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In many cases, the selection process is (unfortunately) labor-intense, as it requires hand-selecting known sequences from reference databases such as SILVA (Pruesse et al., 2007; Quast et al., 2013; Yilmaz et al., 2014), NCBI (Benson et al., 2009; Sayers et al., 2009), GREENGENES (DeSantis et al., 2006; McDonald et al., 2012), or RDP (Wang et al., 2007; Cole et al., 2014). This manual process however also often provides the highest quality, and allows to optimally assemble the RSs for a given project. See also (Balvočiūtė and Huson, 2017) for a comparison of these databases.
Viking Mb 3 Rt Manual
To this end, plotting the histograms or the distribution of the confidences (LWRs) across all placements can be useful, Figure 4C. A more involved metric is the so-called Expected Distance between Placement Locations (EDPL, Masten et al., 2010), which for a given sequence represents the uncertainty-weighted average distance between all placement locations of that sequence, or in other words, the sum of distances between locations, weighted by their respective probability, see Figure 4D. The EDPL is a measure of how far the likely placement locations of a sequence are spread out across the tree. It hence can distinguish between local and global uncertainty of the placements, that is, between cases where nearby edges constitute equally good placement locations versus cases where the sequence does not have a clear placement position in the tree (Matsen et al., 2010). These metrics can be explored with gappa (Czech et al., 2020) and guppy (Matsen et al., 2010); see their respective manuals for the available commands. 2ff7e9595c
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