References
1. Lee D, Redfern O, Orengo C (2007) Predicting protein function from sequence and structure. Nat Rev Mol Cell Biol 8(12):995–1005.
https://doi.org/10.1038/nrm2281
2. Emanuelsson O, Brunak S, von Heijne G, Nielsen H (2007) Locating proteins in the cell
using TargetP, SignalP and related tools. Nat
Protoc 2(4):953–971. https://doi.org/10.
1038/nprot.2007.131
3. Petersen TN, Brunak S, von Heijne G, Nielsen
H (2011) SignalP 4.0: discriminating signal
peptides from transmembrane regions. Nat
Methods 8(10):785–786. https://doi.org/
10.1038/nmeth.1701
4. Krogh A, Larsson B, von Heijne G, Sonnhammer ELL (2001) Predicting transmembrane
protein topology with a hidden Markov
model: application to complete genomes. J
Mol Biol 305(3):567–580. https://doi.org/
10.1006/jmbi.2000.4315
5. Goodswen SJ, Kennedy PJ, Ellis JT (2014)
Vacceed: a high-throughput in silico vaccine
candidate discovery pipeline for eukaryotic
pathogens based on reverse vaccinology. Bioinformatics 30(16):2381–2383. https://doi.
org/10.1093/bioinformatics/btu300
6. Rappuoli R (2000) Reverse vaccinology. Curr
Opin Microbiol 3(5):445–450. https://doi.
org/10.1016/s1369-5274(00)00119-3
7. Goodswen SJ, Kennedy PJ, Ellis JT (2013) A
guide to in silico vaccine discovery for eukaryotic pathogens.
Brief
Bioinform 14
(6):753–774. https://doi.org/10.1093/bib/
bbs066
8. Goodswen SJ, Kennedy PJ, Ellis JT (2013) A
novel strategy for classifying the output from
an in silico vaccine discovery pipeline for
eukaryotic pathogens using machine learning
algorithms. BMC Bioinformatics 14:315.
https://doi.org/10.1186/1471-2105-14315
9. Goodswen SJ, Kennedy PJ, Ellis JT (2017) On
the application of reverse vaccinology to parasitic diseases: a perspective on feature selection
and ranking of vaccine candidates. Int J Parasitol 47(12):779–790. https://doi.org/10.
1016/j.ijpara.2017.08.004
10. Palmieri N, Shrestha A, Ruttkowski B, Beck T,
Vogl C, Tomley F, Blake DP, Joachim A (2017)
The genome of the protozoan parasite Cystoisospora suis and a reverse vaccinology approach
to identify vaccine candidates. Int J Parasitol 47
(4):189–202.
https://doi.org/10.1016/j.
ijpara.2016.11.007
11. Armenteros JJA, Tsirigos KD, Sonderby CK,
Petersen TN, Winther O, Brunak S, von
Heijne G, Nielsen H (2019) SignalP 5.0
improves signal peptide predictions using
deep neural networks. Nat Biotechnol 37
(4):420. https://doi.org/10.1038/s41587019-0036-z
12. Horton P, Park KJ, Obayashi T, Fujita N,
Harada H, Adams-Collier CJ, Nakai K (2007)
WoLF PSORT: protein localization predictor.
Nucleic Acids Res 35:W585–W587. https://
doi.org/10.1093/nar/gkm259
13. Kall L, Krogh A, Sonnhammer ELL (2004) A
combined transmembrane topology and signal
peptide prediction method. J Mol Biol 338
(5):1027–1036. https://doi.org/10.1016/j.
jmb.2004.03.016
14. Armenteros JJA, Sonderby CK, Sonderby SK,
Nielsen H, Winther O (2017) DeepLoc: prediction of protein subcellular localization using
deep
learning.
Bioinformatics
33
(21):3387–3395. https://doi.org/10.1093/
bioinformatics/btx431
15. Vita R, Zarebski L, Greenbaum JA, Emami H,
Hoof I, Salimi N, Damle R, Sette A, Peters B
(2010) The immune epitope database 2.0.
Nucleic Acids Res 38:D854–D862. https://
doi.org/10.1093/nar/gkp1004
16. Bui HH, Sidney J, Peters B, Sathiamurthy M,
Sinichi A, Purton KA, Mothe BR, Chisari FV,
Watkins DI, Sette A (2005) Automated generation and evaluation of specific MHC binding
predictive tools: ARB matrix applications.
Immunogenetics 57(5):304–314. https://doi.
org/10.1007/s00251-005-0798-y
17. Wang P, Sidney J, Dow C, Mothe B, Sette A,
Peters B (2008) A systematic assessment of
MHC class II peptide binding predictions and
evaluation of a consensus approach. PLoS
Comput Biol 4(4). https://doi.org/10.
1371/journal.pcbi.1000048
42
Stephen J. Goodswen et al.
