The Mitogenome of Norway Spruce and a Reappraisal of Mitochondrial Recombination in Plants.

Plant mitogenomes can be difficult to assemble because they are structurally dynamic and prone to intergenomic DNA transfers, leading to the unusual situation where an organelle genome is far outnumbered by its nuclear counterparts. As a result, comparative mitogenome studies are in their infancy and some key aspects of genome evolution are still known mainly from pregenomic, qualitative methods. To help address these limitations, we combined machine learning and in silico enrichment of mitochondrial-like long reads to assemble the bacterial-sized mitogenome of Norway spruce (Pinaceae: Picea abies). We conducted comparative analyses of repeat abundance, intergenomic transfers, substitution and rearrangement rates, and estimated repeat-by-repeat homologous recombination rates. Prompted by our discovery of highly recombinogenic small repeats in P. abies, we assessed the genomic support for the prevailing hypothesis that intramolecular recombination is predominantly driven by repeat length, with larger repeats facilitating DNA exchange more readily. Overall, we found mixed support for this view: Recombination dynamics were heterogeneous across vascular plants and highly active small repeats (ca. 200 bp) were present in about one-third of studied mitogenomes. As in previous studies, we did not observe any robust relationships among commonly studied genome attributes, but we identify variation in recombination rates as a underinvestigated source of plant mitogenome diversity.

miRTrace reveals the organismal origins of microRNA sequencing data.

We present here miRTrace, the first algorithm to trace microRNA sequencing data back to their taxonomic origins. This is a challenge with profound implications for forensics, parasitology, food control, and research settings where cross-contamination can compromise results. miRTrace accurately (> 99%) assigns real and simulated data to 14 important animal and plant groups, sensitively detects parasitic infection in mammals, and discovers the primate origin of single cells. Applying our algorithm to over 700 public datasets, we find evidence that over 7% are cross-contaminated and present a novel solution to clean these computationally, even after sequencing has occurred. miRTrace is freely available at https://github.com/friedlanderlab/mirtrace .