Lernen Visualizing GC Content Across Sequences | Biological Data Visualization

Visualizing GC content across DNA sequences is a powerful way to explore genomic differences and similarities. GC content refers to the percentage of guanine (G) and cytosine (C) bases in a DNA sequence. In genomics, plotting GC content helps you quickly detect patterns, such as regions with unusually high or low GC content, which may indicate functional elements or evolutionary adaptations. For example, regions with high GC content can be associated with gene-rich areas, while low GC content might point to non-coding or repetitive regions. By visualizing GC content, you can compare multiple sequences at a glance and identify outliers or trends that warrant further investigation.


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import matplotlib.pyplot as plt

# Example DNA sequences
sequences = [
    "ATGCGCGTAA",
    "ATATATATAT",
    "GGCCGGCCGG",
    "TATAGCGCTA",
    "CGATCGATCG"
]

def gc_content(seq):
    gc_count = seq.count("G") + seq.count("C")
    return (gc_count / len(seq)) * 100

gc_values = [gc_content(seq) for seq in sequences]
labels = [f"Seq{i+1}" for i in range(len(sequences))]

plt.figure(figsize=(8, 5))
plt.bar(labels, gc_values, color="skyblue")
plt.xlabel("Sequence")
plt.ylabel("GC Content (%)")
plt.title("GC Content Across DNA Sequences")
plt.ylim(0, 100)
plt.show()

To create a GC content plot, you start by calculating the GC content for each DNA sequence. Assign a label to each sequence so you can easily identify them on the plot. When you use matplotlib to build the bar chart, set the x-axis label to "Sequence" and the y-axis label to "GC Content (%)" for clarity. The title, such as "GC Content Across DNA Sequences", helps you and others quickly understand the plot's purpose. The y-axis should be limited from 0 to 100 to reflect percentage values. When interpreting the bar chart, look for sequences with noticeably higher or lower GC content than the others. These outliers might represent functionally important or unusual genomic regions, and could be targets for further analysis.


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import matplotlib.pyplot as plt

sequences = [
    "ATGCGCGTAA",
    "ATATATATAT",
    "GGCCGGCCGG",
    "TATAGCGCTA",
    "CGATCGATCG"
]

def gc_content(seq):
    gc_count = seq.count("G") + seq.count("C")
    return (gc_count / len(seq)) * 100

gc_values = [gc_content(seq) for seq in sequences]
labels = [f"Seq{i+1}" for i in range(len(sequences))]

# Highlight sequences with GC content above 70% or below 30%
colors = []
for gc in gc_values:
    if gc > 70:
        colors.append("red")
    elif gc < 30:
        colors.append("orange")
    else:
        colors.append("skyblue")

plt.figure(figsize=(8, 5))
bars = plt.bar(labels, gc_values, color=colors)
plt.xlabel("Sequence")
plt.ylabel("GC Content (%)")
plt.title("GC Content Across DNA Sequences (Outliers Highlighted)")
plt.ylim(0, 100)
plt.show()

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Abschnitt 3. Kapitel 2

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Suggested prompts:

Can you explain how the color highlighting for outliers works in the plot?

What do the highlighted outlier sequences indicate biologically?

How can I adjust the thresholds for highlighting outliers in the plot?

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              12345678910111213141516171819202122232425
            
import matplotlib.pyplot as plt

# Example DNA sequences
sequences = [
    "ATGCGCGTAA",
    "ATATATATAT",
    "GGCCGGCCGG",
    "TATAGCGCTA",
    "CGATCGATCG"
]

def gc_content(seq):
    gc_count = seq.count("G") + seq.count("C")
    return (gc_count / len(seq)) * 100

gc_values = [gc_content(seq) for seq in sequences]
labels = [f"Seq{i+1}" for i in range(len(sequences))]

plt.figure(figsize=(8, 5))
plt.bar(labels, gc_values, color="skyblue")
plt.xlabel("Sequence")
plt.ylabel("GC Content (%)")
plt.title("GC Content Across DNA Sequences")
plt.ylim(0, 100)
plt.show()


              12345678910111213141516171819202122232425262728293031323334
            
import matplotlib.pyplot as plt

sequences = [
    "ATGCGCGTAA",
    "ATATATATAT",
    "GGCCGGCCGG",
    "TATAGCGCTA",
    "CGATCGATCG"
]

def gc_content(seq):
    gc_count = seq.count("G") + seq.count("C")
    return (gc_count / len(seq)) * 100

gc_values = [gc_content(seq) for seq in sequences]
labels = [f"Seq{i+1}" for i in range(len(sequences))]

# Highlight sequences with GC content above 70% or below 30%
colors = []
for gc in gc_values:
    if gc > 70:
        colors.append("red")
    elif gc < 30:
        colors.append("orange")
    else:
        colors.append("skyblue")

plt.figure(figsize=(8, 5))
bars = plt.bar(labels, gc_values, color=colors)
plt.xlabel("Sequence")
plt.ylabel("GC Content (%)")
plt.title("GC Content Across DNA Sequences (Outliers Highlighted)")
plt.ylim(0, 100)
plt.show()

War alles klar?

Danke für Ihr Feedback!

Abschnitt 3. Kapitel 2

Visualizing GC Content Across Sequences

1. What can a bar chart of GC content reveal about a set of DNA sequences?

2. Fill in the blank: To plot GC content, you first calculate the _____ for each sequence.

3. How would you highlight outlier sequences in a GC content plot?

Visualizing GC Content Across Sequences

1. What can a bar chart of GC content reveal about a set of DNA sequences?

2. Fill in the blank: To plot GC content, you first calculate the _____ for each sequence.

3. How would you highlight outlier sequences in a GC content plot?