nRCFV: a new, dataset-size-independent metric to quantify compositional heterogeneity in nucleotide and amino acid datasets

Abstract

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 effect on RCFV. We discuss the theory behind this, 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 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 at Datadryad: https://doi.org/10.5061/dryad.wpzgmsbpn.