Using Linux to Calculate Frequency of Nucleotides Using Python

Paste a DNA sequence or FASTA content, choose how to treat ambiguous symbols, and instantly calculate A, T, G, C frequencies, GC content, AT content, and visual composition. This premium calculator is built for Linux, Python, and practical bioinformatics workflows.

Nucleotide Frequency Calculator

DNA Sequence or FASTA Input

<div class="wpc-field-group">
            <label for="wpc-input-format">Input Format</label>
            <select id="wpc-input-format" class="wpc-select">
              <option value="auto" selected>Auto-detect</option>
              <option value="raw">Raw sequence only</option>
              <option value="fasta">FASTA text</option>
            </select>
          </div>

<div class="wpc-field-group">
            <label for="wpc-percent-mode">Percentage Basis</label>
            <select id="wpc-percent-mode" class="wpc-select">
              <option value="canonical" selected>Use A/T/G/C total only</option>
              <option value="all">Use all cleaned characters</option>
            </select>
          </div>
        </div>

<div class="wpc-inline-options">
          <div class="wpc-field-group">
            <label for="wpc-ambiguous-mode">Ambiguous Characters</label>
            <select id="wpc-ambiguous-mode" class="wpc-select">
              <option value="ignore" selected>Ignore N and ambiguous bases</option>
              <option value="count">Count N and ambiguous bases separately</option>
            </select>
          </div>

<div class="wpc-field-group">
            <label for="wpc-case-mode">Letter Handling</label>
            <select id="wpc-case-mode" class="wpc-select">
              <option value="normalize" selected>Normalize to uppercase</option>
              <option value="strict">Strict validation after cleanup</option>
            </select>
          </div>
        </div>

<div class="wpc-check-row">
          <input type="checkbox" id="wpc-remove-whitespace" checked>
          <label for="wpc-remove-whitespace">Remove whitespace, tabs, and line breaks automatically</label>
        </div>

<div class="wpc-btn-row">
          <button id="wpc-calc-btn" class="wpc-btn wpc-btn-primary" type="button">Calculate Frequencies</button>
          <button id="wpc-sample-btn" class="wpc-btn wpc-btn-secondary" type="button">Load Sample Sequence</button>
          <button id="wpc-clear-btn" class="wpc-btn wpc-btn-secondary" type="button">Clear</button>
        </div>
      </div>

<aside class="wpc-card">
        <h2>What This Tool Calculates</h2>
        <ul>
          <li>Total sequence length after cleanup</li>
          <li>Counts for A, T, G, and C</li>
          <li>Percent frequency of each nucleotide</li>
          <li>GC content and AT content</li>
          <li>Optional count of N and other ambiguous bases</li>
        </ul>
        <p class="wpc-note">Tip: If you paste FASTA text, header lines beginning with <strong>></strong> are ignored automatically in Auto-detect or FASTA mode.</p>
      </aside>
    </div>

<div id="wpc-results">
      <strong>Ready to analyze.</strong> Enter a sequence and click <strong>Calculate Frequencies</strong> to see counts, percentages, and a chart.
    </div>

<article class="wpc-content">
    <h2>Expert Guide: Using Linux to Calculate Frequency of Nucleotides Using Python</h2>
    <p>Calculating nucleotide frequency is one of the first and most important tasks in bioinformatics. Whether you are examining a short DNA fragment, validating a FASTA file, checking sequencing quality, or preparing features for downstream analysis, base composition matters. On Linux, Python offers a clean and reproducible way to compute nucleotide counts and percentages while integrating smoothly with shell tools, text processing utilities, and larger genomic pipelines.</p>

<p>When people search for “using linux to calculate frequency of nucleotides using python,” they usually need more than a one-line script. They need a practical workflow: how to set up Python on Linux, how to read sequence files, how to handle FASTA headers, how to clean ambiguous characters such as N, and how to summarize A, T, G, and C frequency correctly. They also need to understand what those numbers mean biologically. This guide walks through that process in a way that is useful for students, researchers, and developers building production-grade bioinformatics workflows.</p>

