Paper describing automated validation of splicing mutations using RNASeq data has been indexed in PubMed:
Validation of predicted mRNA splicing mutations using high-throughput transcriptome data.
Viner C, Dorman SN, Shirley BC, Rogan PK. Version 2. F1000Res. 2014 Jan 13 [revised 2014 Apr 7];3:8. doi: 10.12688/f1000research.3-8.v2. eCollection 2014.
PMID: 24741438
We have published an article in Radiation Protection Biodosimetry describing our patented Automated Dicentric Chromosome Identifier Software for both Desktop and Supercomputer systems. The citation is:
Peter K. Rogan, Yanxin Li, Asanka Wickramasinghe, Akila Subasinghe, Natasha Caminsky, Wahab Khan, Jagath Samarabandu, Ruth Wilkins, Farrah Flegal, and Joan H. Knoll. AUTOMATING DICENTRIC CHROMOSOME DETECTION FROM CYTOGENETIC BIODOSIMETRY DATA. Radiat Prot Dosimetry. first published online April 21, 2014 doi:10.1093/rpd/ncu133 (Rogan et al. Radiat Prot Dosimetry. 2014).
This paper was presented at the EPR Biodose 2013 meeting in Leiden, Netherlands. The software identifies highly variable features in a large quantity images in relatively short time frame. Multiple technologies are employed, including SVM machine learning, gradient vector flow, parallelization, and other methods.
Stephanie Dorman presented our paper, “Non-coding mutation analysis reveals previously unrecognized pathways in lymph node-invasive breast cancer,” (Abstract) at the Annual AACR meeting. The poster presentation was very well attended with more than 30 visitors, including representatives from several personalized medicine and genomics companies. Many of the attendees expressed interest in the Shannon human mRNA splicing mutation and Veridical platforms, which were used to generate the results given in this paper.