA, snoRNA, and rsRNA
True whole-transcriptome coverage — from full-length mRNA molecules to short regulatory small RNAs (18–50 nt), all captured in the same reaction
Multi-RNA integration — enables correlation analysis between RNA species, construction of ceRNA networks, and combined biomarker discovery
Cost and sample efficiency — eliminates the need for separate small RNA-seq and long RNA-seq libraries, reducing both sequencing costs and sample input requirements

For researchers studying complex regulatory networks, liquid biopsy biomarkers, or non-coding RNA biology, OmniRNA-seq provides the most complete view of the transcriptome from the smallest possible sample investment. We also offer specialized options for focused analyses including Small RNA Sequencing and lncRNA Sequencing when targeted approaches are preferred.

Feature	OmniRNA-seq	mRNA-seq (PolyA)	Standard Total RNA-seq (Ribo-depletion)
RNA types captured	8 types: mRNA, lncRNA, miRNA, piRNA, tsRNA, ysRNA, snoRNA, rsRNA	1 type: mRNA (polyadenylated)	2–3 types: mRNA + lncRNA + partial ncRNA
Small RNA detection (<200 nt)	Yes — miRNAs, piRNAs, tsRNAs, ysRNAs, snoRNAs, rsRNAs all captured	No — polyA selection excludes small RNAs	No — Ribo-depletion may retain some small ncRNA but size selection step removes <200 nt fragments
Library preparation principle	Modified-independent — captures all RNA regardless of terminal modification	PolyA tail-dependent — mRNA only	Ribo-depletion — removes rRNA, retains remaining long RNAs
Number of libraries needed	1 — single library for all RNA types	1 (mRNA only) + separate small RNA library required	1 (long RNA only) + separate small RNA library required
Input RNA requirement	≥200 ng total RNA; ≥50 ng cfRNA/exosomal RNA	100 ng – 1 μg (polyA-selected)	100 ng – 1 μg (Ribo-depleted)
Strand-specific information	Yes	Optional	Yes
circRNA detection	Yes — back-splice junction analysis included	No — polyA excludes circRNA	Yes
Multi-RNA integration	Built-in — all data from same sample enables direct cross-RNA comparison	Not possible without a separate parallel library	Limited — small RNA data requires a separate sample/library

Comprehensive RNA Coverage: 8 Types in One Library

OmniRNA-seq captures the complete transcriptome by detecting both long and small RNA species from a single library preparation. The two major RNA populations recovered are categorized below.

Long RNA Population (>200 nt)

Principle: Ribo-depletion removes ribosomal RNA while preserving all long RNA species. Fragments are converted to strand-specific cDNA libraries.

RNA types captured:

mRNA — protein-coding transcripts, differential expression, alternative splicing, and allele-specific expression
lncRNA — intergenic, antisense, and intronic long non-coding RNAs; coding potential assessment

Data output: Full transcript-length information, isoform resolution, and integration with small RNA data from the same sample.

Small RNA Population (18–50 nt)

Principle: Small RNA fragments are retained during library preparation without size exclusion. Bioinformatic classification assigns each read to its RNA type.

RNA types captured:

miRNA — microRNA identification, quantification, isomiR analysis, and target prediction
piRNA — Piwi-interacting RNA cluster annotation and ping-pong signature analysis
tsRNA — tRNA-derived small RNA (tRF-5, tRF-3, tRF-1, i-tRF) classification
ysRNA — Y RNA-derived small RNA fragments
snoRNA — small nucleolar RNA and derived fragments
rsRNA — rRNA-derived small RNA fragments

Data output: Six small RNA type expression matrices from the same library as the long RNA data.

Bioinformatic Analysis

Our bioinformatics pipeline processes OmniRNA-seq data through a hierarchical workflow: RNA type classification → type-specific analysis → multi-RNA integration.

Analysis Package	RNA Type	Content Description
Package A: Long RNA Transcriptome	mRNA + lncRNA	QC → genome alignment (STAR/HISAT2) → quantification (featureCounts/Salmon) → mRNA differential expression (DESeq2/edgeR) → lncRNA classification and differential expression → alternative splicing (rMATS) → GO/KEGG enrichment
Package B: Small RNA Profiling	miRNA, piRNA, tsRNA, ysRNA, snoRNA, rsRNA	Size-based classification → sequential database alignment (miRBase, piRBase, GtRNAdb, snoRNABase) → miRNA identification and isomiR analysis → miRNA target prediction → piRNA cluster mapping → tsRNA fragment typing (tRF-5/3/1, i-tRF) → snoRNA/ysRNA/rsRNA quantification
Package C: Multi-RNA Integration	All 8 types	ceRNA network construction (lncRNA-miRNA-mRNA) → cross-RNA-type co-expression correlation → combined biomarker panel identification → integrative pathway enrichment → circRNA detection (CIRI2, find_circ)

Our bioinformatics team delivers a comprehensive analysis report with publication-ready figures, including RNA composition pie charts, multi-RNA heatmaps, ceRNA networks, and pathway enrichment visualizations.