<h3>Why nucleotide frequency matters</h3>
    <p>Nucleotide frequency gives the proportion or count of each DNA base in a sequence. At the simplest level, you count how many A, T, G, and C characters appear. From those counts, you can derive:</p>
    <ul>
      <li><strong>Relative frequency</strong>: percentage of each nucleotide in the sequence.</li>
      <li><strong>GC content</strong>: percentage of G plus C bases.</li>
      <li><strong>AT content</strong>: percentage of A plus T bases.</li>
      <li><strong>Ambiguous content</strong>: fraction of N or other IUPAC ambiguity symbols.</li>
    </ul>

<p>These metrics are useful in sequence quality control, primer design, contamination checks, comparative genomics, machine learning feature engineering, and validation of parsing scripts. In microbial genomics, GC content can vary substantially across species, and in eukaryotic analysis, unusual local nucleotide frequencies may highlight regions with distinct composition or function.</p>

<h3>Why Linux is ideal for this workflow</h3>
    <p>Linux remains the dominant environment for computational biology because it handles large text files efficiently and supports command-line automation. FASTA and FASTQ files are plain text or compressed text, which makes them naturally compatible with Linux tools such as <code>grep</code>, <code>awk</code>, <code>sed</code>, <code>wc</code>, and <code>zcat</code>. Python then adds structured logic, file parsing, and reusable scripting.</p>

<p>A typical Linux-based nucleotide frequency workflow looks like this:</p>
    <ol>
      <li>Open a terminal on Ubuntu, Debian, Fedora, CentOS Stream, or another Linux distribution.</li>
      <li>Confirm Python is installed with <code>python3 --version</code>.</li>
      <li>Store your DNA sequence in a plain text or FASTA file.</li>
      <li>Run a Python script that removes headers, normalizes case, counts bases, and prints percentages.</li>
      <li>Optionally pipe results into larger analysis workflows or save summaries to CSV.</li>
    </ol>

<div class="wpc-code">python3 –version
pwd
ls -lh
nano nucleotide_count.py</div>

<h3>Basic Python logic for nucleotide frequency</h3>
    <p>The core logic is straightforward. You read the sequence, convert it to uppercase, remove non-sequence characters, and count occurrences of A, T, G, and C. Python strings already provide a convenient <code>.count()</code> method, and loops or dictionaries provide even more control if you want to track ambiguous bases.</p>

<div class="wpc-code">sequence = “ATGCGATTAACCGGTTNNATGCATGC”.upper()
counts = {
    “A”: sequence.count(“A”),
    “T”: sequence.count(“T”),
    “G”: sequence.count(“G”),
    “C”: sequence.count(“C”)
}
total = sum(counts.values())
for base, count in counts.items():
    print(base, count, round((count / total) * 100, 2))</div>

<p>That works for a raw sequence string, but Linux workflows often involve FASTA files. FASTA files contain one or more header lines beginning with <strong>></strong>, followed by sequence lines that can span multiple rows. A robust script must skip headers and combine sequence lines before counting nucleotides.</p>

<h3>Reading a FASTA file on Linux with Python</h3>
    <p>Suppose you have a file called <code>sample.fa</code>. A practical script should read the file line by line, ignore headers, strip whitespace, and concatenate sequence lines.</p>

<div class="wpc-code">from collections import Counter

filename = “sample.fa”
sequence_parts = []

with open(filename, “r”, encoding=”utf-8″) as handle:
    for line in handle:
        if line.startswith(“>”):
            continue
        sequence_parts.append(line.strip().upper())

sequence = “”.join(sequence_parts)
counts = Counter(sequence)

a = counts.get(“A”, 0)
t = counts.get(“T”, 0)
g = counts.get(“G”, 0)
c = counts.get(“C”, 0)

canonical_total = a + t + g + c
gc_content = ((g + c) / canonical_total) * 100 if canonical_total else 0

print(“A:”, a)
print(“T:”, t)
print(“G:”, g)
print(“C:”, c)
print(“GC%:”, round(gc_content, 2))</div>

<p>This pattern is portable, easy to maintain, and fully compatible with Linux command-line workflows. You can run it with:</p>

<div class="wpc-code">python3 nucleotide_count.py</div>

<h3>Handling ambiguous bases correctly</h3>
    <p>Real biological data is often messy. You may see <code>N</code> for unknown nucleotides, or ambiguity codes such as R, Y, S, W, K, and M. This raises an important question: what denominator should you use when calculating percentages?</p>

