Consensus assessment of the contamination level of publicly available cyanobacterial genomes

Abstract

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. As reliably detecting 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). 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.

ConFindr: Rapid detection of intraspecies and cross-species contamination in bacterial whole-genome sequence data

“…A recent assessment of 67758 publically-available Salmonella sequences determined that 1.87% of samples had cross-species contamination based on a read classification approach (Robertson et al, 2018). Prevalence of cross-species sequence contamination in public repositories is a known issue that has been described in a number of studies (Merchant et al, 2014;Mukherjee et al, 2015;Lee et al, 2017;Cornet et al, 2018). Very few studies have looked at intraspecies contamination in public repositories, and we could not identify any studies evaluating prevalence of intraspecies contamination in foodborne pathogens.…”

“…The presence of contamination in WGS data is recognized as an important sequence quality issue (Merchant et al, 2014;Ballenghien et al, 2017;Robertson et al, 2018;Cornet et al, 2018). Introduction of contaminants can occur at many stages in the generation of bacterial sequence data.…”

Rapid assessment of phytoplankton assemblages using Next Generation Sequencing – Barcode of Life database: a widely applicable toolkit to monitor biodiversity and harmful algal blooms (HABs)

“…Strains can be mislabelled, lose morphological or biochemical traits or harbour contaminants present at isolation (e.g for cyanobacteria with significant sheaths), with implications for experimental work - as a result, axenic strains are very difficult to obtain [53]. According to the study of Cornet et al [54] on contamination level of publicly accessible cyanobacterial genomes, 21 out of 440 surveyed genomes were highly contaminated (mostly with Proteobacteria and Bacteroidetes). In our study we used primers preferentially amplifying cyanobacterial 16S [47,48], however, some strains failed to sequence due to overlapping heterogeneous signal in Sanger sequencing, which can be indicative of contamination.…”

A constrained SSU-rRNA phylogeny reveals the unsequenced diversity of photosynthetic Cyanobacteria (Oxyphotobacteria)

“…The absence of rRNA genes is explained by the fact that the majority of these genomes (143 out of 193) were assembled with a metagenomic pipeline, according to NCBI metadata. This phenomenon was noticed in Cornet et al (2018a) [38] and is due to the frequent loss of rRNA sequences during metagenomic assembly [28]. Given that neither the isolation source nor the assembly pipeline were easy to determine, we decided not to consider these 193 genomes in our analysis by precautionary principle.…”