Project description:Methods for assembly, taxonomic profiling and binning are key to interpreting metagenome data, but a lack of consensus about benchmarking complicates performance assessment. The Critical Assessment of Metagenome Interpretation (CAMI) challenge has engaged the global developer community to benchmark their programs on highly complex and realistic data sets, generated from ?700 newly sequenced microorganisms and ?600 novel viruses and plasmids and representing common experimental setups. Assembly and genome binning programs performed well for species represented by individual genomes but were substantially affected by the presence of related strains. Taxonomic profiling and binning programs were proficient at high taxonomic ranks, with a notable performance decrease below family level. Parameter settings markedly affected performance, underscoring their importance for program reproducibility. The CAMI results highlight current challenges but also provide a roadmap for software selection to answer specific research questions.
Project description:We developed a set of algorithms for label-free quantification, termed MaxLFQ, embedded into MaxQuant. This contains two datasets to benchmark MaxLFQ: The proteome benchmark dataset consists of of HeLa and E. coli lysates mixed at defined ratios. The dynamic range benchmark dataset consists of UPS1/UPS2 standards (Sigma) spiked into E. coli lysates and quantified against each other.
Project description:While current genetic toxicology practices can detect downstream genotoxicity effects, such as gene mutation and double strand breaks, they are unable to detect the underlying Mode of Action (MoA) of a chemical or differentiate between direct and indirect acting genotoxicants without additional modification. The Adverse Outcome Pathway (AOP) framework is a useful tool to critically identify and evaluate MOAs and can enable subsequent quantitative dose response assessments of genotoxicity endpoints. The recently developed AOP, “Oxidative DNA damage leading to chromosomal aberrations and mutations” ( https://aopwiki.org/aops/296), pertains to one common genetic toxicology relevant MOA: oxidative stress. ROS are key to regulating many biological processes, however, when disrupted, an excess of ROS can eventually lead to DNA damage and double-strand breaks. Here, we look at 18 compounds with a complete or mixed oxidative stress MOA and use a combination of genomic tools such as Pathway analysis, Connectivity mapping and Transcriptional benchmark dose modeling to investigate the diversity of mechanisms that contribute to genotoxicity. TK6 cells were treated with the 18 compounds for 4 hours and the resulting genomic data was analyzed in correlation to the downstream genotoxic endpoint, micronucleus formation. We provide both a qualitative and quantitative analysis of the contribution of oxidative stress MoAs towards the overall genotoxicity outcome of each chemical. These methods have the potential to evolve into Next Generation Risk Assessment (NGRA) tools that can be used for determining the contribution of the oxidative stress MoA in a predictive toxicology setting.
