A major obstacle in single-cell sequencing is given by sample contamination with foreign DNA. To guarantee clean samples and to prevent the introduction of contamination in novel species into public databases, considerable quality control efforts are put into post-sequencing analysis. Contamination screening usually relies on reference-based methods such as database alignment or marker gene search which limits the set of detectable contaminants to known species. But as the majority of species is unknown, a particular challenge is given by the detection of de-novo structure which requires screening techniques that can operate reference-free.

Acdc is a tool specifically developed to aid the quality control process. By combining supervised and unsupervised methods, it reliably detects both known and de-novo contaminants. First, 16S gene prediction and the inclusion of ultrafast exact alignment techniques enable sequence classification using existing knowledge from databases. Second, reference-free inspection is enabled by the use of state-of-the-art machine learning techniques that include fast, non-linear dimensionality reduction of oligonucleotide signatures and subsequent clustering algorithms that automatically estimate the number of clusters. The latter also enables to remove any contaminants, ending up with a clean sample without re-sequencing. Furthermore, given the data complexity and the ill-posedness of clustering, acdc employs bootstrapping techniques to provide statistically profound confidence values. Tested on a large number of samples from diverse sequencing projects, our software is able to quickly and accurately identify contamination. Results are displayed in an interactive result interface. Acdc can be run from the web as well as a dedicated command line application, which allows easy integration into large sequencing projects.

Acdc can reliably detect contamination in single-cell sequencing. In addition to database-driven detection, it complements existing tools by its unsupervised techniques, which allow for the detection of de-novo contaminants, too. As quality control is currently done manually, this contribution bears the potential of drastically reducing the amount of resources put into these processes, particularly in the context of limited availability of reference species, e.g. in de-novo analysis.