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Estimating the Quality of Eukaryotic Genomes Recovered from Metagenomic Analysis

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Estimating the Quality of Eukaryotic Genomes Recovered from Metagenomic Analysis bioRxiv preprint doi: https://doi.org/10.1101/2019.12.19.882753; this version posted December 20, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Estimating the quality of eukaryotic genomes recovered from metagenomic analysis Paul Saary1*, Alex L. Mitchell1, and Robert D. Finn1* 1European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK *Corresponding author: [email protected], [email protected] Abstract Introduction Eukaryotes make up a large fraction of micro- bial biodiversity. However, the field of metage- The past two decades have seen a dramatic nomics has been heavily biased towards the advancement in our understanding of the mi- study of just the prokaryotic fraction. This croscopic organisms present in environments focus has driven the necessary methodolog- (known as microbiomes) such as oceans, soil and ical developments to enable the recovery host-associated sites, like the human gut. Most of prokaryotic genomes from metagenomes, of this knowledge has come from the applica- which has reliably yielded genomes from tion of modern DNA sequencing techniques to thousands of novel species. More recently, mi- the collective genetic material of the microorgan- crobial eukaryotes have gained more atten- isms, using methods such as metabarcoding (am- tion, but there is yet to be a parallel explo- plification of marker genes) or metagenomics sion in the number of eukaryotic genomes re- (shotgun sequencing). Based on the analysis of covered from metagenomic samples. One of such sequence data, it is thought that up to 99 % the current deficiencies is the lack of a uni- of all microorganisms are yet to be cultured versally applicable and reliable tool for the es- (Rinke et al., 2013)). timation of eukaryote genome quality. To ad- To date, the overwhelming number of dress this need, we have developed EukCC, a metabarcoding and metagenomics studies have tool for estimating the quality of eukaryotic focused on the bacteria that are present within genomes based on the dynamic selection of a sample. However, viruses and eukaryotes are single copy marker gene sets, with the aim also important members of the microbial com- of applying it to metagenomics datasets. We munity, both in terms of number and function demonstrate that our method outperforms cur- (Paez-Espino et al., 2016; Carradec et al., 2018; rent genome quality estimators and have ap- Olm et al., 2019; Karin et al., 2019) Indeed, the plied EukCC to datasets from two different unicellular protists and fungi are estimated to biomes to enable the identification of novel account for about 17 % of the global microbial ∼ genomes, including a eukaryote found on the biomass. Within the microbial eukaryotic human skin and a Bathycoccus species ob- biomass, the genetically diverse unicellular tained from a marine sample. organisms known as protists account for as much as 25 % (Bar-On et al., 2018). Today, ∼ the increasing number of completed genomes has revealed that the “protists” classification encompasses a number of divergent sub-clades: 1 bioRxiv preprint doi: https://doi.org/10.1101/2019.12.19.882753; this version posted December 20, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Saary et al. (2019) 1 INTRODUCTION animals and some protists are encompassed are incomplete, so estimating the quality of a in the Opisthokonta clade; others are grouped MAG in terms of completeness and contamina- into the Amoebozoa or (T)SAR ((telonemids), tion can not rely on genomic comparisons. In Stramenopiles, alveolates, and Rhizaria) clades, the absence of a reference genome, quality es- or into further groups. However, the exact root timates for MAGs have used universal single of the overall eukaryotic tree and the number copy marker genes (SCMGs) (Parra et al., 2007; of primary clades remains a topic of discussion Mende et al., 2013; Simão et al., 2015; Parks et (Baldauf, 2003; Burki, 2014; Burki et al., 2019). al., 2015). As these genes are expected to only oc- Despite the increase in the number of com- cur once within a genome, comparing the num- plete and near complete genomes, metabarcod- ber of SCMGs found within a binned genome to ing and metagenomic approaches that have in- the number of expected marker genes provides cluded the analysis of microbial eukaryotes have an estimation of completeness, while additional demonstrated that the true diversity of protists copies of a marker gene can be used as an indi- is far greater than that currently reflected in cator of contamination. After such evaluations, the genomic reference databases (such as Ref- binned genomes