• In-Silico RNA Therapeutic and Biomarker Discovery

    Biomedical data might hold the key to finding the cure for currently incurable diseases. Our mission is to reduce the complexity of biomedical data into innovative therapeutic solutions.

     

    We envision a future in which computerized predictive models will lower time and cost of drug development, impacting society with better and affordable treatments

  • SpliceCore

    Our cloud-based software platform to extract biologically relevant RNA isoforms from raw RNA-seq data.

    • Discover new drug targets and biomarkers through splice isoform quantification coupled with predictive-analytics.
    • Prioritize disease-related genes connecting your data to our databases and algorithms.
    • Rapidly advance to experimental validation with a well supported list of targets.

  • How does SpliceCore work?

    RNA-seq data analysis and interpretation in the cloud

    Identify the best drug targets and biomarkers using RNA-seq

    1. Integrate your data to public data through TXdb, our transcriptomic reference.
    2. Build high-resolution transcriptomic maps to cover all possible splicing isoforms.
    3. Identify specific isoforms differentially expressed in case conditions (e.g. disease) .
    4. Interrogate your results against public and proprietary database through a predictive-analytics step to prioritize therapeutically relevant RNA isoforms.
  • Splicing & Disease

    Splicing Analysis is important to develop new drugs

    ALTERNATIVE SPLICING

    a source of biological diversity

    Splicing is the process of removal of non coding introns and assembly of coding exons into mature mRNAs. About 95% of human genes are alternatively spliced, leading to the expression of multiple mRNA form a same gene. While some splicing isoforms encode fully functional proteins, others lead to partially or non functional variants that can cause disease.

    SPLICING AS A DRUG TARGET

    A large number of diseases are caused by splicing defects

    TYPICAL SPLICING ANALYSIS

    This is how alternative splicing is usually analyzed

    • RNA-seq samples are generated from case and control samples.
    • Junction reads (red) and exon body reads (blue)  are mapped to the reference genome.
    • mRNA level is quantified using all reads.
    • Splicing isoforms are reconstructed using junction reads only.
    • Alternative splicing is estimated by a meta-analysis step.
    • Gene enrichment analysis is performed

    LIMITATIONS AND SOLUTIONS FOR SPLICING ANALYSIS

    Envisagenics delivers the best isoform-level analysis of RNA-seq data

    1. Envisagenics software is more accurate. It does not depend on coverage. Every splicing event is treated as a Bayesian inference problem.
    2. Envisagenics makes you $ per read worth more. Our software cna use both splice junction and exon body reads, saving 50%-70% of data otherwise discarded.
    3. Envisagenics focuses on splicing. By doing that we reduce the complexity of  transcriptome analysis, favoring the discovery of exon candidates that can work as drug targets or biomarkers. 
    4. Envisagenics solution reaches the target. We use machine learning to predict disease-causing alternative splicing, drug targets and biomarkers.

    PREDICTIVE-ANALYTICS FOR SPLICING ANALYSIS

    Envisagencis' approach to prioritize drug targets and biomarkers

    • A machine learning algorithm is trained with a variety of features from public and proprietary data.
    • A predictive model is built based on known splicing functional outcomes.
    • Splicing events form the client's data are tested for their likelihood to produce dysfunctional proteins.
  • Leadership

    Who are we?

    Maria Luisa Pineda Ph.D.

    CEO, Co-founder

    Dr. Maria Luisa Pineda is a biologist with over 10 years of research experience. For her undergraduate studies, Maria was awarded an endowment of $2 million dollars from the Goizueta Foundation and an NIH fellowship with the Minority Access to Research Careers (MARC U*STAR) program.

    Maria received her Ph.D. from the prestigious Watson School of Biological Sciences at Cold Spring Harbor Laboratory as an Arnold and Mabel Beckman graduate student and a William Randolph Hearst foundation scholar. After graduating, she acquired investment experience in technology and life-sciences startup companies at Canrock Ventures and Golden Seeds, LLC.

    Martin Akerman, Ph.D.

