Characterization of the Mitochondrial Proteome in the Ctenophore Mnemiopsis leidyi Using MitoPredictor

Abstract

Mitochondrial proteomes have been experimentally characterized for only a handful of animal species. However, the increasing availability of genomic and transcriptomic data allows one to infer mitochondrial proteins using computational tools. MitoPredictor is a novel random forest classifier, which utilizes orthology search, mitochondrial targeting signal (MTS) identification, and protein domain content to infer mitochondrial proteins in animals. MitoPredictor's output also includes an easy-to-use R Shiny applet for the visualization and analysis of the results. In this article, we provide a guide for predicting and analyzing the mitochondrial proteome of the ctenophore Mnemiopsis leidyi using MitoPredictor.

Keywords: Ctenophora; Machine learning; MitoPredictor; Mitochondria; Mnemiopsis; Proteome; Random Forest.