In 16s sequencing data, we found a few very interesting species as biomarkers, so our laboratory invested good money for metagenomics out of the desire for greater study. However, in metagenomics, those species are not significant. We feel very confused, and don’t know how to integrate the two kinds of data in the analysis. Can you recommend a strategy to integration two datasets or some related papers to study?
Abhijeet Singh
16s rRNA sequencing is an amplicon based squencing and hence has PCR amplification step. PCR is known to incorporate biases in the absolute structure (abundance) of the occuring amplicons. Whereas in while genome metagenome sequencing, there are no such biases involved. In your case, this might be the primary reason. Assuming same DNA was used for both the methods, otherwise DNA extraction is also a big contributing factor. Data analysis is yet another parameter for varying results. If traditional OTU based approach has been used, it might not resolve the lower taxonomic categories sufficiently well. Where as metagnomic bins do not rely on the 16S genes and rarely contains 16S genes. Databases. This is very important factor to visualize/taxonomically annotate the sequenced and processed data. Different database means different possibilities of taxonomic annotations.
I don't feel this is strange disagreement. There are a lot of principle differences which underlies 16S rRNA gene sequencing and whole genome metagenome sequencing.
- Another thing, for your biomarker in 16S data, did you consider the NAs while calculating the relative abundance?
- 16S sequencing is not good at resolving species level.
- I don't think the data (16S rRNA gene and whole genome metagenome ) can be integrated completely. General trends in the community structure can be established but according to my experience the is not a perfect way to pin point the lower taxonomic levels with increased confidence, while comparing both of the datasets.
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Kuldeep Patel
I won't get surprised, as metagenome contain lot of Uncultivated microorganisms(>90%!!!!sometimes), which may not have captured in 16s DATA.
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