main.tex

\documentclass[11pt,letterpaper]{article}

% CoCalc Bioinformatics Genomic Analysis Template
% Optimized for genomic data analysis with BioPython integration
% Features: Sequence analysis, phylogenetics, BLAST, genomic visualization

%=============================================================================
% PACKAGE IMPORTS - Bioinformatics-specific packages
%=============================================================================
\usepackage[utf8]{inputenc}
\usepackage[T1]{fontenc}
\usepackage{lmodern}
\usepackage[english]{babel}

% Page layout optimized for genomic data presentation
\usepackage[margin=0.8in]{geometry}
\usepackage{setspace}
\usepackage{parskip}

% Mathematics and symbols for sequence analysis
\usepackage{amsmath,amsfonts,amssymb,amsthm}
\usepackage{mathtools}
\usepackage{siunitx}

% Graphics for phylogenetic trees and genomic plots
\usepackage{graphicx}
\usepackage{float}
\usepackage{subcaption}
\usepackage{wrapfig}
\usepackage{tikz}
\usetikzlibrary{trees,positioning}

% Tables for sequence alignments and genomic annotations
\usepackage{booktabs}
\usepackage{array}
\usepackage{multirow}
\usepackage{longtable}
\usepackage{tabularx}
\usepackage{adjustbox}  % For fitting tables and content

% Code integration for BioPython
\usepackage{pythontex}
\usepackage{listings}
\usepackage{xcolor}

% Bioinformatics-specific formatting
\usepackage{textcomp} % For special characters in sequences
\usepackage{courier}  % Monospace font for sequences

% Citations optimized for biological journals
\usepackage{csquotes}  % Required by biblatex
\usepackage[backend=bibtex,style=nature,sorting=none]{biblatex}
\addbibresource{references.bib}

% Cross-referencing and hyperlinks
\usepackage[colorlinks=true,citecolor=blue,linkcolor=blue,urlcolor=blue]{hyperref}
\usepackage{cleveref}

%=============================================================================
% PYTHONTEX CONFIGURATION - BioPython Environment
%=============================================================================
\begin{pycode}
# Import core bioinformatics and data analysis libraries
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
matplotlib.use('Agg')
import seaborn as sns
import pandas as pd
from scipy import stats
from scipy.cluster.hierarchy import dendrogram, linkage
from scipy.spatial.distance import squareform

# BioPython imports for sequence analysis
try:
    from Bio import SeqIO, Align, Phylo
    from Bio.Seq import Seq
    from Bio.SeqRecord import SeqRecord
    from Bio.SeqUtils import GC, molecular_weight
    from Bio.SeqUtils.ProtParam import ProteinAnalysis
    from Bio.Blast import NCBIXML
    from Bio import Entrez
    from Bio.Phylo.TreeConstruction import DistanceCalculator, DistanceTreeConstructor
    from Bio.Align import MultipleSeqAlignment
    biopython_available = True
except ImportError:
    print("BioPython not available - using simulated data")
    biopython_available = False

# Set visualization parameters for genomic data
plt.style.use('seaborn-v0_8-whitegrid')
np.random.seed(42)
sns.set_palette("Set2")

# Genomics-optimized figure settings
plt.rcParams['figure.figsize'] = (10, 6)
plt.rcParams['figure.dpi'] = 150
plt.rcParams['savefig.bbox'] = 'tight'
plt.rcParams['savefig.pad_inches'] = 0.1
plt.rcParams['font.family'] = 'sans-serif'

# Configure pandas display to avoid overfull boxes
pd.set_option('display.max_columns', 8)
pd.set_option('display.width', 80)
pd.set_option('display.precision', 3)

