GC Coverage Dropout

Find GC bins with coverage deficits relative to a caller-supplied expected depth.

Description

Coverage deficits in GC bins can reveal compositional bias relative to a supplied expected depth.

Coverage deficits in GC bins can reveal compositional bias relative to a supplied expected depth. A shared baseline makes bins comparable.

Find GC bins with coverage deficits relative to a caller-supplied expected depth.

When to use GC Coverage Dropout

Use this for the following task: Find GC bins with coverage deficits relative to a caller-supplied expected depth.

Prepare the caller-filtered sequences, counts, coordinates or annotations requested below. Keep identifiers and paired lists aligned on the same reference.

How to use GC Coverage Dropout and understand its results

Prepare the caller-filtered sequences, counts, coordinates or annotations requested below. Keep identifiers and paired lists aligned on the same reference.
Enter gcBinFractions, meanDepths, minimumExpectedDepth using the field table, then run the tool.
Read depthDeficitFractions, worstGcBin, maximumDropoutFraction. Interpret flags, units, ordering and placeholder values using the rules below.
Review any reported issue or failed check, correct the supplied values when appropriate, and verify the result in its intended context.

Input field	Meaning	Value type and bounds	Requirement
gcBinFractions	Gc bin fractions	list of number; minimum entries 1; maximum entries 100000; each entry: number; minimum 0; maximum 1	Required
meanDepths	Mean depths	list of number; minimum entries 1; maximum entries 100000; each entry: number; minimum 0	Required
minimumExpectedDepth	Minimum expected depth	number; greater than 0	Required

Align GC fractions in [0,1] with nonnegative depths and require positive expected depth. Return each clipped deficit and the first bin tied for maximum.^[1]

deficit = max(0,1−meanDepth/expectedDepth)

This summarizes the declared operation. Units, indexing, inclusivity and exceptional cases are explained in the surrounding text.

Output field	Meaning	Value type
depthDeficitFractions	Depth deficit fractions	list of number
worstGcBin	Worst gc bin	number
maximumDropoutFraction	Maximum dropout fraction	number

json

{
  "input": {
    "gcBinFractions": [
      0.2,
      0.5,
      0.8
    ],
    "meanDepths": [
      10,
      20,
      10
    ],
    "minimumExpectedDepth": 20
  },
  "output": {
    "depthDeficitFractions": [
      0.5,
      0,
      0.5
    ],
    "worstGcBin": 0.2,
    "maximumDropoutFraction": 0.5
  }
}

Limitations and assumptions

No GC bins are constructed or calibrated. If all deficits are zero, worstGcBin still reports the first bin.

The tool operates on supplied local data; alignment, filtering and annotation must already be appropriate for the stated calculation. Malformed requests produce input errors. A successfully evaluated request can still contain false flags, empty result lists or a result outside the supplied threshold; those outcomes have the meanings stated above.

Alternative or Complementary methods

Choose Sliding GC Content for this operation: Calculate GC content in complete DNA windows at a chosen step size. For a complementary task, use Depth Band Uniformity for this operation: Measure the fraction of covered bases within a caller-defined band around mean depth.

The implementation reference documents the exact calculation, supported input shapes and returned fields.

References

GC Coverage Dropout — tool.io

Similar or alternative tools

Sliding GC Content
Calculate GC content in complete DNA windows at a chosen step size.
Depth Band Uniformity
Measure the fraction of covered bases within a caller-defined band around mean depth.