Calculating Dna Molecular Weight from Gel Electrophoresis

DNA Molecular Weight Calculator from Gel Electrophoresis body { font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; line-height: 1.6; color: #333; background-color: #f8f9fa; margin: 0; padding: 0; } .container { max-width: 960px; margin: 20px auto; padding: 20px; background-color: #ffffff; border-radius: 8px; box-shadow: 0 4px 12px rgba(0, 0, 0, 0.05); } .header { background-color: #004a99; color: white; padding: 20px 0; text-align: center; border-radius: 8px 8px 0 0; margin-bottom: 20px; } .header h1 { margin: 0; font-size: 2.5em; font-weight: 700; } .loan-calc-container { background-color: #ffffff; padding: 30px; border-radius: 8px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.03); margin-bottom: 30px; } .input-group { margin-bottom: 20px; display: flex; flex-direction: column; } .input-group label { display: block; margin-bottom: 8px; font-weight: 600; color: #004a99; } .input-group input[type="number"], .input-group input[type="text"], .input-group select { padding: 12px; border: 1px solid #ced4da; border-radius: 5px; font-size: 1em; transition: border-color 0.3s ease; } .input-group input[type="number"]:focus, .input-group input[type="text"]:focus, .input-group select:focus { border-color: #004a99; outline: none; } .input-group small { color: #6c757d; margin-top: 5px; font-size: 0.85em; } .error-message { color: #dc3545; font-size: 0.8em; margin-top: 5px; display: none; } .button-group { display: flex; justify-content: space-between; margin-top: 25px; } .btn { padding: 12px 25px; border: none; border-radius: 5px; font-size: 1em; font-weight: 600; cursor: pointer; transition: background-color 0.3s ease, transform 0.2s ease; } .btn-primary { background-color: #004a99; color: white; } .btn-primary:hover { background-color: #003366; transform: translateY(-1px); } .btn-secondary { background-color: #6c757d; color: white; } .btn-secondary:hover { background-color: #5a6268; transform: translateY(-1px); } .btn-success { background-color: #28a745; color: white; } .btn-success:hover { background-color: #218838; transform: translateY(-1px); } #results { margin-top: 30px; padding: 25px; background-color: #e9ecef; border-radius: 8px; border: 1px solid #dee2e6; } #results h3 { margin-top: 0; color: #004a99; font-size: 1.8em; text-align: center; margin-bottom: 20px; } .result-item { display: flex; justify-content: space-between; padding: 10px 0; border-bottom: 1px dashed #ccc; } .result-item:last-child { border-bottom: none; } .result-item span:first-child { font-weight: 500; color: #495057; } .result-item span:last-child { font-weight: 600; color: #004a99; } .main-result { background-color: #28a745; color: white; padding: 15px 20px; border-radius: 5px; text-align: center; font-size: 1.5em; margin-bottom: 15px; font-weight: 700; } .main-result-label { font-size: 1em; font-weight: 500; display: block; margin-bottom: 5px; color: rgba(255, 255, 255, 0.8); } .formula-explanation { font-size: 0.95em; color: #555; margin-top: 15px; padding-top: 15px; border-top: 1px solid #ddd; text-align: center; } table { width: 100%; border-collapse: collapse; margin-top: 25px; } th, td { padding: 10px; text-align: left; border: 1px solid #dee2e6; } th { background-color: #004a99; color: white; font-weight: 600; } tbody tr:nth-child(even) { background-color: #f2f2f2; } caption { caption-side: top; font-weight: bold; font-size: 1.1em; margin-bottom: 10px; color: #004a99; } #chartContainer { margin-top: 30px; background-color: #ffffff; padding: 20px; border-radius: 8px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.03); text-align: center; } #chartContainer h3 { margin-top: 0; color: #004a99; font-size: 1.8em; margin-bottom: 20px; } .article-section { margin-top: 40px; padding-top: 20px; border-top: 1px solid #e0e0e0; } .article-section h2 { color: #004a99; font-size: 2em; margin-bottom: 20px; border-bottom: 2px solid #004a99; padding-bottom: 5px; } .article-section h3 { color: #004a99; font-size: 1.6em; margin-top: 30px; margin-bottom: 15px; } .article-section p, .article-section ul, .article-section ol { margin-bottom: 20px; font-size: 1.1em; } .article-section li { margin-bottom: 10px; } .article-section strong { color: #004a99; } .internal-link-list li { margin-bottom: 15px; } .internal-link-list a { color: #004a99; font-weight: 600; text-decoration: none; } .internal-link-list a:hover { text-decoration: underline; } .internal-link-list span { display: block; font-size: 0.9em; color: #555; margin-top: 3px; } .faq-item { border: 1px solid #ddd; border-radius: 5px; margin-bottom: 15px; padding: 15px; background-color: #fdfdfd; } .faq-item h4 { margin: 0 0 10px 0; color: #004a99; font-size: 1.2em; } .faq-item p { margin: 0; font-size: 1em; } .footer { text-align: center; margin-top: 40px; padding: 20px; font-size: 0.9em; color: #6c757d; } canvas { max-width: 100%; height: auto; display: block; margin: 20px auto; } @media (max-width: 768px) { .container { margin: 10px; padding: 15px; } .header h1 { font-size: 2em; } .btn { padding: 10px 18px; font-size: 0.95em; } .button-group { flex-direction: column; gap: 10px; } .button-group .btn { width: 100%; } #results, #chartContainer { padding: 15px; } .article-section h2 { font-size: 1.7em; } .article-section h3 { font-size: 1.4em; } }

