<p>New methods have been published in the time since this question was asked. Now, the relative abundance of organisms across different taxonomic levels can be estimated directly from metagenomic shotgun sequencing data without the need to identify and pull rRNA genes. Here are a few examples of popular tools:</p>
<ul>
<li>Similar to the approach described in the question, <a href="https://github.com/motu-tool/mOTUs" rel="nofollow noreferrer"><strong>mOTUs</strong></a> employs a database of single-copy phylogenetic marker genes to create taxonomic profiles from short-read metagenomic data.</li>
<li><a href="https://github.com/DerrickWood/kraken2" rel="nofollow noreferrer"><strong>Kraken</strong></a> associates short genomic substrings (<em>k</em>-mers) with the lowest common ancestor in its database. The output can then be processed with <a href="https://github.com/jenniferlu717/Bracken/" rel="nofollow noreferrer"><strong>Bracken</strong></a> to get estimates of relative abundance at any taxonomic level, and the Bracken output table can then be passed to a tool like <a href="https://github.com/acvill/bracken_plot" rel="nofollow noreferrer"><strong>bracken_plot</strong></a> to visualize differences in relative abundance between samples.</li>
<li>Like mOTUs, <a href="https://huttenhower.sph.harvard.edu/metaphlan/" rel="nofollow noreferrer"><strong>MetaPhlAn</strong></a> uses a large marker gene database to assign reads to bacterial, archaeal, viral, and eukaryotic clades. This allows for relative abundance estimation in terms of number of cells. <a href="https://elifesciences.org/articles/65088" rel="nofollow noreferrer">If their paper is to be believed</a>, MetaPhlAn3 outperforms both mOTUs2 and Kraken2/Bracken in terms of abundance profile accuracy.</li>
</ul>
<blockquote>
<p>In addition to more accurate species detection, MetaPhlAn3 also quantified taxonomic abundance profiles more accurately compared to MetaPhlAn 2, mOTUs2, and Bracken based on Bray-Curtis similarity in most datasets ... While it was slightly outperformed by mOTUs2 (only in high-recall mode) on the synthetic mouse gut dataset, even in this case, correlation-based measures (Pearson Correlation Coefficient between estimated and expected relative abundances) found MetaPhlAn3 to be more accurate (r = 0.73) than the other considered profilers (MetaPhlAn2 r = 0.63, mOTUs2 precision r = 0.60, mOTUs2 recall r = 0.71, Bracken r = 0.43).</p>
</blockquote>
<p>Although it is now dated, <a href="http://ccb.jhu.edu/software/choosing-a-metagenomics-classifier/" rel="nofollow noreferrer"><em>How to Choose Your Metagenomics Classification Tool</em></a> from the Salzberg lab lists more options and links the associated publications, including <a href="https://github.com/DaehwanKimLab/centrifuge" rel="nofollow noreferrer"><strong>Centrifuge</strong></a> and <a href="https://github.com/fbreitwieser/pavian" rel="nofollow noreferrer"><strong>Pavian</strong></a>, the latter of which is geared specifically toward visualization of metagenomic classification data.</p>

CC BY-SA 4.0
2022-02-08T20:44:39.980Z
Its a thorough answer. Machine learning has enabled classification of bacteria, presumably on 16S. I'm aware of early papers in bioinformatics.
M__
CC BY-SA 4.0
2022-02-08T20:35:11.677Z
2022-02-08T20:35:11.677Z
acvill
<p>This is maybe a very simplified approach but have you thought about simply blasting them against the rRNA/ITS database of NCBI/ SILVA with ie. diamond?</p>
<p>First download the database files from either of the database:</p>
<p>SILVA: <a href="https://www.arb-silva.de/no_cache/download/archive/current/Exports/" rel="nofollow noreferrer">https://www.arb-silva.de/no_cache/download/archive/current/Exports/</a>
NCBI: <a href="https://ftp.ncbi.nlm.nih.gov/blast/db/" rel="nofollow noreferrer">https://ftp.ncbi.nlm.nih.gov/blast/db/</a></p>
<p>Second: Use DIAMOND to align fastas of interest (in your case these will be the 16S rRNA Sequences. These will give you hits per fasta which you can save into tsv or csv format and parse in excel.</p>
<p>To get the abundances, take your fastas of interest and align them back to your raw files (paired reads). You can use BOWTIE + SAMTOOLs for this.</p>
<p>Concatenate the results from the DIAMOND output with the abundances and voila, you should get a nice table with taxa and their abundances. If you have more samples and you would like to compare them, ie. compare the taxa composition. I would strongly suggest that you do some further normalisation ie. normalise the abundances with sequencing depths.</p>
<p>Maybe it's a long way around it but in theory, it should do the job.</p>

CC BY-SA 4.0
2022-03-16T22:11:15.387Z
2022-03-16T22:11:15.387Z
Avamys
<p>I have data, obtained from a single metagenomic DNA sample, that consists of two MiSeq FASTQ files (R1 and R2) that I merged using <a href="https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btt593" rel="noreferrer">PEAR</a>.</p>

<p>Now I want to estimate the abundances of the bacteria taxa to generate a figure like this one:</p>

<p><a href="https://i.sstatic.net/FlJxD.jpg" rel="noreferrer"><img src="https://i.sstatic.net/FlJxD.jpg" alt="enter image description here"></a></p>

<p>Figure from: <em>Panosyan, Hovik, and Nils‐Kåre Birkeland. "Microbial diversity in an Armenian geothermal spring assessed by molecular and culture‐based methods." Journal of basic microbiology 54.11 (2014): 1240-1250.</em></p>

