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I have hundreds of GBs metagenomic 16S rRNA gene sequence data. I want to do microbiome composition profiling (with relative abundance) from the data. Also after that, I will do functional profiling (gene and metabolome level).

Can you please suggest me the best software(s) for these two tasks?

Thank You

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I tried dada2 and is not bad (if you know R).

QIIME2 is also an option. Many other are available, the choice might also depend on your sample and your exact question.

For functional profiling you may try picrust2

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For comprehensive microbial profiling of your 16S rRNA gene sequence data from Illumina MiSeq, I recommend using the web app Microbioma16S (www.microbioma16s.it). This powerful tool offers an end-to-end pipeline that covers everything from preprocessing raw data to conducting alpha and beta diversity analysis. With Microbioma16S, you can explore interactive results of taxonomy associated with features, taxa relative abundances, and perform comparisons between sample groups. It's a user-friendly solution for your microbial profiling needs.

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The best thing to do is a phylogenetic tree in my opinion.

The trendy approach and the one deemed acceptable in publication is deep learning for precisely this application, such as the pipeline published here. This approach is attractive in 16S NGS metagenomics classification because it uses advanced approaches in deep learning, notably convolution neural networks (CNN) and deep belief networks (DBN). I don't really understand DBN, but I suspect it is a variant of recurrent neural networks (RNN) such as long-short term memory (LSTM).

Having said all that the accuracy is low, viz.

For instance, at the genus level, both CNN and DBN reached 91.3% of accuracy with AMP short-reads, whereas RDP classifier obtained 83.8% with the same data.

91% accuarcy for a deep learning neural network is minimal, but it will be quick and will do precisely what you want with little fuss. A phylogenetic tree on the otherhand can take several hundred hours of total processor time. The thing about a 16S tree is that it is 100% accurate, because you can manually identify "grey zones" to assign their classification.

You can retrain CNN models and in future this will likely resolve the accuracy issue. However, I don't know about about DBN and I don't think RSS can retrained, more specifically I don't know how to do it.

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  • $\begingroup$ however, "16S phylogenetics" and "100% accurate" shouldn't go in the same sentence. $\endgroup$ May 26, 2023 at 1:22
  • $\begingroup$ Sorry I mostly disagree @user3479780, but I agree it is better to say "the most accurate". However 1 the sentence is misrepresented, "... 16S tree is that it is 100% accurate, because you can manually identify 'grey zones' to assign their classification". 2 My work (500 cites) and other publications identified it as an excellent taxonomic locus at familial level. 3 Molecular phylogeny is the gold-standard in bacterial taxonomy. 4 Whole genome phylogenetics theory is complicated. 5 At the time many bacterial taxonomic deep-learning studies published had low accuracy (<90%). $\endgroup$
    – M__
    May 26, 2023 at 10:32

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