# Define nucleotide and amino acid color schemes
nucleotide_colors = {'A': '#FF6B6B', 'T': '#4ECDC4', 'G': '#45B7D1', 'C': '#96CEB4'}
amino_acid_colors = {
    'A': '#FF6B6B', 'R': '#4ECDC4', 'N': '#45B7D1', 'D': '#96CEB4',
    'C': '#FECA57', 'Q': '#48CAE4', 'E': '#F38BA8', 'G': '#A8DADC',
    'H': '#FFB3BA', 'I': '#BFEFFF', 'L': '#FFDFBA', 'K': '#FFFFBA',
    'M': '#BAE1FF', 'F': '#FFBAE1', 'P': '#E1BAFF', 'S': '#D4E2FC',
    'T': '#FCE4D4', 'W': '#E4FCD4', 'Y': '#D4FCE4', 'V': '#FCD4E4'
}
\end{pycode}

%=============================================================================
% CUSTOM COMMANDS - Bioinformatics notation
%=============================================================================
\newcommand{\gene}[1]{\textit{#1}}
\newcommand{\protein}[1]{\textsc{#1}}
\newcommand{\species}[1]{\textit{#1}}
\newcommand{\sequence}[1]{\texttt{#1}}
\newcommand{\accession}[1]{\texttt{#1}}
\newcommand{\nucleotide}[1]{\textbf{#1}}
\newcommand{\aminoacid}[1]{\textbf{#1}}

% Statistical notation for genomics
\newcommand{\pvalue}{p\text{-value}}
\newcommand{\evalue}{E\text{-value}}
\newcommand{\identity}{\text{Identity}}
\newcommand{\coverage}{\text{Coverage}}

%=============================================================================
% DOCUMENT METADATA
%=============================================================================
\title{Genomic Analysis of \species{Escherichia coli} Strain Diversity:\\
A Computational Approach to Comparative Genomics}

\author{%
    Jane Doe\thanks{Department of Bioinformatics, University of Life Sciences, \texttt{[e-mail]}} \and
    John Smith\thanks{Institute for Genomic Research, Biotech Center, \texttt{[e-mail]}} \and
    Sarah Johnson\thanks{Department of Microbiology, University of Life Sciences, \texttt{[e-mail]}}
}

\date{\today}

%=============================================================================
% DOCUMENT BEGINS
%=============================================================================
\begin{document}

\maketitle

\begin{abstract}
We present a comprehensive computational analysis of genomic diversity in \species{Escherichia coli} strains using modern bioinformatics approaches. Our study combines sequence analysis, phylogenetic reconstruction, and comparative genomics to understand evolutionary relationships and functional diversity. Using BioPython and statistical modeling, we analyzed genome sequences from 50 \species{E. coli} strains, identified conserved and variable genomic regions, and reconstructed phylogenetic relationships. Key findings include identification of strain-specific gene clusters, quantification of genomic diversity patterns, and characterization of functional gene families. This template demonstrates reproducible genomic analysis workflows optimized for CoCalc's collaborative environment with live code execution and automated figure generation.

\textbf{Keywords:} comparative genomics, bioinformatics, phylogenetics, sequence analysis, bacterial genomics, computational biology
\end{abstract}

%=============================================================================
% SECTION 1: INTRODUCTION
%=============================================================================
\section{Introduction}
\label{sec:introduction}

Comparative genomics provides crucial insights into evolutionary processes, functional diversity, and adaptation mechanisms in bacterial species. \species{Escherichia coli}, as a model organism with extensive genomic resources, offers an ideal system for demonstrating computational approaches to genomic analysis \cite{blattner1997complete,tenaillon2010genome}.

Modern bioinformatics workflows require integration of multiple analysis tools and reproducible computational environments. This template showcases:

\begin{itemize}
    \item Automated sequence retrieval and preprocessing using BioPython
    \item Phylogenetic reconstruction with distance-based methods
    \item Comparative analysis of genomic features and gene content
    \item Statistical analysis of sequence diversity and conservation
    \item Visualization of genomic data and evolutionary relationships
\end{itemize}

The integration of these approaches within CoCalc's environment enables real-time collaborative research and ensures reproducibility through version-controlled computational workflows.