Multi-RNA analytical strategy showing the RNA classification pipeline from raw reads through size-based separation to type-specific analysis and integration

Figure: Multi-RNA analytical strategy. Sequencing reads are first classified by size, then routed to long RNA (mRNA, lncRNA) or small RNA (miRNA, piRNA, tsRNA, ysRNA, snoRNA, rsRNA) analysis pipelines, and finally integrated for multi-RNA network and pathway interpretation.

Applications

OmniRNA-seq is designed for research applications that benefit from comprehensive transcriptome coverage across all RNA species from a single sample and library preparation.

Liquid Biopsy and Circulating RNA Biomarker Discovery

Cell-free RNA (cfRNA) and exosomal RNA in biofluids contain fragments from all RNA types — mRNA, lncRNA, miRNA, piRNA, tsRNA, and others — each carrying disease-specific signatures. OmniRNA-seq's low-input capability (≥50 ng cfRNA) and comprehensive RNA capture make it ideal for plasma, serum, and exosome-based biomarker discovery, where sample quantity is inherently limited.

Multi-RNA Regulatory Network Analysis

Gene regulation involves coordinated interactions between coding and non-coding RNAs. OmniRNA-seq's simultaneous capture of all RNA types from the same sample enables construction of comprehensive ceRNA networks, identification of miRNA-lncRNA-mRNA regulatory axes, and systematic analysis of non-coding RNA-mediated transcriptional and post-transcriptional control mechanisms.

Cancer Transcriptome Profiling

Cancer transcriptomes exhibit complex dysregulation spanning all RNA species — from mRNA splicing alterations and lncRNA misexpression to miRNA oncomir activity and tsRNA fragmentation changes. OmniRNA-seq provides a unified view of these multi-layered transcriptomic alterations from a single tumor sample biopsy.

Non-Coding RNA Biology and Novel RNA Discovery

For researchers focused on understanding the biological roles of specific non-coding RNA families (tsRNA, piRNA, ysRNA, or snoRNA-derived fragments), OmniRNA-seq provides simultaneous detection alongside mRNA and lncRNA — enabling correlation with coding gene expression and functional annotation. The comprehensive capture also facilitates discovery of novel small RNA species.

Extracellular Vesicle and Exosomal RNA Cargo Analysis

Extracellular vesicles (EVs) and exosomes carry a diverse RNA cargo that mediates intercellular communication. OmniRNA-seq captures the full spectrum of EV-associated RNA — from mRNA to small regulatory RNAs — enabling comprehensive characterization of the RNA repertoire packaged into vesicles under physiological and pathological conditions.

Sample Requirements

Sample Type	Minimum Requirement	Recommended Quality
Total RNA (cells or tissue)	≥200 ng	RIN ≥ 7, A260/280 ~1.8–2.0
Cell-free RNA (cfRNA) — plasma/serum	≥50 ng	Use EDTA or citrate tubes; avoid heparin
Exosomal / EV RNA	≥50 ng	Ultracentrifugation or kit-isolated
Supported species	Human, Mouse, Rat	Other species upon consultation

Experimental Design Notes:

Minimum of two experimental groups (e.g., control vs. treatment); three or more biological replicates per group recommended for statistical power.
For cfRNA/exosomal RNA: use EDTA or citrate tubes; avoid heparin-based anticoagulants which inhibit library preparation.
Non-human/non-mouse/non-rat species: please consult with our team for feasibility assessment and reference genome availability.

Case Study: Multi-RNA Profiling of Plasma Exosomes in Osteosarcoma

A 2023 study published in Scientific Data (Nature Portfolio) by Liu and colleagues used comprehensive RNA sequencing of plasma exosomes to simultaneously profile mRNA, lncRNA, and circRNA in osteosarcoma patients and healthy controls — demonstrating the power of multi-RNA analysis from a single library preparation.

Osteosarcoma is the most common primary malignant bone tumor in children and adolescents. Plasma exosomal RNAs represent a promising non-invasive biomarker source, but the full spectrum of coding and non-coding RNAs packaged into osteosarcoma-derived exosomes had not been comprehensively characterized prior to this study.

Figure 1. Study overview and experimental design.
Plasma exosome isolation and characterization from osteosarcoma patients and healthy controls, RNA extraction, library construction, and sequencing workflow. From Liu et al. 2023 (CC BY 4.0).

