A systematic comparison of chloroplast genome assembly tools

Abstract

Chloroplasts are photosynthetic organelles in plant cells and contain their own genomic information. That genome can be utilized in different scientific fields like phylogenetics or biotechnology. Thus, different assemblers have been developed specialized in chloroplast assemblies. Those assemblers often use the output of whole genome sequencing experiments as input. Such sequencing data usually contain the complete chloroplast genome information, even if the sequencing aims for the core genome. Different assembly tools have never been systematically compared. Here we present a benchmark of seven chloroplast assembly tools, capable to succeed in more than 60 % of real data sets. Our results show significant differences between the tested assemblers in terms of generating whole chloroplast genome sequences and computational requirements. Moreover, we suggest further development to improve user experience and success rate. In terms of reproducibility, we created docker images for each tested tool, which are available for the scientific community. Following the presented guidelines, users are able to analyze and screen data sets for chloroplast genomes using only standard computer infrastructure. Thus large scale screening for chloroplasts as hidden treasures within genomic sequencing data is feasible.

GetOrganelle: a fast and versatile toolkit for accurate de novo assembly of organelle genomes

“…Additionally, organelle genomes may produce flip-flop configurations or other assembly isomers mediated by repeats [25][26][27]; these outcomes are not addressed by any of the abovementioned pipelines/toolkits, except GetOrganelle. This capability also was not investigated by Freudenthal et al [24].…”

“…Freudenthal et al [24] showed that GetOrganelle has moderate efficiency in both time and memory usage among tested assemblers. However, they only used the default options for evaluation, which are designed to have high chloroplast genome completion rate, rather than high computational efficiency.…”

“…Fast-Plast (https://github.com/mrmckain/Fast-Plast), NOVOPlasty [23], and ORG.asm (https://git.metabarcoding.org/org-asm/org-asm/orgasm) were proposed as fast tools to conduct de novo assembly of complete organelle genomes from WGS data. However, these tools have not been systematically evaluated until a recent preprint was posted at bioRxiv [24]. Freudenthal et al [24] presented a benchmark comparison of seven chloroplast assembly pipelines/toolkits and found significant differences among those assemblers.…”

“…However, these tools have not been systematically evaluated until a recent preprint was posted at bioRxiv [24]. Freudenthal et al [24] presented a benchmark comparison of seven chloroplast assembly pipelines/toolkits and found significant differences among those assemblers. In their tests, our toolkit, GetOrganelle (https://github.com/Kinggerm/GetOrganelle), significantly outperformed all other assemblers in consistency (unlike consistency under different parameters in this paper), accuracy, and success rate.…”

“…In their tests, our toolkit, GetOrganelle (https://github.com/Kinggerm/GetOrganelle), significantly outperformed all other assemblers in consistency (unlike consistency under different parameters in this paper), accuracy, and success rate. Nevertheless, the broad application and versatile options of GetOrganelle were not explored by Freudenthal et al [24]. Additionally, organelle genomes may produce flip-flop configurations or other assembly isomers mediated by repeats [25][26][27]; these outcomes are not addressed by any of the abovementioned pipelines/toolkits, except GetOrganelle.…”