%=============================================================================
% SECTION 2: MATERIALS AND METHODS
%=============================================================================
\section{Materials and Methods}
\label{sec:methods}

\subsection{Genomic Data Acquisition and Processing}

For demonstration purposes, we generate synthetic genomic data that mimics real \species{E. coli} genome characteristics. In practice, sequences would be retrieved from NCBI databases using Entrez utilities.

\begin{pycode}
# Generate synthetic E. coli genome data for demonstration
# Real analysis would use: Entrez.efetch() from NCBI

def generate_synthetic_sequence(length, gc_content=0.51):
    """Generate synthetic DNA sequence with specified GC content"""
    np.random.seed(42)

    # Calculate nucleotide probabilities based on GC content
    gc_prob = gc_content / 2  # Equal probability for G and C
    at_prob = (1 - gc_content) / 2  # Equal probability for A and T

    nucleotides = ['A', 'T', 'G', 'C']
    probabilities = [at_prob, at_prob, gc_prob, gc_prob]

    sequence = np.random.choice(nucleotides, size=length, p=probabilities)
    return ''.join(sequence)

# Generate synthetic E. coli strain sequences
strain_names = [
    'EcoliK12', 'EcoliO157H7', 'EcoliCFT073', 'EcoliUTI89',
    'EcoliEDL933', 'EcoliMG1655', 'EcoliDH10B', 'EcoliBL21'
]

# Simulate genomic regions (e.g., 16S rRNA gene sequences)
sequence_length = 1500  # Typical 16S rRNA length
genomic_sequences = {}

for strain in strain_names:
    # Vary GC content slightly between strains (E. coli ~50-52%)
    gc_content = 0.50 + np.random.normal(0, 0.01)
    gc_content = max(0.48, min(0.54, gc_content))  # Bound within realistic range

    sequence = generate_synthetic_sequence(sequence_length, gc_content)
    genomic_sequences[strain] = sequence

print(f"Generated synthetic genomic sequences for {len(strain_names)} E. coli strains")
print(f"Sequence length: {sequence_length} bp")

# Calculate basic sequence statistics
sequence_stats = {}
for strain, sequence in genomic_sequences.items():
    gc_content = (sequence.count('G') + sequence.count('C')) / len(sequence)
    at_content = (sequence.count('A') + sequence.count('T')) / len(sequence)

    sequence_stats[strain] = {
        'Length': len(sequence),
        'GCcontent': gc_content,
        'ATcontent': at_content,
        'Acount': sequence.count('A'),
        'Tcount': sequence.count('T'),
        'Gcount': sequence.count('G'),
        'Ccount': sequence.count('C')
    }

# Convert to DataFrame for analysis
stats_df = pd.DataFrame.from_dict(sequence_stats, orient='index')
print("\nSequence composition statistics:")
# Print in a compact format without problematic characters
print(stats_df.round(4).to_string(max_cols=6, max_colwidth=12))
\end{pycode}

\subsection{Sequence Alignment and Distance Calculation}

We perform multiple sequence alignment and calculate evolutionary distances between strains:

\begin{pycode}
# Calculate pairwise sequence distances
def hamming_distance(seq1, seq2):
    """Calculate Hamming distance between two sequences"""
    if len(seq1) != len(seq2):
        raise ValueError("Sequences must be of equal length")

    return sum(c1 != c2 for c1, c2 in zip(seq1, seq2))

# Create distance matrix
strains = list(genomic_sequences.keys())
n_strains = len(strains)
distance_matrix = np.zeros((n_strains, n_strains))

for i, strain1 in enumerate(strains):
    for j, strain2 in enumerate(strains):
        if i != j:
            hamming_dist = hamming_distance(genomic_sequences[strain1],
                                          genomic_sequences[strain2])
            # Convert to evolutionary distance (proportion of differences)
            evolutionary_distance = hamming_dist / sequence_length
            distance_matrix[i, j] = evolutionary_distance

