What is the difference between Total RNA-Seq and mRNA-Seq?

Total RNA-Seq uses ribosomal RNA depletion to remove rRNAs while preserving all other RNA species (mRNA, lncRNA, circRNA, antisense RNA). mRNA-Seq uses poly(A) capture to selectively enrich for polyadenylated transcripts. Total RNA-Seq provides a more comprehensive view of the transcriptome.

Can Total RNA-Seq detect circular RNAs (circRNAs)?

Yes. Total RNA-Seq is the preferred method for circRNA detection because circRNAs lack polyA tails. The rRNA depletion step preserves circRNAs, and our bioinformatics pipeline includes dedicated circRNA detection modules.

How much rRNA remains after depletion?

Our protocol typically reduces rRNA content to <5% of total sequenced reads. Depletion efficiency is assessed by qPCR during library preparation and confirmed by post-sequencing rRNA read counting.

What sequencing depth is recommended for Total RNA-Seq?

Standard: 40-60 million reads per sample (PE150) for gene expression profiling. For low-abundance transcripts, novel transcript discovery, or circRNA detection: 80-100 million reads per sample.

Can Total RNA-Seq be performed on FFPE samples?

Yes, provided DV200 ≥ 40%. Our protocol is optimized for degraded RNA using random hexamer priming and is compatible with FFPE-derived RNA.

Total RNA-Seq Service | Ribosomal Depletion RNA Sequencing for Coding and Non-Coding Transcriptome

Total RNA-Seq Overview

Total RNA sequencing (Total RNA-Seq) is a powerful approach for profiling the complete transcriptome, encompassing both coding (mRNA) and non-coding RNAs (lncRNA, circRNA, snoRNA, and other regulatory transcripts). Unlike poly(A)-selected RNA-seq, which captures only polyadenylated mRNAs and some lncRNAs, total RNA-seq uses ribosomal RNA depletion to remove the highly abundant rRNA fraction (which constitutes >80% of total RNA), allowing deep sequencing of the remaining transcriptome including non-polyadenylated species.

The key technical distinction lies in the RNA selection strategy. Poly(A) capture selectively enriches mRNA by binding the 3' poly-A tail, but systematically loses non-adenylated transcripts such as circRNAs, most lncRNAs, histone mRNAs, and various small non-coding RNAs. Ribosomal RNA depletion, in contrast, removes only the unwanted rRNA and preserves all other RNA species, providing a comprehensive and unbiased view of the transcriptional landscape. Studies have demonstrated that rRNA depletion detects significantly more annotated transcripts and identifies more differentially expressed genes than poly(A) capture, particularly for low-abundance and non-coding transcripts (Zhao et al. 2014).

Our Total RNA-Seq service is designed for research applications requiring complete transcriptome coverage — from comprehensive gene expression profiling and non-coding RNA discovery to alternative splicing analysis and transcript isoform characterization. For studies focused exclusively on protein-coding transcripts, we also offer dedicated mRNA-Seq and Strand-Specific RNA Sequencing services. For targeted analysis of small RNA populations, please refer to our Small RNA Sequencing service.

Feature	Total RNA-Seq	mRNA-Seq (PolyA)	Small RNA-Seq
RNA Selection Method	rRNA depletion (Ribo-off / Ribo-minus)	Poly(A) capture (oligo-dT)	Size selection (18–30 nt) + adapter ligation
mRNA Detection	Yes	Yes (polyadenylated only)	No
lncRNA Detection	Yes — including non-polyadenylated lncRNAs	Partially — only polyadenylated lncRNAs	No
circRNA Detection	Yes — back-splice junction reads detected	No — circRNAs lack polyA tails	No
miRNA / piRNA / Other Small RNAs	Limited — small RNAs are generally lost during size selection for >100 nt fragments	No	Yes — designed for 18–30 nt small RNAs
Histone mRNA	Yes — replicates-independent histone mRNAs lack polyA tails	No — histones lack polyA tails	No
rRNA Content	Low (typically <5% after depletion)	Very low (<1%)	Low (size selection removes most rRNA)
Strand Specificity	Yes — dUTP-based strand-specific library	Optional — both strand-specific and non-stranded available	No — small RNA libraries are typically non-stranded
Recommended Read Length	PE150	PE150	SE50 / SE75
Best For	Comprehensive transcriptome analysis including coding and non-coding RNAs; novel transcript discovery; alternative splicing; circRNA detection	Gene expression profiling focused on protein-coding genes; studies where polyadenylated transcriptome is sufficient	miRNA, piRNA, siRNA, and other small non-coding RNA profiling; small RNA biomarker discovery

