Rational Design of Profile HMMs for Sensitive and Specific Sequence Detection with Case Studies Applied to Viruses, Bacteriophages, and Casposons
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Sources
2.1.1. Microviridae
2.1.2. Flavivirus
2.1.3. Casposons
2.2. Multiple Sequence Alignments
2.3. Profile HMM Construction
2.4. Similarity Searches
2.5. Implementation of TABAJARA Program
2.6. Position-Specific Scoring Metrics
2.6.1. Jensen–Shannon Distance
2.6.2. Shannon Entropy
2.6.3. Mutual Information
2.6.4. Sequence Disharmony
2.7. Protein Similarity Calculation
2.8. K-Fold Cross-Validation
3. Results
3.1. Full-Length Protein Sequences for Profile HMM Construction
3.2. Short Alignment Blocks as a Source for the Construction of Profile HMMs
3.3. A Rational Approach for the Development of Profile HMMs
3.3.1. Motivation
3.3.2. Workflow of the Program
3.3.3. Alignment Block Selection
3.3.4. Profile HMM Construction
3.3.5. Model Validation
3.3.6. Cutoff Score Assignment
3.4. Factors Affecting the Performance of Profile HMMs
3.4.1. Parameter Optimization
3.4.2. Alignment Block Size
3.4.3. Sample Size of the Training Set
3.4.4. Combining Multiple Models for Detection
3.4.5. Generalization Performance
3.5. Conserved Profile HMMs
3.6. Profile HMMs for the Discrimination of Transposable Elements
4. Discussion
4.1. A Generic Approach to Construct Profile HMMs
4.2. Cutoff Scores
4.3. Scope of Applications
4.4. Integration with Other Tools
4.5. Profile HMMs for Viral Classification
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Kimura, M. The Neutral Theory of Molecular Evolution; Cambridge University Press: Cambridge, UK; New York, NY, USA, 1983; 367p. [Google Scholar] [CrossRef]
- Valdar, W.S. Scoring residue conservation. Proteins 2002, 48, 227–241. [Google Scholar] [CrossRef] [PubMed]
- Kalinina, O.V.; Mironov, A.A.; Gelfand, M.S.; Rakhmaninova, A.B. Automated selection of positions determining functional specificity of proteins by comparative analysis of orthologous groups in protein families. Protein Sci. 2004, 13, 443–456. [Google Scholar] [CrossRef] [PubMed]
- Casari, G.; Sander, C.; Valencia, A. A method to predict functional residues in proteins. Nat. Struct. Biol. 1995, 2, 171–178. [Google Scholar] [CrossRef]
- Hannenhalli, S.S.; Russell, R.B. Analysis and prediction of functional sub-types from protein sequence alignments. J. Mol. Biol. 2000, 303, 61–76. [Google Scholar] [CrossRef] [PubMed]
- Lichtarge, O.; Bourne, H.R.; Cohen, F.E. Evolutionarily conserved Galphabetagamma binding surfaces support a model of the G protein-receptor complex. Proc. Natl. Acad. Sci. USA 1996, 93, 7507–7511. [Google Scholar] [CrossRef]
- Zvelebil, M.J.; Barton, G.J.; Taylor, W.R.; Sternberg, M.J. Prediction of protein secondary structure and active sites using the alignment of homologous sequences. J. Mol. Biol. 1987, 195, 957–961. [Google Scholar] [CrossRef]
- Panchenko, A.R.; Kondrashov, F.; Bryant, S. Prediction of functional sites by analysis of sequence and structure conservation. Protein Sci. 2004, 13, 884–892. [Google Scholar] [CrossRef]
- Cover, T.M.; Thomas, J.A. Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing), 2nd ed.; Wiley-Interscience: Hoboken, NJ, USA, 2006; p. 748. [Google Scholar] [CrossRef]
- Capra, J.A.; Singh, M. Predicting functionally important residues from sequence conservation. Bioinformatics 2007, 23, 1875–1882. [Google Scholar] [CrossRef] [Green Version]
