Chromatin organization guides gene regulatory mechanisms and has been subject of extensive research using chromatin accessibility assays. ATAC-seq is commonly applied to elucidate regulatory regions of the genome at both bulk and single-cell resolutions. However, the analysis of single-cell ATAC-seq data is particularly challenging due to issues such as data sparsity, low signal-to-noise ratios, and the lack of standardized quality control (QC) protocols. While QC based on the fragment length distribution (FLD) represents common practice for bulk analyses, an algorithmic solution that utilizes the full potential of the FLD at the single-cell level is missing. To address this limitation, we introduce the python package PEAKQC, a novel tool that provides a robust metric for identifying high-quality cells. PEAKQC quantifies the deviation of individual cells' FLD patterns from the expected distribution using a wavelet transformation-based convolution approach. Benchmarking against alternative metrics revealed favorable selection of high-quality cells, facilitating accurate downstream analysis including cell type identification and cluster separation. PEAKQC is readily installable via the Python Package Index and can be seamlessly integrated into existing single-cell analysis frameworks that utilize Python. By providing a robust and scalable solution for single-cell ATAC-seq QC, PEAKQC addresses a significant knowledge gap in the field and proposes FLD patterns as a novel standard for data quality assessment.

MOTIVATION: High throughput (HT) screens in the omics field are typically analyzed by automated pipelines that generate static visualizations and comprehensive spreadsheet data for scientists. However, exploratory and hypothesis driven data analysis are key aspects of the understanding of biological systems, both generating extensive need for customized and dynamic visualization. RESULTS: Here we describe WIlsON, an interactive workbench for analysis and visualization of multi-omics data. It is primarily intended to empower screening platforms to offer access to pre-calculated HT screen results to the non-computational scientist. Facilitated by an open file format, WIlsON supports all types of omics screens, serves results via a web-based dashboard, and enables end users to perform analyses and generate publication-ready plots. AVAILABILITY AND IMPLEMENTATION: We implemented WIlsON in R with a focus on extensibility using the modular Shiny and Plotly frameworks. A demo of the interactive workbench without limitations may be accessed at http://loosolab.mpi-bn.mpg.de. A standalone Docker container as well as the source code of WIlsON are freely available from our Docker hub https://hub.docker. com/r/loosolab/wilson, CRAN https://cran.r-project.org/web/packages/wilson/, and GitHub repository https://github.molgen.mpg.de/loosolab/wilson-apps, respectively.