# Annotation Challenges with COI Amplicon Analysis in QIIME2
**URL:** https://forum.qiime2.org/t/annotation-challenges-with-coi-amplicon-analysis-in-qiime2/29546
**Category:** General Discussion
**Tags:** taxonomy
**Created:** [March 12, 2024, 10:06am UTC](https://forum.qiime2.org/t/annotation-challenges-with-coi-amplicon-analysis-in-qiime2/29546 "2024-03-12T10:06:20Z")
**Posts on this page:** 4
**Page:** 1
### Author:  [@mengpf0409](https://forum.qiime2.org/u/mengpf0409)
#### Post date: [March 12, 2024, 10:06am UTC](https://forum.qiime2.org/t/annotation-challenges-with-coi-amplicon-analysis-in-qiime2/29546/1 "2024-03-12T10:06:20Z")
Hello everyone,
I'm currently analyzing the COI amplicon region using QIIME2 (version 2022.2) with samples originating from a lake and sequenced using the PE300 sequencing strategy. My amplification primers are as follows:
- Forward: GGWACWGGWTGAACWGTWTAYCCYCC (mlCOIintF)
- Reverse: TANACYTCTGGRTGICCRAARAAYCA (jgHCO219)
In my analysis workflow, I first imported the data and applied DADA2 denoising with the following commands:
```auto
qiime tools import \
--type 'SampleData[PairedEndSequencesWithQuality]' \
--input-path ${Data} \
--input-format CasavaOneEightSingleLanePerSampleDirFmt \
--output-path atcc-paired-end.qza
qiime dada2 denoise-paired \
--i-demultiplexed-seqs atcc-paired-end.qza \
--p-trim-left-f 26 \
--p-trim-left-r 26 \
--p-trunc-len-f 0 \
--p-trunc-len-r 0 \
--p-n-threads 20 \
--o-table atcc_table.qza \
--o-representative-sequences atcc_seqs.qza \
--o-denoising-stats atcc_stats.qza
```
Subsequently, I trained a COI database following the tutorial ["Building a COI database from BOLD references"](https://forum.qiime2.org/t/building-a-coi-database-from-bold-references/16129) on the QIIME2 forum and used the following commands for species annotation:
```auto
qiime feature-classifier classify-sklearn \
--i-classifier COI.qza \
--i-reads atcc_seqs.qza \
--p-n-jobs 8 \
--o-classification taxonomy.qza
```
My issue is that the annotation results using `classify-sklearn` only included 20 genus-level classifications. However, when directly comparing the same sequences with the NCBI NT database via BLAST, I can find many more COI gene matches.
To explore further, I tried `classify-consensus-blast` for comparison:
```auto
qiime feature-classifier classify-consensus-blast \
--i-query atcc_seqs.qza \
--i-reference-reads bold_derep1_seq.qza \
--i-reference-taxonomy bold_derep1_taxa.qza \
--p-perc-identity 0.8 \
--o-classification otu-taxonomy.qza
```
This time, the results showed annotations for 700 genera. However, when adjusting the `p-perc-identity` parameter to 0.9, the number of annotated genera decreased to 30.
I'm curious about why there's such a significant discrepancy between the `classify-sklearn` annotation results and the BLAST comparisons? What's the impact of adjusting the `p-perc-identity` value on the annotation results? Are there recommended approaches to increase the number of genus-level annotations to better match the results from NT database comparisons?
Thank you all for your assistance!
---
### Author:  [@mengpf0409](https://forum.qiime2.org/u/mengpf0409)
#### Post date: [March 12, 2024, 1:29pm UTC](https://forum.qiime2.org/t/annotation-challenges-with-coi-amplicon-analysis-in-qiime2/29546/2 "2024-03-12T13:29:49Z")
Additionally, to eliminate the influence of the database, I utilized a new database available at:
> **[GitHub - meglecz/mkCOInr: Make a non-redundant, comprehensive COI database...](https://github.com/meglecz/mkCOInr)**
>
> Make a non-redundant, comprehensive COI database from NCBI and BOLD and include customizing options
This includes the COI database from both BOLD and NCBI.
However, the results were roughly similar. Using the command:
```bash
qiime feature-classifier fit-classifier-naive-bayes
```
I was able to annotate to 30 genera, while with:
```bash
qiime feature-classifier classify-consensus-blast
```
With **`p-perc-identity`** set to 0.8, it annotated up to 900 genera, but setting **`p-perc-identity`** to 0.9 resulted in annotations for 35 genera.
Moreover, the 900 genera annotated with **`p-perc-identity`** 0.8 had relatively low similarity, between 80-90%.
Why is this the case? How can I annotate more results effectively?
---
### Author:  [@SoilRotifer](https://forum.qiime2.org/u/SoilRotifer)
#### Post date: [March 13, 2024, 6:26pm UTC](https://forum.qiime2.org/t/annotation-challenges-with-coi-amplicon-analysis-in-qiime2/29546/5 "2024-03-13T18:26:26Z")
Hi @mengpf0409,
These are great questions!
> [@mengpf0409](#):
>
> This time, the results showed annotations for 700 genera. However, when adjusting the `p-perc-identity` parameter to 0.9, the number of annotated genera decreased to 30.
> [@mengpf0409](#):
>
> With **`p-perc-identity`** set to 0.8, it annotated up to 900 genera, but setting **`p-perc-identity`** to 0.9 resulted in annotations for 35 genera.
> [@mengpf0409](#):
>
> What's the impact of adjusting the `p-perc-identity` value on the annotation results?
This issue can be partially explained as outlined [here](https://forum.qiime2.org/t/how-is-consensus-calculated-in-qiime-feature-classifier-classify-consensus-blast/24924/8), [here](https://forum.qiime2.org/t/taxonomical-asignment-questions-silva-pr2-and-vsearch-vs-blast/18675/4), [here](https://forum.qiime2.org/t/blast-consensus-question/24200/4) and [here](https://forum.qiime2.org/t/consensus-blast-taxonomy-strings/586/2). That is, the more strict your `--p-perc-identity 0.9` setting, the fewer reference sequences will match. This will result in a smaller pool (this is tied to the `--p-min-consensus` parameter) from which to calculate the lowest common ancestor (LCA) consensus taxonomy. Thus resulting in a consensus taxonomy that is more broad, or at at higher taxonomic rank.
When using `--p-perc-identity 0.8`, you are allowing the retention of more hits to the reference sequences, increasing the pool of taxonomic information that can contribute to the LCA consensus taxonomy, again tied to the `--p-min-consensus` parameter. Kind of a "majority-rule" approach.
Keep in mind, for some datasets & primer-pair choice, it is not unusual for short-read amplicon data to be unable to classify taxa to the genus level. In some cases the more specific classification is the result of incorrect over-classification. That is, returning a more specific identification than it should be able to.
Also, `feature-classifier classify-sklearn` works a differently than `feature-classifier classify-consensus-blast`. I'd highly recommend reading the following papers:
- [Bokulich _et al._ 2018](https://doi.org/10.1186/s40168-018-0470-z)
- [Zeimski _et al._ 2021](https://doi.org/10.3389/fmicb.2021.644487)
---
### Author:  [@mengpf0409](https://forum.qiime2.org/u/mengpf0409)
#### Post date: [March 20, 2024, 3:26am UTC](https://forum.qiime2.org/t/annotation-challenges-with-coi-amplicon-analysis-in-qiime2/29546/6 "2024-03-20T03:26:51Z")
Thank you very much, I will try it