- Lin, J. Divergence measures based on the Shannon entropy. IEEE Trans. Inf. Theory 1991, 37, 145–151. [Google Scholar] [CrossRef]
- Johansson, F.; Toh, H. A comparative study of conservation and variation scores. BMC Bioinform. 2010, 11, 388. [Google Scholar] [CrossRef]
- Feenstra, K.A.; Pirovano, W.; Krab, K.; Heringa, J. Sequence harmony: Detecting functional specificity from alignments. Nucleic Acids Res. 2007, 35, W495–W498. [Google Scholar] [CrossRef]
- Pirovano, W.; Feenstra, K.A.; Heringa, J. Sequence comparison by sequence harmony identifies subtype-specific functional sites. Nucleic Acids Res. 2006, 34, 6540–6548. [Google Scholar] [CrossRef] [PubMed]
- Adami, C. Information theory in molecular biology. Phys. Life Rev. 2004, 1, 3–22. [Google Scholar] [CrossRef]
- Mirny, L.A.; Gelfand, M.S. Using orthologous and paralogous proteins to identify specificity-determining residues in bacterial transcription factors. J. Mol. Biol. 2002, 321, 7–20. [Google Scholar] [CrossRef] [PubMed]
- Marino Buslje, C.; Teppa, E.; Di Domenico, T.; Delfino, J.M.; Nielsen, M. Networks of high mutual information define the structural proximity of catalytic sites: Implications for catalytic residue identification. PLoS Comput. Biol. 2010, 6, e1000978. [Google Scholar] [CrossRef]
- del Sol, A.; Pazos, F.; Valencia, A. Automatic methods for predicting functionally important residues. J. Mol. Biol. 2003, 326, 1289–1302. [Google Scholar] [CrossRef] [PubMed]
- Gribskov, M.; McLachlan, A.D.; Eisenberg, D. Profile analysis: Detection of distantly related proteins. Proc. Natl. Acad. Sci. USA 1987, 84, 4355–4358. [Google Scholar] [CrossRef] [PubMed]
- Henikoff, J.G.; Henikoff, S. Using substitution probabilities to improve position-specific scoring matrices. Comput. Appl. Biosci. 1996, 12, 135–143. [Google Scholar] [CrossRef]
- Eddy, S.R. Accelerated Profile HMM Searches. PLoS Comput. Biol. 2011, 7, e1002195. [Google Scholar] [CrossRef]
- Krogh, A.; Brown, M.; Mian, I.S.; Sjolander, K.; Haussler, D. Hidden Markov models in computational biology. Applications to protein modeling. J. Mol. Biol. 1994, 235, 1501–1531. [Google Scholar] [CrossRef]
- Lu, S.; Wang, J.; Chitsaz, F.; Derbyshire, M.K.; Geer, R.C.; Gonzales, N.R.; Gwadz, M.; Hurwitz, D.I.; Marchler, G.H.; Song, J.S.; et al. CDD/SPARCLE: The conserved domain database in 2020. Nucleic Acids Res. 2020, 48, D265–D268. [Google Scholar] [CrossRef] [PubMed]
- Mistry, J.; Chuguransky, S.; Williams, L.; Qureshi, M.; Salazar, G.A.; Sonnhammer, E.L.L.; Tosatto, S.C.E.; Paladin, L.; Raj, S.; Richardson, L.J.; et al. Pfam: The protein families database in 2021. Nucleic Acids Res. 2021, 49, D412–D419. [Google Scholar] [CrossRef] [PubMed]
- Aalismail, N.A.; Ngugi, D.K.; Diaz-Rua, R.; Alam, I.; Cusack, M.; Duarte, C.M. Functional metagenomic analysis of dust-associated microbiomes above the Red Sea. Sci. Rep. 2019, 9, 13741. [Google Scholar] [CrossRef] [PubMed]
- Fancello, L.; Trape, S.; Robert, C.; Boyer, M.; Popgeorgiev, N.; Raoult, D.; Desnues, C. Viruses in the desert: A metagenomic survey of viral communities in four perennial ponds of the Mauritanian Sahara. ISME J. 2013, 7, 359–369. [Google Scholar] [CrossRef]
- Segobola, J.; Adriaenssens, E.; Tsekoa, T.; Rashamuse, K.; Cowan, D. Exploring Viral Diversity in a Unique South African Soil Habitat. Sci. Rep. 2018, 8, 111. [Google Scholar] [CrossRef]
- Brenner, S.E.; Chothia, C.; Hubbard, T.J. Assessing sequence comparison methods with reliable structurally identified distant evolutionary relationships. Proc. Natl. Acad. Sci. USA 1998, 95, 6073–6078. [Google Scholar] [CrossRef] [PubMed]
