HELIX Genomic Sequencing Engine 🧬

HELIX is a research-grade, algorithmic pipeline and web dashboard designed for genomic sequencing, assembly, and analysis. It combines over 20 classical and advanced computer science algorithms to simulate the end-to-end process of reading, assembling, aligning, and analyzing DNA sequences.

Unlike basic sequence simulators, HELIX utilizes a suite of dynamic programming, backtracking, greedy algorithms, and graph theory techniques to handle complex biological simulations—such as tumor aneuploidy, sex determination, and primer placement.

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🌟 Key Features

1. Advanced Assembly & Alignment

• Eulerian vs Hamiltonian Assembly: Demonstrates the performance shift from NP-Complete (Hamiltonian Path via Backtracking) to Linear Time $O(V+E)$ (Eulerian Path via De Bruijn Graphs) when assembling shredded k-mer reads.
• Smith-Waterman Alignment: Uses dynamic programming to identify exact local alignments and accurately flag mutations (Substitutions, Insertions, Deletions, Frameshifts).

2. Biological Simulation & Intelligence

• Tumor Aneuploidy Simulation: Uses k-Color Graph Coloring to phase haplotypes and determine copy number variations (e.g., detecting 3N or 4N karyotypes in simulated cancer cells).
• Sex Determination Consensus: Aggregates three independent algorithms (Coverage ratio, SRY gene detection, and Heterozygosity rates) to confidently predict the biological sex of the sample.
• Gene-Level Tracking: Tracks the status of critical clinical markers (e.g., TP53, BRCA1, KRAS), especially when cancer simulation is toggled.

3. Resource Optimization & Efficiency

• Huffman Compression: Compresses standard A/T/G/C streams based on frequency, saving disk space dynamically (often achieving ~20-30% savings).
• 0/1 Knapsack Read Selection: Discards low-quality overlapping reads intelligently within a strict RAM budget, optimizing for quality vs memory footprint.
• Job Sequencing with Deadlines: Prioritizes the sequencing of high-value clinical genes (like BRCA1) over intergenic "junk" regions using weighted profit metrics.

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🛠️ Tech Stack

• Backend Engine: Python 3, FastAPI, Uvicorn
• Frontend Dashboard: React, TypeScript, Vite, Tailwind CSS
• Design Pattern: Glassmorphism UI with real-time "Algorithm Explorer" and "Intelligence" visualizers.

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🚀 Quick Start Guide

Prerequisites

Make sure you have Node.js (for the React frontend) and Python 3.8+ installed.

1. Start the Backend API

Navigate to the root directory and install the required dependencies (if you haven't already):

pip install fastapi uvicorn pydantic

Run the FastApi server:

python api.py

(The server will start on http://localhost:8000)

2. Start the Frontend Dashboard

Open a new terminal window, navigate to the frontend folder, and run the Vite dev server:

cd frontend
npm install
npm run dev

(The frontend will be available at http://localhost:5173)

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🧪 How to Use the Dashboard

1. Configure Parameters: On the left panel, select your target species (e.g., Human, Mouse, SARS-CoV-2) or input your own custom DNA sequence.
2. Adjust Simulation Specs: Tweak the genome length, sequencing coverage depth, and expected mutation count. Toggle "Simulate Cancer/Tumor" to observe aneuploidy and targeted gene mutations.
3. Launch the Pipeline: Click Launch HELIX Pipeline. The frontend will fetch data from the Python backend and instantly populate the results.
4. Explore the Tabs:
   • Overview: View the reconstructed genome sequence and the Boy/Girl consensus results.
   • Intelligence: View research-grade assembly metrics (N50, L50, GC Content), deep mutation analytics (SNPs vs INDELs), and human-readable genomic insights.
   • Algorithm Explorer: Click on individual algorithms (like De Bruijn Graphs or Huffman Compression) to see exactly how your specific DNA data was mathematically processed.
   • Comparisons: View the "Anytime Algorithm" charts, comparing Greedy, DP, and B&B time complexities.
   • Advanced: View specific outputs for the Reliability DP, Job Sequencing algorithms, and N-Queens primer placement.

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🧠 Core Algorithms Implemented

Category	Algorithm	Purpose in HELIX
Graphs	De Bruijn Graph	Reconstructing DNA sequences via Eulerian paths.
Graphs	K-Coloring	Phasing haplotypes and estimating aneuploidy.
Dynamic Programming	Smith-Waterman	Optimal local sequence alignment.
Dynamic Programming	0/1 Knapsack	Selecting the highest quality reads within a RAM budget.
Greedy	Huffman Coding	Compressing output DNA text efficiently.
Greedy	Job Sequencing	Prioritizing clinical cancer genes for processing.
Backtracking	N-Queens	Placing primers cleanly without overlapping repeat regions.
Backtracking	Hamiltonian Path	Used purely to demonstrate the inefficiency of NP-Complete approaches vs Eulerian paths.

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👨‍💻 Developer Notes

If you are modifying the Python engine (helix_main.py), note that api.py does not hot-reload by default. If you make changes to the backend algorithms, you must restart the python api.py terminal process for the frontend to receive the new data.