DNA Molecular Weight Calculator from Gel Electrophoresis

Gel Electrophoresis Molecular Weight Estimation

Estimate the molecular weight of DNA fragments based on their migration distance in a gel electrophoresis experiment. Requires a standard curve generated from known DNA markers.

The distance the DNA band has migrated from the origin well.
Size of the first known DNA marker.
Migration distance of the first known marker.
Size of the second known DNA marker.
Migration distance of the second known marker.
Size of the third known DNA marker (optional, for better accuracy).
Migration distance of the third known marker (optional).

Calculation Results

Estimated Molecular Weight bp
Log10 Molecular Weight (Marker 1)
Log10 Molecular Weight (Marker 2)
Log10 Molecular Weight (Marker 3)
Slope of Standard Curve
Y-intercept of Standard Curve
Sample Log10 Molecular Weight
The molecular weight is estimated using a standard curve generated by plotting the logarithm of the molecular weights of known DNA markers against their migration distances. The relationship is typically linear (y = mx + b), where y is log10(molecular weight), x is migration distance, m is the slope, and b is the y-intercept. The migration distance of the unknown sample is then used to interpolate its molecular weight from this curve.

Standard Curve Visualization

Standard Curve Data
Marker Size (bp) Log10 Size Migration Distance (mm)

What is DNA Molecular Weight Calculation from Gel Electrophoresis?

Calculating DNA molecular weight from gel electrophoresis is a fundamental technique in molecular biology used to estimate the size of DNA fragments. When DNA molecules are subjected to an electric field in an agarose or polyacrylamide gel, they migrate towards the positive electrode. Smaller DNA fragments move faster and travel further through the gel matrix than larger fragments. By comparing the migration distance of an unknown DNA fragment to that of DNA fragments with known sizes (molecular weight markers or ladders), researchers can accurately determine the approximate molecular weight of the unknown fragment. This process is crucial for various applications, including DNA fragment analysis, cloning, PCR product verification, and genetic research.

Who should use it: This method is essential for molecular biologists, geneticists, biochemists, researchers in academic and industrial labs, and students learning molecular biology techniques. Anyone working with DNA and needing to assess fragment sizes will find this calculation invaluable.

Common misconceptions: A common misconception is that gel electrophoresis directly measures molecular weight. In reality, it measures migration distance, and the molecular weight is *inferred* by comparing this distance to a standard curve. Another misconception is that the standard curve is always perfectly linear; in practice, deviations can occur, especially at the extremes of the gel or with very large/small fragments. Furthermore, the relationship between migration distance and log of molecular weight is an approximation, and other factors like DNA conformation and buffer conditions can subtly influence migration.

DNA Molecular Weight Calculation from Gel Electrophoresis Formula and Mathematical Explanation

The core principle behind calculating DNA molecular weight from gel electrophoresis relies on establishing a standard curve. This curve represents the relationship between the size (molecular weight) of DNA fragments and how far they migrate in a gel under specific conditions. Typically, the logarithm (base 10) of the molecular weight is plotted against the migration distance. This relationship is often approximated as linear, allowing for interpolation.

The general form of the linear equation is:

\( y = mx + b \)

Where:

  • \( y \) = Logarithm base 10 of the DNA molecular weight (often expressed in base pairs, bp, or Daltons, Da).
  • \( x \) = Migration distance of the DNA fragment from the origin (usually in millimeters, mm).
  • \( m \) = The slope of the standard curve.
  • \( b \) = The y-intercept of the standard curve.