- Drake, J.W. Rates of spontaneous mutation among RNA viruses. Proc. Natl. Acad. Sci. USA 1993, 90, 4171–4175. [Google Scholar] [CrossRef]
- Holland, J.; Spindler, K.; Horodyski, F.; Grabau, E.; Nichol, S.; VandePol, S. Rapid evolution of RNA genomes. Science 1982, 215, 1577–1585. [Google Scholar] [CrossRef]
- Peck, K.M.; Lauring, A.S. Complexities of Viral Mutation Rates. J. Virol. 2018, 92, 14. [Google Scholar] [CrossRef]
- Sanjuan, R.; Domingo-Calap, P. Mechanisms of viral mutation. Cell. Mol. Life Sci. 2016, 73, 4433–4448. [Google Scholar] [CrossRef]
- Sanjuan, R.; Nebot, M.R.; Chirico, N.; Mansky, L.M.; Belshaw, R. Viral mutation rates. J. Virol. 2010, 84, 9733–9748. [Google Scholar] [CrossRef]
- Reyes, A.; Alves, J.M.P.; Durham, A.M.; Gruber, A. Use of profile hidden Markov models in viral discovery: Current insights. Adv. Genom. Genet. 2017, 7, 29–45. [Google Scholar] [CrossRef]
- Mokili, J.L.; Rohwer, F.; Dutilh, B.E. Metagenomics and future perspectives in virus discovery. Curr. Opin. Virol. 2012, 2, 63–77. [Google Scholar] [CrossRef]
- Cobbin, J.C.; Charon, J.; Harvey, E.; Holmes, E.C.; Mahar, J.E. Current challenges to virus discovery by meta-transcriptomics. Curr. Opin. Virol. 2021, 51, 48–55. [Google Scholar] [CrossRef]
- Dutilh, B.E.; Varsani, A.; Tong, Y.; Simmonds, P.; Sabanadzovic, S.; Rubino, L.; Roux, S.; Munoz, A.R.; Lood, C.; Lefkowitz, E.J.; et al. Perspective on taxonomic classification of uncultivated viruses. Curr. Opin. Virol. 2021, 51, 207–215. [Google Scholar] [CrossRef]
- Paez-Espino, D.; Chen, I.A.; Palaniappan, K.; Ratner, A.; Chu, K.; Szeto, E.; Pillay, M.; Huang, J.; Markowitz, V.M.; Nielsen, T.; et al. IMG/VR: A database of cultured and uncultured DNA Viruses and retroviruses. Nucleic Acids Res. 2017, 45, D457–D465. [Google Scholar] [CrossRef] [PubMed]
- Roux, S.; Paez-Espino, D.; Chen, I.A.; Palaniappan, K.; Ratner, A.; Chu, K.; Reddy, T.B.K.; Nayfach, S.; Schulz, F.; Call, L.; et al. IMG/VR v3: An integrated ecological and evolutionary framework for interrogating genomes of uncultivated viruses. Nucleic Acids Res. 2021, 49, D764–D775. [Google Scholar] [CrossRef]
- Park, J.; Karplus, K.; Barrett, C.; Hughey, R.; Haussler, D.; Hubbard, T.; Chothia, C. Sequence comparisons using multiple sequences detect three times as many remote homologues as pairwise methods. J. Mol. Biol. 1998, 284, 1201–1210. [Google Scholar] [CrossRef] [Green Version]
- Yoon, B.J. Hidden Markov Models and their Applications in Biological Sequence Analysis. Curr. Genom. 2009, 10, 402–415. [Google Scholar] [CrossRef] [PubMed]
- Steinegger, M.; Meier, M.; Mirdita, M.; Vohringer, H.; Haunsberger, S.J.; Soding, J. HH-suite3 for fast remote homology detection and deep protein annotation. BMC Bioinform. 2019, 20, 473. [Google Scholar] [CrossRef] [PubMed]
- Koonin, E.V.; Dolja, V.V.; Krupovic, M.; Varsani, A.; Wolf, Y.I.; Yutin, N.; Zerbini, F.M.; Kuhn, J.H. Global Organization and Proposed Megataxonomy of the Virus World. Microbiol. Mol. Biol. Rev. 2020, 84, 2. [Google Scholar] [CrossRef] [PubMed]
- Al-Mutairy, B.; Walter, J.E.; Pothen, A.; Mitchell, D.K. Genome prediction of putative genome-linked viral protein (VPg) of astroviruses. Virus Genes 2005, 31, 21–30. [Google Scholar] [CrossRef] [PubMed]
- Gong, Y.N.; Chen, G.W.; Shih, S.R. Characterization of subtypes of the influenza A hemagglutinin (HA) gene using profile hidden Markov models. J. Microbiol. Immunol. Infect. 2012, 45, 404–410. [Google Scholar] [CrossRef] [PubMed]
