StoatyDive: Evaluation and Classification of Peak Profiles for Sequencing Data

The prediction of binding sites (peak calling) is a common task in the data analysis of methods such as crosslinking or chromatin immunoprecipitation in combination with high-throughput sequencing (CLIP-Seq, ChIP-Seq). The predicted binding sites are often further analyzed to predict sequence motifs or structure patterns as an example. However, the obtained peak set can vary in their profile shapes because of the used peakcaller method, different binding domains of the protein, protocol biases, or other factors. Thus, a tool is missing that evaluates and classifies the predicted peaks based on their shapes. We hereby present StoatyDive, a tool that can be used to filter for specific peak profile shapes of sequencing data such as CLIP and ChIP. StoatyDive therefore fine tunes downstream analysis steps such as structure or sequence motif predictions and acts as a quality control.\n\nWith StoatyDive we were able to classify distinct peak profile shapes from CLIP-seq data of the histone stem-loop-binding protein (SLBP). We show the potential of StoatyDive, as a quality control tool and as a filter to pick different shapes based on biological or methodical questions.\n\nStoatyDive is open source and freely available under GLP-3 at https://github.com/BackofenLab/StoatyDive and at bioconda https://anaconda.org/bioconda/stoatydive.