Step-by-step derivation:

  1. Data Collection: Obtain the migration distances ( \( x_1, x_2, x_3, … \) ) for several DNA fragments of known molecular weights ( \( MW_1, MW_2, MW_3, … \) ). The molecular weights are typically provided by the manufacturer of the DNA ladder or standard.
  2. Logarithmic Transformation: Calculate the logarithm base 10 for each known molecular weight: \( y_i = \log_{10}(MW_i) \).
  3. Standard Curve Plotting: Plot these transformed data points \( (x_i, y_i) \) on a graph, with migration distance on the x-axis and log10(molecular weight) on the y-axis.
  4. Linear Regression: Perform a linear regression analysis on these points to determine the best-fit line. This yields the slope (\( m \)) and the y-intercept (\( b \)). The formulas for \( m \) and \( b \) in linear regression are:
    \( m = \frac{n(\sum xy) – (\sum x)(\sum y)}{n(\sum x^2) – (\sum x)^2} \)
    \( b = \frac{(\sum y) – m(\sum x)}{n} \)
    Where \( n \) is the number of data points (known markers).
  5. Interpolation: Measure the migration distance (\( x_{sample} \)) of the unknown DNA fragment.
  6. Calculate Sample Log Molecular Weight: Use the standard curve equation to find the log10 molecular weight of the sample: \( y_{sample} = m \cdot x_{sample} + b \).
  7. Calculate Sample Molecular Weight: Convert the log10 molecular weight back to the actual molecular weight by taking the antilog (10 to the power of \( y_{sample} \)): \( MW_{sample} = 10^{y_{sample}} \).

Variable Explanations:

The variables used in calculating DNA molecular weight from gel electrophoresis are critical for understanding the estimation process:

Variables Table:

Variables in DNA Molecular Weight Estimation
Variable Meaning Unit Typical Range
\( MW_{known} \) Molecular Weight of a known DNA marker Base Pairs (bp) or Daltons (Da) 100 bp – 50,000 bp (or more, depending on ladder)
\( x \) Migration Distance Millimeters (mm) 0 mm (origin) to gel length (e.g., 100 mm)
\( y \) Logarithm Base 10 of Molecular Weight Log10(bp) 2 (for 100 bp) to 5+ (for >100,000 bp)
\( m \) Slope of the Standard Curve Log10(bp) / mm Typically negative (e.g., -0.02 to -0.05)
\( b \) Y-intercept of the Standard Curve Log10(bp) Varies based on markers and gel conditions (e.g., 3.5 to 5.0)
\( x_{sample} \) Migration Distance of Unknown Sample Millimeters (mm) 0 mm to gel length
\( y_{sample} \) Log10 Molecular Weight of Unknown Sample Log10(bp) Estimated based on \( x_{sample} \)
\( MW_{sample} \) Estimated Molecular Weight of Unknown Sample Base Pairs (bp) or Daltons (Da) Estimated based on \( y_{sample} \)

Practical Examples (Real-World Use Cases)

Here are practical scenarios demonstrating the use of our DNA molecular weight calculator:

Example 1: Verifying PCR Product Size

A researcher performs a Polymerase Chain Reaction (PCR) to amplify a specific gene segment. They run the PCR product on an agarose gel alongside a DNA ladder (markers).

Inputs:

  • Unknown PCR Product Migration Distance: 35 mm
  • Known Marker 1: 1000 bp at 25 mm
  • Known Marker 2: 4000 bp at 15 mm
  • Known Marker 3: 8000 bp at 8 mm

Calculation Process: The calculator first determines \( \log_{10}(1000) \approx 3.0 \), \( \log_{10}(4000) \approx 3.6 \), \( \log_{10}(8000) \approx 3.9 \). It then plots these points with their respective migration distances. Linear regression yields the slope and y-intercept for the standard curve. Using the sample's migration distance of 35 mm, the calculator interpolates the value on the standard curve to estimate the log10 molecular weight of the PCR product, and then converts this back to bp.

Outputs (hypothetical):

  • Slope: -0.035 log10(bp)/mm
  • Y-intercept: 4.2 log10(bp)
  • Sample Log10 Molecular Weight: 2.975 log10(bp)
  • Estimated Molecular Weight: ~944 bp

Interpretation: The PCR product is estimated to be approximately 944 bp in size. This can be compared to the expected size of the amplified gene segment to confirm the success of the PCR reaction. If the expected size was, for instance, 1000 bp, this result suggests the amplification was successful and the product is close to the target size, accounting for slight variations in gel migration.

Example 2: Analyzing Restriction Digest Fragments

A plasmid DNA sample is digested with restriction enzymes, and the resulting fragments are analyzed by gel electrophoresis to determine their sizes.

Inputs:

  • Unknown Fragment Migration Distance: 70 mm
  • Known Marker 1: 500 bp at 40 mm
  • Known Marker 2: 2000 bp at 20 mm

Calculation Process: Similar to the previous example, the calculator computes \( \log_{10}(500) \approx 2.7 \) and \( \log_{10}(2000) \approx 3.3 \). It uses these two points to derive the standard curve's slope and intercept. The sample's migration distance (70 mm) is then used to find its position on the curve, estimating its log10 molecular weight and subsequently its actual molecular weight.

