Abstract
BackgroundThe advent of population-scale genome projects has revolutionized our biological understanding of parasitic protozoa. However, while hundreds to thousands of nuclear genomes of parasitic protozoa have been generated and analyzed, information about the diversity, structure and evolution of their mitochondrial genomes remains fragmentary, mainly because of their extraordinary complexity. Indeed, unicellular flagellates of the order Kinetoplastida contain structurally the most complex mitochondrial genome of all eukaryotes, organized as a giant network of homogeneous maxicircles and heterogeneous minicircles. We recently developed KOMICS, an analysis toolkit that automates the assembly and circularization of the mitochondrial genomes of Kinetoplastid parasites. While this tool overcomes the limitation of extracting mitochondrial assemblies from Next-Generation Sequencing datasets, interpreting and visualizing the genetic (dis)similarity within and between samples remains a time-consuming process.ResultsHere, we present a new analysis toolkit—rKOMICS—to streamline the analyses of minicircle sequence diversity in population-scale genome projects. rKOMICS is a user-friendly R package that has simple installation requirements and that is applicable to all 27 trypanosomatid genera. Once minicircle sequence alignments are generated, rKOMICS allows to examine, summarize and visualize minicircle sequence diversity within and between samples through the analyses of minicircle sequence clusters. We showcase the functionalities of the (r)KOMICS tool suite using a whole-genome sequencing dataset from a recently published study on the history of diversification of the Leishmania braziliensis species complex in Peru. Analyses of population diversity and structure highlighted differences in minicircle sequence richness and composition between Leishmania subspecies, and between subpopulations within subspecies.ConclusionThe rKOMICS package establishes a critical framework to manipulate, explore and extract biologically relevant information from mitochondrial minicircle assemblies in tens to hundreds of samples simultaneously and efficiently. This should facilitate research that aims to develop new molecular markers for identifying species-specific minicircles, or to study the ancestry of parasites for complementary insights into their evolutionary history.
Highlights
The advent of population-scale genome projects has revolutionized our biological understanding of parasitic protozoa
We provide a simplified overview of a straightforward analysis of minicircle sequence diversity, while in-depth analyses are provided in the rKOMICS vignette document
When examining the length distribution of the circularized minicircle sequences using the function msc.length, we found that the majority of minicircles (95.2%) were 720– 760 bp long, which is within the expected length range of minicircles in Leishmania parasites (Fig. 2a). 294 minicircle contigs (4.8%) showed twice this length (1400–1700 bp) (Fig. 2a), which may suggest that these are artificial minicircle dimers introduced by the assembly process, and were subsequently removed
Summary
The advent of population-scale genome projects has revolutionized our biological understanding of parasitic protozoa. We recently developed KOMICS, an analysis toolkit that automates the assembly and circularization of the mitochondrial genomes of Kinetoplastid parasites. While this tool overcomes the limitation of extracting mitochondrial assemblies from Next-Generation Sequencing datasets, interpreting and visualizing the genetic (dis)similarity within and between samples remains a timeconsuming process. In addition to nuclear sequences, next-generation sequencing platforms produce a high copy number of extranuclear sequences (originating from mitochondria or chloroplasts) when DNA is extracted from whole cells Such extranuclear sequences can be de novo assembled using tools such as MITObim [6] or NOVOPlasty [7]. This means that both nuclear and extranuclear sequence diversity could be studied during a single next-generation sequencing experiment
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