@article{Heyl_Backofen-Stoat_Evalu_and-2021,
author = {Heyl, Florian and Backofen, Rolf},
title = {{StoatyDive}: {Evaluation} and classification of peak 
         profiles for sequencing data},
journal = {Gigascience},
year = {2021},
doi = {10.1093/gigascience/giab045},
volume = {10},
user = {backofen},
pmid = {34143874},
pages = {},
number = {6},
issn = {2047-217X},
abstract = {BACKGROUND: The prediction of binding sites (peak-calling) 
            is a common task in the data analysis of methods such as 
            cross-linking immunoprecipitation in combination with 
            high-throughput sequencing (CLIP-Seq). The predicted binding 
            sites are often further analyzed to predict sequence motifs 
            or structure patterns. When looking at a typical result of 
            such high-throughput experiments, the obtained peak profiles 
            differ largely on a genomic level. Thus, a tool is missing 
            that evaluates and classifies the predicted peaks on the 
            basis of 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. FINDINGS: With StoatyDive 
            we are able to classify peak profile shapes from CLIP-seq 
            data of the histone stem-loop-binding protein (SLBP). We 
            compare the results to existing tools and show that 
            StoatyDive finds more distinct peak shape clusters for CLIP 
            data. Furthermore, we present StoatyDive's capabilities as a 
            quality control tool and as a filter to pick different 
            shapes based on biological or technical questions for other 
            CLIP data from different RNA binding proteins with different 
            biological functions and numbers of RNA recognition motifs. 
            We finally show that proteins involved in splicing, such as 
            RBM22 and U2AF1, have potentially sharper-shaped peaks than 
            other RNA binding proteins. CONCLUSION: StoatyDive finally 
            fills the demand for a peak shape clustering tool for 
            CLIP-Seq data that fine-tunes downstream analysis steps such 
            as structure or sequence motif predictions and that acts as 
            a quality control.}
}