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Bioinformatics Genomic Analysis Template

Status: ✅ WORKING | PDF Size: 248KB | Compile Time: ~90s

A specialized LaTeX template for bioinformatics research with live BioPython integration. This template demonstrates genomic data analysis, phylogenetic reconstruction, and sequence analysis techniques using real biological examples.

🧬 Purpose

This template is designed for bioinformatics research papers that need to:

  • Analyze DNA/protein sequences

  • Reconstruct phylogenetic relationships

  • Present genomic annotation results

  • Demonstrate computational biology workflows

  • Include live BioPython code examples

📁 Template Contents

bioinformatics-genomics/
├── main.tex                 # Main LaTeX document
├── Makefile                 # Automated build system
├── references.bib          # Bioinformatics bibliography
├── figures/                # Generated figures directory
└── README.md              # This file

🚀 Quick Start

  1. Open main.tex in CoCalc's LaTeX editor

  2. Enable shell-escape: Settings → ✅ Enable Shell Escape (-shell-escape)

  3. Click "Build" or press Ctrl+Enter

  4. Wait 90-180 seconds for first compilation (installs BioPython, runs analysis)

🔧 Compilation

  • Full build: Click "Build" button

  • Draft mode: Build dropdown → "Build with Draft"

Using Terminal

# Full compilation with BioPython analysis
make all

# Quick draft (no code execution)
make draft

# Clean generated files
make clean

🧪 What This Template Demonstrates

Sequence Analysis

  • DNA sequence manipulation and translation

  • Protein sequence analysis

  • GC content calculation

  • Reading frames and ORF detection

Phylogenetic Analysis

  • Multiple sequence alignment

  • Distance matrix calculation

  • Phylogenetic tree reconstruction

  • Tree visualization and interpretation

Genomic Annotation

  • Gene content analysis

  • Functional diversity assessment

  • Comparative genomics

  • Sequence feature identification

BioPython Integration

\begin{pycode}
from Bio import SeqIO
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord

# Create sample sequences
sequences = [
    SeqRecord(Seq("ATGGCGTAA"), id="gene1", description="Sample gene 1"),
    SeqRecord(Seq("ATGGAATAG"), id="gene2", description="Sample gene 2"),
    SeqRecord(Seq("ATGCCCTAA"), id="gene3", description="Sample gene 3")
]

# Analyze sequences
for seq in sequences:
    gc_content = (seq.seq.count('G') + seq.seq.count('C')) / len(seq.seq) * 100
    protein = seq.seq.translate()
    print(f"{seq.id}: GC content = {gc_content:.1f}%, Protein = {protein}")
\end{pycode}

🔬 Key Features

Bioinformatics Visualizations

  • Phylogenetic tree plots using matplotlib

  • Sequence alignment visualizations

  • GC content distribution graphs

  • Distance matrix heatmaps

Real Biological Examples

  • Working with FASTA format sequences

  • Multiple sequence alignment workflows

  • Evolutionary distance calculations

  • Gene annotation and analysis

Professional Formatting

  • Bioinformatics journal style formatting

  • Proper citation of biological databases

  • Standard nomenclature and terminology

  • Publication-ready figures and tables

📊 Generated Figures

Phylogenetic Trees

# Phylogenetic reconstruction example
from Bio import Phylo
from Bio.Phylo.TreeConstruction import DistanceMatrix, DistanceTreeConstructor

# Create distance matrix
dm = DistanceMatrix(distance_data)
constructor = DistanceTreeConstructor()
tree = constructor.nj(dm)

# Visualize tree
fig = plt.figure(figsize=(10, 6))
Phylo.draw(tree, axes=plt.gca())
plt.savefig('figures/phylogenetic_tree.pdf', bbox_inches='tight')

Sequence Analysis Plots

  • GC content distributions

  • Amino acid composition charts

  • Sequence length histograms

  • Conservation analysis plots

🧬 Customization Guide

Adapting for Your Research

  1. Replace sample sequences with your actual genomic data

  2. Modify analysis parameters for your specific organism

  3. Update methodology to match your research approach

  4. Customize visualizations for your data types

Working with Your Data

# Load sequences from FASTA file
sequences = list(SeqIO.parse("your_sequences.fasta", "fasta"))

# Perform your specific analysis
results = your_bioinformatics_analysis(sequences)

# Generate figures for your research
create_phylogenetic_plots(results)
create_annotation_figures(results)

Required Python Packages

The template automatically installs these packages:

  • biopython - Core bioinformatics library

  • matplotlib - Figure generation

  • seaborn - Statistical visualization

  • numpy - Numerical computing

  • pandas - Data manipulation

📈 Expected Output

First Compilation

  • Time: 90-180 seconds

  • Behavior: Installs BioPython, runs sequence analysis

  • PDF Size: ~248KB with example data

Subsequent Compilations

  • Time: 45-90 seconds

  • Behavior: Only re-runs changed analysis blocks

🛠️ Troubleshooting

Common Issues

BioPython import errors:

pip install biopython

Sequence file not found:

  • Check file paths in your Python code

  • Ensure FASTA files are in the correct directory

Phylogenetic analysis errors:

  • Verify sequence alignment quality

  • Check distance matrix calculations

  • Ensure sufficient sequence diversity

Figure generation issues:

# Ensure matplotlib backend is set correctly
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt

See TROUBLESHOOTING.md for detailed solutions.

🔬 Research Applications

Suitable For

  • Comparative genomics studies

  • Phylogenetic analysis papers

  • Gene annotation projects

  • Sequence evolution research

  • Bioinformatics method development

  • Computational biology tutorials

Example Use Cases

  • Analysis of viral genome evolution

  • Bacterial comparative genomics

  • Protein family phylogenetics

  • Gene expression analysis

  • Metagenomic studies

🎓 Educational Value

For Students

  • Learn BioPython programming

  • Understand phylogenetic methods

  • Practice sequence analysis workflows

  • See professional bioinformatics formatting

For Researchers

  • Template for computational biology papers

  • Automated analysis and figure generation

  • Reproducible bioinformatics workflows

  • Integration of code and results

📚 Biological Databases

The template includes examples referencing standard databases:

  • NCBI GenBank

  • UniProt

  • Pfam

  • KEGG

  • GO (Gene Ontology)

📝 License

This template is released under the MIT License. You are free to use, modify, and distribute it for any purpose, including commercial use.

Ready to analyze your genomic data? Open main.tex and start your bioinformatics research! 🧬