Motivation

Compositional heterogeneity – when the proportions of nucleotides and amino acids are not broadly similar across the dataset – is a cause of a great number of phylogenetic artefacts. Whilst a variety of methods can identify it post-hoc, few metrics exist to quantify compositional heterogeneity prior to the computationally intensive task of phylogenetic tree reconstruction. Here we assess the efficacy of one such existing, widely used, metric: Relative Composition Frequency Variability (RCFV), using both real and simulated data.

Results

Our results show that RCFV can be biased by sequence length, the number of taxa, and the number of possible character states within the dataset. However, we also find that missing data does not appear to have an appreciable value on RCFV. We discuss the theory behind this and the consequences of this for the future of the usage of the RCFV value and propose a new metric, nRCFV, which accounts for these biases. Alongside this, we present a new software that easily calculates both RCFV and nRCFV, called nRCFV_Reader.

Availability and Implementation

nRCFV has been implemented in RCFV_Reader, available at: https://github.com/JFFleming/RCFV_Reader. Both our simulation and real data are available in this dataset.