A knowledge-based approach to predict intragenic deletions or duplications.
BACKGROUND
Despite recent improvements in high-throughput or classic molecular biology approaches it is still challenging to identify intermediate resolution genomic variations (50 bp to 50 kb). Although array-based technologies can be used to detect copy number variations in the human genome they are biased to detect only the largest such deletions or duplications. Several studies have identified deletions or duplications occurring within a gene that directly cause or predispose to disease. We have developed a novel computational system, SPeeDD (system to prioritize deletions or duplications) that utilizes machine learning techniques to predict likely candidate regions that delete or duplicate exon(s) within a gene.


RESULTS
Data mining and machine learning methods were applied to identify sequence features that were predictive of homologous recombination events. The logistic model tree (LMT) method yielded the best results. Sensitivity varied from 20% to 71.6% depending on the specific machine learning model used, but specificity exceeded 90% for all methods evaluated. In addition, the SPeeDD system successfully predicted and prioritized a recently published novel BRCA1 mutation.


CONCLUSIONS
Results suggest that the SPeeDD system is effective at prioritizing candidate deletions and duplications within a gene. Use of SPeeDD enables more focused screening, which reduces the labor and associated costs of the molecular assays and may also lead to targeted design of new array-based screens to focus on candidate areas to accelerate the process of mutation discovery.
Kalari
Krishna R
KR
Department of Biomedical Engineering, Center for Bioinformatics and Computational Biology, University of Iowa, Iowa, USA.
Casavant
Thomas L
TL
Scheetz
Todd E
TE
D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D017353:Gene Deletion; D020440:Gene Duplication; D015894:Genome, Human; D006801:Humans; D017384:Sequence Deletion