StoatyDive: Evaluation and classification of peak profiles for sequencing data
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (GigaScience)
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.
Article activity feed
-
Now published in GigaScience doi: 10.1093/gigascience/giab045
Florian Heyl 1Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Georges-Köhler-Allee 106, 79110 GermanyFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Florian HeylFor correspondence: heylf@informatik.uni-freiburg.de backofen@informatik.uni-freiburg.de
This work has been peer reviewed in GigaScience, which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:
Reviewer 1. (Eric Van Nostrand) http://dx.doi.org/10.5524/REVIEW.102771 Reviewer 2. (Nejc Haberman) http://dx.doi.org/10.5524/REVIEW.102769
Reviewer 3. (William Lai) http://dx.doi.org/10.5524/REVIEW.102770 -
-
