ASSEDA has been incorporated into Cytognomix’s MutationForecaster system. Current paid subscribers will have access to splice.uwo.ca until their subscription ends. Previous trial users of ASSEDA may register with MutationForecaster. After logging in, subscriptions to use ASSEDA and all other products can be activated through the Account menu on the system.
The software is described in “Prediction of mutant mRNA splice isoforms by information theory-based exon definition,” by Eliseos Mucaki, Ben Shirley and Peter Rogan, published in Human Mutation in 2013. The paper (link) was highlighted by the journal editors and is open access.
Since this publication, information models for 10 splicing regulatory binding factors have been added to ASSEDA and incorporated in the computation of exon definition. The next release of the software will filter results to relevant mutations, by limiting results to expressed factors and target genes based on tissue of origin.
Paid subscribers, including renewals, include laboratories at:
- North York General Hospital (Ontario, Canada)
- Trillium Health Care (Ontario, Canada)
- INSERM (Paris, France)
- Beijing Tongren Hospital (Beijing, PRC)
- Tulane University (New Orleans, USA)
- Johns Hopkins Medical Institutions (Baltimore, USA)
- Università di Milano-Bicocca (Monza, Italy)
- The University of Hong Kong (Special Administrative Region, PRC)
- University of Washington Health Sciences Center (Seattle, WA)
- GSTS Pathology (London, England)
- University of Padua (Padova, Italy)
- University of Western Ontario (Ontario, Canada)
- Università di Modena e Reggio Emilia (Modena, Italy)
Resources: Video Tutorial on the use of the ASSEDA server on You Tube.
ASSEDA is is covered by US Patent 5867402 and patents pending.
- February 27, 2017. CytoGnomix finalizes contract with Government of Canada
- Jan. 28, 2017. New version of F1000Research paper on chemotherapy response in breast cancer
- January 25, 2017. Comment from the Transforming Genetic Medicine Initiative Blog
- Jan. 23, 2017. Automated interpretation of digital pathology images is currently at an embryonic stage of development