<ul>
      <li><strong>Canonical denominator</strong>: only A, T, G, and C count toward percentages. This is common for GC content calculations.</li>
      <li><strong>Full cleaned denominator</strong>: all sequence symbols, including N, count toward percentages. This is useful for quality summaries.</li>
    </ul>

<p>There is no universal answer. The correct approach depends on your goal. If you are estimating biological composition, you usually exclude ambiguous symbols. If you are evaluating data cleanliness, include them in a separate summary so that uncertainty is visible.</p>

<div class="wpc-highlight">
      Best practice: report both the canonical nucleotide counts and the ambiguous-base count. This makes your script transparent and avoids confusion in downstream analyses.
    </div>

<h3>Comparison table: approximate genome GC content across common model organisms</h3>
    <p>The value of nucleotide frequency becomes clearer when you compare organisms. GC content is a classic composition metric and differs meaningfully across taxa. The table below lists commonly cited approximate values used in introductory and practical genomics contexts.</p>

<div class="wpc-table-wrap">
      <table class="wpc-table">
        <thead>
          <tr>
            <th>Organism</th>
            <th>Approximate Genome Size</th>
            <th>Approximate GC Content</th>
            <th>Interpretation</th>
          </tr>
        </thead>
        <tbody>
          <tr>
            <td>Human (<em>Homo sapiens</em>)</td>
            <td>3.2 billion base pairs</td>
            <td>About 41%</td>
            <td>Moderate GC content with strong regional variation across chromosomes.</td>
          </tr>
          <tr>
            <td><em>Escherichia coli</em> K-12</td>
            <td>4.64 million base pairs</td>
            <td>About 50.8%</td>
            <td>Near-balanced GC composition, common in bacterial teaching datasets.</td>
          </tr>
          <tr>
            <td>Baker’s yeast (<em>Saccharomyces cerevisiae</em>)</td>
            <td>12.1 million base pairs</td>
            <td>About 38.3%</td>
            <td>AT-richer than many bacteria and useful for eukaryotic comparisons.</td>
          </tr>
          <tr>
            <td><em>Mycobacterium tuberculosis</em></td>
            <td>4.41 million base pairs</td>
            <td>About 65.6%</td>
            <td>High-GC bacterium, illustrating how strongly composition can shift.</td>
          </tr>
        </tbody>
      </table>
    </div>

<p>These approximate statistics are useful because they show why frequency analysis is not just bookkeeping. If your measured GC content is dramatically different from what you expect for a known organism, that may indicate contamination, truncation, parsing problems, or the wrong input file.</p>

<h3>Linux command-line checks before running Python</h3>
    <p>Before launching your Python script, Linux tools can help validate the input. This saves time and catches format problems early.</p>
    <ul>
      <li><code>head sample.fa</code> to inspect headers and the first sequence lines.</li>
      <li><code>grep -c "^>" sample.fa</code> to count FASTA records.</li>
      <li><code>wc -l sample.fa</code> to measure file length.</li>
      <li><code>tr -d "\n\r" < sample.txt | wc -c</code> to estimate sequence length for single-line files.</li>
    </ul>

<p>For compressed files, Linux also makes streaming easy:</p>
    <div class="wpc-code">zcat reads.fa.gz | head
zcat reads.fa.gz | python3 nucleotide_count.py</div>

<h3>Comparison table: practical interpretation of nucleotide frequency outputs</h3>
    <div class="wpc-table-wrap">
      <table class="wpc-table">
        <thead>
          <tr>
            <th>Observed Pattern</th>
            <th>Possible Cause</th>
            <th>Recommended Linux/Python Follow-up</th>
          </tr>
        </thead>
        <tbody>
          <tr>
            <td>Very high N percentage, such as above 5%</td>
            <td>Low-quality sequencing, masked genome region, or incomplete assembly</td>
            <td>Count ambiguous symbols separately and inspect source records with <code>grep</code> or Python filters</td>
          </tr>
          <tr>
            <td>GC content far below or above expected species range</td>
            <td>Contamination, wrong organism, subset bias, or parser error</td>
            <td>Recheck file format, compare against reference statistics, and verify headers</td>
          </tr>
          <tr>
            <td>Only one or two nucleotides dominate unnaturally</td>
            <td>Corrupted input, adapter sequence, synthetic control, or user formatting issue</td>
            <td>Print the cleaned sequence preview and validate non-ATGC characters</td>
          </tr>
          <tr>
            <td>Total canonical count smaller than expected</td>
            <td>Many whitespace or invalid characters removed during cleanup</td>
            <td>Log discarded symbols and report both raw and cleaned lengths</td>
          </tr>
        </tbody>
      </table>
    </div>

