Answers.Body: <p>Use <a href="https://ccb.jhu.edu/software/kraken2/index.shtml?t=downloads" rel="nofollow noreferrer">kraken2</a> with one of the 16S databases. Kraken2 has worked well for me with nanopore sequences. <a href="https://ccb.jhu.edu/software/pavian/" rel="nofollow noreferrer">Pavian</a> can be used to inspect the Kraken2 report files visually.</p>
  
  <p>Using uncorrected called reads has worked for me. Any errors from nanopore sequencing should be inconsistent enough (i.e. not always lead towards the same wrong organism) that the noise doesn't matter.</p>
  
  <p>Note: if you're using the MiniKraken database instead of a comprehensive 16S database, there might be some false positive species assignments depending on which kmers get pruned out.</p>
  
  <p>Another possibility is <a href="https://ccb.jhu.edu/software/centrifuge/" rel="nofollow noreferrer">centrifuge</a>, but I'm not sure if that works for 16S amplicon sequencing; it's primarily for metagenomic analysis.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-06-19T21:08:11.267Z
Answers.Text: @gringer conceptual doubt ..do we need perform denovo assembly on the nanopore data or we can simply use the fastq file for kraken2?
Answers.UserDisplayName: kcm
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-06-20T06:42:41.930Z
Answers.Text: Using uncorrected called reads has worked for me.
Answers.UserDisplayName: gringer
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Answers.CreationDate: 2019-11-30T10:12:13.967Z
Answers.LastActivityDate: 2019-12-10T22:38:14.970Z
Answers.LastEditDate: 2019-12-10T22:38:14.970Z
Answers.UserDisplayName: gringer
Answers.Body: <p>I would second the answer gringer gave recommending Kraken2.  I would suggest using <a href="https://ccb.jhu.edu/software.shtml" rel="nofollow noreferrer">bracken</a> to process the results from Kraken2 if you want to estimate species abundance (the raw Kraken2 results should not be used for this).  I would recommend Pavian for visualising the results, it produces beautiful Sankey plots in a fairly pain-free manner.  If you want to save yourself some work the 16S tool provided by Oxford Nanopore as part of epi2me produces good results for limited effort - although this is a bit of a proprietary black box.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-06-19T21:15:07.587Z
Answers.Text: "lthough this is a bit of a proprietary black box." it takes a lot of time at one time only one barcode sample can be uploaded
Answers.UserDisplayName: kcm
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-12-11T13:00:57.183Z
Answers.LastActivityDate: 2019-12-11T13:00:57.183Z
Answers.UserDisplayName: Graeme
Body: <p>I have recently been tasked with analyzing a heterogeneous microbial sample composed of 16S data generated on a MinION sequencer, with the goal of assigning taxonomies and calculating respective abundance. </p>
  
  <p>It is my understanding that traditional preprocessing methods such as Dada2, Unoise3, and Deblur would likely not be appropriate for this kind of data given the poorer read quality inherent with nanopore sequencing. To give an example, the average read quality of this current experiment is a Phred score of 15 and an average read length of 1500bp. </p>
  
  <p>Furthermore, it would seem best to try and utilize a classifier that makes use of a full length 16S reference, rather than just the V3 region, to try and offset the higher error rate. But are there any classifiers that do this out of the box?</p>
  
  <p>I am very interested to hear of any advice or input the community has on this. Thanks!</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-07-08T06:39:57.620Z
Comments.Text: I'm doing similar work. Kraken2 with silva 138 db plus bracken works fine. Only constrain is that none reads were classified at species level. I'm wondering if you guys has obtained species level assignments? Cheers,
  Gaofeng
Comments.UserDisplayName: Gani
Tags: <nanopore><minion><microbiology>
Title: Microbial Classification with 16S Nanopore Data
ThreadText: # Microbial Classification with 16S Nanopore Data
  
  (Asked by: username_0 on 2019-11-22)
  
  I have recently been tasked with analyzing a heterogeneous microbial sample composed of 16S data generated on a MinION sequencer, with the goal of assigning taxonomies and calculating respective abundance.
  
  It is my understanding that traditional preprocessing methods such as Dada2, Unoise3, and Deblur would likely not be appropriate for this kind of data given the poorer read quality inherent with nanopore sequencing. To give an example, the average read quality of this current experiment is a Phred score of 15 and an average read length of 1500bp.
  
  Furthermore, it would seem best to try and utilize a classifier that makes use of a full length 16S reference, rather than just the V3 region, to try and offset the higher error rate. But are there any classifiers that do this out of the box?
  
  I am very interested to hear of any advice or input the community has on this. Thanks!
  
  **Tags:** nanopore, minion, microbiology
  
  > **Comments:**
  > **username_1**: I'm doing similar work. Kraken2 with silva 138 db plus bracken works fine. Only constrain is that none reads were classified at species level. I'm wondering if you guys has obtained species level assignments? Cheers, Gaofeng  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_2 on 2019-11-30. Score: 3 | Accepted answer
  
  Use [kraken2](https://ccb.jhu.edu/software/kraken2/index.shtml?t=downloads) with one of the 16S databases. Kraken2 has worked well for me with nanopore sequences. [Pavian](https://ccb.jhu.edu/software/pavian/) can be used to inspect the Kraken2 report files visually.
  
  Using uncorrected called reads has worked for me. Any errors from nanopore sequencing should be inconsistent enough (i.e. not always lead towards the same wrong organism) that the noise doesn't matter.
  
  Note: if you're using the MiniKraken database instead of a comprehensive 16S database, there might be some false positive species assignments depending on which kmers get pruned out.
  
  Another possibility is [centrifuge](https://ccb.jhu.edu/software/centrifuge/), but I'm not sure if that works for 16S amplicon sequencing; it's primarily for metagenomic analysis.
  
  > **Comments:**
  > **username_3**: @username_2 conceptual doubt ..do we need perform denovo assembly on the nanopore data or we can simply use the fastq file for kraken2?  
  > **username_2**: Using uncorrected called reads has worked for me.  
  
  ---
  
  ### Answer by username_4 on 2019-12-11. Score: 2
  
  I would second the answer username_2 gave recommending Kraken2. I would suggest using [bracken](https://ccb.jhu.edu/software.shtml) to process the results from Kraken2 if you want to estimate species abundance (the raw Kraken2 results should not be used for this). I would recommend Pavian for visualising the results, it produces beautiful Sankey plots in a fairly pain-free manner. If you want to save yourself some work the 16S tool provided by Oxford Nanopore as part of epi2me produces good results for limited effort - although this is a bit of a proprietary black box.
  
  > **Comments:**
  > **username_3**: "lthough this is a bit of a proprietary black box." it takes a lot of time at one time only one barcode sample can be uploaded  
  
  ---
  
  