acdc – Automated Contamination Detection and Confidence estimation for single-cell genome data

Abstract

BackgroundA major obstacle in single-cell sequencing is sample contamination with foreign DNA. To guarantee clean genome assemblies and to prevent the introduction of contamination into public databases, considerable quality control efforts are put into post-sequencing analysis. Contamination screening generally relies on reference-based methods such as database alignment or marker gene search, which limits the set of detectable contaminants to organisms with closely related reference species. As genomic coverage in the tree of life is highly fragmented, there is an urgent need for a reference-free methodology for contaminant identification in sequence data.ResultsWe present acdc, a tool specifically developed to aid the quality control process of genomic sequence data. By combining supervised and unsupervised methods, it reliably detects both known and de novo contaminants. First, 16S rRNA gene prediction and the inclusion of ultrafast exact alignment techniques allow 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 the removal of any contaminant, yielding a clean sample. 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 user interface. Acdc can be run from the web as well as a dedicated command line application, which allows easy integration into large sequencing project analysis workflows.ConclusionsAcdc can reliably detect contamination in single-cell genome data. In addition to database-driven detection, it complements existing tools by its unsupervised techniques, which allow for the detection of de novo contaminants. Our contribution has the potential to drastically reduce the amount of resources put into these processes, particularly in the context of limited availability of reference species. As single-cell genome data continues to grow rapidly, acdc adds to the toolkit of crucial quality assurance tools.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1397-7) contains supplementary material, which is available to authorized users.

Homoacetogenesis in Deep-Sea Chloroflexi , as Inferred by Single-Cell Genomics, Provides a Link to Reductive Dehalogenation in Terrestrial Dehalococcoidetes

“…Quality-filtered reads were assembled using SPAdes 3.10.0 with the –sc option and kmer sizes 21, 25, 33, 37, 43, 47, 51, 55, 59, 63, 67, 71, 73, 79, 81, and 85. Contaminant screening was performed using the Automated Contamination Detection and Confidence (ACDC) software ( 90 ), which categorizes contigs based on GC content and tetranucleotide frequency. The assembly of well 657K04 was found to have two 16S rRNA gene sequences, indicating that two cells may have been sorted and amplified in one well.…”

“…Each of the 16S rRNA sequences was identical to single-cell genomes 657A03 and 662N06, respectively. Using the ACDC software ( 90 ), we observed that tetranucleotide frequencies and GC content were sufficient to separate the two genomes in assembly 657K04 (see Fig. S2 in the supplemental material).…”

Genomic Variation and Evolution ofVibrio parahaemolyticusST36 over the Course of a Transcontinental Epidemic Expansion

“…Before running the pangenome analysis, contamination in the genomes was assessed with acdc (51). Genomes with unusual gene content or genome size identified during the pangenome analysis and suspected of possible contamination were analyzed to identify contamination and remove any suspected instances where contaminant sequences were identified.…”

Niche Differentiation among Three Closely RelatedCompetibacteraceaeClades at a Full-Scale Activated Sludge Wastewater Treatment Plant and Putative Linkages to Process Performance

“…Paired-end reads were extracted from the binned scaffolds using extract.fastq.for.reassembly.pl (37) and were used for reassembly using SPAdes version 3.10.1 (48), with k values of 21, 33, 55, and 77, as recommended for PE 2 ϫ 150-bp reads (48). Assembly coverage, GC content, and/or tetranucleotide frequencies were used for an additional round of screening with mmgenome, and assemblies were run through the online web tool for ACDC (49) to remove suspected contaminants. Genome completeness and contamination were estimated with CheckM (11).…”