Commit ae1f1904 authored by Bastien MACE's avatar Bastien MACE
Browse files

README redaction

parent ade8baaa
......@@ -154,7 +154,7 @@ obiuniq Aquarium_2.fastq > Aquarium_2.uniq.fasta
```
<a name="step4"></a>
## STEP 4 : Filtering
## STEP 4 : Filtering (OBITools)
The _obigrep_ command filters the sequences according to different criteria which you can chose, such as the sequence length, or the abundance of the amplicons :
```
......@@ -193,6 +193,13 @@ obiannotate -k count Aquarium_2.tag.fasta > Aquarium_2.tag_1.fasta
<a name="step7"></a>
## STEP 7 : Gathering in OTU (swarm)
_swarm_ gathers the sequences in OTU thanks to this algorithm :
- First, sequences are pairwise aligned to count the number of dissimilarities between them
- A threshold _d_ is chosen, when the number of dissimilarities is inferior or equal to _d_, both sequences are gathered in a same OTU
- This process is repeated to add iteratively the sequences to an OTU
- The most abundant sequence of each OTU is chosen to represent the OTU
- The abundance of the OTU is constituted by adding the abundances of each sequence included in the OTU
```
swarm -z -d 1 -o stats_Aq2.txt -w pipeline3_Aq2.fasta < pipeline1_Aq2.tag_1.fasta
# "-z" option permits to accept the abundance in the header, provided that there is no space in the header and that the value is preceded by "size="
......@@ -200,8 +207,16 @@ swarm -z -d 1 -o stats_Aq2.txt -w pipeline3_Aq2.fasta < pipeline1_Aq2.tag_1.fast
# "-o" option returns a ".txt" file in which each line corresponds to an OTU with all the amplicons belonging to this OTU
# "-w" option gives a "fasta" file with the representative sequence of each OTU
```
An option called _fastidious_ can be added, with _-f_, in order to integrate small OTUs in larger related OTUs. We don't use it here because it doesn't change the output at all for this example.
<a name="step8"></a>
## STEP 8 : Analyse your results
Then, you can filtrate your OTUs and the amplicons present in it.
For example, _Elbrecht et al._ recommend in their [publication](https://peerj.com/articles/4644/) to eliminate :
- OTUs with an abundance inferior to 0.01%
- Amplicons with an abundance inferior to 0.003%
- Amplicons with a relative abundance inferior to 5% in their OTU
Now you can make a statistical analysis to evaluate your filtering quality, after comparing the OTUs returned by the pipeline with your reference dataset.
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