# Workflows for Rapid Functional Annotation of Diverse Arthropod Genomes
## 3 Results
### Parameter Optimization  
Like all workflows, parameter optimization is a key part of ensuring quality results. Here we discuss the parameter optimization process for this workflow across a diverse range of arthropod genomes to consider when applying this workflow to their own data sets. For the GOanna tool, the key optimization parameters are the selection of the database \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Species** & **Complete** &
\begin{tabular}{c} **Complete** \\ **Single-Copy** \\ \end{tabular} & **Compleate** & **Fragmented** & **Missing** \\ \hline _Drosophila melanogaster_ (fruit fly) & 99.90 & 53.3 & 46.6 & 0 & 0.1 \\ _Athalia rose_ (turnip sawfly) & 99.70 & 68.9 & 30.8 & 0 & 0.3 \\ _Certainis capitata_ (Mediterranean fruit fly) & 98.40 & 97.5 & 0.9 & 0.4 & 1.2 \\ _Tribolium castnam_ (red flour beetle) & 98.40 & 93.1 & 5.3 & 1.2 & 0.4 \\ _Apis mellifer_ (honey bee) & 97.40 & 96.9 & 0.5 & 1.5 & 1.1 \\ _Varroa destructor_ (parasitic mite) & 95.90 & 43.1 & 52.8 & 0.7 & 3.4 \\ _Cimex declarlaries_ (Cimicidae bed bug) & 95.30 & 93.5 & 1.8 & 2.5 & 2.2 \\ _Copidosoma floridum_ (parasiroid wasp) & 93.70 & 92.5 & 1.2 & 2.9 & 3.4 \\ _Agrilis plantripains_ (emerald ash borer) & 99.00 & 89.1 & 1.8 & 4.6 & 4.5 \\ _Diaphrina citri_ (Asian citrus psyllid) & 87.10 & 55.9 & 31.2 & 2.8 & 10.1 \\ _Proceptus fasciatus_ (large milkweed bug) & 72.90 & 70.8 & 2.1 & 21.4 & 5.7 \\ _Eurytorema affirsa_ (caloal copep) & 57.50 & 55.9 & 1.6 & 20 & 22.5 \\ _Homalladisca vitripennis_ (glassy-winged sharnshooter) & 55.90 & 54.2 & 1.7 & 32.5 & 11.6 \\ _Limenophilus Inatus_ (caddisfly) & 42.40 & 41.4 & 1 & 28.1 & 29.5 \\ _Lattradetus hespreus_ (Western black widow spider) & 31.40 & 30.6 & 0.8 & 26.9 & 41.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Arthropod genomes selected for this study and their BUSCO completeness statistics. The test species are sorted by the BUSCO completeness score. BUSCO version 5.1.2 was used with the protein option and arthropoda_odb10.2019-11-20 database with 1013 markers. and the standard BLAST parameters. It is common practice to do an initial BLAST search against a comprehensive database (e.g., NCBI nr or UniProt-SwissProt databases) to identify the most similar known sequence. While we include the UniProt SwissProt and TrEMBL database as options for GOanna, we note that the databases GOanna uses are not meant to be comprehensive, but rather a subset of proteins that have been assigned GO terms. Moreover, given that searching against larger databases increases the probability of finding spurious matches, we recommend using the phyla-specific database most relevant for your dataset and supplementing the output of GOanna matches with InterProScan results. To ensure high-quality results, BLAST parameters should be optimized. While many analyses report optimizing BLAST solely on the E-value, this varies based upon database size. To determine BLAST parameters, we randomly selected three sets of 1000 sequences from each of the proteomes and manually reviewed the results of alignments from BLAST run with default parameters. The most common error when these sets were re-run with more stringent E-values was identifying short, perfect matches (E-value = 0) that had low query coverage (e.g., less than 50%). To consistently return good matches from a broad range of protein sequences from all the proteomes used in this study, we used cutoffs of 70% identity and 70% coverage for the BLAST parameters. Unlike GOanna, which is BLAST-based, InterProScan searches for near-perfect matches to short motifs and domains [9]. A key consideration for running InterProScan is to decide which databases should be searched. CDD or PFAM are frequently used, and both of these databases are included in the InterPro analysis. Since the computing requirements of InterProScan are considerable, these requirements could be reduced by searching fewer databases.