FAQs Next Generation Sequencing

• General FAQs for Next Generation Sequencing >>

This depends on many factors, such as tissue type, sample quality, development status, existing references, etc. Please refer to the table shown under Product Specifications to find your optimum INVIEW Transcriptome solution. The estimated read numbers shown there refer to human samples. Alternatively, please contact us to discuss your project. In some cases it might be advisable to set up a trial to define how many reads will be required for your specific aim or organism.


INVIEW Transcriptome Discover uses a strand-specific RNA library combined with Illumina’s 150 bp paired-end sequencing and is therefore recommended for the detection of differentially expressed genes, rare and novel transcripts and for discovering splice variants. In addition, the information about the transcript’s orientation allows for a more precise determination of structure and gene expression.

RNA-Seq of bacterial samples most often requires less reads since no splice variants are present. The INVIEW Transcriptome Bacteria is a cost-alternative for all projects interested in the gene expression profile of Bacteria.

The INVIEW Transcriptome Ultra Low is specially designed for all projects with only a tiny amount of starting material.

INVIEW Transcriptome Discover:

Not necessarily. However, if you have a reference, please refer to the recommendations below.

INVIEW Transcriptome Bacteria / Ultra Low:

A clearly defined Ensembl name (e.g., GRCh37 or Rnor_5.0) for the annotated genomic reference sequence has to be provided prior to project start. Alternatively, the genome sequence can be provided in Fasta (along with the corresponding annotation in Gene transfer format (gtf)) or in GenBank format (including annotation).

We recommend starting with total RNA (DNA-free). For detailed sample submission requirements please visit our Sample Submission Guidelines.

To successfully extract RNA for RNA sequencing please follow these guidelines:

• Use Quality Reagents: Choose high-quality reagents and kits specifically designed for RNA extraction to ensure maximum yield and purity.
• Tissue or Cell Handling:
  • If using tissue, immediately snap-freeze or store it in RNA preservation solutions (like RNAlater) to prevent RNA degradation.
  • For cell cultures, process samples promptly and keep them on ice until extraction.
• Homogenization:
  • Homogenize the samples thoroughly to ensure complete lysis of cells and release of RNA.
• Follow Protocols Carefully: Adhere strictly to the manufacturer's protocol of the RNA extraction kit. Pay close attention to:
  • Sample input amount
  • Lysis buffer volume
  • Centrifugation times and speeds
• Avoid RNases:
  • Use RNase-free tubes, pipette tips, and reagents. Wear gloves and use sterile equipment to minimize contamination.
• Check RNA Quality and Quantity:
  • Use a spectrophotometer (e.g., NanoDrop) and a bioanalyzer / Fragment Analyzer to assess RNA purity (A260/A280 ratio) and integrity (RNA Integrity Number, RIN).
• Storage:
  • Store extracted RNA at -80°C for long-term preservation. For short-term storage, RNA can be kept at -20°C.
• Prepare for Library Construction:
  • Ensure that the RNA is free of contaminants (like DNA and proteins) and within the required concentration range for downstream applications.

The integrity of the sample material has a critical impact on the analysis results. Degraded RNA can lead to incomplete or inaccurate sequencing data, as fragmented RNA may not be efficiently captured or aligned. This affects gene-body coverage, resulting in uneven sequencing depth across the gene. Degraded RNA typically shows reduced coverage, particularly in the 3' and 5' regions of genes, leading to biased or incomplete gene expression profiles. High-quality, intact RNA is essential for consistent gene-body coverage and reliable, reproducible RNA sequencing results.

In particular, for poly-A enrichment, degraded RNA can introduce a strong bias towards the 3' end of the transcript. For these samples, we recommend rRNA depletion to improve data quality and reduce this bias.

You will be informed if your sample material does not meet our IQC criteria and will be provided with information about available options. In certain cases, processing at your own risk may be offered. In this case, guarantees for the successful creation of the library and sequencing will be void. If the library fails the quality control prior to sequencing, our Customer Care team will also contact you.

