Table 1 Tools used for the in silico analyses of Haematobia irritans transcripts and translated ORFs
From: Identification of anti-horn fly vaccine antigen candidates using a reverse vaccinology approach
Tool | Description | Website |
|---|---|---|
Virtual Ribosome | Comprehensive tool for translating DNA sequences to the corresponding peptide sequences [21] | |
Vaxign | Vaccine target prediction and analysis system based on the principle of reverse vaccinology [22,23,24] | |
 PSORTb | Program for bacterial protein subcellular localization prediction [29] | |
 TMHMM | ||
 SPAAN | Prediction of adhesins and adhesin-like proteins using neural networks [32] | - |
 BLAST | NCBI sequence similarity alignment and analysis program [33, 34] | |
 IEDB | Immune Epitope Database and Analysis Resource [35] | |
Vacceed | High-throughput in silico vaccine candidate discovery pipeline for eukaryotic pathogens based on reverse vaccinology [25, 26] | - |
 WoLF PSORT | Protein subcellular localization prediction [36] | |
 SignalP 4.1 | Predicts presence and location of signal peptide cleavage sites [37] | |
 TargetP 1.1 | Predicts the subcellular location of eukaryotic proteins [38] | |
 TMHMM | Prediction of transmembrane helices in proteins [30] | TMHMM—2.0—Services—DTU Health Tech |
 MHC I-binding | Peptide binding to MHC class I molecules [39] | |
 MHC II-binding | Peptide binding to MHC class II molecules [40] | |
VaxiJen | Server for alignment-independent prediction of protective antigens. It allows antigen classification solely based on the physicochemical properties of proteins without recourse to sequence alignment [27, 28] | |
 Blast2GOPro | Bioinformatics platform for the functional analysis of genomic datasets [44] | |
  BLASTN | Finds regions of similarity between nucleotide sequences using a nucleotide query [33, 34] | |
  BLASTX | Finds regions of similarity between protein sequences using a translated nucleotide query translated in all six reading frames [33, 34] | |
  InterPro | Provides functional analysis of protein sequences by classifying them into families and predicting the presence of domains and important sites. It uses predictive models known as signatures provided by several different databases [41] | |
  Gene Ontology (GO) | Defines concepts/classes used to describe gene function, and relationships between these concepts. It classifies functions along three aspects: molecular function, cellular component, biological process [42, 43] | |
Conserved Domain Database | Protein annotation resource that consists of a collection of well-annotated multiple sequence alignment models for ancient domains and full-length proteins [45, 46] | |
BepiPred 2.0 | Predicts B-cell epitopes from a protein sequence, using a random forest algorithm trained on epitopes and non-epitope amino acids determined from crystal structures. A sequential prediction smoothing is performed afterwards [47] | |
FBCPred | Predicts flexible length B-cell epitopes using subsequence kernel [48] | |
BCPred | Predicts linear B-cell epitopes using string kernels [49] | |
NetMHC 4.0 Server | Predicts peptide-MHC class I binding using artificial neural networks, and peptides are classified as having a strong or weak binder according to their ranking [50, 51] | |
IEDB-MHC I Binding Predictions | Predicts peptide-MHC class I binding using a combination of artificial neural network, stabilized matrix method, and scoring matrices derived from combinatorial peptide libraries. Predictions were made on 06 February 2018 [35, 50, 52,53,54,55] |