<p>You should also keep in mind that single read accuracy of ONT still is a bit lacking, a 2D or 1D^2 accuracy of 95% still means that on average there's one error every 20 bp, and due to the nature of the data some stretches may be more junky than others. Maybe centrifuge doesn't like that.</p>

<p>Getting a feel of what the data can tell you (and what not) is pretty important.</p>

<p>You may want to try other classification tools to get an idea how well centrifuge works, Kraken comes to mind. But also: what about BLASTing some of these reads at NCBI and simply look at the results to get a first impression?</p>

CC BY-SA 3.0
2017-05-31T15:45:09.357Z
2017-05-31T15:45:09.357Z
BaCh
<p>I recently used the minION (Nanopore, 9.4 flow cell, RAD001 kit) to generate a metagenome out of environmental samples.
Passed reads weren't brilliant (196, average 1,594bp lenght), but working with <a href="https://github.com/infphilo/centrifuge" rel="noreferrer">centrifuge</a> the classification outputs turned out to have quite low hitLength to queryLength scores (average 2%, max 14%). This plus score values don't give me a lot of confidence towards the results I got.</p>

<p>Has anyone else used <em>centrifuge</em> and experienced the same?</p>

CC BY-SA 3.0
2017-06-01T15:31:41.493Z
Yes, this question can be improved, but I think people can understand what OP really wants. I don't think it deserves a closing.
user172818
CC BY-SA 3.0
2017-05-31T13:03:11.483Z
Sorry, but "has anyone else seen this" isn't on topic here (that's just a discussion and asking for personal opinion). Could you [edit] your question to make it request a solution to a specific problem instead? Something like "what sort of threshold should I use for centrifuge" or similar?
terdon
<nanopore><long-reads><minion><metagenome>
Rapid metagenomics classifiers on long read data
# Rapid metagenomics classifiers on long read data

(Asked by: username_0 on 2017-05-31)

I recently used the minION (Nanopore, 9.4 flow cell, RAD001 kit) to generate a metagenome out of environmental samples.
Passed reads weren't brilliant (196, average 1,594bp lenght), but working with [centrifuge](https://github.com/infphilo/centrifuge) the classification outputs turned out to have quite low hitLength to queryLength scores (average 2%, max 14%). This plus score values don't give me a lot of confidence towards the results I got.

Has anyone else used *centrifuge* and experienced the same?

**Tags:** nanopore, long-reads, minion, metagenome
> **Comments:**
> **username_2**: Sorry, but "has anyone else seen this" isn't on topic here (that's just a discussion and asking for personal opinion). Could you [edit] your question to make it request a solution to a specific problem instead? Something like "what sort of threshold should I use for centrifuge" or similar?  
> **username_1**: Yes, this question can be improved, but I think people can understand what OP really wants. I don't think it deserves a closing.  

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## 1 answers

### Answer by username_3 on 2017-05-31. Score: 1

You should also keep in mind that single read accuracy of ONT still is a bit lacking, a 2D or 1D^2 accuracy of 95% still means that on average there's one error every 20 bp, and due to the nature of the data some stretches may be more junky than others. Maybe centrifuge doesn't like that.

Getting a feel of what the data can tell you (and what not) is pretty important.

You may want to try other classification tools to get an idea how well centrifuge works, Kraken comes to mind. But also: what about BLASTing some of these reads at NCBI and simply look at the results to get a first impression?


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