<p>You basically have three options:</p>
<p>a) Normalizing to total read count, so all samples have the same total depth. This is known to perform poorly if there is any compositional changes going on.</p>
<p>b) Normalize with approaches such as DESeq2. That somewhat assumes that most features do not change between samples so the &quot;baseline&quot; is the same.</p>
<p>c) You can normalize so that known features from which you think they should be the same are not differential.</p>

CC BY-SA 4.0
2024-08-19T14:33:56.437Z
Thank you, do you mean normalizing to depth and gene length? RPM and RPKM? using readCount values?
dim
CC BY-SA 4.0
2024-08-14T14:17:07.577Z
2024-08-14T14:17:07.577Z
ATpoint
<p>I'm working on antimicrobial resistance (AMR) surveillance in treated water samples using nanopore long-read DNA sequencing analysis. After analyzing my data with the KMA tool (a k-mer read alignment tool, Clausen et al., 2018), I obtained two tables representing AMR genes under two conditions: &quot;before&quot; and &quot;after&quot; treatment, with no replicates.</p>
<p>The output includes metrics like <strong>Template_Identity</strong>, <strong>Template_Coverage</strong>, <strong>Depth</strong>, and so-called <strong>ConClave score</strong>, that is a measure of the combined alignment quality for each template.</p>
<p>I can also output other metrics, such as <strong>readCount</strong> (Number of reads mapped the template), <strong>bpTotal</strong> (Total number of bases aligned to the template), and others (I don't know if it is important to list all of them here for now)</p>
<p>Given this data, what would be the best approach to normalize and compare the treatment effects ?</p>
<p>Again, I <strong>don't have</strong> replicates or spike-in data.</p>
<p>Below are the examples of tables that I have:</p>
<p>BEFORE</p>
<div class="s-table-container"><table class="s-table">
<thead>
<tr>
<th>Template</th>
<th>readCount</th>
<th>Score</th>
</tr>
</thead>
<tbody>
<tr>
<td>gene1</td>
<td>93</td>
<td>2607</td>
</tr>
<tr>
<td>gene2</td>
<td>104</td>
<td>1982</td>
</tr>
<tr>
<td>gene3</td>
<td>47</td>
<td>3035</td>
</tr>
<tr>
<td>gene4</td>
<td>207</td>
<td>5773</td>
</tr>
<tr>
<td>gene5</td>
<td>36</td>
<td>7544</td>
</tr>
<tr>
<td>gene6</td>
<td>91</td>
<td>5488</td>
</tr>
</tbody>
</table></div>
<p>AFTER</p>
<div class="s-table-container"><table class="s-table">
<thead>
<tr>
<th>Template</th>
<th>readCount</th>
<th>Score</th>
</tr>
</thead>
<tbody>
<tr>
<td>gene1</td>
<td>93</td>
<td>2075</td>
</tr>
<tr>
<td>gene2</td>
<td>104</td>
<td>3488</td>
</tr>
<tr>
<td>gene3</td>
<td>47</td>
<td>1021</td>
</tr>
</tbody>
</table></div>

CC BY-SA 4.0
2024-08-15T20:10:21.083Z
Ideally, comparisons should have full information for all samples; do you have AFTER counts for gene4-gene6? Even if the count is zero, that's important information.
gringer
CC BY-SA 4.0
2024-08-16T13:57:15.233Z
Thanks. I don't even have these genes in my list. Which is kinda straightforward effect of the treatment, if I may guess..
dim
<phylogenetics><nanopore><normalization>
How can I normalize DNAseq nanopore metagenomics data?
# How can I normalize DNAseq nanopore metagenomics data?

(Asked by: username_0 on 2024-08-13)

I'm working on antimicrobial resistance (AMR) surveillance in treated water samples using nanopore long-read DNA sequencing analysis. After analyzing my data with the KMA tool (a k-mer read alignment tool, Clausen et al., 2018), I obtained two tables representing AMR genes under two conditions: "before" and "after" treatment, with no replicates.

The output includes metrics like **Template\_Identity**, **Template\_Coverage**, **Depth**, and so-called **ConClave score**, that is a measure of the combined alignment quality for each template.

I can also output other metrics, such as **readCount** (Number of reads mapped the template), **bpTotal** (Total number of bases aligned to the template), and others (I don't know if it is important to list all of them here for now)

Given this data, what would be the best approach to normalize and compare the treatment effects ?

Again, I **don't have** replicates or spike-in data.

Below are the examples of tables that I have:

BEFORE

| Template | readCount | Score |
| --- | --- | --- |
| gene1 | 93 | 2607 |
| gene2 | 104 | 1982 |
| gene3 | 47 | 3035 |
| gene4 | 207 | 5773 |
| gene5 | 36 | 7544 |
| gene6 | 91 | 5488 |

AFTER

| Template | readCount | Score |
| --- | --- | --- |
| gene1 | 93 | 2075 |
| gene2 | 104 | 3488 |
| gene3 | 47 | 1021 |

**Tags:** phylogenetics, nanopore, normalization
> **Comments:**
> **username_1**: Ideally, comparisons should have full information for all samples; do you have AFTER counts for gene4-gene6? Even if the count is zero, that's important information.  
> **username_0**: Thanks. I don't even have these genes in my list. Which is kinda straightforward effect of the treatment, if I may guess..  

---

## 1 answers

### Answer by username_2 on 2024-08-14. Score: 1 | Accepted answer

You basically have three options:

a) Normalizing to total read count, so all samples have the same total depth. This is known to perform poorly if there is any compositional changes going on.

b) Normalize with approaches such as DESeq2. That somewhat assumes that most features do not change between samples so the "baseline" is the same.

c) You can normalize so that known features from which you think they should be the same are not differential.

> **Comments:**
> **username_0**: Thank you, do you mean normalizing to depth and gene length? RPM and RPKM? using readCount values?  

---

