Comments on detecting rogue taxa using RogueNaRok

Abstract

Rogues are relatively unstable taxa in phylogenetic analyses that are of concern if they obfuscate relationships, or support for relationships, of interest among more stable taxa. RogueNaRok is a recently developed heuristic solution to the problem of identifying rogue taxa. We illustrate the performance of RogueNaRok with simple examples designed to clarify its behaviour. We argue that the optimality criterion currently used by RogueNaRok may sometimes be poorly suited to the task of detecting rogues and that the scores reported by RogueNaRok should not be interpreted as measures of taxon instability. We suggest how RogueNaRok might be enhanced and recommend that it not be relied upon exclusively for detecting rogues.

Causes and analytical impacts of missing data in RADseq phylogenetics: Insights from an African frog (Afrixalus)

“…We interpret these findings as evidence that phylogenetic inference using RADseq data, at least at lower taxonomic levels, is best performed by allowing some missing data (≥60% missing data thresholds) and using high‐weight DNA extraction template (clear, non‐smeared bands visualised via gel electrophoresis). However, we note that there are other caveats to including missing data that we have not addressed here (e.g., model choice, see Leaché et al, ; sequencing error, see Eaton et al (); rogue taxon detection, see Wilkinson & Crotti, ).…”

Target Capture Sequencing Unravels Rubus Evolution

“…To prevent ambiguous placement of taxa in a tree resulting from insufficient phylogenetic signal, RogueNaRok v. 1.0 was used with default settings to identify such "rogue" taxa for each locus using bootstrapped RAxML trees (Aberer et al, 2013). Wilkinson and Crotti (2017) argued that this technique may be poorly suited to detecting rogue taxa, however, the automated reproducible approach RogueNaRok was chosen because this application simultaneously evaluated 941 gene trees. This large gene number supports an automated approach (Borowiec, 2019).…”

Using Information Theory to Detect Rogue Taxa and Improve Consensus Trees

“… Wilkinson and Crotti (2017) demonstrate examples where the RBIC measure employed by RogueNaRok ( Aberer et al 2013 ) undesirably favors one topology over another; the SPIC score ranks these tree pairs correctly ( Fig. 2 ).…”

“… Evaluation of tree pairs presented by Wilkinson and Crotti (2017) using the SPIC score. a) A single rogue leaf X may occupy any of the five possible positions on a four-leaf tree; the reduced consensus reveals the unanimous agreement about the relationships between taxa 1–4 and is thus preferred to the plenary.…”

“… Aberer et al (2013) define the “relative bipartition information content” (RBIC) of a consensus tree as the sum of the support values for each of its constituent splits, where a split’s support is defined as the proportion of underlying trees that contain it. Successively removing rogue taxa can result in substantial improvements to the RBIC score ( Aberer et al 2013 )—but this scoring method exhibits certain shortcomings that hinder the identification of a truly “optimal” consensus tree ( Wilkinson and Crotti 2017 ). The RBIC approach uses the language, but not the mathematics, of information theory.…”