Transcript annotation prioritization and screening system (TrAPSS) for mutation screening

Brian M. O'Leary, Steven G. Davis, Michael F. Smith, Bartley Brown, Mathew B. Kemp, Hakeem Almabrazi, Jason A. Grundstand, Thomas Burns, Vladimir Leontiev, Jeaneen Andorf, Abbot F. Clark, Val C. Sheffield, Thomas L. Casavant, Todd E. Scheetz, Edwin M. Stone, Terry A. Braun

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


When searching for disease-causing mutations with polymerase chain reaction (PCR)-based methods, candidate genes are usually screened in their entirety, exon by exon. Genomic resources (i.e.,, and largely support this paradigm for mutation screening by making it easy to view and access sequence data associated with genes in their genomic context. However, the administrative burden of conducting mutation screening in potentially hundreds of genes and thousands of exons in thousands of patients is significant, even with the use of public genome resources. For example, the manual design of oligonucleotide primers for all exons of the 10 Leber's congenital amaurosis (LCA) genes (149 exons) represents a significant information management challenge. The Transcript Annotation Prioritization and Screening System (TrAPSS) is designed to accelerate mutation screening by (1) providing a gene-based local cache of candidate disease genes in a genomic context, (2) automating tasks associated with optimizing candidate disease gene screening and information management, and (3) providing the implementation of an algorithmic technique to utilize large amounts of heterogeneous genome annotation (e.g. conserved protein functional domains) so as to prioritize candidate genes.

Original languageEnglish
Pages (from-to)1155-1172
Number of pages18
JournalJournal of Bioinformatics and Computational Biology
Issue number6
StatePublished - Dec 2007


  • Bioinformatics
  • Candidate disease gene
  • Mutation screening


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