achieving a certain quality can Seq or ENA). For example, a recent estimate be classified as either medium or high quality based on metabarcoding sequencing suggests (Bowers et al., 2017). that 150,000 eukaryotic species exist in the Due to biases in sampling and extraction oceans alone (Vargas et al., 2015), but only 4,551 methods, the majority of MAGs produced to representative species have an entry in GenBank date correspond to prokaryotic organisms. For (15. Nov 2019). Thus, if the functional role of a prokaryotic MAGs, CheckM (Parks et al., 2015) microbiome is to be completely understood, we is the most widely used tool to estimate com- need to know what these as yet uncharacterised pleteness and contamination, although other ap- organisms are and the functional roles they are proaches have also been used (Pasolli et al., performing. 2019). However, even with size fractionation of Currently, one of the best approaches for un- samples to enrich for prokaryotes prior to li- derstanding microbiome function is through brary preparation, eukaryotic cells frequently re- the assembly of shotgun reads (usually 200- main in the samples, with some eukaryotic DNA 500 bp long) to obtain longer contigs (typi- recovered as MAGs (Delmont et al., 2018; West cally in the range of 2000-500,000 bp). These et al., 2018), while others use size fractionation contigs provide access to complete proteins, specifically to enrich for eukaryotes (Karsenti et which may then be interpreted within the al., 2011; Carradec et al., 2018). context of surrounding genes. In the last As with the quality estimation of bacterial few years, it has become commonplace to ex- MAGs, SCMGs have been used to assess eu- tend this type of analysis to recover puta- karyotic isolate genomes. CEGMA (Parra et al., tive genomes, termed metagenome assembled 2007) used 240 universal single copy marker genomes (MAGs). MAGs are generated by group- genes identified from six model organisms to ing contigs into sets that are believed to have estimate genome completeness, which was then come from a single organism – a process known superseded by BUSCO (Simão et al., 2015; Water- as binning. However, even after binning MAGs, house et al., 2018). The major advance of BUSCO they vary in their completeness and can be compared to CEGMA, was the provision of cu- fragmented, due to a combination of biologi- rated sets of marker genes for several eukaryotic cal (e.g. abundance of microbes), experimental and prokaryotic clades, in addition to the sin- (e.g. depth of sequencing) and technical (e.g. al- gle universal eukaryotic marker gene set. While gorithmic) reasons. Furthermore, the computa- BUSCO provides sets to estimate completeness tional methods used for binning the contigs can of eukaryota, protists, plants and fungi, it re- sometimes fail to distinguish between contigs mains up to the user to select which is the most that have come from different organisms, lead- suitable set when assessing genome quality. Al- ing to a chimeric genome (termed contamina- though BUSCO has been used for quality met- tion). As highlighted above, reference databases ric calculation of eukaryotic MAGs (West et al., 2 bioRxiv preprint doi: https://doi.org/10.1101/2019.12.19.882753; this version posted December 20, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Saary et al. (2019) 2 RESULTS 2018), this manual selection can be challenging, However, BUSCO performed less well on eu- especially when dealing with large numbers of karyotic genomes arising from other taxonomic genomes from unknown species. groups. Notably, the numbers of BUSCOs found Here we investigate the performance of cur- in Amoebozoa genomes varied greatly, with a rent approaches across different eukaryotic median of 88.78 %, but ranging between 69.6 % clades and describe EukCC, an unsupervised for Entamoebidae (number of species, n = 4) to method for the estimation of eukaryotic genome 94.9 % for the four further Amoebozoa families quality in terms of completion and contamina- (n = 6). More surprising was that the Ciliophora tion, with a particular view of applying this tool genomes (n = 4) rarely matched BUSCO eukary- to eukaryotic MAGs. otic marker genes, with a median of 1.16 % of BUSCOs matched. We also evaluated the BUSCO protist set in Results the same way. Somewhat counterintuitively, us- Evaluation of BUSCO across different ing this more specific set the mean proportion of eukaryotic clades matched BUSCOs in Amoebozoa dropped from 88.78 % to 78.37 %, yet increased for Apicom- To determine the applicability of BUSCO for plexa from 61.72 % to 68.37 %. In other taxa, evaluating the quality of eukaryotic MAGs, we such as Stramenopiles, the range of missing BUS- first tested how the more general eukaryotic COs increased (Figure S1).
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