Methodology: Plasma exosomes were isolated from 10 osteosarcoma patients and 5 healthy donors by ultracentrifugation. Total exosomal RNA was extracted, and RNA-seq libraries were constructed using Ribo-depletion (rRNA removal) without polyA selection — enabling simultaneous capture of mRNA, lncRNA, and circRNA. Sequencing was performed on an Illumina NovaSeq 6000 (PE150).

Reads were aligned to the reference genome (hg19) using STAR, and transcript quantification was performed using featureCounts. Differentially expressed RNAs were identified by edgeR with FDR < 0.05 and |log₂FC| > 1. circRNAs were detected and quantified using find_circ and CIRI2 with a minimum of 2 unique back-spliced reads.

Results: The study identified 1,754 lincRNAs, 7,096 known circRNAs, and 1,935 novel circRNAs in plasma exosomes. Comparative analysis between osteosarcoma and healthy controls revealed:

331 differentially expressed mRNAs — genes involved in extracellular matrix organization, cell adhesion, and cancer signaling pathways
132 differentially expressed lincRNAs — including known cancer-associated lncRNAs and novel candidates
489 differentially expressed circRNAs — demonstrating that circular RNAs form a substantial component of the exosomal transcriptome

These results demonstrate that comprehensive multi-RNA profiling from a single library can simultaneously capture dysregulated coding and non-coding transcripts relevant to cancer biology.

Volcano plots and heatmaps of differentially expressed mRNAs, lincRNAs, and circRNAs

Figure 2. Differential expression of mRNAs, lincRNAs, and circRNAs in osteosarcoma exosomes.
Volcano plots and heatmaps showing 331 differentially expressed mRNAs, 132 lincRNAs, and 489 circRNAs identified in plasma exosomes of osteosarcoma patients vs. healthy controls. From Liu et al. 2023 (CC BY 4.0).

FAQ

- 1) What RNA types can OmniRNA-seq detect?
  - OmniRNA-seq simultaneously captures 8 RNA types: mRNA, lncRNA, miRNA, piRNA, tsRNA (tRNA-derived small RNA), ysRNA (Y RNA-derived small RNA), snoRNA (small nucleolar RNA), and rsRNA (rRNA-derived small RNA) — all from a single library preparation.
- 2) How does OmniRNA-seq differ from standard total RNA-seq?
  - Standard total RNA-seq (Ribo-depletion) primarily captures long RNAs (mRNA and lncRNA) and loses small RNAs (<200 nt) during size selection. OmniRNA-seq uses modified-independent library chemistry that retains both long and small RNA populations, enabling detection of miRNA, piRNA, tsRNA, ysRNA, snoRNA, and rsRNA in addition to mRNA and lncRNA — all from the same library.
- 3) What is the minimum RNA input requirement?
  - We recommend ≥200 ng of total RNA for standard samples (cells or tissues) and ≥50 ng for cell-free RNA (cfRNA) or exosomal RNA. Please contact us to discuss options for lower-input or precious samples.
- 4) Can OmniRNA-seq detect circRNAs?
  - Yes. Since OmniRNA-seq uses Ribo-depletion (not polyA selection), it preserves circular RNAs. Our bioinformatics pipeline includes dedicated circRNA detection via back-splice junction analysis (CIRI2, find_circ) alongside linear RNA quantification.
- 5) What sequencing depth is recommended?
  - We typically recommend 40–60 million paired-end reads per sample for comprehensive coverage of both small and long RNA populations. The optimal depth depends on the specific research question and can be adjusted upon consultation.
- 6) Can I run OmniRNA-seq on non-model organisms?
  - Our standard pipeline supports human, mouse, and rat with established reference genomes and curated small RNA databases. Other species may be accommodated upon consultation; however, small RNA annotation (miRNA, piRNA, tsRNA) may be limited for species without well-characterized non-coding RNA databases.
- 7) How is small RNA data analyzed separately from long RNA data?
  - Our bioinformatics pipeline first classifies reads by size: reads 18–50 nt are routed to the small RNA analysis branch, while reads >200 nt proceed to the long RNA (mRNA/lncRNA) branch. Small RNAs are sequentially aligned against specialized databases (miRBase, piRBase, GtRNAdb, snoRNABase, etc.) for type-specific annotation and quantification.
- 8) What kind of multi-RNA integration analysis do you provide?
  - Our advanced analysis includes ceRNA (competing endogenous RNA) network construction linking lncRNA-miRNA-mRNA interactions, cross-RNA-type co-expression correlation, combined biomarker panel identification using multi-RNA signatures, and pathway enrichment analysis integrating all dysregulated RNA species for systems-level interpretation.