@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},
  volume =	 {10},
  number =	 {6},
  pages =	 {},
  user =	 {backofen},
  pmid =	 {34143874},
  doi = 	 {10.1093/gigascience/giab045},
  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.}
}