<p>I've had some success looking for contamination in Illumina and Nanopore reads using <a href="https://github.com/DerrickWood/kraken2" rel="nofollow noreferrer">kraken2</a>.</p>
<p>Kraken2 has a phylogenetic-based approach, which in the first instance tries to find the most specific taxa that the target read likely comes from (going further up in the tree if there's ambiguity), and also produces a summary table indicating the approximate proportion of reads that come from particular taxa. It can be paired with <a href="https://ccb.jhu.edu/software/bracken/" rel="nofollow noreferrer">bracken</a> for additional reliability.</p>

CC BY-SA 4.0
2023-08-10T01:08:28.257Z
Yes I 100% support this approach. Kraken is an excellent algorithm and will identify a whole diversity of contaminants. There are contamination approaches **IF** the contaminant is predicted. When its *anything* Kraken is an extremely good approach. Setting up the Kraken database is a pain, but this case it should be easy because you doesn't have to be very accurate , you don't need the species of the contaminant. Absolutely commend this approach. I don't think bracken is needed in this particular case - outside this case it is needed to correct the count distribution bias.
M__
CC BY-SA 4.0
2023-08-10T00:20:21.900Z
2023-08-10T23:34:46.017Z
2023-08-10T23:34:46.017Z
gringer
<p>I didn't manage to try the ones reported by @gringer and @alexlok here, but I did manage to perform an analysis using the OneCodex webapp: <a href="https://app.onecodex.com/" rel="nofollow noreferrer">https://app.onecodex.com/</a> and based on the experimental information for the samples being analysed, the results were concordant with the expectation. You can do quite a lot on the &quot;free tier&quot;, so at the moment I didn't upgrade to the paid version.</p>

CC BY-SA 4.0
2023-10-12T10:32:54.237Z
2023-10-12T10:32:54.237Z
719016
<p>Maybe less advanced but worth mentioning, I've had good results with <a href="https://www.bioinformatics.babraham.ac.uk/projects/fastq_screen/" rel="nofollow noreferrer">fastq_screen</a>. It's simply trying to align a subset of your sequences against various genomes (comes with a few genomes, you can add more).</p>

CC BY-SA 4.0
2023-08-10T17:54:07.370Z
2023-08-10T17:54:07.370Z
Alexlok
<p>we've done Whole Genome Sequencing with Illumina on DNA extracted from cell lines, where some are suspected to have been infected by something (viral, bacterial, not sure).</p>
<p>I've tried running metaphlan4 (version from github) and got zero hits in all of them.</p>
<p>What other tools could I be trying? Thx.</p>

<metagenome><whole-genome><dna>
detecting DNA of possible infection on cell line
# detecting DNA of possible infection on cell line

(Asked by: username_0 on 2023-08-09)

we've done Whole Genome Sequencing with Illumina on DNA extracted from cell lines, where some are suspected to have been infected by something (viral, bacterial, not sure).

I've tried running metaphlan4 (version from github) and got zero hits in all of them.

What other tools could I be trying? Thx.

**Tags:** metagenome, whole-genome, dna

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## 3 answers

### Answer by username_1 on 2023-08-10. Score: 3

I've had some success looking for contamination in Illumina and Nanopore reads using [kraken2](https://github.com/DerrickWood/kraken2).

Kraken2 has a phylogenetic-based approach, which in the first instance tries to find the most specific taxa that the target read likely comes from (going further up in the tree if there's ambiguity), and also produces a summary table indicating the approximate proportion of reads that come from particular taxa. It can be paired with [bracken](https://ccb.jhu.edu/software/bracken/) for additional reliability.

> **Comments:**
> **username_2**: Yes I 100% support this approach. Kraken is an excellent algorithm and will identify a whole diversity of contaminants. There are contamination approaches **IF** the contaminant is predicted. When its *anything* Kraken is an extremely good approach. Setting up the Kraken database is a pain, but this case it should be easy because you doesn't have to be very accurate , you don't need the species of the contaminant. Absolutely commend this approach. I don't think bracken is needed in this particular case - outside this case it is needed to correct the count distribution bias.  

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### Answer by username_0 on 2023-10-12. Score: 1

I didn't manage to try the ones reported by @gringer and @alexlok here, but I did manage to perform an analysis using the OneCodex webapp: <https://app.onecodex.com/> and based on the experimental information for the samples being analysed, the results were concordant with the expectation. You can do quite a lot on the "free tier", so at the moment I didn't upgrade to the paid version.


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### Answer by username_3 on 2023-08-10. Score: 1

Maybe less advanced but worth mentioning, I've had good results with [fastq\_screen](https://www.bioinformatics.babraham.ac.uk/projects/fastq_screen/). It's simply trying to align a subset of your sequences against various genomes (comes with a few genomes, you can add more).


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