Technical Advantages of Our Total RNA-Seq Service

Comprehensive RNA Coverage from a Single Library

Our rRNA depletion chemistry removes both cytoplasmic (28S, 18S, 5.8S, 5S) and mitochondrial (12S, 16S) rRNAs with >95% efficiency, preserving all other RNA species for sequencing. This enables simultaneous detection of mRNAs, lncRNAs, circRNAs, antisense transcripts, enhancer RNAs (eRNAs), and histone mRNAs from a single library preparation. Unlike poly(A) selection, which systematically biases against non-polyadenylated transcripts, our total RNA-seq approach provides an unbiased view of the full transcriptional landscape. This comprehensive coverage is particularly valuable for studies of non-coding RNA biology, where many regulatory transcripts lack polyA tails and would be missed by conventional mRNA-seq approaches.

Strand-Specific Library Architecture

All Total RNA-Seq libraries are constructed using dUTP-based strand-specific chemistry, which preserves the orientation information of each sequenced transcript. Strand specificity is essential for accurate quantification of antisense transcription, identification of overlapping transcripts transcribed from opposite strands, and correct assignment of reads to the appropriate gene in regions with bidirectional transcription. It also enables precise detection of circRNA back-splice junctions, where reads spanning the junction must be correctly oriented. Our strand-specific libraries achieve >99% strand specificity, ensuring reliable expression estimates and accurate transcript annotation.

Optimised for Low-Input and Challenging Samples

Our total RNA-seq library preparation protocol is optimised for a wide range of RNA input amounts (10 ng – 1 µg total RNA) and sample types, including fresh-frozen tissue, FFPE samples, biofluids, and laser-capture microdissected specimens. For low-input samples, we employ a modified protocol with reduced adapter-dimer formation and increased PCR cycle optimisation to maintain library complexity. The rRNA depletion chemistry is compatible with partially degraded RNA (DV200 > 40%), making our service suitable for challenging sample types where poly(A)-based methods may fail due to RNA fragmentation removing the 3' polyA tail.

Bioinformatics and Data Analysis

Our bioinformatics pipeline for Total RNA-Seq data is designed to extract maximal biological insight from every library. The pipeline handles both coding and non-coding RNA quantification, with dedicated modules for specialised analysis tasks.

Analysis Package	Content Description
Standard Analysis
1. Raw Data QC and Preprocessing	FastQC quality assessment, adapter and quality trimming (Cutadapt/Fastp), rRNA content assessment, read filtering (Q ≥ 30), PCR duplicate identification. Comprehensive QC report including per-base quality, GC content, duplication rates, and alignment statistics.
2. Read Alignment and Transcript Quantification	Strand-aware alignment to reference genome (STAR 2-pass). Gene and transcript-level quantification using featureCounts and RSEM. Detection and quantification of known and novel transcripts. Multi-mapping read resolution for paralogous genes and repetitive regions.
3. Differential Expression Analysis	Identification of differentially expressed genes (DEGs) using DESeq2 or edgeR. Normalisation (TPM, FPKM, RPKM). Analysis of differentially expressed lncRNAs, circRNAs, and mRNAs separately. Multiple testing correction (FDR < 0.05) and fold-change thresholding (\|log2FC\| ≥ 1).
4. circRNA Identification and Quantification	Detection of back-splice junctions using CIRI2, find_circ, or CIRCexplorer2. Circular-to-linear ratio calculation. Identification of circRNA isoforms, exonic vs. intronic circRNA classification. Comparison of circRNA expression between experimental groups.
5. Functional Enrichment Analysis	GO enrichment analysis (Biological Process, Molecular Function, Cellular Component) for differentially expressed genes. KEGG and Reactome pathway enrichment. Gene set enrichment analysis (GSEA) for ranked gene lists. Visualisation: bar charts, dot plots, enrichment maps, GSEA plots.
Advanced Analysis
6. Alternative Splicing Analysis	Identification of differential alternative splicing events using rMATS or MAJIQ. Categorisation of skipped exons (SE), mutually exclusive exons (MXE), alternative 5'/3' splice sites (A5SS/A3SS), and retained introns (RI). Sashimi plot visualisation of splice junctions across conditions.
7. Fusion Gene and Novel Transcript Detection	Identification of gene fusions (STAR-Fusion, FusionCatcher). De novo transcript assembly (StringTie) for novel transcript discovery. Transcript isoform reconstruction and comparison with reference annotation. Coding potential assessment for novel transcripts (CPC2, CNCI).
8. ceRNA Network Analysis	Construction of competing endogenous RNA (ceRNA) networks integrating mRNA-miRNA-lncRNA and mRNA-miRNA-circRNA interactions. miRNA response element (MRE) prediction. Network topology analysis and identification of key regulatory hubs.
9. Variant Detection and Expression Quantitative Trait Loci (eQTL)	SNP/INDEL detection from RNA-seq data (GATK Best Practices). Identification of RNA editing events. Allele-specific expression analysis. Integration with genomic variation data for eQTL mapping (where matched WGS/WES data are available).

