141:
as likelihood mapping, transition/transversions versus divergence plots, and the Xia test for saturation. If phylogenetic signal of an alignment is too low then a longer alignment or an alignment of another gene in the organism may be necessary to perform phylogenetic analysis. Typically substitution saturation is only in issue in data sets with viral sequences. Most algorithms used for phylogenetic analysis do not take into recombination into account, which can alter the molecular clock and coalescent estimates of a multiple sequence alignment. Strains that show signs of recombination should either be excluded from the data set or analyzed on their own.
128:) will align the data set with all selected sequences. After the running a multiple sequence alignment algorithm, manual editing the alignment is highly recommended. Multiple sequence alignment algorithms can leave a large amount of indels in the sequence alignment when the indels do not exist. Manually editing the indels in the data set will allow a more accurate phylogenetic tree.
308:
outbreak, the Nepal strains were not available. Phylodynamic analyses were performed on the
Haitian strain and the Nepalese strain when it became available and affirmed that the Haitian cholera strain was the most similar to the Nepalese cholera strain. This outbreak strain of cholera in Haiti showed signs of an altered or hybrid strain of
103:
phylodynamic analysis. Bacterial genomes are much larger and have a slower evolutionary rate than RNA viruses, limiting studies on the bacterial phylodynamics. The advancement of sequencing technology has made bacterial phylodynamics possible but proper preparation of the whole bacterial genomes is mandatory.
307:
revealed that there was one single point source of the cholera outbreak in Haiti and it was similar to O1 strains circulating in South Asia. Before the MINUSTAH troops from Nepal were sent to Haiti, a cholera outbreak had just occurred in Nepal. In the original research to trace the origin of the
140:
of the sequences, and checking the sequences for possible signs of recombinant strains. Contamination of samples in the data set can be excluded with by various laboratory methods and by proper DNA/RNA extraction methods. There are several way to check for phylogenetic signal in an alignment, such
102:
Sequencing of the genome or genomic regions and what sequencing technique to use is an important experimental setting to phylodynamic analysis. Whole genome sequencing is often performed on bacterial genomes, although depending on the design of the study, many different methods can be utilized for
84:
Studies can be designed to observe intra-host or inter-host interactions. Bacterial phylodynamic studies usually focus on inter-host interactions with samples from many different hosts in a specific geographical location or several different geographical locations. The most important part of a
115:
is frequently executed to find similar strains of the pathogen of interest. Sequences collected from BLAST for an alignment will need the proper information to be added to a data set, such as sample collection date and geographical location of the sample.
443:
Grenfell, Bryan T.; Pybus, Oliver G.; Gog, Julia R.; Wood, James L. N.; Daly, Janet M.; Mumford, Jenny A.; Holmes, Edward C. (2004-01-16). "Unifying the epidemiological and evolutionary dynamics of pathogens".
85:
study design is how to organize the sampling strategy. For example, the number of sampled time points, the sampling interval, and the number of sequences per time point are crucial to phylodynamic analysis.
594:
Norström, Melissa M.; Karlsson, Annika C.; Salemi, Marco (2012-04-01). "Towards a new paradigm linking virus molecular evolution and pathogenesis: experimental design and phylodynamic inference".
51:, ecological, and evolutionary processes to better understand of the mechanisms that drive spatiotemporal incidence and phylogenetic patterns of bacterial pathogens. Bacterial phylodynamics uses
136:
In order to have an accurate phylodynamic analysis, quality control methods must be performed. This includes checking the samples in the data set for possible contamination, measuring
636:
Azarian, Taj; Ali, Afsar; Johnson, Judith A.; Mohr, David; Prosperi, Mattia; Veras, Nazle M.; Jubair, Mohammed; Strickland, Samantha L.; Rashid, Mohammad H. (2014-12-31).
47:, and population dynamics of infectious disease pathogen phylogenies during pandemics and studying intra-host evolution of viruses. Phylodynamics combines the study of
203:
Testing the reliability of the tree after inferring its phylogeny, is a crucial step in the phylodynamic pipeline. Methods to test the reliability of a tree include
297:, which is the major water source in the surrounding area. Soon after the MINUSTAH troops arrival, the first cholera case was reported near the location of the
89:
causes problems when looking at a diverse taxological samples. For example, sampling from a limited geographical location may impact effective population size.
1134:
Katz, Lee S.; Petkau, Aaron; Beaulaurier, John; Tyler, Shaun; Antonova, Elena S.; Turnsek, Maryann A.; Guo, Yan; Wang, Susana; Paxinos, Ellen E. (2013-08-30).
59:(SNP) in order to better understand the evolutionary mechanism of bacterial pathogens. Many phylodynamic studies have been performed on viruses, specifically
856:
Larkin, M. A.; Blackshields, G.; Brown, N. P.; Chenna, R.; McGettigan, P. A.; McWilliam, H.; Valentin, F.; Wallace, I. M.; Wilm, A. (2007-11-01).
