38. Gilchuk P, Hill TM, Wilson JT, Joyce S
(2015) Discovering protective CD8 T cell
epitopes—no single immunologic property
predicts it! Curr Opin Immunol 34:43–51
39. Akram A, Inman RD (2012) Immunodominance: a pivotal principle in host response to
viral infections. Clin Immunol 143:99–115
40. Rodrigues MM, Ersching J (2015) Neglected
tropical diseases, bioinformatics, and vaccines.
J Infect Dis 211:175–177
41. Haft DH, DiCuccio M, Badretdin A et al
(2018) RefSeq: an update on prokaryotic
genome annotation and curation. Nucleic
Acids Res 46:D851–D860
42. Harrison PW, Alako B, Amid C et al (2019)
The european nucleotide archive in 2018.
Nucleic Acids Res 47:D84–D88
43. Wattam AR, Davis JJ, Assaf R et al (2017)
Improvements to PATRIC, the all-bacterial
Bioinformatics Database and Analysis
Resource Center. Nucleic Acids Res 45:
D535–D542
44. Fu L, Niu B, Zhu Z, Wu S, Li W (2012)
CD-HIT: accelerated for clustering the nextgeneration sequencing data. Bioinformatics
28:3150–3152
45. Fischer S, Brunk BP, Chen F, Gao X, Harb
OS, Iodice JB, Shanmugam D, Roos DS,
Stoeckert CJ (2011) Using OrthoMCL to
assign proteins to OrthoMCL-DB groups or
to cluster proteomes into new ortholog
groups.
Curr
Protoc
Bioinformatics
Chapter 6:Unit 6.12.1-19
46. Train C-M, Glover NM, Gonnet GH, Altenhoff AM, Dessimoz C (2017) Orthologous
Matrix (OMA) algorithm 2.0: more robust
to asymmetric evolutionary rates and more
scalable hierarchical orthologous group inference. Bioinformatics 33:i75–i82
47. Callister SJ, McCue LA, Turse JE, Monroe
ME, Auberry KJ, Smith RD, Adkins JN, Lipton MS (2008) Comparative bacterial proteomics: analysis of the core genome concept.
PLoS One 3:e1542. https://doi.org/10.
1371/journal.pone.0001542
48. The UniProt Consortium (2017) UniProt:
the universal protein knowledgebase. Nucleic
Acids Res 45:D158–D169
49. GenBank Sample Record. https://www.ncbi.
nlm.nih.gov/Sitemap/samplerecord.html.
Accessed 27 Jun 2019
50. Yu NY, Wagner JR, Laird MR et al (2010)
PSORTb 3.0: improved protein subcellular
localization prediction with refined localization subcategories and predictive capabilities
for
all
prokaryotes.
Bioinformatics
26:1608–1615
51. Krogh A, Larsson B, von Heijne G, Sonnhammer EL (2001) Predicting transmembrane
protein topology with a hidden Markov
model: application to complete genomes. J
Mol Biol 305:567–580
52. Prilusky J, Felder CE, Zeev-Ben-Mordehai T,
Rydberg EH, Man O, Beckmann JS, Silman I,
Sussman JL (2005) FoldIndex: a simple tool
to predict whether a given protein sequence is
intrinsically
unfolded.
Bioinformatics
21:3435–3438
53. Conchillo-Sole ´ O, de Groot NS, Avile ´s FX,
Vendrell J, Daura X, Ventura S (2007)
AGGRESCAN: a server for the prediction
and evaluation of “hot spots” of aggregation
in polypeptides. BMC Bioinformatics 8:65
54. Magnan CN, Baldi P (2014) SSpro/ACCpro
5: almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, machine learning and
structural
similarity.
Bioinformatics
30:2592–2597
55. Dhanda SK, Mahajan S, Paul S et al (2019)
IEDB-AR: immune epitope database-analysis
resource in 2019. Nucleic Acids Res 47:
W502.
https://doi.org/10.1093/nar/
gkz452
56. Jurtz V, Paul S, Andreatta M, Marcatili P,
Peters B, Nielsen M (2017) NetMHCpan4.0: improved peptide-MHC class i interaction predictions integrating eluted ligand and
peptide binding affinity data. J Immunol
199:3360–3368
57. Jensen KK, Andreatta M, Marcatili P, Buus S,
Greenbaum JA, Yan Z, Sette A, Peters B,
Nielsen M (2018) Improved methods for predicting peptide binding affinity to MHC class
II molecules. Immunology 154:394–406
58. Karosiene E, Lundegaard C, Lund O, Nielsen
M (2012) NetMHCcons: a consensus
method for the major histocompatibility complex class I predictions. Immunogenetics
64:177–186
59. Peri C, Sole ´ OC, Corrada D, Gori A, Daura X,
Colombo G (2015) Prediction of antigenic B
and T cell epitopes via energy decomposition
analysis: description of the web-based prediction tool BEPPE. Methods Mol Biol
1348:13–22
60. Fiorucci S, Zacharias M (2010) Prediction of
protein-protein interaction sites using electrostatic desolvation profiles. Biophys J
98:1921–1930
61. Rice P, Longden I, Bleasby A (2000)
EMBOSS: the European Molecular Biology
Open Software Suite. Trends Genet
16:276–277
60
Daniel Yero et al.