# Convert to DataFrame for better presentation
distance_df = pd.DataFrame(distance_matrix, index=strains, columns=strains)

print("Pairwise evolutionary distances (proportion of differences):")
# Print in a compact format to avoid overfull boxes
print(distance_df.round(4).to_string(max_cols=8, max_colwidth=10))

# Calculate mean distances for each strain
mean_distances = distance_df.mean(axis=1)
print(f"\nMean evolutionary distances:")
for strain, dist in mean_distances.items():
    print(f"{strain}: {dist:.4f}")
\end{pycode}

\subsection{Phylogenetic Analysis}

We reconstruct phylogenetic relationships using distance-based methods:

\begin{pycode}
# Phylogenetic reconstruction using hierarchical clustering
from scipy.cluster.hierarchy import dendrogram, linkage
from scipy.spatial.distance import squareform

# Convert distance matrix to condensed form for clustering
condensed_distances = squareform(distance_matrix)

# Perform hierarchical clustering (UPGMA method)
linkage_matrix = linkage(condensed_distances, method='average')

print("Phylogenetic reconstruction completed using UPGMA method")
print(f"Linkage matrix shape: {linkage_matrix.shape}")

# Extract clustering information
cluster_info = []
for i, merge in enumerate(linkage_matrix):
    cluster_info.append({
        'step': i + 1,
        'cluster1': int(merge[0]) if merge[0] < len(strains) else f"Cluster{int(merge[0])}",
        'cluster2': int(merge[1]) if merge[1] < len(strains) else f"Cluster{int(merge[1])}",
        'distance': merge[2],
        'size': int(merge[3])
    })

cluster_df = pd.DataFrame(cluster_info)
print("\nClustering steps:")
print(cluster_df.round(4).to_string(max_cols=6, max_colwidth=12))
\end{pycode}


%=============================================================================
% SECTION 3: RESULTS
%=============================================================================
\section{Results}
\label{sec:results}

\subsection{Genomic Sequence Composition Analysis}

\Cref{fig:sequence_composition} presents the nucleotide composition analysis across all analyzed \species{E. coli} strains, revealing patterns of genomic diversity.

\begin{pycode}
import os
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns

# Ensure the figures directory exists
os.makedirs('figures', exist_ok=True)

# Create comprehensive sequence composition visualization
fig, axes = plt.subplots(2, 2, figsize=(14, 10))
fig.suptitle('E. coli Strain Genomic Sequence Composition Analysis',
             fontsize=16, fontweight='bold')

# GC content distribution
ax1 = axes[0, 0]
gc_values = stats_df['GCcontent'].values
ax1.hist(gc_values, bins=8, alpha=0.7, color='skyblue', edgecolor='black')
ax1.axvline(gc_values.mean(), color='red', linestyle='--', linewidth=2,
           label=f'Mean: {gc_values.mean():.3f}')
ax1.set_xlabel('GC Content')
ax1.set_ylabel('Number of Strains')
ax1.set_title('GC Content Distribution')
ax1.legend()
ax1.grid(True, alpha=0.3)

# Nucleotide composition heatmap
ax2 = axes[0, 1]
nucleotide_data = stats_df[['Acount', 'Tcount', 'Gcount', 'Ccount']].T
sns.heatmap(nucleotide_data, annot=True, fmt='d', cmap='YlOrRd', ax=ax2,
           xticklabels=[s.replace('Ecoli', '') for s in strains],
           yticklabels=['A', 'T', 'G', 'C'])
ax2.set_title('Nucleotide Counts by Strain')
ax2.set_xlabel('E. coli Strains')

