# Elusive Recurrent Bacterial Contamination in a Diatom Culture: A Case Study
## 4 Discussion
As noted before [32], 16S amplicon sequencing can be a useful addition, as this method is relatively cheap and does not make any assumptions about the taxonomy of possible contaminants (as would PCR or RT-PCR used for this purpose in older works [29]). It can even have some limited utility in detecting eukaryotic contaminants by recovering sequences of their plastids or mitochondria. On the other hand, this method requires a molecular biology laboratory and could be prone to false positives due to the contamination of DNA samples after isolation. It also requires careful interpretation: even in a clean sample, some reads would inevitably be classified as non-organellar bacteria due to a combination of in vitro amplification/sequencing artifacts and in silico classification errors. However, real contamination is likely to be caused by one bacterial strain, which will be represented by one or a few numerous phylotypes (or OTUs). Random noise, on the other hand, is likely to be distributed evenly along the bacterial taxonomy. There is also an issue of unclassified reads that could be coming from the contaminant bacterium, host organelles, amplification artifacts, or any other source. Analysis of _F. radians_ shotgun read libraries for bacterial sequences has produced both false negatives (missing the contamination when it is present) and false positives (finding contamination in clean libraries). False positive due to contamination with a 16S amplicon should be a rare occasion; a high amount of bacterial 16S rRNA reads usually does imply contamination. Technically, a positive is not even false: the library has been contaminated, after all! Still, we believe that this case deserves a mention. Although the library shows high content of bacterial 16S rRNA, it is safe to use in genomic assembly. These reads are unlikely to map to anything other than SSU genes, and they are trivial to filter out. False negatives are harder to detect, as it is difficult to establish a baseline number of reads that should map to bacterial 16S rRNAs without a clean reference library. However, both types of errors could be avoided with careful examination of taxonomic distribution of recovered reads: overall approach should be similar to that described above for amplicon libraries. The same advice applies to other bacterial sequences in eukaryotic genomic libraries. Most eukaryotes have at least a few HGT-derived genes, so the fact that some reads map to bacterial genomes does not constitute evidence of contamination. This is especially true for diatom algae, who are known to have high rates of horizontal gene acquisition [10] and generally mosaic genome composition caused by multiple endosymbioses in their evolutionary history. Another complication is introduced by a relative scarcity and non-model status of diatom genomes. At least in the absence of high-quality reference genome, any seemingly bacterial read could be interpreted either as an HGT-derived gene or as a part of contaminant's genome. However, real HGTs are relatively rare and involve various bacterial donors, while contamination introduces a single bacterial genome in its entirety into the library. In case of protein-coding genes one could also rely on low-level statistics such as GC content or frequencies of short k-mers, as was done in [16]. These parameters are relatively species-specific and differ significantly between eukaryotes and bacteria. In case of HGT, newly acquired genes slowly mutate until their statistics are close to the (host) genome average. Unlike them, genes within a bacterial genome do not need to match eukaryotic standards, so the corresponding reads or contigs would be different from the rest of the assembly. In conclusion, we suggest that limiting contamination control to any single method could possibly mislead the researcher in either direction.