- Van der Auwera, S.; Bulla, I.; Ziller, M.; Pohlmann, A.; Harder, T.; Stanke, M. ClassyFlu: Classification of influenza A viruses with Discriminatively trained profile-HMMs. PLoS ONE 2014, 9, e84558. [Google Scholar] [CrossRef] [PubMed]
- Alves, J.M.; de Oliveira, A.L.; Sandberg, T.O.; Moreno-Gallego, J.L.; de Toledo, M.A.; de Moura, E.M.; Oliveira, L.S.; Durham, A.M.; Mehnert, D.U.; Zanotto, P.M.; et al. GenSeed-HMM: A Tool for Progressive Assembly Using Profile HMMs as Seeds and its Application in Alpavirinae Viral Discovery from Metagenomic Data. Front. Microbiol. 2016, 7, 269. [Google Scholar] [CrossRef]
- Phan, M.V.T.; Ngo Tri, T.; Hong Anh, P.; Baker, S.; Kellam, P.; Cotten, M. Identification and characterization of Coronaviridae genomes from Vietnamese bats and rats based on conserved protein domains. Virus Evol. 2018, 4, vey035. [Google Scholar] [CrossRef] [PubMed]
- Fonseca, P.; Ferreira, F.; da Silva, F.; Oliveira, L.S.; Marques, J.T.; Goes-Neto, A.; Aguiar, E.; Gruber, A. Characterization of a Novel Mitovirus of the Sand Fly Lutzomyia longipalpis Using Genomic and Virus-Host Interaction Signatures. Viruses 2020, 13, 9. [Google Scholar] [CrossRef]
- Brito, A.F.; Pinney, J.W. The evolution of protein domain repertoires: Shedding light on the origins of the Herpesviridae family. Virus Evol. 2020, 6, veaa001. [Google Scholar] [CrossRef]
- Masembe, C.; Phan, M.V.T.; Robertson, D.L.; Cotten, M. Increased resolution of African swine fever virus genome patterns based on profile HMMs of protein domains. Virus Evol. 2020, 6, veaa044. [Google Scholar] [CrossRef]
- Oliveira, L.S.; Gruber, A. Rational design of profile HMMs for viral classification and discovery. In Bioinformatics; Nakaya, H., Ed.; Exon Publications: Brisbane, Australia, 2021; pp. 151–170. [Google Scholar] [CrossRef]
- Grazziotin, A.L.; Koonin, E.V.; Kristensen, D.M. Prokaryotic Virus Orthologous Groups (pVOGs): A resource for comparative genomics and protein family annotation. Nucleic Acids Res. 2017, 45, D491–D498. [Google Scholar] [CrossRef]
- Skewes-Cox, P.; Sharpton, T.J.; Pollard, K.S.; DeRisi, J.L. Profile hidden Markov models for the detection of viruses within metagenomic sequence data. PLoS ONE 2014, 9, e105067. [Google Scholar] [CrossRef] [PubMed]
- Bigot, T.; Temmam, S.; Perot, P.; Eloit, M. RVDB-prot, a reference viral protein database and its HMM profiles. F1000Reseach 2019, 8, 530. [Google Scholar] [CrossRef]
- Goodacre, N.; Aljanahi, A.; Nandakumar, S.; Mikailov, M.; Khan, A.S. A Reference Viral Database (RVDB) To Enhance Bioinformatics Analysis of High-Throughput Sequencing for Novel Virus Detection. mSphere 2018, 3, 2. [Google Scholar] [CrossRef]
- Huerta-Cepas, J.; Szklarczyk, D.; Heller, D.; Hernandez-Plaza, A.; Forslund, S.K.; Cook, H.; Mende, D.R.; Letunic, I.; Rattei, T.; Jensen, L.J.; et al. eggNOG 5.0: A hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res. 2019, 47, D309–D314. [Google Scholar] [CrossRef] [PubMed]
- Zayed, A.A.; Lucking, D.; Mohssen, M.; Cronin, D.; Bolduc, B.; Gregory, A.C.; Hargreaves, K.R.; Piehowski, P.D.; White, R.A.; Huang, E.L.; et al. efam: An expanded, metaproteome-supported HMM profile database of viral protein families. Bioinformatics 2021, 37, 4202–4208. [Google Scholar] [CrossRef]
- Terzian, P.; Olo Ndela, E.; Galiez, C.; Lossouarn, J.; Perez Bucio, R.E.; Mom, R.; Toussaint, A.; Petit, M.A.; Enault, F. PHROG: Families of prokaryotic virus proteins clustered using remote homology. NAR Genom. Bioinform. 2021, 3, lqab067. [Google Scholar] [CrossRef]