(2015) Discovering protective CD8 T cell
epitopes—no single immunologic property
predicts it! Curr Opin Immunol 34:43–51
39. Akram A, Inman RD (2012) Immunodominance: a pivotal principle in host response to
viral infections. Clin Immunol 143:99–115
40. Rodrigues MM, Ersching J (2015) Neglected
tropical diseases, bioinformatics, and vaccines.
J Infect Dis 211:175–177
41. Haft DH, DiCuccio M, Badretdin A et al
(2018) RefSeq: an update on prokaryotic
genome annotation and curation. Nucleic
Acids Res 46:D851–D860
42. Harrison PW, Alako B, Amid C et al (2019)
The european nucleotide archive in 2018.
Nucleic Acids Res 47:D84–D88
43. Wattam AR, Davis JJ, Assaf R et al (2017)
Improvements to PATRIC, the all-bacterial
Bioinformatics Database and Analysis
Resource Center. Nucleic Acids Res 45:
D535–D542
44. Fu L, Niu B, Zhu Z, Wu S, Li W (2012)
CD-HIT: accelerated for clustering the nextgeneration sequencing data. Bioinformatics
28:3150–3152
45. Fischer S, Brunk BP, Chen F, Gao X, Harb
OS, Iodice JB, Shanmugam D, Roos DS,
Stoeckert CJ (2011) Using OrthoMCL to
assign proteins to OrthoMCL-DB groups or
to cluster proteomes into new ortholog
groups.
Curr
Protoc
Bioinformatics
Chapter 6:Unit 6.12.1-19
46. Train C-M, Glover NM, Gonnet GH, Altenhoff AM, Dessimoz C (2017) Orthologous
Matrix (OMA) algorithm 2.0: more robust
to asymmetric evolutionary rates and more
scalable hierarchical orthologous group inference. Bioinformatics 33:i75–i82
47. Callister SJ, McCue LA, Turse JE, Monroe
ME, Auberry KJ, Smith RD, Adkins JN, Lipton MS (2008) Comparative bacterial proteomics: analysis of the core genome concept.
PLoS One 3:e1542. https://doi.org/10.
1371/journal.pone.0001542
48. The UniProt Consortium (2017) UniProt:
the universal protein knowledgebase. Nucleic
Acids Res 45:D158–D169
49. GenBank Sample Record. https://www.ncbi.
nlm.nih.gov/Sitemap/samplerecord.html.
Accessed 27 Jun 2019
50. Yu NY, Wagner JR, Laird MR et al (2010)
PSORTb 3.0: improved protein subcellular
localization prediction with refined localization subcategories and predictive capabilities
for
all
prokaryotes.
Bioinformatics
26:1608–1615
51. Krogh A, Larsson B, von Heijne G, Sonnhammer EL (2001) Predicting transmembrane
protein topology with a hidden Markov
model: application to complete genomes. J
Mol Biol 305:567–580
52. Prilusky J, Felder CE, Zeev-Ben-Mordehai T,
Rydberg EH, Man O, Beckmann JS, Silman I,
Sussman JL (2005) FoldIndex: a simple tool
to predict whether a given protein sequence is
intrinsically
unfolded.
Bioinformatics
21:3435–3438
53. Conchillo-Sole ´ O, de Groot NS, Avile ´s FX,
Vendrell J, Daura X, Ventura S (2007)
AGGRESCAN: a server for the prediction
and evaluation of “hot spots” of aggregation
in polypeptides. BMC Bioinformatics 8:65
54. Magnan CN, Baldi P (2014) SSpro/ACCpro
5: almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, machine learning and
structural
similarity.
Bioinformatics
30:2592–2597
55. Dhanda SK, Mahajan S, Paul S et al (2019)
IEDB-AR: immune epitope database-analysis
resource in 2019. Nucleic Acids Res 47:
W502.
https://doi.org/10.1093/nar/
gkz452
56. Jurtz V, Paul S, Andreatta M, Marcatili P,
Peters B, Nielsen M (2017) NetMHCpan4.0: improved peptide-MHC class i interaction predictions integrating eluted ligand and
peptide binding affinity data. J Immunol
199:3360–3368
57. Jensen KK, Andreatta M, Marcatili P, Buus S,
Greenbaum JA, Yan Z, Sette A, Peters B,
Nielsen M (2018) Improved methods for predicting peptide binding affinity to MHC class
II molecules. Immunology 154:394–406
58. Karosiene E, Lundegaard C, Lund O, Nielsen
M (2012) NetMHCcons: a consensus
method for the major histocompatibility complex class I predictions. Immunogenetics
64:177–186
59. Peri C, Sole ´ OC, Corrada D, Gori A, Daura X,
Colombo G (2015) Prediction of antigenic B
and T cell epitopes via energy decomposition
analysis: description of the web-based prediction tool BEPPE. Methods Mol Biol
1348:13–22
60. Fiorucci S, Zacharias M (2010) Prediction of
protein-protein interaction sites using electrostatic desolvation profiles. Biophys J
98:1921–1930
61. Rice P, Longden I, Bleasby A (2000)
EMBOSS: the European Molecular Biology
Open Software Suite. Trends Genet
16:276–277
60
Daniel Yero et al.