Novogene RNA Seq: A Practical Guide to Sequencing, Analysis, and Interpretation
Novogene RNA Seq is a commercial high throughput RNA sequencing (RNA seq) service that delivers transcriptome wide expression data for a wide range of biological samples. This guide explains the core concepts, decision points, typical workflow, common pitfalls, and limits of interpretation. If you are a researcher considering outsourcing RNA seq or evaluating Novogene’s offerings, this guide will help you design a robust study and interpret results correctly. RNA seq has become a standard tool for quantifying gene expression, detecting alternative splicing, and discovering novel transcripts NCBI Bookshelf.
The technology behind Novogene RNA Seq typically uses Illumina short read sequencing, though specific platforms may vary by project. The output is a set of raw sequencing reads that must be processed through bioinformatics pipelines to obtain meaningful biological results. Practical guidance on these pipelines is available through community training resources Galaxy Training Network.
At a Glance
| Aspect | Typical Parameter or Consideration |
|---|---|
| Sequencing platform | Illumina NovaSeq, HiSeq, or equivalent |
| Read length | 100 150 bp paired end (most common) |
| Recommended read depth | 20 40 million reads per sample for standard gene expression, deeper for splice variants or low expression |
| RNA input requirement | 0.5 2 µg total RNA (varies by library kit) |
| RNA quality threshold | RIN >= 7 or DV200 > 70% for mammalian samples, lower for degraded samples |
| Library type | Poly A enrichment (mRNA) or ribosomal RNA depletion (total RNA) |
| Data format delivered | FASTQ files for raw reads, optional processed counts |
| Typical turnaround | 2 4 weeks after sample receipt |
| Bioinformatics support | Offered as an add on service, or you can analyze independently |
This table summarizes common service parameters, but you should always confirm exact specifications with Novogene for your project. Quality control at each step is essential EMBL EBI Training.
Decision Points for Choosing Novogene RNA Seq
When deciding whether to use Novogene or a similar provider, consider the following criteria.
Study design and biological question. If your goal is to compare transcript abundance between conditions, standard poly A RNA seq with 30 million reads per sample is usually sufficient. For non coding RNAs or degraded samples, ribosomal RNA depletion may be required.
Sample type and number. Novogene accepts a variety of sample types: tissue, cells, blood, and even some plant or microbial samples. However, sample quality is critical. For clinical or difficult samples, ask about their experience with similar material.
Bioinformatics capacity. If you lack in house bioinformatics, Novogene offers standard analysis packages that include differential expression, principal component analysis, and pathway enrichment. Alternatively, you can perform your own analysis using open source tools Bioconductor. Bioconductor provides hundreds of packages for RNA seq analysis, from alignment to visualization.
Budget and timeline. Novogene is often cost effective for large numbers of samples, but small projects may be better served by smaller core facilities. Turnaround times can vary, especially if you request custom analysis.
Data storage and sharing. Novogene typically returns raw data that can be submitted to public repositories like the NCBI Sequence Read Archive NCBI Sequence Read Archive. Plan for data management early.
Practical Workflow
A typical Novogene RNA Seq project proceeds through these steps.
1. RNA Extraction and Quality Control
Extract total RNA using a method appropriate for your sample. For mammalian tissues, a column based kit or TRIzol works well. Quantify RNA (e.g., NanoDrop, Qubit) and assess integrity using a Bioanalyzer or TapeStation. The RNA integrity number (RIN) should be above 7 for most standard libraries. Degraded samples may still be sequenced with specific protocols, but results will be less reliable.
2. Library Preparation
Novogene will prepare sequencing libraries from your submitted RNA. Two main approaches exist. Poly A enrichment captures messenger RNA (mRNA) by binding to poly A tails, it is the most popular for gene expression studies. Ribosomal RNA depletion removes abundant ribosomal RNAs, retaining both mRNA and other non coding RNAs. Your choice depends on the RNA types of interest. The library preparation includes fragmentation, reverse transcription, adapter ligation, and PCR amplification. Quality control checks include fragment size distribution and quantification.
3. Sequencing
Libraries are pooled and sequenced on a NovaSeq or similar platform. Most projects use paired end 150 bp reads, which improve mapping accuracy and enable detection of splice junctions. The sequencing depth (number of reads per sample) should be planned according to your study’s power analysis. For human or mouse transcriptomes, 30 million paired reads per sample is a common starting point.
4. Data Delivery
Novogene delivers raw FASTQ files. Some projects include processed data such as read counts per gene or transcript. You are encouraged to verify the data by running your own quality control and alignment.
5. Bioinformatics Analysis
If you choose to analyze the data yourself, a standard pipeline consists of:
- Quality trimming and adapter removal (e.g., Fastp or Trimmomatic)
- Alignment to a reference genome or transcriptome (e.g., STAR, HISAT2)
- Quantification of transcript abundance (e.g., featureCounts, Salmon)
- Differential expression analysis (e.g., DESeq2 or edgeR in Bioconductor)
- Functional enrichment (e.g., GSEA, clusterProfiler)
Detailed workflows are available from the Galaxy Training Network Galaxy Training Network. For example, their “RNA Seq analysis using reference genomes” tutorial covers each step.
