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.

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.Electronic supplementary materialThe online version of this article (10.1186/s13059-018-1588-9) contains supplementary material, which is available to authorized users.

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27Plant mitogenomes can be difficult to assemble because they are structurally dynamic and prone 28 to intergenomic DNA transfers, leading to the unusual situation where an organelle genome is far 29 outnumbered by its nuclear counterparts. As a result, comparative mitogenome studies are in 30 their infancy and some key aspects of genome evolution are still known mainly from pre-31 genome, qualitative methods. To help address these limitations, we combined machine learning 32 and in silico enrichment of mitochondrial-like long reads to assemble the bacterial-sized 33 mitogenome of Norway spruce (Pinaceae: Picea abies). We conducted comparative analyses of 34 repeat abundance, intergenomic transfers, substitution and rearrangement rates, and estimated 35 repeat-by-repeat homologous recombination rates. Prompted by our discovery of highly 36 recombinogenic small repeats in P. abies, we assessed the genomic support for the prevailing 37 hypothesis that intramolecular recombination is predominantly driven by repeat length, with 38 larger repeats facilitating DNA exchange more readily. Overall, we found mixed support for this 39 view: recombination dynamics were heterogeneous across vascular plants and highly active 40 small repeats (ca. 200 bp) were present in about a third of studied mitogenomes. As in previous 41 studies, we did not observe any robust relationships among commonly-studied genome 42 attributes, but we identify variation in recombination rates as a underinvestigated source of plant 43 mitogenome diversity. 44 45 forest tree, Norway spruce (Pinaceae: Picea abies), from whole-genome shotgun sequencing 77 reads. The P. abies mitogenome helps to fill a phylogenetic gap in comparative analyses, and to 78 that end we analyzed gene repertoires; sources of genome size heterogeneity; intraspecific 79 variation; and mutation, recombination, and rearrangement rates in gymnosperms. Prompted by 80 the detection of highly recombinogenic small repeats in P. abies, we reevaluated published 81 recombination rates in seed plants. Despite early recognition of recombination as a factor in 82 generating the diversity of eukaryotic mitogenomes (Palmer & Herbon 1988; Gray et al. 1999), 83 surprisingly little attention has been given to recombinational dynamics as a source of 84 mitogenomic diversity within plants. As in previous studies, we found no clear relationship 85 between mitogenome traits and potential mechanisms infor example, genome size and the 86 proportion of intergenomically transferred DNAbut the role of recombination as a driver of 87 mitogenomic diversity within plants merits further scrutiny. 88 Results and Discussion 89Mitogenome assembly 90 We combined machine learning, in silico enrichment of long sequencing reads, and assembly 91 reconciliation to produce a highly contiguous P. abies draft mitogenome (Fig. 1). First, we 92 developed a support vector machine (SVM) to identify mitochondrial-like scaffolds from the P. 93 abies v. 1.0 genome assembly (Nystedt et al. 2013). Training the SVM classifier...