Outputs (hypothetical):

  • Slope: -0.03 log10(bp)/mm
  • Y-intercept: 3.5 log10(bp)
  • Sample Log10 Molecular Weight: 1.4 log10(bp)
  • Estimated Molecular Weight: ~25 bp

Interpretation: The digested DNA sample contains a fragment estimated to be around 25 bp. This might be a small fragment generated by the restriction digest, or it could indicate degradation of the DNA sample, especially if larger fragments were also expected. The accuracy depends heavily on the quality of the ladder and the gel run. Adding more markers would improve confidence.

How to Use This DNA Molecular Weight Calculator

Our calculator simplifies the process of estimating DNA molecular weight from gel electrophoresis data. Follow these steps for accurate results:

  1. Prepare Your Gel Data: Before using the calculator, you need the migration distances (in millimeters) for your known DNA markers (ladder) and your unknown DNA sample(s). Ensure all measurements are taken consistently from the origin well to the leading edge of the DNA band.
  2. Input Known Marker Data: Enter the size (in base pairs, bp) and migration distance (in mm) for at least two known DNA markers. For greater accuracy, input a third marker if available. The calculator uses these points to construct a standard curve.
  3. Input Unknown Sample Data: Enter the migration distance (in mm) of your DNA sample or fragment of interest.
  4. Calculate: Click the "Calculate Molecular Weight" button. The calculator will process the inputs, generate the standard curve parameters (slope and intercept), estimate the molecular weight of your sample, and display intermediate values.
  5. Understand the Results:
    • Estimated Molecular Weight: This is the primary result, indicating the approximate size of your DNA fragment in base pairs (bp).
    • Intermediate Values: The slope, y-intercept, and log values provide insight into the standard curve and the sample's position on it.
    • Standard Curve Table: Review the table to see how your known markers were used.
    • Visualization: The chart dynamically displays your standard curve and the position of your sample, aiding in visual interpretation.
  6. Decision-Making Guidance:
    • PCR Verification: Does the estimated size match your expected PCR product size? A close match indicates successful amplification.
    • Restriction Digest Analysis: Are the fragment sizes consistent with the expected fragments after enzyme digestion?
    • Cloning: Do the insert and vector fragment sizes align for successful ligation?
    • Troubleshooting: If results are unexpected, check your marker sizes, ensure accurate distance measurements, verify gel conditions (voltage, time, buffer), and consider running more markers for a more robust curve.
  7. Reset and Recalculate: Use the "Reset" button to clear all fields and start over. Use "Copy Results" to save the calculated data.

Key Factors That Affect DNA Molecular Weight Calculation Results

While the calculator provides an estimate, several factors can influence the accuracy of DNA molecular weight determination via gel electrophoresis:

  • Accuracy of DNA Ladder: The precision of the molecular weight estimations is directly dependent on the accuracy and resolution of the DNA ladder (markers) used. If the ladder's band sizes are not precisely known or are poorly resolved, the standard curve will be less reliable.
  • Number and Distribution of Markers: Using only two markers can lead to a less accurate standard curve, especially if the unknown sample's size falls far outside the range of the markers. A wider distribution of markers covering the expected size range of the unknown sample provides a more robust and accurate interpolation.
  • Gel Conditions (Agarose Concentration): The percentage of agarose in the gel dictates its pore size and affects DNA migration. Higher percentages resolve smaller fragments better, while lower percentages are better for larger fragments. Using an inappropriate agarose concentration can compress or distort the bands, impacting migration distances.
  • Electrophoresis Running Conditions: Factors like voltage, running time, buffer composition (e.g., TAE, TBE), and temperature can influence how DNA fragments migrate. Inconsistent or non-optimal conditions can lead to distorted bands or altered migration patterns, affecting the standard curve's linearity.
  • DNA Conformation: The shape of the DNA molecule (e.g., supercoiled, linear, nicked circular) can affect its migration rate independent of its length. For accurate molecular weight estimation, it's best to use linear DNA markers and assume the unknown sample is also linear. Denaturing conditions can sometimes be used to ensure linear conformations.
  • Measurement Accuracy: Precisely measuring the migration distance from the origin well to the *leading edge* of each band is crucial. Variations in measurement technique, parallax error, or uneven band migration can introduce significant inaccuracies.
  • Ethidium Bromide Staining and Visualization: Over- or under-staining with intercalating dyes like ethidium bromide can subtly alter the apparent size of DNA fragments. The intensity and clarity of bands also affect the precision of measurement.
  • Gel Compression/Distortion: At the beginning of the gel, migration can be non-linear due to the initial electric field strength and gel pores. Similarly, very large fragments might migrate slowly and compress together towards the end of the gel, making accurate size determination difficult.