<h3>How to build a more production-ready Python script</h3>
    <p>For serious Linux use, your script should accept command-line arguments and produce reproducible outputs. Useful improvements include:</p>
    <ol>
      <li>Accept an input path via <code>sys.argv</code> or <code>argparse</code>.</li>
      <li>Support both raw sequence text and FASTA files.</li>
      <li>Report cleaned length, canonical length, and ambiguous count separately.</li>
      <li>Write results to stdout and optionally save a CSV report.</li>
      <li>Exit with a helpful error code if the file is empty or malformed.</li>
    </ol>

<p>These features matter in Linux pipelines because you often run the same script over many files using shell loops, workflow managers, or job schedulers. A well-designed script should be predictable enough to use in <code>bash</code>, <code>cron</code>, Snakemake, Nextflow, or SLURM environments.</p>

<h3>Python libraries that can help</h3>
    <p>You do not always need external libraries, but some are worth knowing:</p>
    <ul>
      <li><strong>Biopython</strong> for FASTA parsing and broader sequence utilities.</li>
      <li><strong>pandas</strong> for summarizing results from many files.</li>
      <li><strong>matplotlib</strong> or web charting tools for visualizing base composition.</li>
    </ul>

<p>If your project is simple, standard Python is enough. For teaching and deployment, fewer dependencies are often better. On Linux servers, this reduces environment issues and makes scripts easier to share.</p>

<h3>Common mistakes when calculating nucleotide frequency</h3>
    <ul>
      <li>Forgetting to remove FASTA headers.</li>
      <li>Using lowercase sequence without normalization.</li>
      <li>Including whitespace or hidden characters in counts.</li>
      <li>Ignoring ambiguous symbols without documenting that choice.</li>
      <li>Using the wrong denominator for percentage calculations.</li>
      <li>Assuming one file contains only one sequence record.</li>
    </ul>

<p>Even small mistakes can distort percentages. On Linux, reproducibility comes from scripting every step clearly. If you document your cleanup rules and denominator choice, other researchers can reproduce your values exactly.</p>

<h3>Authoritative reference links for Linux, Python, and genomics data</h3>
    <p>When validating sequence-based calculations, it helps to use trusted reference resources. The following sources are authoritative and relevant to Python-based nucleotide frequency work in Linux bioinformatics environments:</p>
    <ul>
      <li><a href="https://www.ncbi.nlm.nih.gov/" target="_blank" rel="noopener noreferrer">NCBI</a> for reference sequences, FASTA records, and genome resources.</li>
      <li><a href="https://medlineplus.gov/genetics/understanding/basics/dna/" target="_blank" rel="noopener noreferrer">MedlinePlus Genetics (.gov)</a> for a government-backed overview of DNA and nucleotide basics.</li>
      <li><a href="https://bioinformatics.oxfordjournals.org/" target="_blank" rel="noopener noreferrer">Oxford academic bioinformatics resources</a> are useful, but for a strict .edu option you can also consult <a href="https://genome.ucsc.edu/" target="_blank" rel="noopener noreferrer">UCSC Genome Browser (.edu)</a> for genome data context.</li>
    </ul>

<h3>Putting it all together</h3>
    <p>If your goal is “using linux to calculate frequency of nucleotides using python,” the fastest reliable approach is this: inspect the file in Linux, parse it in Python, count A, T, G, and C, report percentages, and always state how ambiguous bases were handled. That gives you a reproducible method suitable for notebooks, scripts, teaching labs, and real research pipelines.</p>

<p>The calculator on this page follows that same philosophy. It lets you paste raw sequence or FASTA text, choose whether percentages should be based only on canonical nucleotides or all cleaned symbols, and optionally count ambiguous bases separately. The chart helps you see base composition instantly, which is particularly helpful for teaching, quick QC, and sanity checks before running more computationally expensive analyses.</p>

<p>In practice, nucleotide frequency is often the first checkpoint before moving into motif analysis, codon bias studies, k-mer counting, primer design, or taxonomic comparison. A clean Linux plus Python workflow gives you speed, transparency, and scalability. Once you trust your counting logic on small examples, you can extend the same principles to whole genomes and large sequencing datasets.</p>

