Phylogenetic visualization of the spread of H7 influenza A viruses

Janies, Daniel; Pomeroy, Laura W.; Krueger, Chris; Zhang, Yuqi; Senturk, Izzet F.; Kaya, Kamer; Çatalyürek, Ümit V.

doi:10.1111/cla.12107

Cited by 9 publications

(5 citation statements)

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“…Utilizing a similar approach as Janies [ 15 ] we used betweenness centrality to calculate the connectedness of hosts. To generate a transmission network, an apomorphy list (changes of host) was extracted from each phylogenetic tree.…”

Section: Methodsmentioning

confidence: 99%

Giardiasis as a neglected disease in Brazil: Systematic review of 20 years of publications

et al. 2017

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IntroductionGiardiasis is an intestinal infection that affects more than two hundred million people annually worldwide; it is caused by the flagellated protozoan Giardia duodenalis. In tropical countries and in low or middle-income settings, like Brazil, its prevalence can be high. There is currently no systematic review on the presence of G. duodenalis in patients, animals or water sources in Brazil.MethodsThis systematic review was performed according to recommendations established by Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). As databases for our searches, we have used PubMed, Embase, Scopus and the Brazilian database SciELO using the keywords Giardia* and Brazil.ResultsThis systematic review identified research studies related to G. duodenalis in water, giardiasis in animals, prevalence of giardiasis across Brazilian regions, genotyping of strains isolated in humans, and giardiasis in indigenous populations. We also propose a network of G. duodenalis transmission in Brazil based on genotypes analyses.ConclusionThis is the first time within the last twenty years that a review is being published on the occurrence of G. duodenalis in Brazil, addressing relevant issues such as prevalence, molecular epidemiology and analytical methods for parasite detection.

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Section: Methodsmentioning

confidence: 99%

Giardiasis as a neglected disease in Brazil: Systematic review of 20 years of publications

et al. 2017

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“…For the betweenness centrality analysis we used methods similar to those used in Janies et al [40]. Betweenness measures the centrality of a node by computing how many times a node of interest is on the shortest paths between any two other nodes normalized by the number of pairs of other nodes.…”

Section: Methodsmentioning

confidence: 99%

Tracking a serial killer: Integrating phylogenetic relationships, epidemiology, and geography for two invasive meningococcal disease outbreaks

et al. 2018

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BackgroundWhile overall rates of meningococcal disease have been declining in the United States for the past several decades, New York City (NYC) has experienced two serogroup C meningococcal disease outbreaks in 2005–2006 and in 2010–2013. The outbreaks were centered within drug use and sexual networks, were difficult to control, and required vaccine campaigns.MethodsWhole Genome Sequencing (WGS) was used to analyze preserved meningococcal isolates collected before and during the two outbreaks. We integrated and analyzed epidemiologic, geographic, and genomic data to better understand transmission networks among patients. Betweenness centrality was used as a metric to understand the most important geographic nodes in the transmission networks. Comparative genomics was used to identify genes associated with the outbreaks.ResultsNeisseria meningitidis serogroup C (ST11/ET-37) was responsible for both outbreaks with each outbreak having distinct phylogenetic clusters. WGS did identify some misclassifications of isolates that were more distant from the outbreak strains, as well as those that should have been included based on high genomic similarity. Genomes for the second outbreak were more similar than the first and no polymorphism was found to either be unique or specific to either outbreak lineage. Betweenness centrality as applied to transmission networks based on phylogenetic analysis demonstrated that the outbreaks were transmitted within focal communities in NYC with few transmission events to other locations.ConclusionsNeisseria meningitidis is an ever changing pathogen and comparative genomic analyses can help elucidate how it spreads geographically to facilitate targeted interventions to interrupt transmission.

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“…For the betweenness centrality analysis we used methods similar to those used in Janies et al [38]. We used the subset of isolates with home addresses that could be geocoded plus an isolate from Paris, LNP27256 (GenBank: AVOU01000001.1) related to the outbreak [31].…”

Section: Methodsmentioning

confidence: 99%

Tracking a Serial Killer: Integrating Phylogenetic Relationships, Epidemiology, and Geography for Two Invasive Meningococcal Disease Outbreaks

Ezeoke

Galac

Lin

et al. 2018

Preprint

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31 Methods Whole Genome Sequencing (WGS) was used to analyze preserved meningococcal isolates33 collected before and during the two outbreaks. We integrated and analyzed epidemiologic, 34 geographic, and genomic data to better understand transmission networks among patients.35 Betweenness centrality was used as a metric to understand the most important geographic nodes 36 in the transmission networks. Comparative genomics was used to identify genes associated with 37 the outbreaks. 39 Results 40Neisseria meningitidis serogroup C (ST11/ET-37) was responsible for both outbreaks with each 41 outbreak having distinct phylogenetic clusters. WGS did identify some misclassifications of 42 isolates that were more distant from the rest of the outbreak, as well as those that should have 43 been included based on high genomic similarity. Genomes for the second outbreak were more 44 similar than the first and no mutation was found to either be unique or specific to either outbreak 45 lineage. Betweenness centrality as applied to transmission networks based on phylogenetic for use under a CC0 license.This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint (which . http://dx.doi.org/10.1101/387597 doi: bioRxiv preprint first posted online Aug. 8, 2018; 3 46 analysis demonstrated that the outbreaks were transmitted within focal communities in NYC 47 with few transmission events to other locations. 49 Conclusions50 Neisseria meningitidis is an ever changing pathogen and comparative genomic analyses can help 51 elucidate how it spreads geographically to facilitate targeted interventions to interrupt 52 transmission.for use under a CC0 license. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. 65 N. meningitidis is found in the nasopharynx in up to 35% of healthy adults [11] and it is this66 colonization state that is believed to be responsible for IMD transmission and outbreaks.67 Secondary IMD cases are rare and outbreaks occur in either organizational settings, such as 68 universities, or in members of discreet community groups, such as MSM. Pulsed-field gel 69 electrophoresis (PFGE) has been used in NYC to detect links among IMD cases not identified by 70 case investigations [12]. However, PFGE may misclassify due to a lack of discriminatory power 71 [13]. Additionally, PFGE does not provide details on mutations in the N. meningitidis genome 72 that can be used to track its spread in space and time. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. This art...

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Phylogenetic visualization of the spread of H7 influenza A viruses

Cited by 9 publications

References 43 publications

Giardiasis as a neglected disease in Brazil: Systematic review of 20 years of publications

Giardiasis as a neglected disease in Brazil: Systematic review of 20 years of publications

Tracking a serial killer: Integrating phylogenetic relationships, epidemiology, and geography for two invasive meningococcal disease outbreaks

Tracking a Serial Killer: Integrating Phylogenetic Relationships, Epidemiology, and Geography for Two Invasive Meningococcal Disease Outbreaks

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