Frequently Asked Questions (FAQ)

Can I use just one known marker to calculate molecular weight?

No, you need at least two known markers to establish a standard curve (a line). One marker only gives you a reference point, not a relationship between size and distance.

What units should I use for DNA size and migration distance?

The calculator expects DNA sizes in Base Pairs (bp) and migration distances in millimeters (mm). Ensure consistency.

Is the result always exact?

No, the result is an estimation. Gel electrophoresis is not a precise measurement tool for molecular weight. Factors like marker accuracy, gel conditions, and measurement precision introduce variability.

What should I do if my unknown sample migrates faster than my smallest marker?

Your estimation might be unreliable. Consider using a DNA ladder with smaller fragments or accepting that the size is less than the smallest marker's size (e.g., "smaller than 100 bp").

What if my unknown sample migrates slower than my largest marker?

The calculator can estimate this, but accuracy decreases significantly outside the range of your markers. It's best to use a ladder that covers the expected size range. You can state the estimate, but note the potential inaccuracy.

Why is the standard curve usually plotted on a log scale?

The relationship between DNA size and migration distance is generally inverse and non-linear on a linear scale. Plotting the log10 of the molecular weight against migration distance typically yields a linear relationship, making it easier to calculate and interpolate.

Can I use this for RNA molecular weight calculation?

The principle is similar, but the relationship between size and migration might differ for RNA compared to DNA. You would need RNA molecular weight markers and potentially a different standard curve derivation if the relationship is non-linear.

How does DNA conformation affect migration?

Supercoiled or nicked circular DNA molecules migrate differently than linear molecules of the same base pair count due to their compact shapes. For accurate size estimation, it's crucial that both the markers and the sample are in the same conformation, typically linear.

Related Tools and Internal Resources

© 2023 Your Company Name. All rights reserved.