Our bioinformatics team provides a comprehensive analysis report with publication-ready figures including expression heatmaps, volcano plots, pathway enrichment visualisations, splice event sashimi plots, and circRNA genomic annotations. Data are delivered in standard formats (FASTQ, BAM, count matrices, FPKM/TPM tables, DEG lists, circRNA coordinates) for downstream analysis and deposition in public repositories.

Applications

Total RNA-Seq is broadly applicable across transcriptomics research, offering particular advantages in areas where non-coding RNAs and transcript diversity are central to the biological question.

Comprehensive Transcriptome Profiling

Total RNA-Seq provides the most complete view of the transcriptome, capturing protein-coding and non-coding RNAs simultaneously. This is essential for studies aiming to characterise the full transcriptional response to a perturbation, disease state, or developmental transition. The ability to detect lncRNAs, circRNAs, and other non-coding transcripts alongside mRNAs enables researchers to build a complete picture of gene regulatory networks and identify RNA species that may serve as biomarkers or therapeutic targets. Applications range from baseline transcriptome cataloguing in non-model organisms to comprehensive characterisation of disease-associated transcriptional changes.

Non-coding RNA Discovery and Functional Characterisation

Total RNA-Seq is the method of choice for lncRNA and circRNA discovery because it captures non-polyadenylated transcripts missed by other methods. Numerous studies have used total RNA-seq to identify novel lncRNAs involved in cancer progression, cardiovascular disease, and neurodevelopment. The strand-specific nature of our libraries allows accurate determination of transcript orientation, which is critical for annotating antisense lncRNAs and distinguishing overlapping sense/antisense transcript pairs. Combined with RNA-seq after perturbation (knockdown/knockout), total RNA-seq can also identify downstream targets of specific non-coding RNAs.

Alternative Splicing and Isoform Diversity Analysis

Alternative splicing generates transcript diversity that vastly expands the coding capacity of the genome. Total RNA-Seq with deep sequencing coverage (80–100 M reads per sample) enables comprehensive detection and quantification of alternatively spliced transcripts. Our advanced analysis pipeline identifies all major splicing event types (skipped exons, alternative 5'/3' splice sites, mutually exclusive exons, retained introns) and quantifies their usage differences between conditions. This application is particularly valuable in cancer research, where splicing dysregulation is a hallmark, and in neurobiology, where tissue-specific splicing patterns shape neuronal function and plasticity.

Biomarker Discovery in Liquid Biopsy and Tissue Studies

Total RNA-Seq of biofluids (plasma, serum, urine, CSF) and tissue biopsies enables unbiased biomarker discovery by capturing all RNA species without a priori selection. Circulating lncRNAs and circRNAs in biofluids have emerged as promising non-invasive biomarkers for cancer, cardiovascular disease, and neurological disorders, often providing complementary information to protein-coding biomarkers. Total RNA-seq's ability to simultaneously assess multiple RNA biotypes increases the probability of identifying robust multi-analyte biomarker panels. For FFPE tissue archives, our optimised low-input protocol enables retrospective biomarker discovery studies using clinical specimens.

Integrative and Multi-Omics Transcriptome Analysis

Total RNA-Seq data can be integrated with other omics data types — including whole-genome sequencing (WGS), ChIP-seq, ATAC-seq, DNA methylation, and proteomics — to build mechanistic models of gene regulation. Integration with genomic variation data enables expression quantitative trait locus (eQTL) mapping and allele-specific expression analysis. Comparison with matched epigenomic data (H3K27ac, H3K4me3, H3K27me3 ChIP-seq) reveals the chromatin regulatory landscape underlying differential expression. For translational research, total RNA-seq data can be integrated with drug response datasets to identify biomarkers of treatment response and resistance mechanisms.

