ProDeGe: a computational protocol for fully automated decontamination of genomes

Tennessen, Kristin; Andersen, Evan; Clingenpeel, Scott; Rinke, Christian; Lundberg, Derek S.; Han, Jianxin; Dangl, Jeff; Ivanova, Natalia; Woyke, Tanja; Kyrpides, Nikos C.; Pati, Amrita

doi:10.1038/ismej.2015.100

Cited by 71 publications

(64 citation statements)

References 17 publications

(15 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We compared acdc to the state-of-the-art contamination detection tool ProDeGe [11] both in terms of supervised and unsupervised detection capabilities. ProDeGe has been optimized to obtain a high precision in the context of a known taxonomic level and database support.…”

Section: Resultsmentioning

confidence: 99%

“…In those cases, mostly all contigs were marked as contaminants, resulting in an empty clean sequences file. This fact can be attributed both to ProDeGes behavior of selecting contaminants with high specificity [11] and to it’s missing ability to distinguish between clean and contaminated samples. Results of 4 samples could not be obtained, because computation didn’t provide any output.…”

Section: Resultsmentioning

confidence: 99%

“…Given those obstacles, ProDeGe, an automated Protocol for the Decontamination of Genomes was recently developed [11]. ProDeGe combines the BLAST algorithm [12] as a popular choice for database sequence alignment with reference-free PCA-reduced oligonucleotide profiling to enhance classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2016

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2016

Self Cite

View full text Add to dashboard Cite

show abstract

“…Nevertheless, the separation of contigs into bins that represent different co-occurring organisms is not straightforward, even though special tools are designed for this, e.g. ProDeGe (Tennessen et al 2016). New automated methods for binning contigs of different taxa retrieved from the same SAG are urgently needed.…”

Section: Single-cell Genomicsmentioning

confidence: 99%

Exploring the oceanic microeukaryotic interactome with metaomics approaches

Krabberød¹,

Bjorbækmo²,

Shalchian-Tabrizi³

et al. 2017

Aquat. Microb. Ecol.

View full text Add to dashboard Cite

THE MICROBIAL OCEANOur collective awareness of the significance of marine ecosystems has increased steadily during the last 40 yr, progressing in concert with the development of new technologies that have allowed us to dig deeper into the microbial world. A number of key events can be identified from this period, for example the formulation of the microbial-loop concept, the discovery of abundant photoautotrophic (Synechococcus and Prochlorococcus) and heterotrophic (SAR11) pico-sized prokaryotic plankton as well as novel lineages of heterotrophic picoeukaryotes (e.g. MALV and MAST), the recognition of the importance of Archaea in oceanic plankton, the realization that the majority of marine microbes cannot be cultured, the discovery of the rare biosphere and the key role of viruses in oceanic communities (Kirchman 2008 ABSTRACT: Biological communities are systems composed of many interacting parts (species, populations or single cells) that in combination constitute the functional basis of the biosphere. Animal and plant ecologists have advanced substantially our understanding of ecological interactions. In contrast, our knowledge of ecological interaction in microbes is still rudimentary. This represents a major knowledge gap, as microbes are key players in almost all ecosystems, particularly in the oceans. Several studies still pool together widely different marine microbes into broad functional categories (e.g. grazers) and therefore overlook fine-grained species/population-specific interactions. Increasing our understanding of ecological interactions is particularly needed for oceanic microeukaryotes, which include a large diversity of poorly understood symbiotic relationships that range from mutualistic to parasitic. The reason for the current state of affairs is that determining ecological interactions between microbes has proven to be highly challenging. However, recent technological developments in genomics and transcriptomics (metaomics for short), coupled with microfluidics and high-performance computing are making it increasingly feasible to determine ecological interactions at the microscale. Here, we present our views on how this field will advance thanks to the progress in metaomics approaches as well as potential avenues for future research.

show abstract

“…Anvi'o and another recently developed software package, CheckM, estimate genome completeness and contamination based on the presence of single-copy marker genes. ProDeGe [40] and acdc [41] are additional tools that perform automated contamination screens returning separate fasta files for clean and contaminant contigs. These tools can be used in combination with tools like CheckM and Anvi'o, especially on large sets of SAGs where manual curation is challenging.…”

Section: Contig-level Quality Assurancementioning

confidence: 99%