Mitochondrial DNA Analysis Tool

A Python-based tool for analyzing mitochondrial DNA sequences, implementing sequence alignment, motif searching, and statistical analysis.

Table of Contents

1. Project Overview
2. System Design
3. Installation
4. Usage
5. Examples
6. Project Structure
7. Design Documentation

Project Overview

This tool provides functionality for:

• FASTA file parsing and data management
• Mitochondrial DNA sequence analysis
• Motif searching and pattern matching
• Global and local sequence alignment
• GC content analysis and visualization

System Design

CRC Cards

MitochondrialDNA

• Class: MitochondrialDNA
• Responsibilities:
  • Store DNA sequence data
  • Calculate sequence properties
  • Extract subsequences
• Collaborators: none

MotifFinder

• Class: MotifFinder
• Responsibilities:
  • Search for patterns in sequences
  • Count motif occurrences
• Collaborators: MitochondrialDNA

SequenceAlignment

• Class: SequenceAlignment
• Responsibilities:
  • Perform sequence alignments
  • Calculate alignment scores
• Collaborators: MitochondrialDNA

UML Diagram

classDiagram
    class MitochondrialDNA {
        +str seq
        +str ID
        +str description
        +get_subsequence(start, end)
        +get_GC_content()
        +get_length()
    }
    
    class MotifFinder {
        +str motif_seq
        +count_occurrences(target_seq)
        +search_motif(target_seq)
    }
    
    class SequenceAlignment {
        +str seq1
        +str seq2
        +align_sequences()
        +get_alignment_scores()
    }

    MotifFinder ..> MitochondrialDNA : uses
    SequenceAlignment ..> MitochondrialDNA : uses

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Installation

1. Clone the repository:

git clone https://github.com/hades-k/dna-analysis.git
cd dna-analysis

2. Install dependencies:

pip install -r requirements.txt

Usage

Web Interface

1. Start the Flask server:

python app.py

2. Open your browser to http://localhost:5000

3. Upload a FASTA file and use the interface to:

   • View sequence statistics
   • Perform sequence alignments
   • Search for motifs
   • Visualize results

Examples

Input Example (FASTA format)

>NC_000001 Species_1 mitochondrion, complete genome
CCGGCAGGAACTGCTAACACATAATCGGCGTTTTGAAGTTCGCAAGGAGAGTGCCTTCCGCGTTCCCGGCTTCGTATCAA
GATATGGGACAAGGAGGGACTGTGACATTTACATGTTACGACCCCCAGTAGCTTAAATACAACGGTTTAACTACAATAGT
CGGCCGGGCAGGGTTCGGATAGGTTTACAACTACTTTTCAACATTCTCGTGATTACTAAAGCAGTCAGGCAGAAGTGATC
GAAGCGCTCTTAGATAGTGCGCCAGACCCGCTGAGCCCGCGGCATACTAGCAGTGAACCACGTTAGTACTGCTTGTATCG
ATTCGATACCCCTGAGGCCCGGACAATCTTCAGCCTTTCATAGAGAGTAAGTCTCATTTGAATTATAAACCTCGTTTATC
CGTATCCCCTGGTCCTTTAGACCCCTGTTAAGTCTCCGGCTCGTTAGCTAGCTCGTAAGTTACTGTATTACTGGGTAGCG
TTGTATAGATTTTTCGTGAGCCGTTTGGCTGCCTAGCCACAGGAACGTCAAGGCGACGGTCCCTGATGATATGTAGAGTT
GTGCTTCAGTGGGGGTCGTGGTTGACCAACAGGACCTCCTCATGCATTACACGGTGTAGTAGAAACAGTTAAAAGGTTTA
TTCAGAAACCCCTTATGGTGGCTGCTTCGCAAGCCAGTGCCCATAGTTCACGTGAGCCTAATAATGGAATATAGGCCTGT
GTGACTATGGCCGGTTCCCTTCTCAAAGGAAGGTATCTACCATCGCCGTTACGTTCACCATAAGTTAACGTAAGGCATGC
GTTCTACTCATTTTCCGCGAGGTAGCATAAGTTTTCTTAATCCACACATTGGACGCACTGGTGGGGTGAGCCAGAACCAC
GCATAGTTATAGGAGCGTGCGATTAAGCCTGCGACGACTCTTCGACCTATAACTCAAATTCATGCCACTGCTATCCAATC
ATTCTCCAATTGCGCAACATGGGATTTTACGCCGATTCTC

Output Examples

1. Sequence Statistics:

Sequence ID: NC_000001
Description: NC_000001 Species_1 mitochondrion, complete genome
Length: 1000
GC Content: 48.2%

2. Motif Search:

Motif: GCT
Count: 14
Positions: 12, 68, 130, 245, 269, 310, 438, 446, 450, 507, 562, 660, 663, 949

3. Alignment Results:

Sequence 1 ID: NC_000001
Sequence 2 ID: NC_000002
Matches: 399
Mismatches: 532
Gaps: 138
Total Length: 1069

Project Structure

.
├── app.py                # Flask web application
├── Part 3.py             # Analysis module
├── models.py             # Core DNA analysis classes
├── parser.py             # FASTA file parsing
├── global_alignment_algo.py  # Global sequence alignment
├── local_alignment_algo.py   # Local sequence alignment
├── fm_index_query.py     # Pattern matching
├── templates/            # HTML templates
│   ├── home.html
│   ├── statistics.html
│   ├── alignment.html
│   └── motif_search.html
├── static/              # Static files
└── uploads/             # Uploaded files

Design Documentation

Genomic Element Modeling

1. MitochondrialDNA Class

   • Represents individual mitochondrial DNA sequences
   • Encapsulates sequence data and analysis methods
   • Provides methods for sequence manipulation and analysis

2. MotifFinder Class

   • Implements pattern searching using FM-Index
   • Provides efficient motif searching capabilities
   • Returns both counts and positions of motifs

3. SequenceAlignment Class

   • Implements both global and local alignment algorithms
   • Provides configurable alignment parameters
   • Returns alignment results and scores

Component Interactions

graph TD
    subgraph WebInterface
        Flask[app.py]
        Templates[templates/]
        Static[static/]
    end

    subgraph CoreAnalysis
        Models[models.py]
        subgraph ModelsClasses
            MTDNA[MitochondrialDNA]
            Motif[MotifFinder]
            Align[SequenceAlignment]
        end
    end

    subgraph FileProcessing
        Parser[parser.py]
    end

    subgraph AnalysisAlgorithms
        Global[global_alignment_algo.py]
        Local[local_alignment_algo.py]
        FMIndex[fm_index_query.py]
    end

    subgraph DataStorage
        Uploads[uploads/]
    end

    subgraph AnalysisScript
        Part3[part3.py]
    end

   
    Flask --> Part3
    Models --> Global
    Models --> Local
    Models --> FMIndex
    Part3 --> Parser
    Parser --> Uploads
    Part3 --> Models

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Final note

This project was created for the Advanced Programming course at University of Bologna