Frequently Asked Questions About Our Products And Services

• General FAQs for Next Generation Sequencing >>

Common sample types include stool, saliva, skin swabs, environmental samples (soil, water), and more. Each sample type may require specific handling protocols. Please refer to our sample submission guide for more details. 

Microbiome profiling through next-generation sequencing (NGS) enables the detection of a wide range of species, including:

• Bacteria: All bacterial species with published sequences in the NCBI nucleotide database can be identified.
• Archaea: Archaeal species represented in the NCBI database can also be detected.
• Fungi: Fungal species can be identified through the analysis of Internal Transcribed Spacer (ITS) regions or 18S region, provided their sequences are available in the NCBI database.
• Cyanobacteria: Currently, identification of cyanobacteria is not possible due to the limitations of the existing primer setup. If cyanobacteria are a key focus for your study, we recommend exploring alternative primer sets or methodologies that are specifically designed for their amplification and detection.

Important considerations:

• Resolution Limits: Closely related species with nearly identical DNA sequences may not be distinguishable, and results will typically report only at the genus or family level.
• Sample Quality: The quality of the DNA and the specific regions targeted can impact the detection capabilities.
• Database Coverage: The identification of species relies on the completeness and accuracy of the reference sequences in the NCBI database.

Distinguishing Closely Related Species: NGS may struggle to differentiate closely related species with nearly identical DNA sequences, often resulting in identification only at the genus or family level.

DNA Quality and Degradation: The presence of degraded DNA, especially in highly processed samples or those with low pH, can lead to incomplete or inconclusive species identification.

Sensitivity Issues: While NGS is highly sensitive, this can also be a drawback. Low-abundance sequences or those with unexpected lengths may be detected but could be due to PCR or sequencing errors, requiring filtering out during data analysis.

False Positives/Negatives: Due to the method's sensitivity, there is a risk of false positives if sequences present at low levels are misidentified. Conversely, species present in very low abundance (typically below 0.5% of all assigned reads) may not be reported.

Dependence on Reference Databases: The accuracy of species identification relies heavily on the completeness and quality of reference sequences in databases like NCBI. Uncharacterized or poorly annotated species may be missed.

Primer Bias: Different primer sets may amplify certain species better than others, leading to an incomplete representation of the microbial community. It’s important to choose primers carefully based on their known limitations.

Yes, 16S primers can sometimes amplify plant organelles, specifically the 16S rRNA gene found in the chloroplasts of plants. Chloroplasts contain their own ribosomal RNA genes, which are similar to those found in bacteria due to their evolutionary origin.

However, the degree of amplification will depend on several factors, including:

• Primer Specificity: Some 16S primers are designed specifically for bacterial targets and may not efficiently amplify chloroplast DNA. Others may be more inclusive.
• Sample Composition: In samples with a complex mixture of organisms, such as environmental samples, the presence of plant DNA can lead to amplification of chloroplast sequences.
• Target Region: Different regions of the 16S gene may show varying levels of conservation between bacterial and chloroplast DNA, influencing the likelihood of successful amplification.

If you aim to specifically analyze bacterial communities and want to avoid amplifying plant organelles, it may be necessary to choose primers that are specifically designed to target bacterial 16S rRNA genes.

False Negatives:

• Degraded DNA: Samples with significantly degraded DNA may lead to no species identification, resulting in false negatives.
• Low Abundance: Species present at very low levels (typically below 0.5% of all assigned reads) may not be detected or reported, even if they are present in the sample.

False Positives:

• High Sensitivity: NGS detects all successfully amplified DNA amplicons. Sequences with low abundance or unexpected lengths may be incorrectly identified due to PCR or sequencing errors.
• Filtering Process: To mitigate false positives, stringent bioinformatics filters are applied during data analysis.

Negative Controls:

• To reduce false positives, a negative control (sterile water) is analyzed alongside samples.

When conducting 16S microbiome analyses, consider the following guidelines for selecting target regions and determining the number of targets:

Select Target Regions: For comprehensive microbial profiling, choose at least one target region from the 16S rRNA gene. Commonly analyzed regions include V1-V3, V3-V4, or V3-V5. The choice depends on your research objectives and the specific taxa of interest.

Consider Multiple Targets: To maximize coverage and capture the diversity of your microbial community, it’s beneficial to analyze multiple 16S target regions. This can help compensate for the limitations of individual primer sets, as some may fail to amplify specific species.

Literature Review: Review existing studies and literature related to your chosen target regions. This will provide insights into potential biases or limitations associated with different primer sets and can guide your selection.

Sample Type: Consider the nature of your samples (e.g., environmental, clinical) and their expected diversity. More complex samples may require a broader range of target regions to accurately reflect the community structure.

Experimental Design: If your analysis aims to compare different samples or conditions, ensure that the same target regions are used across all samples for consistency in data interpretation.

We utilize the amplicon generation process as our primary entry quality control. While our PCR conditions are optimized, the success of amplicon generation heavily depends on the amount of bacterial, archaeal, or fungal DNA present in the sample.

DNA Extraction Quality: If we extract the DNA, the quantity of extracted DNA is evaluated using spectrophotometry.

Initial Assessment: If we are unable to generate an amplicon or if the generated amplicon is very weak, it serves as a strong indication that the DNA content may be insufficient.

Negative Controls: Each batch includes negative controls (sterile water) to detect any potential contamination during sample preparation.

Sequencing Quality Assessment:

• Read Quality Monitoring: After sequencing, the quality of the reads is assessed using metrics like Q-scores. This helps identify any potential issues with sequencing accuracy.
• Filtering of Low-Quality Reads: Low-quality reads and adapter sequences are removed during the bioinformatics analysis to enhance data quality.

No Amplicon Generation:

If no amplicon can be generated, we will inform you and halt processing for those samples and reimburse you for the sequencing portion that was not completed.

Weak Amplicon Generation:

If only a weak amplicon is generated, we will inform you and Eurofins Genomics can include these amplicons in the sequencing pool on customer request. However, experience shows that weak amplicons typically result in very few reads. Interpretation of these reads should be approached with caution.

Genetic Variation: Different species have varying lengths of target regions within their genomes. For example, the 16S rRNA gene can vary significantly among bacterial species, leading to differences in amplicon lengths when amplified.

Intraspecies Diversity: Even within a single species, genetic variations can lead to differences in amplicon lengths due to polymorphisms or mutations.

Yes, especially regarding human samples. Ensure compliance with relevant ethical guidelines and obtain necessary approvals for research involving human subjects.