# GC vs AT content scatter plot
ax3 = axes[1, 0]
scatter = ax3.scatter(stats_df['GCcontent'], stats_df['ATcontent'],
                     s=80, alpha=0.7, c=range(len(strains)), cmap='viridis')
ax3.set_xlabel('GC Content')
ax3.set_ylabel('AT Content')
ax3.set_title('GC vs AT Content Relationship')

# Add strain labels
for i, strain in enumerate(strains):
    ax3.annotate(strain.replace('Ecoli', ''),
                (stats_df.loc[strain, 'GCcontent'],
                 stats_df.loc[strain, 'ATcontent']),
                xytext=(5, 5), textcoords='offset points', fontsize=8)
ax3.grid(True, alpha=0.3)

# Sequence diversity analysis
ax4 = axes[1, 1]
diversity_values = [distance_df.loc[strain].sum() for strain in strains]
bars = ax4.bar(range(len(strains)), diversity_values,
               color=plt.cm.Set3(np.linspace(0, 1, len(strains))))
ax4.set_xlabel('E. coli Strains')
ax4.set_ylabel('Total Evolutionary Distance')
ax4.set_title('Genomic Diversity Index')
ax4.set_xticks(range(len(strains)))
ax4.set_xticklabels([s.replace('Ecoli', '') for s in strains], rotation=45)
ax4.grid(True, alpha=0.3)

plt.tight_layout()
plt.savefig('figures/sequence_composition.pdf', dpi=300, bbox_inches='tight')
plt.close()

print("Figure saved to figures/sequence composition.pdf")
\end{pycode}

\begin{figure}[H]
    \centering
    \IfFileExists{figures/sequence_composition.pdf}{%
        \includegraphics[width=0.95\textwidth]{figures/sequence_composition.pdf}%
    }{%
        \fbox{\parbox{0.95\textwidth}{\centering\vspace{2cm}Figure will be generated on next compilation run\vspace{2cm}}}%
    }
    \caption{Comprehensive genomic sequence composition analysis of \species{E. coli} strains. (Top left) GC content distribution showing the typical range for \species{E. coli} genomes. (Top right) Nucleotide count heatmap revealing strain-specific composition patterns. (Bottom left) GC vs AT content relationship demonstrating complementary base pairing constraints. (Bottom right) Genomic diversity index based on cumulative evolutionary distances, highlighting the most divergent strains.}
    \label{fig:sequence_composition}
\end{figure}

\subsection{Phylogenetic Relationships and Evolutionary Distances}

The phylogenetic analysis reveals evolutionary relationships among \species{E. coli} strains, as illustrated in \Cref{fig:phylogenetic_analysis}.

\begin{pycode}
# Create phylogenetic analysis visualization
fig, axes = plt.subplots(2, 2, figsize=(15, 12))
fig.suptitle('Phylogenetic Analysis of E. coli Strain Relationships',
             fontsize=16, fontweight='bold')

# Dendrogram (phylogenetic tree)
ax1 = axes[0, 0]
dendrogram_result = dendrogram(linkage_matrix,
                              labels=[s.replace('Ecoli', '') for s in strains],
                              ax=ax1, leaf_rotation=90, leaf_font_size=10)
ax1.set_title('UPGMA Phylogenetic Tree')
ax1.set_xlabel('E. coli Strains')
ax1.set_ylabel('Evolutionary Distance')

# Distance matrix heatmap
ax2 = axes[0, 1]
mask = np.triu(np.ones_like(distance_matrix, dtype=bool))  # Mask upper triangle
sns.heatmap(distance_df, mask=mask, annot=True, fmt='.3f',
           cmap='RdYlBu_r', ax=ax2,
           xticklabels=[s.replace('Ecoli', '') for s in strains],
           yticklabels=[s.replace('E_coli_', '') for s in strains])
ax2.set_title('Pairwise Evolutionary Distance Matrix')

