## 3 Results
### A Comparison to the MiFish eDNA Pipeline  
MiFish is a mature eDNA processing pipeline that is under active development [11; 12]. We were interested in testing the performance of the _eDNA-container_ app against this well-established pipeline. Therefore, we used the _eDNA-container_ app to analyze the test dataset provided by _MiFish_, which is based on an eDNA dataset generated by [27]. The species lists reported across all samples by both packages were very similar, with 18 of 19 species identified by the _eDNA-container app_ (Supplemental File S3) also being found by _MiFish_ (Supplemental File S4). The _eDNA-container_ app identified a _Bos taurus_ barcode that was not in the _MiFish_ outputs, but this was a low confidence assignment (<90%). _MiFish_ shared 19 of 21 species with the eDNA-container app, with the two unique species in the _MiFish_ results being _Anas platyrhnchos_ and _Blicca bjoerkua_. Once again, these species assignments unique to _MiFish_ were flagged as low confidence by the software. Table 3 shows a comparison of the number of species detected in each sample found in the tested dataset. There is a high level of consistency across both pipelines, and differences were detected only for low-count taxa. The detection of low-count taxa is strongly influenced by sampling biases due to the different read quality filtering and ASV algorithms adopted by the _eDNA-container app_ and _MiFish_.  
The main difference in the outputs from the two pipelines is that _MiFish_ consistently reports higher read counts for each taxon. The reason for these higher taxa counts is that _MiFish_ reports single-end read counts, whilst the _eDNA-container app_ reports amplicon fragment counts (the R1 and R2 read pairs are considered as a single fragment). Another reason for the difference in taxa counts between the two pipelines is that the _eDNA-container app_ conservatively only counts amplicons if the PCR primers can be identified at the ends of the paired-end reads. Although this latter strategy reduces the influence of putative PCR artifacts, it does come at the cost of sampling depth.  
In summary, despite differences in the detection of low-count taxa, the outputs from the _eDNA-container app_ and _MiFish_ are very similar in terms of high confidence identifications and abundance rankings of taxa. Users familiar with the popular _QIIME2_ ecosystem of tools will benefit from _eDNA-container app_ outputs as they will be compatible with existing downstream processing pipelines. The ability to view raw data outputs from the _eDNA-container app_ using the drag-and-drop _QIIME2_ viewer is also advantageous for scientists who prefer web-based interactive plotting tools. As _MiFish_ and the _eDNA-container app_ use different underlying algorithms, the availability of both packages provides scientists with two alternative pipelines to assess the robustness of captured eDNA profiles.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Output File** & **Explanation** \\ \hline final-report.pdf & PDF report summarizing QC and ASV metrics \\ asv\_count\_tax\_seq\_summary.csv & Spreadsheet of eDNA taxa counts and ASV sequences \\ barchart.qzv & Taxonomic barplot \\ alpha\_rarefaction.qzv & Alpha diversity rarefaction plots \\ paired-end-demux.qzv & Read quality plots \\ asvs & Amplified Sequence Variant raw files \\ Logs & _DADA2_ and _cutadapt_ plugin log files \\ boxplot-forward.png & Boxplot of forward read used in report \\ boxplot-reverse.png & Boxplot of reverse read used in report \\ manifest.tsv & Sample metadata \\ \hline \hline \end{tabular}
\end{table}
Table 2: A summary of the output files generated by the _eDNA-container app_ (qzv files can be viewed interactively using the _QIIME2_ viewer).