When processing sample material beyond the specs, several issues may arise and should be carefully considered:

• Decreased RNA Quality: Sample material that deviates from optimal conditions (e.g., improper storage, extended handling, or delays) may lead to RNA degradation, resulting in poor quality data.
• Lower Input Quantity: Reduced input RNA quantity can lead to insufficient material for accurate analysis, potentially causing biased or incomplete results due to low coverage or poor detection of lowly expressed genes. These samples may also fail to produce sufficient amounts of libraries for subsequent sequencing.
• Bias in Data: Alterations in sample handling or processing can introduce biases in gene expression profiles, such as uneven gene-body coverage, particularly in the 3' or 5' ends.
• Reduced Sensitivity: Handling samples outside of recommended conditions can affect the detection of lowly expressed genes, making the analysis less sensitive.

RNA must be shipped on dry ice, unless the RNA is precipitated in ethanol. Tissues / cell cultures must be flash frozen in liquid nitrogen or dry ice and have to be shipped on dry ice. Alternatively, fresh material can be stabilised in “RNAlater” (e.g., Ambion, SIGMA or QIAGEN) and be sent at room temperature. Please verify in advance that the couriers you are using accept dry-ice shipments.

Since ribosomal RNA (rRNA) constitutes the majority of a total RNA sample, we recommend removing it to enrich the sample for other RNA types. This can be achieved through either a poly-A selection or rRNA depletion method, both of which help to focus the analysis on more informative RNA populations

RNA sequencing (RNA-seq) is a powerful tool for transcriptome analysis, and choosing between polyA enrichment and rRNA depletion depends on the specific goals of the experiment and the types of RNA you wish to study. Here’s a breakdown of when each method makes sense:

1. PolyA Enrichment

PolyA enrichment selectively isolates mRNA by binding to the polyA tails found in the 3’ end of most eukaryotic mRNAs. This is useful when you are primarily interested in coding RNA (mRNA), and it simplifies the analysis by reducing the complexity of the sample.

When to use PolyA Enrichment?

• Focus on mRNA: If your goal is to analyze protein-coding genes, polyA enrichment is ideal because it captures most of the mRNA that has a polyA tail (which is the majority of the transcriptome in eukaryotic cells).
• Eukaryotic Samples: Most eukaryotic mRNAs have polyA tails, making polyA enrichment particularly useful for these species.
• Reducing rRNA Interference: In RNA samples with high rRNA content, polyA enrichment helps reduce rRNA contamination by selectively enriching for mRNA, although it won’t remove rRNA entirely.
• mRNA Profiling: If you are performing transcriptomics to understand gene expression levels or alternative splicing in coding regions, polyA enrichment is effective.
• Limitations:
  • Non-Polyadenylated RNAs: It won’t capture non-polyadenylated RNA, such as long non-coding RNAs (lncRNAs), miRNAs, rRNAs, and some small RNAs.

Considerations for Low-Quality RNA (PolyA Enrichment):

• Reduced mRNA Capture: If the mRNA is fragmented or degraded, polyA enrichment capture primarily the 3´end of mRNA, and there could be a loss of important data. This is often the case for RNA extracted from FFPE slides.
• Moderate Degradation: PolyA enrichment can still be useful when the RNA degradation is moderate (i.e., not severely fragmented), as long as there is a sufficient amount of intact polyadenylated RNA.

 2. rRNA Depletion

rRNA depletion removes the vast majority of ribosomal RNA (rRNA), which makes up 80-90% of the total RNA in most eukaryotic cells. By depleting rRNA, you can analyze other types of RNA that may be less abundant, such as mRNA (in cases where polyA enrichment is not preferred), lncRNAs, small RNAs, and other non-coding RNAs.

When to use rRNA Depletion?

• Comprehensive Transcriptome Analysis: If your goal is to study all types of RNA, including non-coding RNAs (like lncRNAs and miRNAs), small RNAs, or even non-polyadenylated mRNAs, rRNA depletion is a better option because it preserves these RNA types.
• Prokaryotic Samples: In prokaryotes (which have rRNA as the most abundant RNA), rRNA depletion is commonly used to improve the quality of the RNA-seq data, as prokaryotic mRNAs do not have polyA tails.
• Limitations:
  • Partial rRNA Removal: In some cases, rRNA depletion methods may not remove all rRNA species, leaving some residual.

Considerations for Low-Quality RNA (rRNA Depletion)

• Severe RNA Degradation: rRNA depletion is better suited for low-quality or degraded RNA, e.g. RNA extracted from FFPE slides, because it doesn’t rely on the integrity of polyA tails. Even with fragmented RNA, rRNA depletion can effectively reduce rRNA contamination.