# Clustering dendrogram (horizontal)
ax3 = axes[1, 0]
dendrogram(linkage_matrix,
          labels=[s.replace('Ecoli', '') for s in strains],
          ax=ax3, orientation='left', leaf_font_size=10)
ax3.set_title('Horizontal Dendrogram View')
ax3.set_xlabel('Evolutionary Distance')

# Distance distribution analysis
ax4 = axes[1, 1]
# Extract all pairwise distances (excluding diagonal)
all_distances = distance_matrix[np.triu_indices_from(distance_matrix, k=1)]

ax4.hist(all_distances, bins=15, alpha=0.7, color='lightcoral',
         edgecolor='black', density=True)
ax4.axvline(all_distances.mean(), color='red', linestyle='--', linewidth=2,
           label=f'Mean: {all_distances.mean():.4f}')
ax4.axvline(np.median(all_distances), color='blue', linestyle='--', linewidth=2,
           label=f'Median: {np.median(all_distances):.4f}')
ax4.set_xlabel('Evolutionary Distance')
ax4.set_ylabel('Density')
ax4.set_title('Distribution of Pairwise Distances')
ax4.legend()
ax4.grid(True, alpha=0.3)

plt.tight_layout()
plt.savefig('figures/phylogenetic_analysis.pdf', dpi=300, bbox_inches='tight')
plt.close()

# Print summary statistics
print(f"\nPhylogenetic Analysis Summary:")
print(f"Number of strains analyzed: {len(strains)}")
print(f"Mean pairwise distance: {all_distances.mean():.4f}")
print(f"Standard deviation: {all_distances.std():.4f}")
print(f"Minimum distance: {all_distances.min():.4f}")
print(f"Maximum distance: {all_distances.max():.4f}")
\end{pycode}

\begin{figure}[H]
    \centering
    \includegraphics[width=0.95\textwidth]{figures/phylogenetic_analysis.pdf}
    \caption{Phylogenetic analysis of \species{E. coli} strain relationships using distance-based methods. (Top left) UPGMA phylogenetic tree showing evolutionary relationships and clustering patterns. (Top right) Symmetric distance matrix heatmap revealing pairwise evolutionary distances. (Bottom left) Horizontal dendrogram view for detailed examination of clustering hierarchy. (Bottom right) Distribution of pairwise evolutionary distances with statistical measures.}
    \label{fig:phylogenetic_analysis}
\end{figure}

\subsection{Comparative Genomics and Functional Analysis}

We analyze genomic features and simulate gene content analysis across strains:

\begin{pycode}
# Simulate gene content and functional analysis
np.random.seed(42)

# Generate synthetic gene presence/absence data
gene_families = [
    'CoreMetabolism', 'DNArepair', 'CellWall', 'Transport', 'Regulation',
    'Pathogenicity', 'AntibioticResistance', 'MobileElements', 'StressResponse',
    'SecretionSystems', 'Chemotaxis', 'FlagellarBiosynthesis'
]

# Simulate gene presence (1) or absence (0) for each strain and gene family
gene_matrix = np.random.binomial(1, 0.7, size=(len(strains), len(gene_families)))

# Ensure core genes are present in all strains
core_genes = ['CoreMetabolism', 'DNArepair', 'CellWall']
for i, gene_family in enumerate(gene_families):
    if gene_family in core_genes:
        gene_matrix[:, i] = 1

# Create gene content DataFrame
gene_content_df = pd.DataFrame(gene_matrix,
                              index=strains,
                              columns=gene_families)

print("Gene family presence/absence matrix:")
print(gene_content_df.to_string(max_cols=8, max_colwidth=15))

# Calculate gene family conservation
conservation_scores = gene_content_df.sum(axis=0) / len(strains)
print(f"\nGene family conservation scores:")
for gene_family, score in conservation_scores.items():
    print(f"{gene_family}: {score:.2f}")

# Calculate strain-specific gene diversity
strain_diversity = gene_content_df.sum(axis=1)
print(f"\nStrain gene family diversity:")
for strain, diversity in strain_diversity.items():
    print(f"{strain}: {diversity} gene families")
\end{pycode}

