1. Spliceogen: an integrative, scalable tool for the discovery of splice-altering variants
- Author
-
Steven Monger, Eleni Giannoulatou, Michael Troup, Eddie Ip, and Sally L. Dunwoodie
- Subjects
Statistics and Probability ,chemistry.chemical_classification ,0303 health sciences ,Computer science ,RNA Splicing ,In silico ,030305 genetics & heredity ,Exons ,Genomics ,Computational biology ,Biochemistry ,Computer Science Applications ,03 medical and health sciences ,Computational Mathematics ,INDEL Mutation ,Computational Theory and Mathematics ,chemistry ,RNA splicing ,Scalability ,splice ,Nucleotide ,Indel ,Molecular Biology ,Software ,030304 developmental biology - Abstract
Motivation In silico prediction tools are essential for identifying variants which create or disrupt cis-splicing motifs. However, there are limited options for genome-scale discovery of splice-altering variants. Results We have developed Spliceogen, a highly scalable pipeline integrating predictions from some of the individually best performing models for splice motif prediction: MaxEntScan, GeneSplicer, ESRseq and Branchpointer. Availability and implementation Spliceogen is available as a command line tool which accepts VCF/BED inputs and handles both single nucleotide variants (SNVs) and indels (https://github.com/VCCRI/Spliceogen). SNV databases with prediction scores are also available, covering all possible SNVs at all genomic positions within all Gencode-annotated multi-exon transcripts. Supplementary information Supplementary data are available at Bioinformatics online.
- Published
- 2019
- Full Text
- View/download PDF