Amplicon sequencing can be a very powerful approach for detecting toxic cyanobacteria or any other kind of microorganism during monitoring programs. However, owing to the huge size of next-generation sequencing (NGS) datasets (up to several Gb), there is an obvious need for semi-automatic data processing and statistical analysis, as well as visualization of the patterns found. Importantly, raw NGS data contain errors, some of which are easily detected (e.g. too short or low-quality reads), while others remain hidden even after the most stringent quality controls (e.g. chimeras, contaminations, reads with large insertions or deletions, referred to as “indels”). As a consequence, NGS data need to be interpreted with caution, and bioinformatics analysis implementing poor error identification can easily lead to erroneous conclusions. Hence, a crucial step in the analysis of NGS data is the detection and removal of as many erroneous reads as possible. Moreover, bioinformatics involve additional preprocessing steps, including demultiplexing (i.e. grouping reads to samples according to the barcode sequence), deleting non-biological tags together with the adaptors and primer sequences, and removing chimeric sequences. In addition, the bioinformatics pipelines enable the quality-filtered sequences to be clustered into biologically relevant operational taxonomic units (OTUs), which form the basis of the statistical analysis, including the calculation of alpha- and beta-diversity.

B-BLOOMS, CCAMBIO, CYANOCOST, PYROCYANO