Sample Requirements

Sample Type	Recommended Amount	Quality Requirements
Total RNA (intact)	100 ng – 1 µg	RIN ≥ 7; 28S/18S ratio ≥ 1.5; A260/A280 ≥ 1.8; A260/A230 ≥ 1.5
Total RNA (degraded / FFPE)	200 ng – 1 µg	DV200 ≥ 40%; no severe DNA contamination; A260/A280 ≥ 1.6
Fresh-frozen tissue	10–30 mg	Snap-frozen in liquid nitrogen; stored at −80°C; no RNAlater required
FFPE tissue sections	5–10 sections (10 µm thick)	H&E QC available; RNA extraction performed in-house for optimal yield
Biofluid (plasma/serum)	200 µL – 1 mL	EDTA or citrate tubes preferred; haemolysis-free; processed within 2 h of collection
Cell pellet / Cultured cells	1 × 10⁶ – 1 × 10⁷ cells	Washed in PBS; snap-frozen or TRIzol-lysed; RNA stabilisation reagent accepted

Important Notes:

All samples should be shipped on dry ice or in RNA stabilisation reagent (RNAlater or similar). Avoid multiple freeze-thaw cycles.
For total RNA samples, please provide concentration (ng/µL), A260/A280, A260/A230, and RIN/DV200 values with the submission form.
Minimum 3 biological replicates per experimental group is recommended for robust statistical analysis. 5+ replicates are recommended for studies with high expected biological variability or complex designs.
For studies requiring circRNA detection, we recommend higher sequencing depth (≥80 M reads per sample) and at least 4 biological replicates per group to achieve sufficient power for circRNA-specific differential expression analysis.
Please include a minimum of one control sample per batch (technical replicate or reference RNA) to enable batch effect assessment in multi-batch studies.
For species without a well-annotated reference genome, please contact our team for a feasibility assessment — de novo transcriptome assembly may be recommended.

Case Study: Whole Transcriptome Sequencing Reveals ceRNA Regulatory Networks in Colorectal Cancer

A 2024 study published in Scientific Reports by Zhang and colleagues used whole transcriptome sequencing (total RNA-seq with rRNA depletion) to systematically characterise coding and non-coding RNA expression in colorectal cancer (CRC) and construct comprehensive ceRNA regulatory networks linking lncRNAs, circRNAs, miRNAs, and mRNAs.

Colorectal cancer (CRC) is one of the most common malignancies worldwide, with complex molecular heterogeneity that complicates diagnosis, prognosis, and treatment selection. While protein-coding genes have been extensively studied in CRC, the roles of non-coding RNAs — particularly lncRNAs and circRNAs — in CRC pathogenesis are less well understood. Competing endogenous RNA (ceRNA) networks, in which lncRNAs and circRNAs sequester miRNAs away from their mRNA targets through shared miRNA response elements (MREs), represent an important layer of post-transcriptional regulation that has been implicated in cancer progression, metastasis, and drug resistance. However, a systematic characterisation of the ceRNA landscape in CRC using total RNA-seq had been lacking.

Study design and workflow for whole transcriptome sequencing of colorectal cancer tissues Figure 1. Study design for whole transcriptome sequencing-based ceRNA network analysis in colorectal cancer.
Total RNA was extracted from 10 paired CRC and adjacent normal tissues. Ribosomal RNA was depleted (Ribo-off kit), and strand-specific libraries were sequenced on Illumina NovaSeq 6000 (PE150). Bioinformatic analysis identified differentially expressed mRNAs, lncRNAs, circRNAs, and miRNAs, which were used to construct mRNA-miRNA-lncRNA and mRNA-miRNA-circRNA ceRNA networks. Key network hubs were validated using TCGA-COAD data and in vitro experiments. Adapted from Zhang et al. 2024 (CC BY 4.0).

Whole transcriptome sequencing approach: The authors collected 10 paired CRC tumour and adjacent normal tissue samples from patients undergoing surgical resection. Total RNA was extracted and subjected to ribosomal RNA depletion using a Ribo-off rRNA Depletion Kit, which removes both cytoplasmic and mitochondrial rRNAs. Strand-specific RNA-seq libraries were constructed and sequenced on the Illumina NovaSeq 6000 platform with PE150 read length, generating approximately 60–80 million reads per sample. Bioinformatic analysis was performed using a multi-layer pipeline: (1) alignment to the human reference genome (GRCh38) using STAR; (2) gene-level quantification with featureCounts for mRNAs and lncRNAs; (3) circRNA detection and quantification using CIRI2 and CIRCexplorer2; (4) miRNA quantification from the same total RNA-seq data; (5) differential expression analysis using DESeq2 with FDR correction; (6) ceRNA network construction using miRanda and RNAhybrid for MRE prediction, with network visualisation in Cytoscape. Key findings were validated using TCGA-COAD and GEO public datasets (GSE50760, GSE90652), and in vitro functional experiments (KPNA2 knockdown in HCT116 and RKO cell lines).