function getElement(id) { return document.getElementById(id); } function setInnerHtml(id, value) { getElement(id).innerHTML = value; } function setStyleDisplay(id, display) { getElement(id).style.display = display; } function isValidNumber(value, min = -Infinity, max = Infinity) { if (value === null || value === "") return false; var num = parseFloat(value); return !isNaN(num) && num >= min && num <= max; } function showErrorMessage(elementId, message, show) { var errorElement = getElement(elementId); if (show) { errorElement.innerText = message; errorElement.style.display = 'block'; } else { errorElement.innerText = ''; errorElement.style.display = 'none'; } } function calculateLinearRegression(xData, yData) { var n = xData.length; var sumX = 0, sumY = 0, sumXY = 0, sumXX = 0; for (var i = 0; i < n; i++) { sumX += xData[i]; sumY += yData[i]; sumXY += xData[i] * yData[i]; sumXX += xData[i] * xData[i]; } var denominator = n * sumXX – sumX * sumX; if (denominator === 0) { return { slope: 0, intercept: sumY / n }; // Handle vertical line case, though unlikely here } var slope = (n * sumXY – sumX * sumY) / denominator; var intercept = (sumY – slope * sumX) / n; return { slope: slope, intercept: intercept }; } function updateChart(markers) { var canvas = getElement('electrophoresisChart'); var ctx = canvas.getContext('2d'); ctx.clearRect(0, 0, canvas.width, canvas.height); // Clear previous drawing var chartHeight = canvas.height; var chartWidth = canvas.width; var padding = 40; // Determine data ranges var minX = 0, maxX = 0; var minY = 0, maxY = 0; var xValues = []; var yValues = []; for (var i = 0; i < markers.length; i++) { xValues.push(markers[i].migration); yValues.push(markers[i].logMw); } // Add dummy points for axis range if only one marker if (xValues.length === 1) { xValues.push(xValues[0] + 1); // Add a small delta if only one point yValues.push(yValues[0]); } else if (xValues.length === 0) { xValues.push(0); yValues.push(0); // Default if no markers } minX = Math.min.apply(null, xValues); maxX = Math.max.apply(null, xValues); minY = Math.min.apply(null, yValues); maxY = Math.max.apply(null, yValues); // Adjust ranges for better visualization var xRange = maxX – minX; var yRange = maxY – minY; minX = minX – xRange * 0.1; // Add 10% padding on left maxX = maxX + xRange * 0.1; // Add 10% padding on right minY = minY – yRange * 0.1; // Add 10% padding on bottom maxY = maxY + yRange * 0.1; // Add 10% padding on top if (minX == maxX) { maxX = minX + 1; } // Prevent division by zero if all x are same if (minY == maxY) { maxY = minY + 1; } // Prevent division by zero if all y are same // Scaling functions var scaleX = (chartWidth – 2 * padding) / (maxX – minX); var scaleY = (chartHeight – 2 * padding) / (maxY – minY); var getGraphX = function(x) { return padding + (x – minX) * scaleX; }; var getGraphY = function(y) { return chartHeight – padding – (y – minY) * scaleY; }; // Draw Axes ctx.strokeStyle = '#cccccc'; ctx.lineWidth = 1; ctx.beginPath(); // X-axis ctx.moveTo(padding, chartHeight – padding); ctx.lineTo(chartWidth – padding, chartHeight – padding); // Y-axis ctx.moveTo(padding, padding); ctx.lineTo(padding, chartHeight – padding); ctx.stroke(); // Draw X-axis labels and ticks var numXTicks = 5; for (var i = 0; i <= numXTicks; i++) { var x = minX + (maxX – minX) * i / numXTicks; var graphX = getGraphX(x); ctx.beginPath(); ctx.moveTo(graphX, chartHeight – padding); ctx.lineTo(graphX, chartHeight – padding + 5); // Tick mark ctx.stroke(); ctx.textAlign = (i === 0) ? 'left' : ((i === numXTicks) ? 'right' : 'center'); ctx.fillText(x.toFixed(1), graphX, chartHeight – padding + 20); } // Draw Y-axis labels and ticks var numYTicks = 5; for (var i = 0; i <= numYTicks; i++) { var y = minY + (maxY – minY) * i / numYTicks; var graphY = getGraphY(y); ctx.beginPath(); ctx.moveTo(padding, graphY); ctx.lineTo(padding – 5, graphY); // Tick mark ctx.stroke(); ctx.textAlign = 'right'; ctx.fillText(y.toFixed(2), padding – 10, graphY + 5); } // Draw Standard Curve Points ctx.fillStyle = '#004a99'; for (var i = 0; i = 2) { var regression = calculateLinearRegression(markers.map(m => m.migration), markers.map(m => m.logMw)); var lineStartX = minX; var lineEndX = maxX; var lineStartY = regression.intercept + regression.slope * lineStartX; var lineEndY = regression.intercept + regression.slope * lineEndX; ctx.strokeStyle = '#004a99'; ctx.lineWidth = 2; ctx.beginPath(); ctx.moveTo(getGraphX(lineStartX), getGraphY(lineStartY)); ctx.lineTo(getGraphX(lineEndX), getGraphY(lineEndY)); ctx.stroke(); } // Draw Sample Point (if calculated) var sampleLogMw = getElement('sampleLogMw').innerText; var sampleMigration = getElement('migrationDistance').value; if (sampleLogMw !== '-' && sampleMigration && isValidNumber(sampleMigration)) { var sampleLogMwNum = parseFloat(sampleLogMw); var sampleMigrationNum = parseFloat(sampleMigration); ctx.fillStyle = '#28a745'; // Green for sample point ctx.beginPath(); ctx.arc(getGraphX(sampleMigrationNum), getGraphY(sampleLogMwNum), 5, 0, Math.PI * 2); ctx.fill(); } // Add labels/legend var legendHtml = '