<p>The problem is that there wasn't a step of amplification of the 16S region as the goal of the sequencing was to discover new genes. I've already isolated 16S reads from my sample using <a href="http://bioinfo.lifl.fr/RNA/sortmerna/" rel="noreferrer">SortMeRNA</a>, but it seems like softwares that do OTU picking, taxonomic assignment and diversity analyses (such as mothur and QIIME) require that all the reads come from the same region of the 16S gene.</p>

<p>Is there a way of using these 16S reads that I've filtered using SortMeRNA in a diversity analysis using mothur/QIIME?</p>

CC BY-SA 3.0
2017-08-22T20:24:16.403Z
Cross-posted on [biostars](https://www.biostars.org/p/268754/)
elsoja
<metagenome><taxonomy>
Microbial diversity analysis using whole-genome metagenomic data
# Microbial diversity analysis using whole-genome metagenomic data

(Asked by: username_0 on 2017-08-22)

I have data, obtained from a single metagenomic DNA sample, that consists of two MiSeq FASTQ files (R1 and R2) that I merged using [PEAR](https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btt593).

Now I want to estimate the abundances of the bacteria taxa to generate a figure like this one:

[![enter image description here](https://i.sstatic.net/FlJxD.jpg)](https://i.sstatic.net/FlJxD.jpg)

Figure from: *Panosyan, Hovik, and Nils‐Kåre Birkeland. "Microbial diversity in an Armenian geothermal spring assessed by molecular and culture‐based methods." Journal of basic microbiology 54.11 (2014): 1240-1250.*

The problem is that there wasn't a step of amplification of the 16S region as the goal of the sequencing was to discover new genes. I've already isolated 16S reads from my sample using [SortMeRNA](http://bioinfo.lifl.fr/RNA/sortmerna/), but it seems like softwares that do OTU picking, taxonomic assignment and diversity analyses (such as mothur and QIIME) require that all the reads come from the same region of the 16S gene.

Is there a way of using these 16S reads that I've filtered using SortMeRNA in a diversity analysis using mothur/QIIME?

**Tags:** metagenome, taxonomy
> **Comments:**
> **username_0**: Cross-posted on [biostars](https://www.biostars.org/p/268754/)  

---

## 2 answers

### Answer by username_1 on 2022-02-08. Score: 1

New methods have been published in the time since this question was asked. Now, the relative abundance of organisms across different taxonomic levels can be estimated directly from metagenomic shotgun sequencing data without the need to identify and pull rRNA genes. Here are a few examples of popular tools:

* Similar to the approach described in the question, [**mOTUs**](https://github.com/motu-tool/mOTUs) employs a database of single-copy phylogenetic marker genes to create taxonomic profiles from short-read metagenomic data.
* [**Kraken**](https://github.com/DerrickWood/kraken2) associates short genomic substrings (*k*-mers) with the lowest common ancestor in its database. The output can then be processed with [**Bracken**](https://github.com/jenniferlu717/Bracken/) to get estimates of relative abundance at any taxonomic level, and the Bracken output table can then be passed to a tool like [**bracken\_plot**](https://github.com/acvill/bracken_plot) to visualize differences in relative abundance between samples.
* Like mOTUs, [**MetaPhlAn**](https://huttenhower.sph.harvard.edu/metaphlan/) uses a large marker gene database to assign reads to bacterial, archaeal, viral, and eukaryotic clades. This allows for relative abundance estimation in terms of number of cells. [If their paper is to be believed](https://elifesciences.org/articles/65088), MetaPhlAn3 outperforms both mOTUs2 and Kraken2/Bracken in terms of abundance profile accuracy.

> In addition to more accurate species detection, MetaPhlAn3 also quantified taxonomic abundance profiles more accurately compared to MetaPhlAn 2, mOTUs2, and Bracken based on Bray-Curtis similarity in most datasets ... While it was slightly outperformed by mOTUs2 (only in high-recall mode) on the synthetic mouse gut dataset, even in this case, correlation-based measures (Pearson Correlation Coefficient between estimated and expected relative abundances) found MetaPhlAn3 to be more accurate (r = 0.73) than the other considered profilers (MetaPhlAn2 r = 0.63, mOTUs2 precision r = 0.60, mOTUs2 recall r = 0.71, Bracken r = 0.43).

Although it is now dated, [*How to Choose Your Metagenomics Classification Tool*](http://ccb.jhu.edu/software/choosing-a-metagenomics-classifier/) from the Salzberg lab lists more options and links the associated publications, including [**Centrifuge**](https://github.com/DaehwanKimLab/centrifuge) and [**Pavian**](https://github.com/fbreitwieser/pavian), the latter of which is geared specifically toward visualization of metagenomic classification data.

> **Comments:**
> **username_2**: Its a thorough answer. Machine learning has enabled classification of bacteria, presumably on 16S. I'm aware of early papers in bioinformatics.  

---

### Answer by username_3 on 2022-03-16. Score: 0

This is maybe a very simplified approach but have you thought about simply blasting them against the rRNA/ITS database of NCBI/ SILVA with ie. diamond?

First download the database files from either of the database:

SILVA: <https://www.arb-silva.de/no_cache/download/archive/current/Exports/>
NCBI: <https://ftp.ncbi.nlm.nih.gov/blast/db/>

Second: Use DIAMOND to align fastas of interest (in your case these will be the 16S rRNA Sequences. These will give you hits per fasta which you can save into tsv or csv format and parse in excel.

To get the abundances, take your fastas of interest and align them back to your raw files (paired reads). You can use BOWTIE + SAMTOOLs for this.

Concatenate the results from the DIAMOND output with the abundances and voila, you should get a nice table with taxa and their abundances. If you have more samples and you would like to compare them, ie. compare the taxa composition. I would strongly suggest that you do some further normalisation ie. normalise the abundances with sequencing depths.

Maybe it's a long way around it but in theory, it should do the job.


---

