Atharv Ved
Built a computational framework for predicting safe, high-specificity CRISPR-Cas9 guide RNAs targeting cardiac remodelling genes — accepted at IEEE ICPCSN 2026.
Where Atharv Started
His Background
- • High school student passionate about biology and computation
- • Interest in genomics, genetic diseases, and molecular therapeutics
- • Self-taught in Python and bioinformatics tools
- • No prior peer-reviewed research experience
His Goals
- • Publish original research at an IEEE conference
- • Work on something with real clinical impact
- • Bridge computational methods with molecular biology
- • Build a standout profile for college applications
The Problem He Wanted to Solve
Pathological cardiac remodelling is a primary precursor to heart failure. Conventional therapies treat symptoms, not the root genetic drivers. CRISPR-Cas9 gene editing offers a direct way to reprogram disease at its source — but its clinical use is limited by off-target mutations and unpredictable guide RNA efficiency. Atharv set out to build a computational pipeline that could predict safer, more specific CRISPR targets for cardiac disease.
The Research
Working with YRI mentors, Atharv developed a computational framework for CRISPR target prediction specifically optimized for cardiac remodelling genes — MYH7, ACTC1, TNNT2, and NPPA. By integrating biochemical sequence features with thermodynamic modelling of RNA-DNA hybridization, he systematically evaluated candidate guide RNAs for both editing efficiency and off-target safety.
AI-based Computational Pipelines for Genomic Bioinformatics and Precision Medicine Applications in Modern Healthcare Systems
CRISPR-Cas9 gene editing is limited by off-target mutations and variable guide RNA efficiency, blocking clinical translation for heart failure therapies
Retrieved cardiac remodelling gene sequences from NCBI GenBank, scanned for SpCas9 PAM sites using Biopython, and scored candidate guides via BLAST-based off-target profiling
MYH7, ACTC1, TNNT2, NPPA — key sarcomeric and regulatory drivers of hypertrophic cardiomyopathy
gRNA2 (targeting MYH7) identified as strongest clinical candidate — highest specificity score, lowest off-target burden, ~100% editing efficiency
Efficiency vs. Specificity Tradeoff
Atharv's key insight: high editing efficiency alone isn't enough. All three tested guide RNAs hit ~100% efficiency, but their safety profiles diverged sharply. By combining BLAST alignment, mismatch heatmapping, and specificity scoring, he filtered out high-risk candidates that traditional efficiency-only pipelines would have approved — a critical safety check for any future clinical translation.
Genes Analyzed
gRNAs Evaluated
Editing Efficiency
Off-Target Sites Mapped
The Outcome
Accepted for Presentation at the 6th International Conference on Pervasive Computing and Social Networking
IEEE ICPCSN 2026, Salem
May 6–8, 2026
ICPCSN2026 / IEEE / GP-114
15-minute oral presentation + Q&A
No research experience, interested in genomics and computational biology but no clear project or mentor
Built a CRISPR target prediction pipeline for cardiac disease, accepted at an IEEE international conference as a high schooler
The Bigger Picture
People worldwide living with heart failure — conventional therapies only treat symptoms, not root genetic causes
Primary gene driver of hypertrophic cardiomyopathy — Atharv's pipeline identified the safest CRISPR guide for editing it
Published and presenting at an international IEEE conference as a high school student
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