    CTO, Co-Founder

    Dr. Martin Akerman was a postdoctoral fellow at Cold Spring Harbor Laboratory. He received his PhD in Bioinformatics from Technion - Israel, following his BSc and MSc in Biology also at the Technion. He started research as a cell biologist investigating infectious diseases.

    He later transitioned into bioinformatics and developed new algorithms to analyze and interpret genomic data. Since the beginning of his PhD in 2005, his impact on the scientific community is reflected in over 600 citations in scientific journals. The software developed by Dr. Akerman is used by hundreds of scientists every month and has been experimentally validated in the field of cancer research.

  • Scientific advisors

    Adrian Krainer, Ph.D.

    Professor and Program Chair Cancer & Molecular Biology Cold Spring Harbor Laboratory.

     

    Yaniv Erlich, 

    Ph.D.

    Assistant Professor, Columbia University Core Member New York Genome Center.

     

    Michael Schatz, Ph.D.

    Associate research professor, Johns Hopkins University

    Michael Q. Zhang, Ph.D.

    Professor and Director Center for Systems Biology University of Texas at Dallas Professor, Tsinghua University.
     

  • News & press

    Breakout labs funds Envisagenics with $350K

    MedCityNews

    Envisagenics at Grand Central Tech 2017 class

    Business Insider

    2015 Innovator Of The Year Winners

    Innovate LI

    East Coast Biotech Roundup: Editas, Checkmate, Ovid & More

    eXome, Xconomny

    Envisagenics Lands $225K NIH Award

    InnovateLI

    LI biotech startup receives $225,000 grant from NIH

    Newsday

    Startup Envisagenics Nabs NIH Grant to Develop Alternative Splicing Analysis Tech

    Genome Web

    Accelerate Long Island and the Long Island Emerging Technologies Fund Announce $100,000 Investment in Envisagenics

    Street Insider

    Accelerate Long Island and the Long Island Emerging Technologies Fund Announce $100,000 Investment in Envisagenics

    Accelerate Long Island

    Accelerate LI, Eyeing New Seed Fund, Debuts Ninth Grad: Envisagenics

    Xconomy

    Envisagenics snags $100K

    Long Island Business News

    Tech startup Envisagenics receives $100,000 in funding

    Newsday

    Envisagenics Gets $100K In Seed Funding

    Innovate LI

    Helping Big Pharma Master Big Data

    Innovate LI

    Job fairs, beyond résumés

    Long Island Business News

    Long Island Technology Startups 2015

    Accelerate Long Island

    Building Long Island's Tech Industry

    WLIW21

  • Scientific publications

    Differentiation of mammary tumors and reduction in metastasis upon Malat1lncRNA loss

    Genes & Development

    Application of SpliceCore algorithms to investigate alternative splicing implications in MALAT1-dependent breast cancer

    Differential connectivity of splicing activators and repressors to the human spliceosome

     

    Genome Biology

    Our method to predict  protein-protein interactions between spliceosomal proteins

    Interactome analysis brings splicing into focus

     

    Genome Biology

    Exclusive research highlight on our recent publication

    SRSF1-regulated alternative splicing in breast cancer

    Molecular Cell

    over a hundred experimental validations for SpliceCore algorithms reveal the regulatory network of the SRSF1 oncogene. The CASC4 gene is predicted and confirmed to be involved in cancer progression

    SpliceTrap: a method to quantify alternative splicing under single cellular conditions

    Bioinformatics

    SpliceTrap, our core algorithm for alternative splicing quantification

  • Investors & Partners

  • Request SpliceTrap download link

    The use of SpliceTrap by academic users (academic institutions and non-profit organizations) requires the acceptance of Envisagenics standard end user license agreement and it is limited to internal research and development activities aimed at the production of scientific knowledge or teaching. It excludes any research activity by academic users sponsored by for-profit organizations and for any commercial use, whether it is to supply services to third parties or to sell information, data or any product of commercial value. Users desiring to use, copy, modify and/or incorporate Envisagenics' software into additional products or use it for commercial purposes require a partnership agreement.

    Please contact Martin Akerman at [email protected]

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