<div class="wpc-footer-note">
      <strong>Professional recommendation:</strong> save your nucleotide frequency script in version control, keep a tiny FASTA test file with known counts, and compare your outputs against expected values whenever you modify parsing logic. In bioinformatics, simple validation prevents expensive downstream errors.
    </div>
  </article>
</div>

function wpcParseSequence(inputText, inputFormat, removeWhitespace, caseMode) {
    let text = inputText || "";

if (inputFormat === "fasta" || (inputFormat === "auto" && text.indexOf(">") !== -1)) {
      const lines = text.split(/\r?\n/);
      const seqLines = [];
      for (let i = 0; i < lines.length; i++) {
        if (!lines[i].startsWith(">")) {
          seqLines.push(lines[i]);
        }
      }
      text = seqLines.join("");
    }

if (removeWhitespace) {
      text = text.replace(/\s+/g, "");
    }

if (caseMode === "normalize" || caseMode === "strict") {
      text = text.toUpperCase();
    }

return text;
  }

function wpcCalculate() {
    const inputText = document.getElementById("wpc-sequence-input").value;
    const inputFormat = document.getElementById("wpc-input-format").value;
    const percentMode = document.getElementById("wpc-percent-mode").value;
    const ambiguousMode = document.getElementById("wpc-ambiguous-mode").value;
    const caseMode = document.getElementById("wpc-case-mode").value;
    const removeWhitespace = document.getElementById("wpc-remove-whitespace").checked;
    const resultsEl = document.getElementById("wpc-results");

const cleaned = wpcParseSequence(inputText, inputFormat, removeWhitespace, caseMode);

if (!cleaned) {
      resultsEl.innerHTML = "<strong>No sequence found.</strong> Please paste a DNA sequence or FASTA content.";
      wpcRenderChart([0, 0, 0, 0, 0], ["A", "T", "G", "C", "Ambiguous"]);
      return;
    }

const validChars = /^[ATGCNRYSWKMBDHVU.-]*$/i;
    if (caseMode === "strict" && !validChars.test(cleaned)) {
      resultsEl.innerHTML = "<strong>Invalid characters detected.</strong> Strict mode allows A, T, G, C, N and standard ambiguity symbols only after cleanup.";
      wpcRenderChart([0, 0, 0, 0, 0], ["A", "T", "G", "C", "Ambiguous"]);
      return;
    }

const chars = cleaned.split("");
    let countA = 0;
    let countT = 0;
    let countG = 0;
    let countC = 0;
    let ambiguous = 0;
    let discarded = 0;

for (let i = 0; i < chars.length; i++) {
      const ch = chars[i];
      if (ch === "A") {
        countA++;
      } else if (ch === "T" || ch === "U") {
        countT++;
      } else if (ch === "G") {
        countG++;
      } else if (ch === "C") {
        countC++;
      } else if ("NRYSWKMBDHV.-".indexOf(ch) !== -1) {
        ambiguous++;
      } else {
        discarded++;
      }
    }

const canonicalTotal = countA + countT + countG + countC;
    const cleanedRecognizedTotal = canonicalTotal + ambiguous;
    const denominator = percentMode === "all" ? cleanedRecognizedTotal : canonicalTotal;

function pct(value) {
      return denominator > 0 ? ((value / denominator) * 100).toFixed(2) : "0.00";
    }

const gc = canonicalTotal > 0 ? (((countG + countC) / canonicalTotal) * 100).toFixed(2) : "0.00";
    const at = canonicalTotal > 0 ? (((countA + countT) / canonicalTotal) * 100).toFixed(2) : "0.00";

let html = "";
    html += "<strong>Calculation complete.</strong>";
    html += "<div class='wpc-result-grid'>";
    html += "<div class='wpc-stat'><strong>" + canonicalTotal + "</strong>Canonical A/T/G/C Total</div>";
    html += "<div class='wpc-stat'><strong>" + cleanedRecognizedTotal + "</strong>Recognized Cleaned Length</div>";
    html += "<div class='wpc-stat'><strong>" + discarded + "</strong>Discarded Characters</div>";
    html += "<div class='wpc-stat'><strong>" + countA + " (" + pct(countA) + "%)</strong>Adenine (A)</div>";
    html += "<div class='wpc-stat'><strong>" + countT + " (" + pct(countT) + "%)</strong>Thymine (T)</div>";
    html += "<div class='wpc-stat'><strong>" + countG + " (" + pct(countG) + "%)</strong>Guanine (G)</div>";
    html += "<div class='wpc-stat'><strong>" + countC + " (" + pct(countC) + "%)</strong>Cytosine (C)</div>";
    html += "<div class='wpc-stat'><strong>" + gc + "%</strong>GC Content</div>";
    html += "<div class='wpc-stat'><strong>" + at + "%</strong>AT Content</div>";
    html += "</div>";

