SpliceCore: A cloud-based platform to detect, quantify and interpret alternative splicing variation from next-generation sequencing data.

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 DESCRIPTION (provided by applicant): This Small Business Innovation Research (SBIR) Phase I project will yield the first prototype of SpliceCore, a cloud-based resource for the discovery, analysis and interpretation of Alternative Splicing (AS) from RNA-seq data. 15% of all known diseases are triggered by defects in AS, an mRNA maturation process that conveys functional diversity to genes. Defective AS is treatable by small molecules and RNA therapeutic compounds, some of which are currently in clinical trials. SpliceCore will discover new drug targets and biomarkers by extracting disease-relevant AS events from RNA-seq data. The SpliceCore suite combines three algorithms developed and validated at Cold Spring Harbor Laboratory (CSHL): SpliceTrap, for the detection of AS profiles; SpliceDuo, for the identification of significant AS variation; and SpliceImpact, for the prioritization of biologically relevant AS events with therapeutic potential. We are currently applying these algorithms at CSHL for the discovery of AS events causative of Breast Cancer and to study the role of AS in the mechanism of the Spinal Muscular Atrophy disease. The Transcriptomics market was valued at $1.7 billion in 2013 and it is expected to reach $3.7 billion by 2019 at a CAGR of 13.7% from 2014 to 2019. RNA-seq data is quickly accumulating in public repositories such as The Cancer Genome Atlas (TCGA), Geuvadis and the ENCODE project. It is expected that the number of pre-clinical studies involving AS profiling will increase as a result of the reduced costs of Next Generation Sequencing and the early success of RNA therapeutics. SpliceCore will reduce the cost, time and complexity associated with AS analysis. To deliver a commercial prototype, it is necessary to anticipate the demands of multiple users operating simultaneously in a cloud-based environment. Our objective for this project is to investigate cost-effective computing strategies that comply with user-tailored specifications. Therefore our aims are (1) to develop data processing methods and predictive heuristics that increase computing performance while reducing cloud expenditures; (2) to increase detection sensitivity by enabling the discovery of novel AS, and use this new capacity to generate a database for cancer-specific AS events; and (3) Improve SpliceImpact biological interpretation by developing human-computer interaction through object recognition and new quantitative metrics that capitalize on "omics" datasets. There is a great challenge in the market in making cost- effective, fast and robust data analysis with experimentally testable solutions which Envisagenics innovative technology could relief. Envisagenics has a tremendous opportunity due to the increased demand for AS analysis in the biomedical sector, reinforced by new high-throughput capabilities and promising clinical trials. This work is a close collaboration with one of the leading bioinformaticians in the AS field, Dr. Gunnar Rätsch Associate Member at Memorial Sloan-Kettering Institute for Cancer Research, expert in computational methods for the analysis of big biomedical data and the renown scientists Dr. Adrian Krainer, Professor at Cold Spring Harbor Laboratory, who has deciphered much of the AS mechanism and its implications to Cancer and other genetic disorders.