Figure 2. Differential expression and ceRNA network analysis results.
Total RNA-seq identified thousands of differentially expressed RNAs spanning all major biotypes, including mRNAs, lncRNAs, circRNAs, and miRNAs. ceRNA networks were constructed based on shared miRNA response elements. Validation using TCGA-COAD data and public GEO datasets confirmed the robustness of the identified regulatory interactions. Adapted from Zhang et al. 2024 (CC BY 4.0).

Key findings: (1) Whole transcriptome sequencing identified 2,465 differentially expressed mRNAs (1,236 up-regulated, 1,229 down-regulated), 2,852 differentially expressed lncRNAs (1,498 up, 1,354 down), 1,477 differentially expressed circRNAs (789 up, 688 down), and 77 differentially expressed miRNAs in CRC tissues compared with adjacent normal controls. (2) Functional enrichment analysis revealed that target genes of dysregulated ceRNAs were enriched in pathways related to cell cycle, DNA replication, p53 signalling, PI3K-Akt signalling, and Wnt signalling — all pathways with established roles in colorectal carcinogenesis. (3) The mRNA-miRNA-lncRNA ceRNA network comprised 425 nodes and 875 edges, while the mRNA-miRNA-circRNA network comprised 336 nodes and 702 edges. (4) KPNA2 (karyopherin subunit alpha 2) was identified as a key hub gene in the ceRNA network, and its knockdown in CRC cell lines significantly suppressed cell proliferation, migration, and invasion. (5) Validation using TCGA-COAD data confirmed that KPNA2 was significantly overexpressed in CRC and associated with poor prognosis, and that the ceRNA interactions identified from the study's total RNA-seq data were reproducible in this independent cohort. This study demonstrates the power of total RNA-seq for comprehensive transcriptome analysis and its utility in identifying clinically relevant regulatory mechanisms in cancer.