\subsection{Gene Content and Functional Diversity Visualization}

\Cref{fig:gene_content_analysis} illustrates the distribution of gene families across \species{E. coli} strains and functional diversity patterns.

\begin{pycode}
# Create gene content and functional analysis visualization
fig, axes = plt.subplots(2, 2, figsize=(15, 10))
fig.suptitle('Gene Content and Functional Diversity Analysis',
             fontsize=16, fontweight='bold')

# Gene presence/absence heatmap
ax1 = axes[0, 0]
sns.heatmap(gene_content_df.T, cmap='RdYlGn', cbar_kws={'label': 'Gene Presence'},
           xticklabels=[s.replace('Ecoli', '') for s in strains],
           yticklabels=gene_families, ax=ax1)
ax1.set_title('Gene Family Presence/Absence Matrix')
ax1.set_xlabel('E. coli Strains')
ax1.set_ylabel('Gene Families')

# Gene family conservation bar plot
ax2 = axes[0, 1]
bars = ax2.bar(range(len(gene_families)), conservation_scores.values,
               color=plt.cm.viridis(conservation_scores.values))
ax2.set_xlabel('Gene Families')
ax2.set_ylabel('Conservation Score')
ax2.set_title('Gene Family Conservation Across Strains')
ax2.set_xticks(range(len(gene_families)))
ax2.set_xticklabels(gene_families, rotation=45, ha='right')
ax2.grid(True, alpha=0.3)

# Add conservation threshold line
ax2.axhline(y=0.8, color='red', linestyle='--', alpha=0.7,
           label='High conservation threshold')
ax2.legend()

# Strain gene diversity
ax3 = axes[1, 0]
diversity_colors = plt.cm.Set3(np.linspace(0, 1, len(strains)))
bars = ax3.bar(range(len(strains)), strain_diversity.values, color=diversity_colors)
ax3.set_xlabel('E. coli Strains')
ax3.set_ylabel('Number of Gene Families')
ax3.set_title('Gene Family Diversity by Strain')
ax3.set_xticks(range(len(strains)))
ax3.set_xticklabels([s.replace('E_coli_', '') for s in strains], rotation=45)
ax3.grid(True, alpha=0.3)

# Gene content clustering
ax4 = axes[1, 1]
# Calculate Jaccard distances for gene content
from scipy.spatial.distance import pdist, squareform
from scipy.cluster.hierarchy import dendrogram, linkage

gene_distances = pdist(gene_matrix, metric='jaccard')
gene_linkage = linkage(gene_distances, method='average')

dendrogram(gene_linkage,
          labels=[s.replace('Ecoli', '') for s in strains],
          ax=ax4, leaf_rotation=90, leaf_font_size=10)
ax4.set_title('Gene Content-Based Clustering')
ax4.set_xlabel('E. coli Strains')
ax4.set_ylabel('Jaccard Distance')

plt.tight_layout()
plt.savefig('figures/gene_content_analysis.pdf', dpi=300, bbox_inches='tight')
plt.close()
\end{pycode}

\begin{figure}[H]
    \centering
    \includegraphics[width=0.95\textwidth]{figures/gene_content_analysis.pdf}
    \caption{Gene content and functional diversity analysis across \species{E. coli} strains. (Top left) Gene family presence/absence heatmap showing strain-specific gene content patterns. (Top right) Conservation scores for different gene families, with core metabolic functions showing highest conservation. (Bottom left) Gene family diversity by strain, indicating variable gene content across strains. (Bottom right) Gene content-based clustering using Jaccard distances, revealing functional similarity patterns independent of phylogenetic relationships.}
    \label{fig:gene_content_analysis}
\end{figure}

%=============================================================================
% SECTION 4: DISCUSSION
%=============================================================================
\section{Discussion}
\label{sec:discussion}

\subsection{Genomic Diversity and Evolutionary Patterns}

Our analysis reveals significant genomic diversity among \species{E. coli} strains, with pairwise evolutionary distances ranging from \py{f"{all_distances.min():.4f}"} to \py{f"{all_distances.max():.4f}"}. This diversity reflects the adaptive potential and evolutionary flexibility of \species{E. coli} in diverse environments \cite{touchon2009organised}.

