StoatyDive: Evaluation and classification of peak profiles for sequencing data
Author(s) -
Florian Heyl,
Rolf Backofen
Publication year - 2021
Publication title -
gigascience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.947
H-Index - 54
ISSN - 2047-217X
DOI - 10.1093/gigascience/giab045
Subject(s) - computational biology , computer science , cluster analysis , rna splicing , filter (signal processing) , sequence motif , data mining , pattern recognition (psychology) , biology , artificial intelligence , bioinformatics , rna , genetics , gene , computer vision
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.
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