Exploring biological data programmatically with Python & Biopython

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🧬 Python Bioinformatics – Academic Practice Repository

This repository contains Python and Biopython programs developed as part of my first-semester undergraduate coursework for the subject Introduction to Biological Data / Biological Systems.

The focus of this repository is on fundamental bioinformatics tasks implemented using Python, with an emphasis on biological sequence analysis, data handling, and visualization.

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📘 Academic Context

• Course: Introduction to Biological Data / Biological Systems
• Level: Undergraduate (First Semester)
• Purpose: To gain hands-on experience in applying programming concepts to biological sequence data and basic bioinformatics workflows.

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🧬 Topics Covered

🔹 Biological Sequence Analysis (Python)

• Reverse complement of DNA sequences
• DNA → RNA transcription
• RNA → Protein translation
• Counting individual nucleotides (A, T, G, C)
• Percentage composition of nucleotides
• Processing sequences from input files

🔹 Biopython

• Sequence manipulation using Biopython
• Reading biological sequences from files
• Performing transcription and translation using Biopython utilities
• Counting number of sequences in an input file
• Nucleotide frequency analysis across multiple sequences

🔹 Data Handling & Analysis

• Reading sequence data from text and FASTA files
• Working with tabular data using pandas
• Organizing biological data for downstream analysis

🔹 Visualization

• Line plots for biological data
• Scatter plots for sequence-related analysis
• Basic visualization using Matplotlib

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🛠️ Tools & Libraries Used

• Python 3
• Biopython
• pandas
• NumPy
• Matplotlib
• RDKIT

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📁 Repository Structure

The full directory structure is shown below:

Python-BioInformatics/
├── sequences/
│   ├── manual-transcription.py
│   ├── transcription-biopython.py
│   ├── fasta-to-dna-mrna-protein-sequences.py
│   ├── fasta-to-protein-sequence.py
│   ├── file-translation-to-stop-at-codon.py
│   ├── stop-at-logic-for-translation.py
│   ├── central-dogma-of-biology.py
│   ├── translation-to-stop-at-codon.py
│   ├── manual-translation.py
│   ├── purine-pirimidine-translation.py
│
├── complements/
│   ├── complement-and-reverse_complement.py
│   ├── complement-reverse_complement.py
│   ├── complement-reverse_complement-from-sequence.py
│
├── statistics/
│   ├── length-of-sequence.py
│   ├── nucleotide-count-from-file.py
│   ├── nucleotide-count-and-length-of-sequence.py
│   ├── nucleotide-count-from-file-plot.py
│
├── file_handling/
│   ├── sequence-conversions-from-file.py
│   ├── multiple-files-to-sequeces.py
│
├── medical_imaging/
│   ├── displaying-dicom-file.py
│   ├── reading-info-from-dicom-file.py
│
├── cheminformatics/
│   ├── smiles-to-png-with-atom-numbers.py
│   ├── Smiles2image-using-RDKIT.py
│   ├── descriptors-from-smiles.py
│   ├── smiles2png-with-atomnumbers.py
│   ├── smiles-to-pdb-hydrogen.py
│   ├── smiles-to-descriptors-RDKIT.py
│   ├── smiles-to-morganfingerprint.py
│   ├── smile-descriptors-to-aromaticity.py
│   ├── smiles-to-PDB.py
│   ├── descriptors-from-smiles-as-a-file.py
│   ├── descriptors-from-smiles-with-atoms-aromaticity.py

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📄 Script Descriptions

📂 sequences/

| File | Description |
|------|-------------|
| manual-transcription.py | Performs DNA → RNA transcription manually by replacing T → U. |
| transcription-biopython.py | Uses Biopython to transcribe DNA sequences into RNA. |
| fasta-to-dna-mrna-protein-sequences.py | Reads FASTA and generates DNA, mRNA, and protein sequences. |
| fasta-to-protein-sequence.py | Converts FASTA DNA sequences directly into protein. |
| file-translation-to-stop-at-codon.py | Translates DNA sequences but stops when encountering a STOP codon. |
| stop-at-logic-for-translation.py | Demonstrates algorithmic logic for STOP-aware translation. |
| central-dogma-of-biology.py | Complete DNA → RNA → Protein transformation. |
| translation-to-stop-at-codon.py | Translates sequences until the first STOP codon. |
| manual-translation.py | Manually maps codons to amino acids without external libraries. |
| purine-pirimidine-translation.py | Identifies purines (A,G) and pyrimidines (C,T,U) in sequences. |

📂 complements/

| File | Description |
|------|-------------|
| complement-and-reverse_complement.py | Generates DNA complement and reverse complement. |
| complement-reverse_complement.py | Alternative method for generating complement strands. |
| complement-reverse_complement-from-sequence.py | Takes a user-provided sequence and returns complement + reverse complement. |

📂 statistics/

| File | Description |
|------|-------------|
| length-of-sequence.py | Calculates length of a nucleotide sequence. |
| nucleotide-count-from-file.py | Reads sequence from file and counts A, T, C, G. |
| nucleotide-count-and-length-of-sequence.py | Outputs both nucleotide frequency and length. |
| nucleotide-count-from-file-plot.py | Generates a plotted visualization of nucleotide counts. |

📂 file_handling/

| File | Description |
|------|-------------|
| sequence-conversions-from-file.py | Reads DNA file and converts it to RNA and protein. |
| multiple-files-to-sequeces.py | Loads multiple sequence files and extracts sequences. |

📂 medical_imaging/

| File | Description |
|------|-------------|
| displaying-dicom-file.py | Displays medical DICOM images. |
| reading-info-from-dicom-file.py | Extracts and prints metadata from DICOM files. |

📂 cheminformatics/

| File | Description |
|------|-------------|
| smiles-to-png-with-atom-numbers.py | Converts SMILES to PNG with atom numbers labeled. |
| Smiles2image-using-RDKIT.py | Generates molecular images using RDKit. |
| descriptors-from-smiles.py | Extracts basic molecular descriptors from SMILES. |
| smiles2png-with-atomnumbers.py | Additional SMILES-to-image tool with atom indices. |
| smiles-to-pdb-hydrogen.py | Converts SMILES to PDB and adds hydrogens. |
| smiles-to-descriptors-RDKIT.py | Generates descriptor values using RDKit utilities. |
| smiles-to-morganfingerprint.py | Produces Morgan (circular) fingerprints. |
| smile-descriptors-to-aromaticity.py | Calculates aromaticity-related descriptors. |
| smiles-to-PDB.py | Converts SMILES to a PDB structure. |
| descriptors-from-smiles-as-a-file.py | Reads multiple SMILES from file and generates descriptors. |
| descriptors-from-smiles-with-atoms-aromaticity.py | Computes descriptors + atom-level aromaticity features. |

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🎯 Learning Outcomes

Through this coursework and practice, I developed:

• A strong foundation in biological sequence representation
• Practical experience using Biopython for sequence analysis
• Confidence in handling biological data programmatically
• Basic skills in visualizing biological datasets
• An interdisciplinary understanding of programming applied to life sciences

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📌 Notes

• This repository represents academic learning and practice, not a production-level bioinformatics pipeline.
• Code is written with a focus on clarity and understanding.
• The repository may be extended in the future with advanced bioinformatics or machine learning–based analyses.

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📄 Reference

This work is based on material covered during the course Introduction to Biological Data / Biological Systems. Reference material is not publicly included to respect academic and copyright boundaries.

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📜 License

This project is licensed under the MIT License.

Copyright (c) 2026
Krish Singh (github.com/wasitkrish)

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