To facilitate KEGG pathway analysis, differentially expressed transcripts were mapped to KEGG ortholog IDs. The transcripts were aligned to Swiss‐Prot (The Uniprot Consortium, 2017 ) using blastx (Camacho et al., 2009 ). Swiss‐Prot hits were filtered using an e‐value cutoff of 1e−5 and matched to KEGG orthologs using the KEGG API. For each Manduca transcript, the KEGG ortholog corresponding to the lowest BLAST e‐value was selected.
Swiss prot
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Swiss-Prot is a manually curated protein sequence database. It provides a high level of annotation, a minimal level of redundancy, and a high level of integration with other databases.
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6 protocols using swiss prot
1
KEGG Pathway Analysis of Differentially Expressed Transcripts
2
Genome Annotation and Analysis Pipeline
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Using the error corrected genome, the origin and terminus of replication were determined using GenSkew (http://genskew.csb.univie.ac.at ). tRNA and rRNA were predicted using tRNAscan-SE1.3154 (link) and RNAmmer 1.255 (link), respectively. Genome prediction and annotation were performed using Prodigal56 (link) and the Swiss-Prot (from Uniprot) database, respectively. The other unknown predicted genes were annotated using the non-redundant protein database of GenBank. Subsequently, the amino acid sequences of annotated genes were aligned against the protein database to identify Gene Ontology (GO), KO ID, and Clusters of Orthologous Groups (COG)57 (link),58 (link).
3
Identification of MYB Transcription Factors
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All candidate protein sequences from the genome of P. campanulata were scanned by Hmmer v3.1 with default parameters employing the hidden Markov model profiles of the Myeloblastosis (MYB) domain (PF00249) downloaded from the Pfam (v30.0) databases. A database of transcription factors, constructed for the MYB gene family from A. thaliana downloaded from the Pfam (v30.0) database, was used as a query to search against the protein datasets of P. campanulata using BlastP. Subsequently, the protein datasets were analyzed with Swiss-Prot (http://www.uniprot.org/ , accessed on 20 December 2020), using the method of auto-blasting two sequence sets to verify the domain composition. Based on the above methods, the preliminarily identified candidate sequences were determined by merging all the datasets and removing repeating sequences. The resulting candidate sequences were then confirmed by the Pfam (v30.0) and NCBI Conserved Domain Database (CDD, https://www.ncbi.nlm.nih.gov/cdd/ (accessed on 20 December 2020)). The sequences with MYB functional domains were retained for the following analysis.
4
N. caninum Protein Identification
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The bands (1D SDS-PAGE) or spots (2D SDS-PAGE) were excised from acrylamide gels and analysed by nano LC-MS/MS as described (Pollo-Oliveira et al., 2013) . The resulting data were searched against N. caninum predicted protein database ToxoDB version 28 and Swissprot (The UniProt Consortium, 2017), using Mascot software version 2.6.1 (Matrix Science, London, UK). The search was performed using carbamidomethylation as fixed modification of cysteine and oxidation of methionine as variable modification. The peptide tolerance was set to 50 ppm and 0.6 Da of ion tolerances, allowing for two missed cleavages. Proteins and peptide identifications >95% probability were accepted.
5
Creating a Challenging Protein Localization Test Set
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To catch over-fitting on a static standard dataset, we created a new independent test set from SwissProt (The UniProt Consortium, 2021 (link)). Applying the same filters as DeepLoc (only eukaryotes; all proteins ≥40 residues; no fragments; only experimental annotations) gave 5947 proteins. Using MMseqs2 (Steinegger and Söding, 2017 (link)), we removed all proteins from the new set with ≥20% PIDE to any protein in any other set. Next, we mapped location classes from DeepLoc to SwissProt, merged duplicates, and removed multi-localized proteins (protein X both in class Y and Z). Finally, we clustered at ≥20% PIDE leaving only one representative of each cluster in the new, more challenging test set (dubbed setHARD; 490 proteins; Supplementary Appendix : Datasets).
6
Genomic Islands Annotation and Analysis
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We used Geneious Prime version 2020.1.2 to manually inspect the ΦSA3, vSa-α, vSa-β, vSa-ɣ, and SaPI-1 genomic islands (22 (link),23 (link)) and Swiss-Prot (Uniprot Consortium, https://www.uniprot.org ) to annotate paralogues. To map the genetic context of genomic islands, we randomly selected representative genome sequences from different phylogenetic locations of the tree showing the most common CC5 lineages in the Rio de Janeiro metropolitan area (Figure 1 ). We determined gene presence or absence using BLAST analysis (https://blast.ncbi.nlm.nih.gov ).
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