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 to facilitate the development of 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 extracts 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 SpliceCore Works
RNA-seq data analysis and interpretation in the cloud
Identify the best drug targets and biomarkers using RNA-seq
- Integrate your data to public data through TXdb, our transcriptomic reference.
- Build high-resolution transcriptomic maps to cover all possible splicing isoforms.
- Identify specific isoforms differentially expressed in case conditions (e.g. disease).
- Interrogate your results against public and proprietary database through a predictive analytics step to prioritize therapeutically relevant RNA isoforms.
Splicing & Disease
Splicing Analysis unlocks vast untapped potential for drug target discovery
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 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 AS errors.
- 50% of synonymous cancer-driver mutations are known to impair AS.
- Alternative splicing defects can be corrected with small molecules or RNA therapeutics such as Nusinersen, which was developed at CSHL and approved for the treatment of Spinal Muscular Atrophy (SMA) by FDA in 2016.
- Envisagenics' SpliceCore accelerates the discovery of druggable splicing events.
TYPICAL SPLICING ANALYSIS
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 "$-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.
Leadership
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
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
-