6. Validation and Interpretation
Computationally identified differences should be validated by independent methods such as qPCR or western blot. The biological interpretation must be grounded in your experimental context. The transcriptome does not always reflect protein abundance.
Common Mistakes
Even experienced researchers make errors when outsourcing RNA seq. Avoid these pitfalls.
Insufficient biological replicates. RNA seq is highly reproducible but biological variation can be large. Use at least three replicates per condition, and ideally more for complex organisms or when effect sizes are small. Without replicates, differential expression results are unreliable.
Poor RNA quality. RIN values below 7 can introduce bias because degraded RNA leads to 3 prime bias in poly A libraries. If samples are degraded, ask about ribosomal RNA depletion or specialized kits.
Wrong library type. If you are interested in non coding RNAs or total transcriptome, poly A selection will miss many transcripts. Similarly, if you are working with prokaryotes, you need a different protocol.
Ignoring batch effects. If samples are processed in different sequencing runs, batch effects can confound biological signals. Randomize samples across lanes and include a pooled control sample to check for technical artifacts.
Overlooking alignment issues. For non model organisms, a high quality reference genome may be absent. In that case, de novo transcriptome assembly or mapping to a closely related species may be needed, but with lower accuracy.
Data hoarding without submission. Public data sharing is a common requirement. Plan to submit raw data to the NCBI Sequence Read Archive NCBI Sequence Read Archive to enable reproducibility.
Limits of Interpretation
RNA seq is powerful but has important limitations that affect how you interpret results.
Relative abundance only. RNA seq measures relative expression, not absolute transcript copy numbers. Comparisons between genes within a sample or across different tissues require careful normalization (e.g., reads per kilobase per million, or TPM).
Low expression detection. Very lowly expressed genes may be missed due to sequencing depth limits. Even with deep sequencing, noise increases for rare transcripts.
Transcript assembly challenges. For organisms without a complete reference, reconstructing full length transcripts from short reads is difficult. Isoform quantification remains an active research area.
Batch effects and confounders. Technical variation from library preparation, sequencing runs, or different reagent lots can obscure biological signals. Always include proper controls and randomize sample processing.
Biological validation required. RNA seq identifies candidate genes and pathways, but these must be functionally validated. The transcriptome does not always reflect protein activity due to post transcriptional regulation, protein degradation, or modifications.
Studies using RNA seq have uncovered important biological insights, such as metabolic dysregulation in steatohepatitis Novel Potential Markers of Metabolic Dysfunction Associated Steatohepatitis Prone to Hepatocellular Carcinoma, lipid heterogeneity in leukemia High content stimulated Raman pathology imaging and transcriptomics reveal leukemia subtype specific lipid metabolic heterogeneity, and transcriptomic responses in endometrium The Effect of Seminal Plasma on the Equine Endometrial Transcriptome. However, in each case the authors also used complementary methods to confirm key findings.
Frequently Asked Questions
What is the minimum RNA input required for Novogene RNA Seq?
Typical library preparation kits require 0.5 2 µg of total RNA. For low input samples, specialized kits can work with as little as 10 ng, but you should discuss this with Novogene before shipping.
How long does it take from sample submission to data delivery?
Standard turnaround is 2 4 weeks, depending on queue and library complexity. Rushed services may be available at extra cost.
Can I use Novogene RNA Seq for non model organisms without a reference genome?
Yes, but the analysis will require de novo transcriptome assembly, which is more computationally intensive and may produce fragmented transcript models. Novogene may offer de novo assembly as an add on, or you can perform it yourself using tools like Trinity.
Does Novogene provide differential expression analysis, or do I have to do it myself?
Novogene offers standard bioinformatics packages that include read alignment, quantification, and differential expression analysis using common methods such as DESeq2. However, you may have more flexibility and reproducibility by running your own analysis with public tools.
References and Further Reading
- NCBI Bookshelf. RNA Seq: A Practical Guide. NCBI Bookshelf
- EMBL EBI Training. RNA Seq Analysis. EMBL EBI Training
- Galaxy Training Network. RNA Seq Analysis Using a Reference Genome. Galaxy Training Network
- Bioconductor. RNA Seq Workflow. Bioconductor
- NCBI Sequence Read Archive. Submit and Access Raw Sequencing Data. NCBI Sequence Read Archive
- Novel Potential Markers of Metabolic Dysfunction Associated Steatohepatitis Prone to Hepatocellular Carcinoma. Can J Gastroenterol Hepatol. PubMed
- High Content Stimulated Raman Pathology Imaging and Transcriptomics Reveal Leukemia Subtype Specific Lipid Metabolic Heterogeneity. Front Immunol. PubMed
- The Effect of Seminal Plasma on the Equine Endometrial Transcriptome. Reprod Domest Anim. PubMed
- Chromosome Level Assembly of Flowering Cherry Provides Insight into Anthocyanin Accumulation. Genes (Basel). PubMed
- Chromosome Level Genome Assemblies of Four Wild Peach Species Provide Insights into Genome Evolution and Genetic Basis of Stress Resistance. BMC Biol. PubMed