638:"Phylodynamic Analysis of Clinical and Environmental Vibrio cholerae Isolates from Haiti Reveals Diversification Driven by Positive Selection"
348:
298:
290:
286:
282:
320:
sequences are used for phylodynamic analysis. Using phylodynamic analysis to study cholera helps prediction and understanding of
162:
is the first step in phylodynamic analysis. This can be accomplished with several different algorithms (e.g., IQTREE, MEGA).
67:) which have high mutation rates. The field of bacterial phylodynamics has increased substantially due to the advancement of
235:, and selection analysis. Phylodynamic results of a data set can also influence better study designs in future experiments.
227:
Several methods are used to assess phylodynamic reliability of a data set. These methods include estimating the data set's
170:
There are several different methods to infer phylogenies. These include methods include tree building algorithms such as
111:
When a new dataset with samples for phylodynamic analysis are obtained, the sequences in the new data set are aligned. A
301:
camp. Phylodynamic analysis was used to look into the source of the Haiti cholera outbreak. Whole genome sequencing of
271:, which caused critical infrastructure damage, leading to the conclusion that the outbreak was most likely due to the
314:
associated with high virulence. Typically high quality single-nucleotide polymorphisms (hqSNP) from whole genome
277:
bacterium being introduced naturally to the waters in Haiti from the earthquake. Soon after the earthquake, the
56:
1202:
208:
338:
333:
264:
159:
117:
204:
68:
907:"IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies"
155:
1207:
1197:
1136:"Evolutionary Dynamics of Vibrio cholerae O1 following a Single-Source Introduction to Haiti"
801:"MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform"
268:
212:
112:
48:
453:
386:
8:
353:
285:
troops from Nepal to Haiti. Rumors started circulating about terrible conditions of the
137:
64:
905:
Nguyen, Lam-Tung; Schmidt, Heiko A.; von
Haeseler, Arndt; Minh, Bui Quang (2015-01-01).
457:
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44:
40:
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1088:
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993:
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771:
755:
714:
706:
665:
649:
531:
523:
489:
461:
412:
394:
294:
175:
507:
1093:
964:"MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets"
799:
Katoh, Kazutaka; Misawa, Kazuharu; Kuma, Kei-ichi; Miyata, Takashi (2002-07-15).
527:
399:
316:
310:
303:
273:
259:
253:
228:
710:
278:
693:
Biek, Roman; Pybus, Oliver G.; Lloyd-Smith, James O.; Didelot, Xavier (2015).
1191:
1159:
1102:
989:
930:
883:
824:
767:
661:
607:
473:
408:
86:
28:
980:
922:
744:"MUSCLE: multiple sequence alignment with high accuracy and high throughput"
465:
1177:
1120:
1061:
1043:
1007:
948:
891:
842:
785:
728:
679:
615:
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481:
426:
36:
24:
1151:
653:
816:
759:
60:
20:
1077:"The 2010 Cholera Outbreak in Haiti: How Science Solved a Controversy"
232:
39:
role of these pathogens. Phylodynamic analysis includes analyzing
263:
has been a popular bacterium for phylodynamic analysis after the
248:
125:
52:
962:
Kumar, Sudhir; Stecher, Glen; Tamura, Koichiro (2016-07-01).
904:
855:
343:
171:
121:
1075:
Orata, Fabini D.; Keim, Paul S.; Boucher, Yan (2014-04-03).
373:
Volz, Erik M.; Koelle, Katia; Bedford, Trevor (2013-03-21).
692:
1133:
251:
is a diarrheal disease that is caused by the bacterium
593:
798:
635:
506:
Frost, Simon D.W.; Pybus, Oliver G.; Gog, Julia R.;
442:
505:
961:
695:"Measurably evolving pathogens in the genomic era"
79:
510:; Bonhoeffer, Sebastian; Bedford, Trevor (2015).
372:
267:. The cholera outbreak happened right after the
198:
1189:
1074:
349:United Nations Stabilisation Mission in Haiti
231:, demographic history, population structure,
512:"Eight challenges in phylodynamic inference"
1028:"Understanding the Cholera Epidemic, Haiti"
293:troops were deposing of their waste in the
243:
156:nucleotide or amino acid substitution model
289:camp, as well as people claiming that the
222:
1167:
1110:
1092:
1051:
997:
979:
938:
873:
832:
775:
718:
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535:
416:
398:
1025:
97:
1190:
324:evolution during bacterial epidemics.
165:
118:Multiple sequence alignment algorithms
1021:
1019:
1017:
858:"Clustal W and Clustal X version 2.0"
741:
589:
587:
585:
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149:
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501:
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438:
436:
13:
1014:
131:
92:
71:and the amount of data available.
14:
1219:
699:Trends in Ecology & Evolution
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622:
552:
496:
433:
144:
1127:
1068:
968:Molecular Biology and Evolution
955:
911:Molecular Biology and Evolution
898:
742:Edgar, Robert C. (2004-01-01).