1. Lee D, Redfern O, Orengo C (2007) Predicting protein function from sequence and structure. Nat Rev Mol Cell Biol 8(12):995–1005.
https://doi.org/10.1038/nrm2281
2. Emanuelsson O, Brunak S, von Heijne G, Nielsen H (2007) Locating proteins in the cell
using TargetP, SignalP and related tools. Nat
Protoc 2(4):953–971. https://doi.org/10.
1038/nprot.2007.131
3. Petersen TN, Brunak S, von Heijne G, Nielsen
H (2011) SignalP 4.0: discriminating signal
peptides from transmembrane regions. Nat
Methods 8(10):785–786. https://doi.org/
10.1038/nmeth.1701
4. Krogh A, Larsson B, von Heijne G, Sonnhammer ELL (2001) Predicting transmembrane
protein topology with a hidden Markov
model: application to complete genomes. J
Mol Biol 305(3):567–580. https://doi.org/
10.1006/jmbi.2000.4315
5. Goodswen SJ, Kennedy PJ, Ellis JT (2014)
Vacceed: a high-throughput in silico vaccine
candidate discovery pipeline for eukaryotic
pathogens based on reverse vaccinology. Bioinformatics 30(16):2381–2383. https://doi.
org/10.1093/bioinformatics/btu300
6. Rappuoli R (2000) Reverse vaccinology. Curr
Opin Microbiol 3(5):445–450. https://doi.
org/10.1016/s1369-5274(00)00119-3
7. Goodswen SJ, Kennedy PJ, Ellis JT (2013) A
guide to in silico vaccine discovery for eukaryotic pathogens.
Brief
Bioinform 14
(6):753–774. https://doi.org/10.1093/bib/
bbs066
8. Goodswen SJ, Kennedy PJ, Ellis JT (2013) A
novel strategy for classifying the output from
an in silico vaccine discovery pipeline for
eukaryotic pathogens using machine learning
algorithms. BMC Bioinformatics 14:315.
https://doi.org/10.1186/1471-2105-14315
9. Goodswen SJ, Kennedy PJ, Ellis JT (2017) On
the application of reverse vaccinology to parasitic diseases: a perspective on feature selection
and ranking of vaccine candidates. Int J Parasitol 47(12):779–790. https://doi.org/10.
1016/j.ijpara.2017.08.004
10. Palmieri N, Shrestha A, Ruttkowski B, Beck T,
Vogl C, Tomley F, Blake DP, Joachim A (2017)
The genome of the protozoan parasite Cystoisospora suis and a reverse vaccinology approach
to identify vaccine candidates. Int J Parasitol 47
(4):189–202.
https://doi.org/10.1016/j.
ijpara.2016.11.007
11. Armenteros JJA, Tsirigos KD, Sonderby CK,
Petersen TN, Winther O, Brunak S, von
Heijne G, Nielsen H (2019) SignalP 5.0
improves signal peptide predictions using
deep neural networks. Nat Biotechnol 37
(4):420. https://doi.org/10.1038/s41587019-0036-z
12. Horton P, Park KJ, Obayashi T, Fujita N,
Harada H, Adams-Collier CJ, Nakai K (2007)
WoLF PSORT: protein localization predictor.
Nucleic Acids Res 35:W585–W587. https://
doi.org/10.1093/nar/gkm259
13. Kall L, Krogh A, Sonnhammer ELL (2004) A
combined transmembrane topology and signal
peptide prediction method. J Mol Biol 338
(5):1027–1036. https://doi.org/10.1016/j.
jmb.2004.03.016
14. Armenteros JJA, Sonderby CK, Sonderby SK,
Nielsen H, Winther O (2017) DeepLoc: prediction of protein subcellular localization using
deep
learning.
Bioinformatics
33
(21):3387–3395. https://doi.org/10.1093/
bioinformatics/btx431
15. Vita R, Zarebski L, Greenbaum JA, Emami H,
Hoof I, Salimi N, Damle R, Sette A, Peters B
(2010) The immune epitope database 2.0.
Nucleic Acids Res 38:D854–D862. https://
doi.org/10.1093/nar/gkp1004
16. Bui HH, Sidney J, Peters B, Sathiamurthy M,
Sinichi A, Purton KA, Mothe BR, Chisari FV,
Watkins DI, Sette A (2005) Automated generation and evaluation of specific MHC binding
predictive tools: ARB matrix applications.
Immunogenetics 57(5):304–314. https://doi.
org/10.1007/s00251-005-0798-y
17. Wang P, Sidney J, Dow C, Mothe B, Sette A,
Peters B (2008) A systematic assessment of
MHC class II peptide binding predictions and
evaluation of a consensus approach. PLoS
Comput Biol 4(4). https://doi.org/10.
1371/journal.pcbi.1000048
42
Stephen J. Goodswen et al.