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

Lux M, Krüger J, Rinke C, et al. acdc – Automated Contamination Detection and Confidence estimation for single-cell genome data. BMC Bioinformatics. 2016;17(1): 543.Background A 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. Results We 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. Conclusions Acdc 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

De novo Nd-1 genome assembly reveals genomic diversity of Arabidopsis thaliana and facilitates genome-wide non-canonical splice site analysis across plant species

Pucker B. De novo Nd-1 genome assembly reveals genomic diversity of Arabidopsis thaliana and facilitates genome-wide non-canonical splice site analysis across plant species. Bielefeld: Universität Bielefeld; 2019

Discovery of Pseudomonas Natural Products Involved in the Biological Control of Potato Pathogens

Potato common scab and late blight, caused by Streptomyces scabies and Phytophthora infestans, respectively, are serious diseases affecting one of the world’s largest and most important food crops. The lack of stable interventions has shifted recent focus toward biological control (biocontrol) methods. Pseudomonas isolates have shown significant promise as bacterial biocontrol agents, occurring in soils worldwide with high inter-strain diversity and potential for natural product biosynthesis. This thesis details investigations into the biosynthetic potential of environmental Pseudomonas strains isolated from a potato field, with a focus on discovering novel natural products active against plant pathogens. Investigations focused on a strain showing strong biocontrol phenotypes, Ps652. Initially, this strain showed strong inhibition of phytopathogens but with few biosynthetic gene clusters (BGCs) identified by common methods. A variety of methods were used to identify the determinants of the strong biocontrol phenotype shown by this strain, including activity-guided isolation of natural products and transposon mutagenesis. 3,7-dihydroxytropolone (3,7-HT) is reported here as being produced by a Pseudomonas isolate for the first time. 3,7-HT shows improved activity towards Streptomyces scabies compared to 7-hydroxytropolone, but does not fully explain activity of Ps652 against P. infestans. Additionally, investigations were made into putative RiPP BGCs containing DUF692 proteins in environmental Pseudomonas isolates Ps706 and Ps708. These BGCs appeared associated with phytopathogen inhibition in previous work, and were studied here using bioinformatics, gene deletions, and heterologous expression approaches

Embryonic Stem Cells

Embryonic stem cells are one of the key building blocks of the emerging multidisciplinary field of regenerative medicine, and discoveries and new technology related to embryonic stem cells are being made at an ever increasing rate. This book provides a snapshot of some of the research occurring across a wide range of areas related to embryonic stem cells, including new methods, tools and technologies; new understandings about the molecular biology and pluripotency of these cells; as well as new uses for and sources of embryonic stem cells. The book will serve as a valuable resource for engineers, scientists, and clinicians as well as students in a wide range of disciplines