- Moreno-Gallego, J.L.; Reyes, A. Informative Regions In Viral Genomes. Viruses 2021, 13, 6. [Google Scholar] [CrossRef]
- Rangel-Pineros, G.; Almeida, A.; Beracochea, M.; Sakharova, E.; Marz, M.; Muñoz, A.R.; Hölzer, M.; Finn, R.D. VIRify: An integrated detection, annotation and taxonomic classification pipeline using virus-specific protein profile hidden Markov models. bioRxiv 2022. [Google Scholar] [CrossRef]
- Tisza, M.J.; Belford, A.K.; Dominguez-Huerta, G.; Bolduc, B.; Buck, C.B. Cenote-Taker 2 democratizes virus discovery and sequence annotation. Virus Evol. 2021, 7, veaa100. [Google Scholar] [CrossRef]
- Krupovic, M.; Makarova, K.S.; Forterre, P.; Prangishvili, D.; Koonin, E.V. Casposons: A new superfamily of self-synthesizing DNA transposons at the origin of prokaryotic CRISPR-Cas immunity. BMC Biol. 2014, 12, 36. [Google Scholar] [CrossRef]
- Roux, S.; Krupovic, M.; Poulet, A.; Debroas, D.; Enault, F. Evolution and Diversity of the Microviridae Viral Family through a Collection of 81 New Complete Genomes Assembled from Virome Reads. PLoS ONE 2012, 7, e40418. [Google Scholar] [CrossRef] [PubMed]
- Krupovic, M.; Shmakov, S.; Makarova, K.S.; Forterre, P.; Koonin, E.V. Recent Mobility of Casposons, Self-Synthesizing Transposons at the Origin of the CRISPR-Cas Immunity. Genome Biol. Evol. 2016, 8, 375–386. [Google Scholar] [CrossRef] [PubMed]
- Makarova, K.S.; Wolf, Y.I.; Alkhnbashi, O.S.; Costa, F.; Shah, S.A.; Saunders, S.J.; Barrangou, R.; Brouns, S.J.; Charpentier, E.; Haft, D.H.; et al. An updated evolutionary classification of CRISPR-Cas systems. Nat. Rev. Microbiol. 2015, 13, 722–736. [Google Scholar] [CrossRef] [PubMed]
- Edgar, R.C. MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004, 32, 1792–1797. [Google Scholar] [CrossRef] [PubMed]
- Shannon, C.E. A Mathematical Theory of Communication. Bell Syst. Tech. J. 1948, 27, 379–423. [Google Scholar] [CrossRef]
- Shannon, C.E.; Weaver, W. The Mathematical Theory of Communication; University of Illinois Press: Urbana, IL, USA, 1949; p. 117. [Google Scholar]
- Rice, P.; Longden, I.; Bleasby, A. EMBOSS: The European Molecular Biology Open Software Suite. Trends Genet. 2000, 16, 276–277. [Google Scholar] [CrossRef] [PubMed]
- Kristensen, D.M.; Waller, A.S.; Yamada, T.; Bork, P.; Mushegian, A.R.; Koonin, E.V. Orthologous gene clusters and taxon signature genes for viruses of prokaryotes. J. Bacteriol. 2013, 195, 941–950. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Krupovic, M.; Beguin, P.; Koonin, E.V. Casposons: Mobile genetic elements that gave rise to the CRISPR-Cas adaptation machinery. Curr. Opin. Microbiol. 2017, 38, 36–43. [Google Scholar] [CrossRef]
- Pearson, W.R.; Li, W.; Lopez, R. Query-seeded iterative sequence similarity searching improves selectivity 5-20-fold. Nucleic Acids Res. 2017, 45, e46. [Google Scholar] [CrossRef]
- Dogan, T.; Karacali, B. Automatic identification of highly conserved family regions and relationships in genome wide datasets including remote protein sequences. PLoS ONE 2013, 8, e75458. [Google Scholar] [CrossRef]
- Guerrero, D.; Bautista, R.; Villalobos, D.P.; Canton, F.R.; Claros, M.G. AlignMiner: A Web-based tool for detection of divergent regions in multiple sequence alignments of conserved sequences. Algorithms Mol. Biol. 2010, 5, 24. [Google Scholar] [CrossRef] [PubMed]