Summary:

• PolyA Enrichment is optimal when you are focusing on mRNA and the RNA quality is high to moderate. It is less effective when RNA is degraded or when you need to analyze non-polyadenylated RNAs.
• rRNA Depletion works well for all non rRNA types and prokaryotic mRNA, especially if you want to capture a broader spectrum of RNA, including non-polyadenylated RNAs like lncRNAs. This method is also ideal when working with severely degraded RNA.

We offer rRNA depletion using specialized kits for a variety of organisms, including human, mouse, rat, bacteria, and plants. These kits are tailored to efficiently remove ribosomal RNA, allowing for a more focused analysis of the transcriptome in these species. 

Our rRNA depletion service is compatible with a wide range of mammalian species, though the depletion efficiency may vary depending on the species. Below is a summary of rRNA depletion efficiencies for several species based on estimates:

• Chicken: 82%, Cow: 98%, Cynomolgus monkey (Macaca fascicularis): 88%, Dog: 90%, Hamster: 95%, Horse: 98%, Pig: 80%, Rabbit: 81%, Sheep: 95%

Our rRNA depletion service is compatible with a wide range of plant species, though the depletion efficiency may vary depending on the species. Below is a summary of rRNA depletion efficiencies for several species based on estimates:

Arabidopsis: 99%, Barley: 92%, Corn: 90%, Cotton: 99%, Flaxseed: 87%, Maple: 89%, Oat: 99%, Potato: 93%, Rice: 91%, Rye: 99%, Sorghum: 79%, Soybean: 93%, Wheat: 94%

For RNA analysis from blood samples, we recommend both rRNA depletion and globin mRNA depletion. Since globin mRNA makes up a significant portion of the RNA in blood, depleting it alongside rRNA helps to enrich the sample for other less abundant but biologically relevant RNA species.  

For Ultra-Low Input RNA-Seq, we use poly-A selection to enrich for mRNA, as it is highly effective in capturing the transcriptome from small RNA quantities. Please note that rRNA depletion is not available for this service due to the limited amount of input RNA.

As a consequence, Ultra-Low Input RNA-Seq is only available for eukaryotic RNA.

The required number of reads depends on factors such as genome size, the number of known genes, and transcripts. As a general guideline, we recommend 5-10 million read pairs per sample for small genomes (e.g., bacteria), and 20-30 million reads per sample for larger genomes (e.g., human, mouse). For medium-sized genomes (e.g., yeast), the required number of reads can vary based on the project, but we typically suggest between 15-20 million reads per sample. For de novo transcriptome assembly projects, we recommend around 100 million reads per sample. 

We provide raw data as FASTQ files for all projects. We also have advanced bioinformatics capabilities to provide optional data analysis services. The bioinformatics services available for RNA-Seq projects are as follows:

• Standard Analysis:
  • Quality assessment of sequencing data
  • Alignment of reads to a reference genome
  • Assembly and quantification of transcripts
  • Normalization of gene counts
  • Identification of differentially expressed genes
  • Principal component analysis (PCA)
  • Significance testing
  • Generation of detailed reports and visualizations
• Optional Analysis:
  • Alternative splicing (AS) analysis
  • Gene fusion analysis
  • Variant detection and its effect on protein alteration
• Advanced Analysis:
  • Gene Set Enrichment Analysis (GSEA)

Each service includes specific deliverables and has associated costs and turnaround times.

When total RNA is sequenced, you can expect to detect a variety of RNA types, including:

• mRNA: Provides gene expression information.
• rRNA: Dominates most RNA samples, unless depleted.
• tRNA and non-coding RNAs: Smaller amounts, but play important regulatory roles.

If no specific enrichment is done, rRNA will likely dominate the data.

During the RNA-Seq – Ultra Low library preparation process, primers and adapter sequences are added to the RNA during cDNA synthesis and amplification. These sequences are necessary for the sequencing process, but they are not part of the actual biological transcript. If left in the data, they can introduce noise and reduce the quality of the sequencing results. To ensure high-quality data, Eurofins will trim these sequences prior to delivery, which may result in slightly shortened sequencing reads.