DSA_Case_Study: Genomic Variant Processing System (Creative Conceptual Theme)

Project Overview

This project demonstrates how multiple data structures work together to manage genomic variant data. Primary focus: Data Structures (Bloom Filter, Priority Queue, Hash Table, BST) and their role in the pipeline. UI is intentionally omitted to emphasize DSA concepts.

Repository Structure

• data_structures.py -> implementations of BloomFilter, PriorityQueue, VariantMetadata, BST
• genomic_system.py -> integration and persistence logic (uses JSON files in /data)
• test_cases.py -> automated test cases that demonstrate behavior
• data/ -> initial JSON files (known_variants.json, metadata.json, processed_cases.json)
• presentation.pptx -> Creative conceptual PPT (not auto-generated)
• DSA_Case_Study.pdf -> Detailed report (not auto-generated)

How to run

1. Ensure you are in the repository root (where these files are located)
2. Run tests:

python3 test_cases.py

3. Or run interactive (import GenomicSystem in a REPL):

python3
>>> from genomic_system import GenomicSystem
>>> s = GenomicSystem()
>>> s.add_case(1, 'P1', 'VAR_1_mut', 1500)
>>> s.save_all()

• Emphasis is on correct usage of data structures, their reasoning and complexity
• BloomFilter: O(k) insertion/check (k = number of hashes), memory efficient
• PriorityQueue: O(log n) insert/pop
• Hash Table: O(1) average lookup/insert
• BST (unbalanced): average O(log n) insert/search for random data; worst-case O(n)

Team contributions suggestion

• Karthikeya: Bloom Filter + explanation slides
• Sairam: Priority Queue + test cases
• Mouliiswar: Hash Table + JSON persistence
• Yaswanth: BST + range query demonstration + README