80:Novel hypothesis (study design)
57:single-nucleotide polymorphisms
849:
792:
735:
366:
265:2010 cholera outbreak in Haiti
199:Assessing phylogenetic support
1:
875:10.1093/bioinformatics/btm404
359:
209:maximum likelihood estimation
1094:10.1371/journal.ppat.1003967
1032:Emerging Infectious Diseases
528:10.1016/j.epidem.2014.09.001
400:10.1371/journal.pcbi.1002947
106:
7:
339:Computational phylogenetics
334:2010 Haiti cholera outbreak
327:
238:
160:multiple sequence alignment
10:
1224:
711:10.1016/j.tree.2015.03.009
379:PLOS Computational Biology
74:
69:next-generation sequencing
1026:Piarroux, Renaud (2011).
35:to better understand the
269:2010 earthquake in Haiti
244:Phylodynamics of cholera
466:10.1126/science.1090727
223:Phylodynamics inference
213:posterior probabilities
17:Bacterial phylodynamics
1044:10.3201/eid1707.110059
805:Nucleic Acids Research
748:Nucleic Acids Research
596:The New Microbiologica
1152:10.1128/mBio.00398-13
981:10.1093/molbev/msw054
923:10.1093/molbev/msu300
654:10.1128/mBio.01824-14
375:"Viral Phylodynamics"
98:Experimental settings
49:phylogenetic analysis
1203:Evolutionary biology
458:2004Sci...303..327G
391:2013PLSCB...9E2947V
354:Viral phylodynamics
166:Phylogeny inference
138:phylogenetic signal
65:Viral phylodynamics
33:bacterial pathogens
817:10.1093/nar/gkf436
760:10.1093/nar/gkh340
194:Hypothesis testing
184:maximum likelihood
150:Evolutionary model
868:(21): 2947–2948.
811:(14): 3059–3066.
452:(5656): 327–332.
217:Bayesian analysis
188:Bayesian analysis
180:maximum parsimony
154:The best fitting
45:natural selection
41:genetic diversity
1215:
1182:
1181:
1171:
1146:(4): e00398–13.
1131:
1125:
1124:
1114:
1096:
1072:
1066:
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1055:
1038:(7): 1161–1168.
1023:
1012:
1011:
1001:
983:
974:(7): 1870–1874.
959:
953:
952:
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902:
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847:
846:
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796:
790:
789:
779:
754:(5): 1792–1797.
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732:
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690:
684:
683:
673:
648:(6): e01824–14.
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295:Artibonite River
176:neighbor joining
19:is the study of
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1128:
1087:(4): e1003967.
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385:(3): e1002947.
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254:Vibrio cholerae
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229:molecular clock
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152:
147:
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132:Quality control
120:(e.g., MUSCLE,
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100:
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93:Generating data
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12:
11:
5:
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1200:
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1081:PLOS Pathogens
1067:
1013:
954:
917:(1): 268–274.
897:
862:Bioinformatics
848:
791:
734:
705:(6): 306–313.
685:
621:
602:(2): 101–111.
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508:Viboud, Cecile
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1208:Phylogenetics
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29:phylogenetics
26:
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1198:Bacteriology
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169:
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135:
113:BLAST search
110:
101:
83:
37:evolutionary
25:epidemiology
16:
15:
322:V. cholerae
317:V. cholerae
311:V. cholerae
304:V. cholerae
274:V. cholerae
260:V. cholerae
61:RNA viruses
1192:Categories
360:References
21:immunology
1160:2150-7511
1103:1553-7374
990:1537-1719
931:0737-4038
884:1367-4811
825:0305-1048
768:1362-4962
662:2150-7511
608:1121-7138
522:: 88–92.
516:Epidemics
474:1095-9203
409:1553-7358
233:gene flow
107:Alignment
1178:23820394
1121:24699938
1062:21762567
1008:27004904
949:25371430
892:17846036
843:12136088
786:15034147
729:25887947
680:25538191
616:22707126
546:25843391
482:14726583
427:23555203
328:See also
299:MINUSTAH
291:MINUSTAH
287:MINUSTAH
283:MINUSTAH
239:Examples
126:CLUSAL W
1169:3705451
1112:3974815
1053:3381400
999:8210823
940:4271533
720:4457702
671:4278535
537:4383806
490:4017704
454:Bibcode
446:Science
418:3605911
387:Bibcode
249:Cholera
75:Methods
1176:
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488:
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425:
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407:
211:, and
186:, and
158:for a
124:, and
55:-wide
53:genome
27:, and
486:S2CID
344:MAFFT
281:sent
172:UPGMA
122:MAFFT
63:(see
1174:PMID
1156:ISSN
1140:mBio
1117:PMID
1099:ISSN
1058:PMID
1004:PMID
986:ISSN
945:PMID
927:ISSN
888:PMID
880:ISSN
839:PMID
821:ISSN
782:PMID
764:ISSN
725:PMID
676:PMID
658:ISSN
642:mBio
612:PMID
604:ISSN
542:PMID
478:PMID
470:ISSN
423:PMID
405:ISSN
1164:PMC
1148:doi
1107:PMC
1089:doi
1048:PMC
1040:doi
994:PMC
976:doi
935:PMC
919:doi
870:doi
829:PMC
813:doi
772:PMC
756:doi
715:PMC
707:doi
666:PMC
650:doi
532:PMC
524:doi
462:doi
450:303
413:PMC
395:doi
215:in
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