Answers.Body: <p>I tried <a href="https://benjjneb.github.io/dada2/" rel="nofollow noreferrer">dada2</a> and is not bad (if you know R).</p>
  
  <p><a href="https://qiime2.org/" rel="nofollow noreferrer">QIIME2</a> is also an option.
  Many other are available, the choice might also depend on your sample and your exact question.</p>
  
  <p>For functional profiling you may try <a href="https://github.com/picrust/picrust2/wiki" rel="nofollow noreferrer">picrust2</a></p>
  
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Answers.UserDisplayName: Fabio Marroni
Answers.Body: <p>For comprehensive microbial profiling of your 16S rRNA gene sequence data from Illumina MiSeq, I recommend using the web app Microbioma16S (<a href="http://www.microbioma16s.it" rel="nofollow noreferrer">www.microbioma16s.it</a>). 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.</p>
  
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Answers.LastEditDate: 2023-05-25T13:20:53.277Z
Answers.UserDisplayName: cami
Answers.Body: <p>The best thing to do is a phylogenetic tree in my opinion. </p>
  
  <p>The trendy approach and the one deemed acceptable in publication is deep learning for precisely this application, such as the pipeline published <a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-018-2182-6" rel="nofollow noreferrer">here</a>. 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). </p>
  
  <p>Having said all that the accuracy is low, viz. </p>
  
  <blockquote>
    <p>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.</p>
  </blockquote>
  
  <p>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.</p>
  
  <p>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.</p>
  
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Answers.Text: however, "16S phylogenetics" and "100% accurate" shouldn't go in the same sentence.
Answers.UserDisplayName: user3479780
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Answers.Text: 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%).
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Body: <p>I have hundreds of GBs metagenomic 16S rRNA gene sequence data. I want to do <strong>microbiome composition profiling</strong> (with relative abundance) from the data. Also after that, I will do functional profiling (<strong>gene and metabolome level</strong>). </p>
  
  <p>Can you please suggest me the <strong>best software(s)</strong> for these two tasks?</p>
  
  <p>Thank You</p>
  
Tags: <software-recommendation><metagenome><multi-omics><systems-biology>
Title: Software for microbial profiling from 16S rRNA gene sequence
ThreadText: # Software for microbial profiling from 16S rRNA gene sequence
  
  (Asked by: username_0 on 2020-03-27)
  
  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
  
  **Tags:** software-recommendation, metagenome, multi-omics, systems-biology
  
  
  ---
  
  ## 3 answers
  
  ### Answer by username_1 on 2020-04-01. Score: 2 | Accepted answer
  
  I tried [dada2](https://benjjneb.github.io/dada2/) and is not bad (if you know R).
  
  [QIIME2](https://qiime2.org/) 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](https://github.com/picrust/picrust2/wiki)
  
  
  ---
  
  ### Answer by username_2 on 2023-05-14. Score: 1
  
  For comprehensive microbial profiling of your 16S rRNA gene sequence data from Illumina MiSeq, I recommend using the web app Microbioma16S ([www.microbioma16s.it](http://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.
  
  
  ---
  
  ### Answer by username_3 on 2020-04-03. Score: 0
  
  The best thing to do is a phylogenetic tree in my opinion.
  
  The trendy approach and the one deemed acceptable in publication is username_0 learning for precisely this application, such as the pipeline published [here](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-018-2182-6). This approach is attractive in 16S NGS metagenomics classification because it uses advanced approaches in username_0 learning, notably convolution neural networks (CNN) and username_0 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 username_0 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.
  
  > **Comments:**
  > **username_4**: however, "16S phylogenetics" and "100% accurate" shouldn't go in the same sentence.  
  > **username_3**: Sorry I mostly disagree @username_4, 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 username_0-learning studies published had low accuracy (<90%).  
  
  ---
  
  