- Bzhalava, Z.; Hultin, E.; Dillner, J. Extension of the viral ecology in humans using viral profile hidden Markov models. PLoS ONE 2018, 13, e0190938. [Google Scholar] [CrossRef] [PubMed]
- Punta, M.; Coggill, P.C.; Eberhardt, R.Y.; Mistry, J.; Tate, J.; Boursnell, C.; Pang, N.; Forslund, K.; Ceric, G.; Clements, J.; et al. The Pfam protein families database. Nucleic Acids Res. 2012, 40, D290–D301. [Google Scholar] [CrossRef] [PubMed]
- Aramaki, T.; Blanc-Mathieu, R.; Endo, H.; Ohkubo, K.; Kanehisa, M.; Goto, S.; Ogata, H. KofamKOALA: KEGG ortholog assignment based on profile HMM and adaptive score threshold. Bioinformatics 2019, 36, 2251–2252. [Google Scholar] [CrossRef]
- Chen, G.W.; Hsiung, C.A.; Chyn, J.L.; Shih, S.R.; Wen, C.C.; Chang, I.S. Revealing molecular targets for enterovirus type 71 detection by profile hidden Markov models. Virus Genes 2005, 31, 337–347. [Google Scholar] [CrossRef]
- Pagnuco, I.A.; Revuelta, M.V.; Bondino, H.G.; Brun, M.; Ten Have, A. HMMER Cut-off Threshold Tool (HMMERCTTER): Supervised classification of superfamily protein sequences with a reliable cut-off threshold. PLoS ONE 2018, 13, e0193757. [Google Scholar] [CrossRef]
- Castresana, J. Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis. Mol. Biol. Evol. 2000, 17, 540–552. [Google Scholar] [CrossRef] [Green Version]
- Lee, A.J.; Bhattacharya, R.; Scheuermann, R.H.; Pickett, B.E. Identification of diagnostic peptide regions that distinguish Zika virus from related mosquito-borne Flaviviruses. PLoS ONE 2017, 12, e0178199. [Google Scholar] [CrossRef]
- Simmonds, P.; Aiewsakun, P. Virus classification—Where do you draw the line? Arch. Virol. 2018, 163, 2037–2046. [Google Scholar] [CrossRef]
- Lefkowitz, E.J.; Dempsey, D.M.; Hendrickson, R.C.; Orton, R.J.; Siddell, S.G.; Smith, D.B. Virus taxonomy: The database of the International Committee on Taxonomy of Viruses (ICTV). Nucleic Acids Res. 2018, 46, D708–D717. [Google Scholar] [CrossRef]
- Baltimore, D. Expression of animal virus genomes. Bacteriol. Rev. 1971, 35, 235–241. [Google Scholar] [CrossRef] [PubMed]
- Simmonds, P.; Adams, M.J.; Benko, M.; Breitbart, M.; Brister, J.R.; Carstens, E.B.; Davison, A.J.; Delwart, E.; Gorbalenya, A.E.; Harrach, B.; et al. Consensus statement: Virus taxonomy in the age of metagenomics. Nat. Rev. Microbiol. 2017, 15, 161–168. [Google Scholar] [CrossRef] [PubMed]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Oliveira, L.S.; Reyes, A.; Dutilh, B.E.; Gruber, A. Rational Design of Profile HMMs for Sensitive and Specific Sequence Detection with Case Studies Applied to Viruses, Bacteriophages, and Casposons. Viruses 2023, 15, 519. https://doi.org/10.3390/v15020519
Oliveira LS, Reyes A, Dutilh BE, Gruber A. Rational Design of Profile HMMs for Sensitive and Specific Sequence Detection with Case Studies Applied to Viruses, Bacteriophages, and Casposons. Viruses. 2023; 15(2):519. https://doi.org/10.3390/v15020519
Chicago/Turabian StyleOliveira, Liliane S., Alejandro Reyes, Bas E. Dutilh, and Arthur Gruber. 2023. "Rational Design of Profile HMMs for Sensitive and Specific Sequence Detection with Case Studies Applied to Viruses, Bacteriophages, and Casposons" Viruses 15, no. 2: 519. https://doi.org/10.3390/v15020519
APA StyleOliveira, L. S., Reyes, A., Dutilh, B. E., & Gruber, A. (2023). Rational Design of Profile HMMs for Sensitive and Specific Sequence Detection with Case Studies Applied to Viruses, Bacteriophages, and Casposons. Viruses, 15(2), 519. https://doi.org/10.3390/v15020519