MOTIVATION: Multiple factors can impact accuracy and reproducibility of mass spectrometry data. There is a need to integrate quality assessment and control into data analytic workflows. RESULTS: The MsQuality package calculates 43 low-level quality metrics based on the controlled mzQC vocabulary defined by the HUPO-PSI on a single mass spectrometry-based measurement of a sample. It helps to identify low-quality measurements and track data quality. Its use of community-standard quality metrics facilitates comparability of quality assessment and control (QA/QC) criteria across datasets. AVAILABILITY AND IMPLEMENTATION: The R package MsQuality is available through Bioconductor at https://bioconductor.org/packages/MsQuality.

MOTIVATION: First-line data quality assessment and exploratory data analysis are integral parts of any data analysis workflow. In high-throughput quantitative omics experiments (e.g. transcriptomics, proteomics and metabolomics), after initial processing, the data are typically presented as a matrix of numbers (feature IDs × samples). Efficient and standardized data quality metrics calculation and visualization are key to track the within-experiment quality of these rectangular data types and to guarantee for high-quality datasets and subsequent biological question-driven inference. RESULTS: We present MatrixQCvis, which provides interactive visualization of data quality metrics at the per-sample and per-feature level using R's shiny framework. It provides efficient and standardized ways to analyze data quality of quantitative omics data types that come in a matrix-like format (features IDs × samples). MatrixQCvis builds upon the Bioconductor SummarizedExperiment S4 class and thus facilitates the integration into existing workflows. AVAILABILITY AND IMPLEMENTATION: MatrixQCVis is implemented in R. It is available via Bioconductor and released under the GPL v3.0 license. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Among the main challenges in metabolomics are the rapid dereplication of previously characterized metabolites across a range of biological samples and the structural prediction of unknowns from MS/MS data. Here, we developed MetCirc to comprehensively align and calculate pairwise similarity scores among MS/MS spectral data and visualize these across a range of biological samples. MetCirc comprises functionalities to interactively organize these data according to compound familial groupings and to accelerate the discovery of shared metabolites and hypothesis formulation for unknowns. As such, MetCirc provides a significant advance to address biological questions in areas where chemodiversity plays a role. AVAILABILITY AND IMPLEMENTATION: MetCirc , implemented in the open-source R language, together with its vignette are available in the Bioconductor project and at https://github.com/PlantDefenseMetabolism/MetCirc . CONTACT: [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.