Consensus assessment of the contamination level of publicly available cyanobacterial genomes [article]

Luc Cornet, Loic Meunier, Mick Van Vlierberghe, Raphael R. Leonard, Benoit Durieu, Yannick Lara, Agnieszka Misztak, Damien Sirjacobs, Emmanuelle J. Javaux, Herve Philippe, Annick Wilmotte, Denis Baurain
2018 bioRxiv   pre-print
BACKGROUND: Publicly available genomes are crucial for phylogenetic and metagenomic studies, in which contaminating sequences can be the cause of major problems. This issue is expected to be especially important for Cyanobacteria because axenic strains are notoriously difficult to obtain and keep in culture. Yet, despite their great scientific interest, no data are currently available concerning the quality of publicly available cyanobacterial genomes. RESULTS: As reliably detecting
more » ... tecting contaminants is a complex task, we designed a pipeline combining six methods in a consensus strategy to assess the contamination level of 440 genome assemblies of Cyanobacteria. Two methods are based on published reference databases of ribosomal genes (SSU rRNA 16S and ribosomal proteins), one is indirectly based on a reference database of marker genes (CheckM), and three are based on complete genome analysis. Among those genome-wide methods, Kraken and DIAMOND blastx share the same reference database that we derived from Ensembl Bacteria, whereas CONCOCT does not require any reference database, instead relying on differences in DNA tetramer frequencies. Given that all the six methods appear to have their own strengths and limitations, we used the consensus of their rankings to infer that >5% of cyanobacterial genome assemblies are highly contaminated by foreign DNA (i.e., contaminants were detected by 5 or 6 methods). CONCLUSIONS: Our results will help researchers to check the quality of publicly available genomic data before use in their own analyses. Moreover, we argue that journals should make mandatory the submission of raw read data along with genome assemblies in order to facilitate the detection of contaminants in sequence databases.
doi:10.1101/301788 fatcat:gjbjce3z7zdg3bvmvvq5wwphuq