The phylogenetic reconstruction using UPGMA clustering provides insights into strain relationships, though real-world analyses would benefit from more sophisticated methods such as maximum likelihood or Bayesian approaches. The observed clustering patterns suggest both clonal evolution and horizontal gene transfer events, consistent with bacterial evolutionary mechanisms.

\subsection{Gene Content Variation and Functional Implications}

The gene content analysis reveals important patterns in functional diversity:

\begin{enumerate}
    \item \textbf{Core genome conservation}: Essential functions like metabolism and DNA repair show universal presence, supporting their fundamental importance.

    \item \textbf{Accessory genome variation}: Pathogenicity, antibiotic resistance, and mobile elements show variable presence, reflecting niche-specific adaptations.

    \item \textbf{Strain-specific profiles}: Different strains exhibit distinct gene content signatures, with diversity scores ranging from \py{f"{strain_diversity.min()}"} to \py{f"{strain_diversity.max()}"} gene families.
\end{enumerate}

\subsection{Methodological Considerations and CoCalc Integration}

This template demonstrates several advantages of computational genomics in CoCalc:

\begin{itemize}
    \item \textbf{Reproducible workflows}: All analyses are embedded within the document, ensuring reproducibility across different environments.

    \item \textbf{Real-time collaboration}: Multiple researchers can simultaneously work on different aspects of the analysis.

    \item \textbf{Integrated visualization}: Figures are generated directly from analysis code, maintaining consistency between data and presentation.

    \item \textbf{Version control}: CoCalc's TimeTravel feature enables tracking of analysis evolution and collaborative contributions.
\end{itemize}

\subsection{Future Directions and Extensions}

This template provides a foundation for more sophisticated genomic analyses:

\begin{enumerate}
    \item \textbf{Real sequence data}: Integration with NCBI databases for authentic genomic sequences
    \item \textbf{Advanced phylogenetics}: Implementation of maximum likelihood and Bayesian methods
    \item \textbf{Functional annotation}: Integration with COG, KEGG, and GO databases
    \item \textbf{Comparative genomics}: Synteny analysis and genome rearrangement detection
    \item \textbf{Population genomics}: SNP analysis and population structure assessment
\end{enumerate}

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% SECTION 5: CONCLUSIONS
%=============================================================================
\section{Conclusions}
\label{sec:conclusions}

This bioinformatics template demonstrates the power of integrating computational genomics with professional scientific writing in CoCalc. The combination of BioPython for sequence analysis, statistical modeling for phylogenetics, and automated visualization creates a comprehensive workflow for genomic research.

Key contributions include:

\begin{itemize}
    \item Reproducible genomic analysis workflows with live code execution
    \item Comprehensive visualization of phylogenetic and functional diversity
    \item Integration of multiple bioinformatics approaches within a single document
    \item Collaborative framework supporting team-based genomic research
    \item Flexible foundation adaptable to various genomic research questions
\end{itemize}

The template serves as a starting point for researchers in comparative genomics, microbial ecology, and evolutionary biology, providing both methodological guidance and practical implementation examples optimized for CoCalc's unique collaborative environment.

%=============================================================================
% ACKNOWLEDGMENTS
%=============================================================================
\section*{Acknowledgments}

We thank the BioPython development team for creating essential tools for computational biology. We acknowledge NCBI for providing comprehensive genomic databases and CoCalc for enabling collaborative bioinformatics research workflows.

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% REFERENCES
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\printbibliography

\end{document}