Alignment-Free Approaches for Predicting Novel Nuclear Mitochondrial Segments (NUMTs) in the Human Genome

Abstract

Regions of the nuclear genome may harbor sequences of mitochondrial origin, indicating an ancestral transfer of mitochondrial DNA into the nuclear genome. These Nuclear Mitochondrial Segments (NUMTs) are traditionally detected by sequence similarity search, as implemented in the Basic Local Alignment Search Tool (BLAST). Confidently detecting such NUMTs is important for the comprehensive annotation of the human genome. Here we explore the possibility of detecting NUMTs in the human genome by alignment-free approaches, such as k-mers (k-tuples, k-grams, oligos of length k) distributions. We find that when k=6 or larger, the k-mer approach and BLAST search produce almost identical results, e.g., detect the same set of NUMTs longer than 3kb. However, when k=5 or k=4, certain signals are only detected by the alignment-free approach, and these may indicate yet unrecognized, and potentially more ancestral NUMTs. We introduce a "Manhattan plot" style representation of NUMTs predictions across the genome, which are calculated based on the reciprocal of the Jensen-Shannon divergence between the nuclear and mitochondrial k-mer frequencies. An inspection of Genome Browser annotations shows that most of the k-mer specific NUMT predictions contain Long Terminal Repeat (LTR), whereas BLAST based NUMT predictions do not.