Example Usage

Interactive Colab Notebook

For an interactive demonstration of the GenomeVisualizer toolbox, visit the Colab notebook:

Colab Notebook

Data Handling

The basic.py module offers utility functions for loading and analyzing genome sequences. These are used as the foundation for further analytical steps in replication analysis and motif detection.

Steps:

  1. Load genome data from plain text.

  2. Perform basic k-mer frequency analysis.

  3. Identify most frequent substrings.

from GenomeVisualizer.basic import load_genome_from_txt, FrequencyMap, FrequentWords

# Load genome
genome = load_genome_from_txt("data/ecoli.txt")

# Count k-mer frequencies
freq_map = FrequencyMap(genome, 10)

# Most frequent 10-mers
print(FrequentWords(genome, 10))

Replication Analysis

The replication.py module supports analysis related to DNA replication, including skew diagrams and symbol frequency plots that help infer the origin of replication (oriC).

Steps:

  1. Compute skew array to estimate origin of replication.

  2. Plot skew profile and symbol frequency variations.

from GenomeVisualizer.replication import SkewArray, MinimumSkew, FasterSymbolArray
from GenomeVisualizer.visualization import plot_skew_array_with_ori, plot_symbol_array

# Skew analysis
skew = SkewArray(genome)
ori_positions = MinimumSkew(genome)
plot_skew_array_with_ori(skew, ori_positions, genome_label="E. coli")

# Symbol frequency analysis
symbol_array = FasterSymbolArray(genome, "C")
plot_symbol_array(symbol_array, "C", genome_label="E. coli")

Motif Analysis

The motifs.py module contains implementations for motif finding algorithms such as Greedy Motif Search, Randomized Motif Search, and Gibbs Sampling. It also includes motif scoring and profile matrix utilities.

Steps:

  1. Run Greedy or Gibbs-based motif search.

  2. Generate motif profile.

  3. Visualize motif conservation.

from GenomeVisualizer.motifs import GreedyMotifSearchWithPseudocounts, GibbsSampler
from GenomeVisualizer.visualization import plot_motiflogo

Dna = [
    "CGCCCCTCTCGGGGGTGTTCAGTAAACGGCCA",
    "GGGCGAGGTATGTGTAAGTGCCAAGGTGCCAG",
    "TAGTACCGAGACCGAAAGAAGTATACAGGCGT",
    "TAGATCAAGTTTCAGGTGCACGTCGGTGAACC",
    "AATCCACCAGCTCCACGTGCAATGTTGGCCTA"
]

# Run Gibbs Sampler
motifs = GibbsSampler(Dna, 8, 5, 100)
print("Best motifs:", motifs)

# Plot motif logo
plot_motiflogo(motifs)

Explanation:

The algorithms return candidate motifs that are most conserved across DNA sequences. Visualization with a sequence logo reveals information-rich regions.

This walkthrough demonstrates how GenomeVisualizer can support DNA replication analysis and motif discovery with robust utilities and clear visualizations. For hands-on exploration, check the Colab link above.