Hello everyone,
I am starting a soil mapping project in a specific crop region, and my goal is to perform shotgun sequencing (Illumina NovaSeq) to identify microorganisms and potential genes. This includes aspects such as soil biological quality, antimicrobial resistance genes, phytopathogenics, etc. I would like to ask:
What is the ideal coverage (number of reads) that I should consider to obtain a robust and detailed analysis of these factors in my soil samples? An Illumina protocol mentions that above 10 million reads, you can already see a lot (though it is very generic about this). However, in discussions with specialists from Illumina, they mentioned 15, 18, and 25 million reads, but they always say that I need to decide on the coverage. We already know that more than 25 million reads will be financially unfeasible, but we want to optimize as much as possible without losing quality and money.
Any practical experience or references to scientific articles that can provide precise guidance would be extremely helpful!
Thank you!
Shotgun metagenomic characterization of a microbial community is an alternative approach based on constructing a random sample of the total DNA in a specimen and performing deep sequencing. Per-base coverage is the average number of times a base of a genome is sequenced. The coverage depth of a genome is calculated as the number of bases of all short reads that match a genome divided by the length of this genome. One study found that shallow shotgun sequencing (0.5 million reads) and ultra-deep sequencing (2.5 billion reads) were 97% correlated for species composition and 99% correlated for metagenomic profiles. Biodiversity research, and particularly the investigation of hard-to-observe ecological communities, increasingly relies on DNA- and RNA-based tools such as metabarcoding.A shotgun metagenomics project workflow typically follows the same six steps:
1. Sample preparation,
2. DNA extraction,
3. Quantification and
4. Quality control,
5. Library construction, sequencing, and
6. Bioinformatics analysis.
https://www.researchgate.net/publication/358938865_Shotgun_Metagenomics_of_Deep_Forest_Soil_Layers_Show_Evidence_of_Altered_Microbial_Genetic_Potential_for_Biogeochemical_Cycling
https://www.researchgate.net/publication/344390975_Shotgun_Metagenomic_Survey_of_Maize_Soil_Rhizobiome
Determining the ideal coverage for shotgun sequencing in soil samples involves balancing the depth of sequencing required to capture the microbial diversity and genetic elements of interest with the cost considerations. Here’s a detailed approach to help you make an informed decision:
Factors to Consider
General Guidelines and Practical Recommendations
Scientific References
Recommendations
Given your constraints and objectives:
- Aim for 18-20 million reads per sample as a balanced approach. This should provide a detailed and comprehensive analysis of soil biological quality, antimicrobial resistance genes, and phytopathogens, without excessively high costs.
- Consider starting with a pilot study using a few samples at different coverage levels (e.g., 15, 20, 25 million reads) to evaluate the depth and quality of data, helping to fine-tune the optimal coverage for your specific samples.
Conclusion
While 25 million reads would offer the highest resolution, aiming for 18-20 million reads per sample should suffice for a detailed and robust analysis of soil microorganisms and potential genes, providing a good balance between quality and cost. Use previous studies and a pilot experiment to confirm this for your specific project needs.
Glacy Jaqueline da Silva
Steven Senabulya
Prem Baboo
The ideal cover in shotgun soil metagenomics refers to the comprehensive representation of microbial diversity and genetic material in a soil sample. Achieving optimal coverage ensures that the sequencing process captures a wide array of microorganisms, including rare species, and their functional genes. This is crucial for accurately assessing the microbial community structure, dynamics, and potential functional capabilities within the soil ecosystem. High coverage minimizes biases and gaps in the data, providing a more complete and reliable picture of the soil's microbiome. This thorough understanding is essential for applications such as soil health assessment, agricultural productivity, and ecological studies, enabling better management and conservation strategies.
Thank you very much for your contribution. We´ll do a pilot test but we´d really appreciate all the answers. It helped me a lot!!