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References
|
Ahola V, Aittokallio T, Uusipaikka E, Vihinen M (2003). Efficient estimation of emission probabilities in profile hidden Markov models. Bioinformatics 19:2359-2368. |
|
|
Bernardes JS, Davila AM, Costa VS, Zaverucha G (2007). Improving model construction of profile HMMs for remote homology detection through structural alignment. BMC Bioinformatics 8:435. |
|
|
Castillo-Esparza JF, Hernandez-Gonzalez I, Ibarra JE (2019). Search for Cry proteins expressed by Bacillus spp. genomes, using hidden Markov model profiles. 3 Biotech 9(1):13. |
|
|
Eddy SR (2004). What is a hidden Markov model? Nature Biotechnology 22:1315e6. |
|
|
Eddy SR (2011). Accelerated Profile HMM searches. PLoS Computational Biology 7(10):e1002195. |
|
|
Edgar RC (2004). MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Research 32:1792-1797. |
|
|
Ghadaksaz A, Fooladi AAI, Hooseini HM, Amin M (2015). The prevalence of some Pseudomonas virulence genes related to biofilm formation and alignate production among clinical isolates. Journal of Applied Biomedicine 13(1):61-68. |
|
|
Gong YN, Chen GW, Shih SR (2012). Characterization of subtypes of the influenza A hemagglutinin (HA) gene using profile hidden Markov models. Journal of Microbiology, Immunology and Infection 45:404-410. |
|
|
Henikoff S, Greene EA, Pietrokovski S, Bork P, Attwood TK, Hood L (1997). Gene families: The taxonomy of protein paralogs and chimeras. Science 278(5338):609-614. |
|
|
Kirsip H, Abroi A (2019). Protein structure-guided Hidden Markov Models (HMMs) as a powerful method in the detection of ancestral endogenous viral elements. Viruses 11(4):320. |
|
|
Okkotsu Y, Little AS, Schurr MJ (2014). The Pseudomonas aeruginosa AlgZR two-component system coordinates multiple phenotypes. Frontiers in Cellular and Infection Microbiology 4:82. |
|
|
Okonechnikov K, Golosova O, Fursov M (2012). Unipro UGENE: a unified bioinformatics toolkit. Bioinformatics 28:1166-1167. |
|
|
Olsen I (2015). Biofilm-specific antibiotic tolerance and resistance. European Journal of Clinical Microbiology 34:877-886. |
|
|
R Core Team (2015). R: a language and environment for statistical computing. Vienna: The R Foundation for Statistical Computing. Available at |
|
|
Segev-Zarko LA, Kapach G. Josten M, Klug YA, Sahl SG, Shai Y (2018). Deficient lipid A remodeling by the arnB gene promotes biofilm formation in antimicrobial peptide susceptible P. aeruginosa. Biochemistry 57(13):2024-2034. |
|
|
Skewes-Cox P, Sharpon TJ, Pollard KS, DeRisi JL (2014). Profile hidden Markov models for the detection of viruses within metagenomic sequence data. PLoS One 9(8):e105067. |
|
|
Sonnhammer EL, Eddy SR, Durbin R (1997). Pfam: a comprehensive database of protein domain families based on seed alignments. Proteins 28:405e20. |
|
|
Valadbeigi H, Sadeghifard N, Salehi MB (2017). The prevalence of pilA and algD virulence genes in P. aeruginosa urinary tract and tracheal isolate. Infectious Disorders Drug Targets 17(2):86-89. |
|
|
Yoon BJ (2009). Hidden Markov models and their applications in biological sequence analysis. Current Genomics 10:402-415. |
|
|
Zheng W, Wuyun Q, Li Y, Mortuza SM, Zhang C, Pearce R, Ruan J, Zhang Y (2019). Detecting distant-homology protein structures by aligning deep neural-network based contact maps. PLoS Computational Biology 15(10):e1007411. |
|
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