Paper describing automated validation of splicing mutations using RNASeq data has been indexed in PubMed:
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.
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.
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.
March 9, 2014. Commentary on Mutation interpretation: current limitations and approaches to overcome them
Why do we sequence gene panels, exomes and complete genomes? To find mutations in genes, of course. This is the currency that allows bioinformaticists and clinicians to determine which metabolic pathways are dysregulated in patients with gene-based disease.
What has surprised me most about NGS since the development of this wonderful technology for finding variation has been the under-investment and significant ignorance (deliberate or incidental) about what constitutes a disease-related mutation. There are a number sources of false positive and negative mutations, which will invariably impact which abnormal pathways are inferred from mutation data. This has significant implications for personalizing diagnosis and therapy.
So much emphasis has placed on coding mutations that alter amino acids and create nonsense codons, in part, because of the false sense of security conferred by apparent understanding of changes in protein coding. All of the tools available for inferring pathogenicity from missense changes are to some extent inaccurate and generally produce results inconsistent with one another. Even when they are consistent, it is not a guarantee of accuracy*. Leading researchers, recognizing this, have typically shunned incorporation of these results landmark NGS papers.
Nonsense codons and intronic dinucleotides at exon boundaries recognized in splicing are thought to be the most rigorous evidence for pathogenicity. Many nonsense codons induce exon skipping, and these events frequently preserve reading frame, which should dampen confidence about pathogenicity of such mutations, a potential source of false positives. The analyses of dinucleotides in splice sites are known to comprise only a small fraction of known splicing mutations. We and others have demonstrated that splicing mutations can occur widely throughout genes (in exons and introns), either inactivating splice site and exon recognition, reducing splicing efficiency, or creating novel cryptic splice isoforms. These account for a substantial fraction of unrecognized, highly deleterious mutations in NGS data. Our company, Cytognomix, has released peer-reviewed software which predicting such mutations in genomic sequences (shannonpipeline.cytognomix.com), and companion software (veridical.org) using RNASeq data for validating these predictions.
Our ongoing analysis of complete gene sequences in breast cancer has revealed breathtaking levels of sequence variation in introns, promoter and downstream regions that dwarf that seen in exons alone. Variants in promoter regions have been proven to explain expression levels in normal individuals. It seems likely that these gene regions will harbor disease causing variants in many patients. Using information theory-based methods we are prioritizing the most likely regulatory mutation candidates. Our aim is to produce a full catalogue of likely disease-causing variants in each patient, to improve our understanding of dyregulated pathways in each individual.
The blog entry has also been posted on the NGSLeaders Discussion Board on Data Analysis and Informatics at www.ngsleaders.org
*The fact that an opinion has been widely held is no evidence whatever that it is not utterly absurd.”
— Bertrand Russell
The article has been published for 7 weeks and has been downloaded or viewed 562 times. Three peer reviews have been submitted to date: one approved and two approved with reservations. We are addressing these comments now and modifying the manuscript accordingly.
Viner C, Dorman SN, Shirley BC and Rogan PK (2014) Validation of predicted mRNA splicing mutations using high-throughput transcriptome data [v1; ref status: indexed, http://f1000r.es/2no] F1000Research 2014, 3:8 (doi: 10.12688/f1000research.3-8.v1)
The paper has also been highlighted on the RNA-Seq Blog.
March 3, 2014. Oral presentation at the The Fifth International Symposium on Hereditary Breast and Ovarian Cancer Conference
Our abstract, “Identification, Prediction and Prioritization of Non-Coding Variants of Uncertain Significance in Heritable Breast/Ovarian Cancer,” has been accepted for oral presentation at the BRCA: Twenty Years of Advances – The Fifth International Symposium on Hereditary Breast and Ovarian Cancer Conference in Montreal, Quebec (Apr 23-25).
The authors are:
E.J. Mucaki(1), N. Caminsky(1), A. Stuart(1), C. Viner(1), B. Shirley(2), J.H. Knoll(1,2), P. Ainsworth(1), P.K. Rogan(1,2)
1) University of Western Ontario, 2) Cytognomix Inc., London, Canada
The Shannon mutation pipeline is now available by online subscription.
See our announcement !
The paper that we presented at the 2013 EPR BioDose meeting is now in press in the journal: Radiation Protection Biodosimetry:
Automating dicentric chromosome detection from cytogenetic biodosimetry data
Peter K. Rogan*1, Yanxin Li1, Asanka Wickramasinghe1, Akila Subasinghe1, Natasha Caminsky1, Wahab Khan1, Jagath Samarabandu1, Joan H. Knoll1, Ruth Wilkins2, and Farrah Flegal3
1University of Western Ontario, 1151 Richmond Street, London, ON, Canada, N6A 3K7, 2Health Canada, 775 Brookfield Road, PL 6303B, Ottawa, ON, Canada K1A 1C1, 3Atomic Energy of Canada Ltd., STN 51, Bldg 513, Chalk River, ON, Canada K0J 1J0
Stephanie Dorman’s presentation of our analysis of Cancer Genome Atlas genomic and clinical data of somatic breast cancer on January 10, 2014 has been highlighted in the Breast Cancer Society of Canada blog:
Jan. 13, 2014. New paper and software for experimental evaluation of predicted mutations in genomes or exomes
We have just published a paper describing method and companion software for experimental validation of mutations with NGS (RNASeq) data:
Validation of predicted mRNA splicing mutations using high-throughput transcriptome data. Coby Viner, Stephanie N. Dorman, Ben C. Shirley, Peter K. Rogan. published in F1000Research ( http://f1000research.com/articles/3-8/v1 )
This approach fills a critical unmet need in genome-scale mutation analysis. Contact us if you want to trial the software.
- April 22, 2014. Validation of predicted splicing mutations paper in F1000Research – now in PubMed
- April 22, 2014. New paper describing Automated Biodosimetry Software published
- April 9, 2014. Presentation at the American Association for Cancer Research meeting
- March 9, 2014. Commentary on Mutation interpretation: current limitations and approaches to overcome them