FAQs — Frequently Asked Questions

- 1) What is the difference between Total RNA-Seq and mRNA-Seq?
  - The key difference lies in the RNA selection method. Total RNA-Seq uses ribosomal RNA depletion to remove rRNAs while preserving all other RNA species, including both polyadenylated and non-polyadenylated transcripts. This allows detection of mRNAs, lncRNAs, circRNAs, antisense RNAs, and other non-coding RNAs from a single library. mRNA-Seq uses poly(A) capture (oligo-dT beads) to selectively enrich for polyadenylated transcripts, which are primarily protein-coding mRNAs and some lncRNAs. mRNA-Seq is more cost-effective if you are only interested in protein-coding gene expression, but Total RNA-Seq provides a more comprehensive view of the transcriptome including non-coding RNA species.
- 2) Can Total RNA-Seq detect circular RNAs (circRNAs)?
  - Yes — Total RNA-Seq is the preferred method for circRNA detection because circRNAs lack polyA tails and would be missed by poly(A)-based methods. The rRNA depletion step in total RNA-seq preserves circRNAs, and the strand-specific library construction enables accurate detection of back-splice junctions (the unique hallmark of circRNA formation). Our bioinformatics pipeline includes dedicated circRNA detection modules (CIRI2, CIRCexplorer2, or find_circ) that identify back-splice junction reads and quantify circular-to-linear ratios. However, note that the linear fragments generated from circRNAs during fragmentation may be indistinguishable from linear RNA fragments; therefore, circRNA quantification relies specifically on reads spanning back-splice junctions.
- 3) How much rRNA remains after depletion?
  - Our optimised rRNA depletion protocol typically reduces rRNA content to <5% of total sequenced reads (compared to >80% in non-depleted total RNA). We assess depletion efficiency at two stages: (1) during library preparation by qPCR (ΔCq between pre- and post-depletion samples) and (2) post-sequencing by counting reads aligning to rRNA reference sequences. If depletion efficiency falls below 90%, the sample is flagged and, where sufficient material permits, re-processed. The exact residual rRNA percentage can vary depending on sample quality, RNA integrity, and the specific depletion chemistry used.
- 4) What sequencing depth is recommended for Total RNA-Seq?
  - We recommend 40–60 million reads per sample (PE150) for standard gene expression profiling and differential expression analysis. For studies requiring detection of low-abundance transcripts, novel transcript discovery, or comprehensive alternative splicing analysis, we recommend 80–100 million reads per sample. For circRNA detection specifically, deeper sequencing (≥80 M reads) improves the detection of back-splice junctions, which are typically present at low abundance. The optimal sequencing depth depends on your specific research objectives, the expected expression levels of your genes/transcripts of interest, and the complexity of your experimental design. Our project managers will work with you to determine the appropriate sequencing depth.
- 5) Can Total RNA-Seq be performed on FFPE samples?
  - Yes — our Total RNA-Seq protocol is compatible with FFPE-derived RNA, provided the RNA quality meets minimum thresholds (DV200 ≥ 40%). FFPE RNA is typically degraded and fragmented, with reduced average fragment size compared to fresh-frozen samples. Our protocol has been optimised for degraded RNA: the rRNA depletion chemistry is effective even on fragmented RNA templates, and the library preparation uses random hexamer priming rather than oligo-dT, avoiding the 3' bias that would affect poly(A)-based methods on degraded RNA. However, please note that FFPE RNA generally yields lower library complexity and requires additional PCR cycles. We recommend submitting at least 200 ng of FFPE-derived total RNA and discussing expected data yields with our project management team before initiating the project.
- 6) What are the limitations of Total RNA-Seq?
  - Total RNA-Seq has several important limitations to consider: (1) Small RNAs (miRNAs, piRNAs, tRFs < 30 nt) are generally not captured efficiently because the library preparation workflow includes a size selection step that retains fragments >100 nt. For small RNA analysis, we recommend our dedicated Small RNA Sequencing service. (2) rRNA depletion efficiency can vary between sample types and species; some species-specific depletion probes may not be available for non-model organisms. (3) The cost per sample is typically higher than mRNA-Seq due to the additional depletion step and the need for deeper sequencing to achieve equivalent coverage of protein-coding transcripts. (4) Computational analysis is more complex due to the presence of non-coding RNA species and the need for multi-level quantification.
- 7) How long does the Total RNA-Seq service take?
  - A standard Total RNA-Seq project typically takes 6–8 weeks from sample receipt to data delivery, depending on sample number, sequencing depth, and analysis complexity. This includes RNA QC (2–3 business days), rRNA depletion and library preparation (5–7 business days), sequencing (10–14 business days), and standard bioinformatic analysis (5–7 business days). Express sequencing options are available for time-sensitive projects — please contact our team to discuss expedited timelines. Advanced bioinformatics analysis (alternative splicing, ceRNA networks, fusion gene detection) adds an additional 7–10 business days to the standard analysis timeline.
- 8) What species are supported by your rRNA depletion probes?
  - Our standard rRNA depletion probes cover human, mouse, rat, zebrafish, Drosophila, Arabidopsis, C. elegans, E. coli, and yeast rRNAs. For other species, we offer custom probe design services or can use universal depletion approaches. Contact our team to confirm probe availability for your species of interest before submitting samples. For species without validated rRNA depletion probes, we recommend discussing alternative strategies, which may include poly(A) selection (mRNA-seq) or custom rRNA probe design based on the species' rRNA sequences.

References:

Zhao W, He X, Hoadley KA, et al. Comparison of RNA-Seq by poly (A) capture, ribosomal RNA depletion, and DNA microarray for expression profiling. BMC Genomics. 2014;15:419.
Conesa A, Madrigal P, Tarazona S, et al. A survey of best practices for RNA-seq data analysis. Genome Biology. 2016;17:13.
Zhang L, Li Y, Fu C, et al. Exploration and validation of ceRNA regulatory networks in colorectal cancer based on associations whole transcriptome sequencing. Scientific Reports. 2024;14:20446.
Adiconis X, Borges-Rivera D, Satija R, et al. Comparative analysis of RNA sequencing methods for degraded or low-input samples. Nature Methods. 2013;10(7):623-629.

For Research Use Only. This service is intended for transcriptome profiling and gene expression analysis applications. It is not intended for clinical diagnosis, treatment selection, patient stratification, or therapeutic decision-making.

Total RNA-Seq Service

Total RNA-Seq Overview

Total RNA-Seq vs. mRNA-Seq vs. Small RNA-Seq