• Science of RNA Splicing Therapeutics

  • RNA Splicing

    What is RNA splicing & and how do we use it for drug discovery?

    What is Alternative Splicing in RNA?

    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 isoforms from the 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

    • It is predicted that 15% of all diseases are caused by disrupted splicing.
    • 50% of rare genetic disorders described in literature are caused by alternative splicing errors.
    • 50% of synonymous cancer-driver mutations are known to impair alternative splicing.
    • Alternative splicing defects can be corrected with small molecules or RNA therapeutics such as Nusinersen, a drug developed at Cold Spring Harbor Laboratory and approved for the treatment of Spinal Muscular Atrophy (SMA) by the FDA in 2016.
    • Envisagenics' SpliceCore accelerates the discovery of druggable splicing events.

    Typical Splicing Analysis

    The previous methodology for alternative splicing analysis

    • 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

    • Envisagenics software is more accurate. It does not depend on coverage. Every splicing event is treated as a Bayesian inference problem.
    • Envisagenics makes your cost-per-read worth more. Our software uses both splice junction and exon body reads, saving 50%-70% of data that would have been discarded using previous methods.
    • Envisagenics focuses on splicing. By focusing analysis on splicing heterogeneity, we reduce the complexity of  transcriptome analysis, favoring the discovery of exon candidates that can work as drug targets or biomarkers.
    • Envisagenics' solution reaches the target. We use machine learning to predict disease-causing alternative splicing, drug targets and biomarkers.

    Predictive Analytics for Splicing Analysis

    Envisagenics' novel approach for prioritizing drug targets and biomarkers

    • We have developed a machine learning algorithm trained with a variety of features from public and proprietary data.
    • We built a predictive model based on known splicing functional outcomes.
    • Splicing events from proprietary partner data are tested for the likelihood of producing dysfunctional proteins.
  • Publications

    Genes & Development 2016. 30: 34-51

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

    Genome Biology 2015 16:119

    Our method to predict protein-protein interactions between spliceosomal proteins.

    Genome Biology 2015 16: 135

    An exclusive research highlight on our recent publication.

    Molecular Cell 2015 1: 105-117

    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.

    Bioinformatics 2011 21: 3010–3016

    Here we introduce SpliceTrap, a method to quantify exon inclusion levels using paired-end RNA-seq data. Unlike other tools, which focus on full-length transcript isoforms, SpliceTrap approaches the expression-level estimation of each exon as an independent Bayesian inference problem. In addition, SpliceTrap can identify major classes of alternative splicing events under a single cellular condition, without requiring a background set of reads to estimate relative splicing changes. We tested SpliceTrap both by simulation and real data analysis, and compared it to state-of-the-art tools for transcript quantification. SpliceTrap demonstrated improved accuracy, robustness and reliability in quantifying exon-inclusion ratios.