Novel hypothesis (study design) Studies can be designed to observe intra-host or inter-host interactions.<unk> 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 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<unk> analysis. Sampling bias causes problems when looking at a diverse taxological samples. For example, sampling from a limited geographical location may impact effective population size. Generating data Experimental settings Sequencing of the genome or genomic regions and what sequencing technique to use is an important experimental setting to<unk> analysis. Whole genome sequencing is often performed on<unk>  genomes, although depending on the design of the study, many different methods can be utilized for<unk> analysis.<unk>  genomes are much larger and have a slower evolutionary rate then RNA viruses, limiting studies on the<unk> . The advancement of sequencing technology has made<unk>  possible but proper preparation of the whole<unk>  genomes is mandatory. Alignment When a new dataset with samples for<unk> analysis are obtained, the sequences in the new data set are aligned. A BLAST search 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. Multiple sequence alignment algorithms (e.g., MUSCLE, MAFFT, and CLUSAL W) 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. Quality control In order to have an accurate<unk> analysis, quality control methods must be performed. This includes checking the samples in the data set for possible contamination, measuring phylogenetic signal 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 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. Data analysis Evolutionary model The best fitting nucleotide or amino acid substitution model for a multiple sequence alignment is the first step in<unk> analysis. This can be accomplished with several different algorithms (e.g., IQTREE, MEGA). Phylogeny inference There are several different methods to infer phylogenies. These include methods include tree building algorithms such as UPGMA, neighbor joining, maximum parsimony, maximum likelihood, and Bayesian analysis. Hypothesis testing Assessing phylogenetic support Testing the reliability of the tree after inferring its phylogeny, is a crucial step in the<unk> pipeline. Methods to test the reliability of a tree include bootstrapping, maximum likelihood estimation, and posterior probabilities in Bayesian analysis.<unk>  inference Several methods are used to assess<unk> reliability of a data set. These methods include estimating the data set's molecular clock, demographic history, population structure, gene flow, and selection analysis.<unk> results of a data set can also influence better study designs in future experiments.
Bacterial phylodynamics
Vaccine efficacy
Disease ecology
Epidemiology of pneumonia
Functional molecular infection epidemiology
Public health surveillance
Discovery of disease-causing pathogens
Mutational signatures