if (ambiguousMode === "count") {
      html += "<p class='wpc-note'><strong>Ambiguous bases:</strong> " + ambiguous + " (" + (cleanedRecognizedTotal > 0 ? ((ambiguous / cleanedRecognizedTotal) * 100).toFixed(2) : "0.00") + "% of recognized cleaned length)</p>";
    } else {
      html += "<p class='wpc-note'>Ambiguous bases detected but excluded from the visible percentage summary: <strong>" + ambiguous + "</strong></p>";
    }

html += "<p class='wpc-note'>Percentages are based on <strong>" + (percentMode === "all" ? "all recognized cleaned characters" : "canonical A/T/G/C only") + "</strong>. U is treated as T for convenience when RNA-like input appears.</p>";

resultsEl.innerHTML = html;

const chartLabels = ambiguousMode === "count"
      ? ["A", "T", "G", "C", "Ambiguous"]
      : ["A", "T", "G", "C"];

const chartData = ambiguousMode === "count"
      ? [countA, countT, countG, countC, ambiguous]
      : [countA, countT, countG, countC];

wpcRenderChart(chartData, chartLabels);
  }

function wpcRenderChart(data, labels) {
    const ctx = document.getElementById("wpc-chart").getContext("2d");

if (wpcChartInstance) {
      wpcChartInstance.destroy();
    }

const backgroundColors = labels.length === 5
      ? ["#2563eb", "#0ea5e9", "#16a34a", "#f59e0b", "#94a3b8"]
      : ["#2563eb", "#0ea5e9", "#16a34a", "#f59e0b"];

const borderColors = labels.length === 5
      ? ["#1d4ed8", "#0284c7", "#15803d", "#d97706", "#64748b"]
      : ["#1d4ed8", "#0284c7", "#15803d", "#d97706"];

wpcChartInstance = new Chart(ctx, {
      type: "bar",
      data: {
        labels: labels,
        datasets: [{
          label: "Nucleotide Count",
          data: data,
          backgroundColor: backgroundColors,
          borderColor: borderColors,
          borderWidth: 2,
          borderRadius: 10
        }]
      },
      options: {
        responsive: true,
        maintainAspectRatio: false,
        scales: {
          y: {
            beginAtZero: true,
            ticks: {
              color: "#334155"
            },
            grid: {
              color: "#e2e8f0"
            }
          },
          x: {
            ticks: {
              color: "#334155"
            },
            grid: {
              display: false
            }
          }
        },
        plugins: {
          legend: {
            labels: {
              color: "#0f172a"
            }
          },
          tooltip: {
            backgroundColor: "#0f172a",
            titleColor: "#ffffff",
            bodyColor: "#ffffff"
          }
        }
      }
    });
  }

document.getElementById("wpc-calc-btn").addEventListener("click", wpcCalculate);

document.getElementById("wpc-sample-btn").addEventListener("click", function () {
    document.getElementById("wpc-sequence-input").value = ">demo_sequence\nATGCGATTAACCGGTTNNATGCATGCCCGTAAGTTCGATCGATCGNNNATATGC";
    wpcCalculate();
  });

document.getElementById("wpc-clear-btn").addEventListener("click", function () {
    document.getElementById("wpc-sequence-input").value = "";
    document.getElementById("wpc-results").innerHTML = "<strong>Ready to analyze.</strong> Enter a sequence and click <strong>Calculate Frequencies</strong> to see counts, percentages, and a chart.";
    wpcRenderChart([0, 0, 0, 0, 0], ["A", "T", "G", "C", "Ambiguous"]);
  });

wpcRenderChart([0, 0, 0, 0, 0], ["A", "T", "G", "C", "Ambiguous"]);
})();
</script>

</div>
</div>

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