'; legendHtml += ' Known Markers'; if (markers.length >= 2) { legendHtml += ' Standard Curve Line'; } if (sampleLogMw !== '-') { legendHtml += ' Sample Point'; } legendHtml += '
'; getElement('chartLegend').innerHTML = legendHtml; } function calculateDnaWeight() { var migrationDistance = getElement('migrationDistance').value; var knownSize1 = getElement('knownSize1').value; var migrationDistance1 = getElement('migrationDistance1').value; var knownSize2 = getElement('knownSize2').value; var migrationDistance2 = getElement('migrationDistance2').value; var knownSize3 = getElement('knownSize3').value; var migrationDistance3 = getElement('migrationDistance3').value; var errors = false; // Validate Sample Migration Distance if (!isValidNumber(migrationDistance) || parseFloat(migrationDistance) < 0) { showErrorMessage('migrationDistanceError', 'Please enter a valid, non-negative migration distance.', true); errors = true; } else { showErrorMessage('migrationDistanceError', '', false); } // Validate Marker 1 if (!isValidNumber(knownSize1) || parseFloat(knownSize1) <= 0) { showErrorMessage('knownSize1Error', 'Please enter a valid, positive known size.', true); errors = true; } else { showErrorMessage('knownSize1Error', '', false); } if (!isValidNumber(migrationDistance1) || parseFloat(migrationDistance1) <= 0) { showErrorMessage('migrationDistance1Error', 'Please enter a valid, positive migration distance.', true); errors = true; } else { showErrorMessage('migrationDistance1Error', '', false); } // Validate Marker 2 if (!isValidNumber(knownSize2) || parseFloat(knownSize2) <= 0) { showErrorMessage('knownSize2Error', 'Please enter a valid, positive known size.', true); errors = true; } else { showErrorMessage('knownSize2Error', '', false); } if (!isValidNumber(migrationDistance2) || parseFloat(migrationDistance2) 0 && migrationDistance3 && isValidNumber(migrationDistance3) && parseFloat(migrationDistance3) > 0) { hasMarker3 = true; showErrorMessage('knownSize3Error', ", false); showErrorMessage('migrationDistance3Error', ", false); } else if (knownSize3 || migrationDistance3) { // If one is present but not valid/complete if (!isValidNumber(knownSize3) || parseFloat(knownSize3) <= 0) { showErrorMessage('knownSize3Error', 'Please enter a valid, positive known size for marker 3.', true); errors = true; } else { showErrorMessage('knownSize3Error', '', false); } if (!isValidNumber(migrationDistance3) || parseFloat(migrationDistance3) <= 0) { showErrorMessage('migrationDistance3Error', 'Please enter a valid, positive migration distance for marker 3.', true); errors = true; } else { showErrorMessage('migrationDistance3Error', '', false); } } if (errors) { setInnerHtml('estimatedMolecularWeight', '-'); setInnerHtml('logMw1', '-'); setInnerHtml('logMw2', '-'); setInnerHtml('logMw3', '-'); setInnerHtml('slope', '-'); setInnerHtml('yIntercept', '-'); setInnerHtml('sampleLogMw', '-'); setStyleDisplay('mainResult', 'none'); updateChart([]); // Clear chart updateTable([]); // Clear table return; } var knownSizes = []; var migrationDistances = []; var markersData = []; // Marker 1 var size1 = parseFloat(knownSize1); var dist1 = parseFloat(migrationDistance1); var logMw1 = Math.log10(size1); knownSizes.push(size1); migrationDistances.push(dist1); markersData.push({ size: size1, logMw: logMw1, migration: dist1 }); setInnerHtml('logMw1', logMw1.toFixed(3)); // Marker 2 var size2 = parseFloat(knownSize2); var dist2 = parseFloat(migrationDistance2); var logMw2 = Math.log10(size2); knownSizes.push(size2); migrationDistances.push(dist2); markersData.push({ size: size2, logMw: logMw2, migration: dist2 }); setInnerHtml('logMw2', logMw2.toFixed(3)); // Marker 3 (if provided and valid) if (hasMarker3) { var size3 = parseFloat(knownSize3); var dist3 = parseFloat(migrationDistance3); var logMw3 = Math.log10(size3); knownSizes.push(size3); migrationDistances.push(dist3); markersData.push({ size: size3, logMw: logMw3, migration: dist3 }); setInnerHtml('logMw3', logMw3.toFixed(3)); } else { setInnerHtml('logMw3', '-'); } // Calculate linear regression var regression = calculateLinearRegression(migrationDistances, knownSizes.map(function(size, index) { return Math.log10(size); })); var slope = regression.slope; var yIntercept = regression.intercept; setInnerHtml('slope', slope.toFixed(5)); setInnerHtml('yIntercept', yIntercept.toFixed(5)); // Calculate sample molecular weight var sampleMigration = parseFloat(migrationDistance); var sampleLogMw = slope * sampleMigration + yIntercept; var estimatedMw = Math.pow(10, sampleLogMw); setInnerHtml('sampleLogMw', sampleLogMw.toFixed(3)); setInnerHtml('estimatedMolecularWeight', estimatedMw.toFixed(0)); setStyleDisplay('mainResult', 'block'); // Update Table updateTable(markersData); // Update Chart updateChart(markersData); } function updateTable(markersData) { var tableBody = getElement('tableBody'); tableBody.innerHTML = ''; // Clear existing rows if (markersData.length === 0) { var row = tableBody.insertRow(); var cell1 = row.insertCell(0); var cell2 = row.insertCell(1); var cell3 = row.insertCell(2); cell1.innerHTML = '-'; cell2.innerHTML = '-'; cell3.innerHTML = '-'; return; } markersData.forEach(function(marker) { var row = tableBody.insertRow(); var cell1 = row.insertCell(0); var cell2 = row.insertCell(1); var cell3 = row.insertCell(2); cell1.innerHTML = marker.size.toLocaleString(); cell2.innerHTML = marker.logMw.toFixed(3); cell3.innerHTML = marker.migration.toFixed(1); }); } function resetCalculator() { getElement('migrationDistance').value = ''; getElement('knownSize1').value = '1000'; getElement('migrationDistance1').value = '25'; getElement('knownSize2').value = '5000'; getElement('migrationDistance2').value = '15'; getElement('knownSize3').value = '10000'; getElement('migrationDistance3').value = '8'; setInnerHtml('estimatedMolecularWeight', '-'); setInnerHtml('logMw1', '-'); setInnerHtml('logMw2', '-'); setInnerHtml('logMw3', '-'); setInnerHtml('slope', '-'); setInnerHtml('yIntercept', '-'); setInnerHtml('sampleLogMw', '-'); setStyleDisplay('mainResult', 'none'); // Clear errors showErrorMessage('migrationDistanceError', '', false); showErrorMessage('knownSize1Error', '', false); showErrorMessage('migrationDistance1Error', '', false); showErrorMessage('knownSize2Error', '', false); showErrorMessage('migrationDistance2Error', '', false); showErrorMessage('knownSize3Error', '', false); showErrorMessage('migrationDistance3Error', '', false); // Clear chart and table updateChart([]); updateTable([]); // Re-initialize chart with default values if desired, or just keep it empty var canvas = getElement('electrophoresisChart'); var ctx = canvas.getContext('2d'); ctx.clearRect(0, 0, canvas.width, canvas.height); getElement('chartLegend').innerHTML = ''; } function copyResults() { var mainResultText = getElement('estimatedMolecularWeight').innerText; var logMw1 = getElement('logMw1').innerText; var logMw2 = getElement('logMw2').innerText; var logMw3 = getElement('logMw3').innerText; var slope = getElement('slope').innerText; var yIntercept = getElement('yIntercept').innerText; var sampleLogMw = getElement('sampleLogMw').innerText; var assumptions = "Assumptions:\n"; assumptions += "- Known Marker 1 Size (bp): " + getElement('knownSize1').value + "\n"; assumptions += "- Marker 1 Migration (mm): " + getElement('migrationDistance1').value + "\n"; assumptions += "- Known Marker 2 Size (bp): " + getElement('knownSize2').value + "\n"; assumptions += "- Marker 2 Migration (mm): " + getElement('migrationDistance2').value + "\n"; if (getElement('knownSize3').value) { assumptions += "- Known Marker 3 Size (bp): " + getElement('knownSize3').value + "\n"; assumptions += "- Marker 3 Migration (mm): " + getElement('migrationDistance3').value + "\n"; } assumptions += "- Sample Migration (mm): " + getElement('migrationDistance').value + "\n"; var resultString = "— DNA Molecular Weight Calculation Results —\n\n"; resultString += "Estimated Molecular Weight: " + mainResultText + " bp\n"; resultString += "Sample Log10 Molecular Weight: " + sampleLogMw + "\n"; resultString += "Slope of Standard Curve: " + slope + "\n"; resultString += "Y-intercept of Standard Curve: " + yIntercept + "\n"; resultString += "Log10 Molecular Weight (Marker 1): " + logMw1 + "\n"; resultString += "Log10 Molecular Weight (Marker 2): " + logMw2 + "\n"; if (getElement('logMw3').innerText !== '-') { resultString += "Log10 Molecular Weight (Marker 3): " + logMw3 + "\n"; } resultString += "\n" + assumptions; // Use Clipboard API navigator.clipboard.writeText(resultString).then(function() { // Success! Optionally provide feedback to the user. var originalText = getElement('copyResultsButton').innerText; getElement('copyResultsButton').innerText = 'Copied!'; setTimeout(function() { getElement('copyResultsButton').innerText = originalText; }, 2000); }, function(err) { // Fallback for older browsers or if clipboard API is not available alert('Failed to copy. Please copy manually.\n\n' + resultString); console.error('Clipboard write failed: ', err); }); } // Initial call to potentially set default values or update chart/table if defaults are loaded document.addEventListener('DOMContentLoaded', function() { // Set default values on load getElement('knownSize1').value = '1000'; getElement('migrationDistance1').value = '25'; getElement('knownSize2').value = '5000'; getElement('migrationDistance2').value = '15'; getElement('knownSize3').value = '10000'; getElement('migrationDistance3').value = '8'; // Update table and chart with initial defaults to visualize var initialMarkers = [ { size: parseFloat(getElement('knownSize1').value), logMw: Math.log10(parseFloat(getElement('knownSize1').value)), migration: parseFloat(getElement('migrationDistance1').value) }, { size: parseFloat(getElement('knownSize2').value), logMw: Math.log10(parseFloat(getElement('knownSize2').value)), migration: parseFloat(getElement('migrationDistance2').value) }, { size: parseFloat(getElement('knownSize3').value), logMw: Math.log10(parseFloat(getElement('knownSize3').value)), migration: parseFloat(getElement('migrationDistance3').value) } ]; updateTable(initialMarkers); updateChart(initialMarkers); });

Leave a Comment