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Methods for Outbreaks Using Genomic Data
Published in Leonhard Held, Niel Hens, Philip O’Neill, Jacco Wallinga, Handbook of Infectious Disease Data Analysis, 2019
Don Klinkenberg, Caroline Colijn, Xavier Didelot
A NJ tree is a binary tree built with the NJ algorithm, which iteratively connects clusters (which are single samples or groups of samples connected in previous steps) by selecting the pair of least distant clusters, until all samples are connected. If possible, this algorithm returns the tree where all distances between pairs of leaves are equal to the distances in the distance matrix, which is the case with the FMD outbreak data (Figures 13.2b and c). NJ trees are unrooted, but they can be used as the basis to make (rooted) phylogenies, where the direction of evolution is known for each branch, and the internal nodes represent ancestors. The root can be defined as the midpoint of the tree, but more often it is defined by including samples known to be well separated from the sequences under study, called outgroups. Figure 13.2c shows such a tree for the FMD outbreak, where four reference genomes that are not part of the outbreak were used to root the tree. NJ trees are examples of phylogenetic trees which describe the inferred evolutionary relationships between the samples (or taxa). There are many more approaches to infer phylogenetic trees, for example UPGMA, maximum parsimony, maximum likelihood, and various Bayesian approaches [1, 3].
The Role of the Computer in Estimates of DNA Nucleotide Sequence Divergence
Published in S. K. Dutta, DNA Systematics, 2019
To this end then, Fitch and Margoliash62 use a distance method designed to choose the phylogenetic tree that minimized the difference between the tree distances (phyletic distances) and the observed sequence differences. Matrix methods, such as that of Farris66 use a table of sequence differences among all possible pairs of sequences. These require only the number of sequence differences and not the actual changes. Maximum parsimony methods construct trees with the minimum total length. Dayhoff’s approach builds a phylogeny by simultaneously determining a branch order and an ancestral sequence at each branch point. This determines a minimum length tree, but does not consider the genetic code. In all cases, there is a need for indication of possible ambiguities in branching order due to nearly equivalent alternative trees (“confidence limits”). Also required is some measure of similarity between molecular and nonmolecular phylogenetic trees, as well as between the trees produced from different sorts of macromolecules for a given set of species.
Molecular Biology of the Amelogenin Gene
Published in Colin Robinson, Jennifer Kirkham, Roger Shore, Dental Enamel, 2017
James P. Simmer, Malcolm L. Snead
The protein alignments described above were analyzed using two different methods to determine sequence divergence and to construct phylogenetic trees. Trees were constructed using both a distance matrix and a maximum parsimony method. Distance matrix methods involve first performing pairwise comparisons of the amino acid sequences in multiple alignments and establishing a table or distance matrix. This distance matrix provides the fraction of nonidentical positions between each pair. The data is then utilized
wsp-based analysis of Wolbachia strains associated with Phlebotomus papatasi and P. sergenti (Diptera: Psychodidae) main cutaneous leishmaniasis vectors, introduction of a new subgroup wSerg
Published in Pathogens and Global Health, 2018
Fateh Karimian, Hassan Vatandoost, Yavar Rassi, Naseh Maleki-Ravasan, Nayyereh Choubdar, Mona Koosha, Kourosh Arzamani, Eslam Moradi-Asl, Arshad Veysi, Hamzeh Alipour, Manouchehr Shirani, Mohammad Ali Oshaghi
Sixty-nine (69) Wolbachia sequences belong to super groups A and B related to Phelebtominae (22 species) and other insect species (n = 47) were subjected to molecular phylogenetic analysis. Due to the different lengths of the sequences, they were trimmed to obtain a consistent region for phylogenetic analysis. Phylogeny was reconstructed by using 585 nucleotides of wsp gene. All three methods of neighbor joining, maximum parsimony, and maximum likelihood analysis revealed similar topology. The phylogenetic analysis scattered the Wolbachia sequences of this study among the obtained sequences from Genbank (Figure 2). Representatives of Wolbachia Super groups A and B clustered in two main separate clades. Twenty-five known subgroups of Super groups A and B resolved well and formed separate clusters supported by a high bootstrap values. The bootstrap support values for clusters were more than 50%. More than 85 percent of the subgroup’ clusters were supported by more than 80% bootstrap values (Figure 2). Wolbachia wsp sequences in P.sergenti with 94% bootstrap value formed new independent subgroups in Supergroup A, and have a close relationship with wAlbA and wMel subgroups of Supergroup A. In addition the wsp sequences of Wolbachia in P.mongolensis and some P.papatasi specimens were positioned within the new subgroup wSerg.
De novo sequencing of proteins by mass spectrometry
Published in Expert Review of Proteomics, 2020
Rui Vitorino, Sofia Guedes, Fabio Trindade, Inês Correia, Gabriela Moura, Paulo Carvalho, Manuel A. S. Santos, Francisco Amado
A group of peptides can be common to several protein sequences. Therefore, regardless of the quality of the analytical work, it is often impossible to select the specific protein being studied [87]. As proteins are cleaved into peptides in the first step of peptide-centric analysis, there is no direct method to restore the link between peptides and their parent proteins [91]. As such, it is usually impossible to determine the number of proteins identified, as only the number of peptides is reported. To address such limitations, besides reporting all possible protein sequences, the best practice is to report a list of proteins according to maximum parsimony, that is, the minimum number of proteins that correspond to all identified peptides [92].
Novel strain of Pseudoruminococcus massiliensis possesses traits important in gut adaptation and host-microbe interactions
Published in Gut Microbes, 2022
Kaisa Hiippala, Imran Khan, Aki Ronkainen, Fredrik Boulund, Helena Vähä-Mäkilä, Maiju Suutarinen, Maike Seifert, Lars Engstrand, Reetta Satokari
We used EzBioCloud: database of 16S rRNA and whole genome assemblies40 together with TYGS41 for species identification and phylogenetic inference. For genome-scale taxonomic analysis, the genome assemblies were searched using BBMap’s MinHash Sketch35 to first identify the closely related type strain genomes. EzBioCloud: database of 16S rRNA and whole genome assemblies40 was also used for identification of similar closely related species to bacterium genome. OrthoANI measures the overall similarity between two genome sequences.15 ANI and OrthoANI are comparable algorithms: they share the same species demarcation cutoff at 95 ~ 96% and large comparison studies have demonstrated both algorithms to produce near identical reciprocal similarities. TYGS clusters species and subspecies using the dedicated clustering algorithm and established thresholds42 analogous to 70% and ca. 79% DDH, respectively. Further 16S rRNA gene phylogenies were also inferred using the DSMZ phylogenomics pipeline at http://ggdc.dsmz.de/. A multiple sequence alignment was created with MUSCLE.43 Maximum likelihood (ML) and maximum parsimony (MP) trees were inferred from the alignment with RAxML44 and TNT,45 respectively. For ML, rapid bootstrapping in conjunction with the autoMRE bootstopping criterion46 and subsequent search for the best tree was used; for MP, 1000 bootstrapping replicates were used in conjunction with tree-bisection-and-reconnection branch swapping and ten random sequence addition replicates. The sequences were checked for a compositional bias using the Χ2 test as implemented in PAUP*.47