SBIR-STTR Award

?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.

Public Health Relevance Statement:


Public Health Relevance:
At least 15% of all human diseases are triggered by structural changes of the mRNA, known as alternative splicing (AS). The goal of this project is to develop a preliminary cost-effective and robust computer program for the analysis of AS using biomedical Big Data. This cloud-based bioinformatics software will help biomedical researchers worldwide in the development of new therapies and diagnostics. Drug target screenings are increasingly focusing on AS, motivated by recent success in clinical trials and the introduction of Next Generation Sequencing technologies such as RNA-seq. Envisagenics' platform will fill the gap between basic science discoveries and therapeutic solutions through comprehensive AS analysis for Cancer and rare genetic diseases. By the completion of this project a prototype of SpliceCore will be ready to enter the commercialization phase.

NIH Spending Category:
Bioengineering; Biotechnology; Cancer; Genetics; Human Genome; Networking and Information Technology R&D

Project Terms:
Algorithms; Alternative Splicing; anticancer research; Basic Science; Big Data; Bioinformatics; Biological; Biological Markers; Businesses; cancer genetics; cancer therapy; Clinical Trials; clinically relevant; cloud based; Code; Collaborations; commercialization; computer human interaction; Computer Programs and Programming; Computer software; computerized data processing; Computing Methodologies; cost; cost effective; Coupled; Data; Data Analyses; Data Set; Databases; Decision Making; Defect; design; Detection; Development; Diagnostic; Disease; drug discovery; Drug Targeting; Environment; Event; Exons; Expenditure; flexibility; Genes; Goals; Hereditary Disease; heuristics; High-Throughput RNA Sequencing; Hour; human disease; improved; innovative technologies; Institutes; Laboratories; Language; laptop; Literature; malignant breast neoplasm; Malignant Neoplasms; Marketing; member; Messenger RNA; Methods; Modeling; Modification; Mutation; Names; next generation sequencing; Normal tissue morphology; novel; Nucleotides; object recognition; Oncogenes; Other Genetics; Pathway interactions; Performance; Phase; preclinical study; Prevalence; Process; professor; programs; protein structure function; prototype; public health relevance; repository; Research Personnel; Resources; Reverse Transcriptase Polymerase Chain Reaction; RNA; RNA Sequence Analysis; RNA Sequences; Role; Running; Sampling; Scientist; Services; Small Business Innovation Research Grant; small molecule; Small RNA; Solutions; Speed (motion); Spinal Muscular Atrophy; success; Technology; Testing; The Cancer Genome Atlas; Therapeutic; Time; transcriptome sequencing; transcriptomics; Variant; Work

Over 30 million people in the US suffer from genetic diseases or cancers caused by mutations of which ~15% disrupt the regulation of splicing. Alternative splicing (AS) errors have been reported in literature to drive 370 genetic diseases out of ~800 described to date. In addition, due to the recent success of FDA-approved splicing modulators like Nusinersen, along with fascinating pre- clinical results underlining the importance of AS as therapeutic targets; splicing research has become of major interest to pharmaceutical companies. Envisagenics is developing SpliceCoreTM, an innovative cloud-based software platform using biomedical big data for AS analysis to discover new therapies and biomarkers for complex diseases. Our breakthrough platform combines algorithms and databases developed and experimentally validated at Cold Spring Harbor Laboratory (CSHL): SpliceTrapTM, for the detection of splicing activity using RNA-seq data; SpliceDuoTM, for the identification of significant splicing variation across biological samples; SpliceImpact2TM, for the prioritization of biologically relevant AS variants with therapeutic potential; and TXdbTM, a splicing isoform database that connects client?s proprietary data to public repositories such as the Cancer Genome Atlas (TCGA). Thanks to the Phase I award SpliceCore was adapted as a cloud-based software, accelerating scalability and adaptation to the fast- evolving market of biomedical Big Data. We now have deployed SpliceCore?s back-end on three cloud-service providers, increased its overall run-time by a factor of 12, developed tools to discover disease-specific AS isoforms, finalized and tested a machine-learning algorithm to predict the biological impact of AS, and experimentally validated some of our new predictions with a success rate of 82.5%. The goal for Phase II is to accelerate client acquisition through the development of user-interactive applications informed from client?s feedback by substantially expanding the platform?s knowledgebase and predictive functions with novel AS isoforms extracted from ~37,000 public datasets. Thus, a new version of SpliceCore will be developed to predict regulatory interactions between RNA-binding proteins and their RNA targets to assist in the interpretation of aberrant splicing factors through a collaboration with world renowned HHMI Professor Dr. Tom Tuschl from Rockefeller University and developer of Nusinersen, Professor Dr. Adrian Krainer from CSHL. Envisagenics is targeting the global bioinformatics market valued at $4 billion in 2014 with a CAGR of over 21%. SpliceCore could capture ~10% of the market, identify novel drug targets, and design RNA therapeutics from aberrant splicing events prevalent in cancer and a multitude of genetic diseases while increasing the efficiency of R&D in biopharma.

Thesaurus Terms:
Achievement; Advanced Development; Affect; Algorithms; Alternative Splicing; Amyotrophic Lateral Sclerosis; Award; Back; Basic Science; Big Biomedical Data; Big Data; Bioinformatics; Biological; Biological Markers; Biological Process; Biological Products; Cancer Etiology; Cancer Genetics; Case Control; Client; Cloud Based; Cloud Computing; Cloud Service; Collaborations; Commercial Application; Complex; Computer Simulation; Computer Software; Computerized; Cost; Crosslink; Data; Data Mining; Data Set; Data Warehouse; Databases; Defect; Design; Detection; Development; Disease; Drops; Drug Discovery; Drug Targeting; Dysmyelopoietic Syndromes; Ensure; Event; Experimental Study; Face; Failure; Fascinate; Fda Approved; Feedback; Flexibility; Food And Drug Administration Drug Approval; Frequencies; Gene Product; Genetic Diseases; Genotype-Tissue Expression Project; Goals; Growth; Human Disease; Imagery; Immunoprecipitation; Improved; Innovation; Interest; Knowledge Base; Laboratories; Learning Strategy; Literature; Machine Learning; Malignant Neoplasms; Manuals; Maps; Memorial Sloan-Kettering Cancer Center; Meta-Analysis; Methods; Mutation; New Therapeutic Target; Novel; Novel Therapeutics; Nucleotides; Pathway Interactions; Patients; Performance; Petabyte; Pharmacologic Substance; Phase; Pre-Clinical; Preclinical Study; Predictive Analytics; Predictive Modeling; Prevalence; Price; Privatization; Probability; Professor; Protein Binding Domain; Protein Isoforms; Protein Splicing; Regulation; Reporting; Repository; Research; Research And Development; Research Infrastructure; Research Personnel; Ribonucleosides; Risk; Rna; Rna Splicing; Rna-Binding Protein Fus; Rna-Binding Proteins; Running; Sampling; Secure; Service Providers; Services; Small Business Innovation Research Grant; Specificity; Spinal Muscular Atrophy; Srsf2 Gene; Structure; Success; System; System Architecture; Targeted Biomarker; Technology; Testing; The Cancer Genome Atlas; Therapeutic; Therapeutic Rna; Therapeutic Target; Time; Tissues; Tool; Transcriptome Sequencing; Translating; Universities; User-Friendly; Validation; Variant; Work;