Mutadelic: mutation analysis using description logic inferencing capabilities.
BACKGROUND
As next generation sequencing gains a foothold in clinical genetics, there is a need for annotation tools to characterize increasing amounts of patient variant data for identifying clinically relevant mutations. While existing informatics tools provide efficient bulk variant annotations, they often generate excess information that may limit their scalability.


RESULTS
We propose an alternative solution based on description logic inferencing to generate workflows that produce only those annotations that will contribute to the interpretation of each variant. Workflows are dynamically generated using a novel abductive reasoning framework called a basic framework for abductive workflow generation (AbFab). Criteria for identifying disease-causing variants in Mendelian blood disorders were identified and implemented as AbFab services. A web application was built allowing users to run workflows generated from the criteria to analyze genomic variants. Significant variants are flagged and explanations provided for why they match or fail to match the criteria.


METHODS
The Mutadelic web application is available for use at http://krauthammerlab.med.yale.edu/mutadelic.


BACKGROUND
michael.krauthammer@yale.edu.


BACKGROUND
Supplementary data are available at Bioinformatics online.
Program in Computational Biology and Bioinformatics and.
Matthew E
ME
Holford
Program in Computational Biology and Bioinformatics and Department of Pathology, Yale University School of Medicine, New Haven, CT, USA.
Michael
M
Krauthammer
D004252:DNA Mutational Analysis / Q000379:methods*; D023281:Genomics / Q000379:methods; D006402:Hematologic Diseases / Q000235:genetics; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods; D006801:Humans; D058977:Molecular Sequence Annotation; D009154:Mutation; D012984:Software; D057188:Workflow