<p>The specifics will depend on the experimental setup and data, but a few general comments...</p>

<p>1) Metagenomic data is often large volume and high in microbial diversity but low and uneven in coverage; single genome assemblers may suffer particularly since abundant (or closely related) species/strains will tend to be interpreted as repeats within a genome rather than deriving from different genomes, leading to assembly errors. Checkout <a href="https://www.nature.com/articles/nmeth.4458" rel="nofollow noreferrer">CAMI</a> for some recent benchmarks of mategenomic assmeblers.</p>

<p>2) You might want to think about filtering your database to certain viruses, e.g. you don't expect to find RNA viruses in DNA sequencing. If the sample is an animal body site (like human gut) then consider restricting to viruses found in that organism (if it's an environmental sample this may not be possible).</p>

<p>3) BLAST will be slow, consider <a href="https://github.com/bbuchfink/diamond" rel="nofollow noreferrer">DIAMOND</a> for a drop in faster replacement for BLASTP. Even faster approaches use only specific marker genes (like Metaphlan2 or Mocat2) or k-mer methods (like kraken or clark). Again CAMI benchmarks many of these and other metagenomic taxonomic classifiers.</p>

<p>Otherwise, I think possibly a missing step is filtering the non-viral sequences. You haven't said if your sequencing was enriched for viruses or depleted for bacteria/host prior to sequencing but if it isn't then the vast majority of your sequences will likely be non-viral (e.g. bacterial or host). Removing or marking these early can speed up downstream analysis and remove false positives (e.g. the human genome contains integrated viruses, bacteria contain prophages etc.). Also it is normally important to look for and remove potential contaminants like lab reagents or cloning vectors.</p>

CC BY-SA 4.0
2018-06-06T04:18:13.047Z
Your answer was really insightful. Yes, I use DNAse/RNAse to deplete host DNA and the sample was enriched for viral nucleic acids.I am trying to use DIAMOND and let you know about the results. Thanks !
LoveVirology
CC BY-SA 4.0
2018-06-04T09:11:15.370Z
2018-06-04T09:11:15.370Z
Chris_Rands
<p>I am analyzing <strong>viral metagenomics</strong> data (Illumina Miseq) for the first time. I have used <strong><a href="http://denovoassembler.sourceforge.net/" rel="nofollow noreferrer">Ray</a></strong> (reference below) for <em>de novo</em> viral genome assembly before but I haven't done metagenomics analysis before.</p>
<p>I know that there are some tools like Metavelvet for metagenomics. But I still need to find time to learn those tools.</p>
<p>For now, can I do quick and dirty analysis using following method ?</p>
<ol>
<li><p>De novo assemby using <a href="http://denovoassembler.sourceforge.net/" rel="nofollow noreferrer">Ray</a></p>
</li>
<li><p>Create a personal database of all the viral ref genomes using NCBI database</p>
</li>
<li><p>Blast all the contigs generated by Ray against personal database.</p>
</li>
</ol>
<p>I assume this should give me some idea about the viral genomes present in my reads. What could be the potential flaws of this method in comparison to the tools like Metavelvet that are dedicated to metagenomics?</p>
<hr />
<p>Ray Meta: scalable de novo metagenome assembly and profiling.
Sébastien Boisvert, Frédéric Raymond, Élénie Godzaridis, François Laviolette and Jacques Corbeil.
Genome Biology (BioMed Central Ltd).
13:R122, Published: 22 December 2012</p>

<assembly><covid-19><metagenome><illumina>
Viral Metagenomics
# Viral Metagenomics

(Asked by: username_0 on 2018-06-01)

I am analyzing **viral metagenomics** data (Illumina Miseq) for the first time. I have used **[Ray](http://denovoassembler.sourceforge.net/)** (reference below) for *de novo* viral genome assembly before but I haven't done metagenomics analysis before.

I know that there are some tools like Metavelvet for metagenomics. But I still need to find time to learn those tools.

For now, can I do quick and dirty analysis using following method ?

1. De novo assemby using [Ray](http://denovoassembler.sourceforge.net/)
2. Create a personal database of all the viral ref genomes using NCBI database
3. Blast all the contigs generated by Ray against personal database.

I assume this should give me some idea about the viral genomes present in my reads. What could be the potential flaws of this method in comparison to the tools like Metavelvet that are dedicated to metagenomics?

---

Ray Meta: scalable de novo metagenome assembly and profiling.
Sébastien Boisvert, Frédéric Raymond, Élénie Godzaridis, François Laviolette and Jacques Corbeil.
Genome Biology (BioMed Central Ltd).
13:R122, Published: 22 December 2012

**Tags:** assembly, covid-19, metagenome, illumina

---

## 1 answers

### Answer by username_1 on 2018-06-04. Score: 2 | Accepted answer

The specifics will depend on the experimental setup and data, but a few general comments...

1) Metagenomic data is often large volume and high in microbial diversity but low and uneven in coverage; single genome assemblers may suffer particularly since abundant (or closely related) species/strains will tend to be interpreted as repeats within a genome rather than deriving from different genomes, leading to assembly errors. Checkout [CAMI](https://www.nature.com/articles/nmeth.4458) for some recent benchmarks of mategenomic assmeblers.

2) You might want to think about filtering your database to certain viruses, e.g. you don't expect to find RNA viruses in DNA sequencing. If the sample is an animal body site (like human gut) then consider restricting to viruses found in that organism (if it's an environmental sample this may not be possible).

3) BLAST will be slow, consider [DIAMOND](https://github.com/bbuchfink/diamond) for a drop in faster replacement for BLASTP. Even faster approaches use only specific marker genes (like Metaphlan2 or Mocat2) or k-mer methods (like kraken or clark). Again CAMI benchmarks many of these and other metagenomic taxonomic classifiers.

Otherwise, I think possibly a missing step is filtering the non-viral sequences. You haven't said if your sequencing was enriched for viruses or depleted for bacteria/host prior to sequencing but if it isn't then the vast majority of your sequences will likely be non-viral (e.g. bacterial or host). Removing or marking these early can speed up downstream analysis and remove false positives (e.g. the human genome contains integrated viruses, bacteria contain prophages etc.). Also it is normally important to look for and remove potential contaminants like lab reagents or cloning vectors.

> **Comments:**
> **username_0**: Your answer was really insightful. Yes, I use DNAse/RNAse to deplete host DNA and the sample was enriched for viral nucleic acids.I am trying to use DIAMOND and let you know about the results. Thanks !  

---

