Fragments Per Kilobase Million

Calculate fragments per kilobase of transcript per million mapped fragments.

Description

FPKM measures assigned fragments per effective transcript kilobase per library million fragments.

FPKM measures assigned fragments per effective transcript kilobase per library million fragments. Paired reads from one fragment require an upstream counting convention.^[1]

Calculate fragments per kilobase of transcript per million mapped fragments.

When to use Fragments Per Kilobase Million

Use this tool to calculate fragments per kilobase of transcript per million mapped fragments.

Prepare the upstream counts, expression values, sequences or record lists requested by this tool. Keep corresponding features in the same order and confirm count units, transformations and caller-selected criteria.

How to use Fragments Per Kilobase Million and understand its results

Prepare the upstream counts, expression values, sequences or record lists requested by this tool. Keep corresponding features in the same order and confirm count units, transformations and caller-selected criteria.
Enter fragmentCounts, effectiveLengthsBp, mappedFragments using the field table and calculation rules, then run the tool.
Read fpkm, sumFpkm. The explanation below defines the units, signs, ordering and any flags or placeholder values.
If the tool reports an input issue, correct the values and run it again. Apply the stated assumptions when interpreting a successfully returned result.

Input field	Meaning	Value type; see rules below	Requirement
fragmentCounts	Fragment counts	list of number	Required
effectiveLengthsBp	Effective lengths bp	list of number	Required
mappedFragments	Mapped fragments	number	Required

Provide aligned nonempty nonnegative fragment counts and positive effective lengths with positive mapped-fragment total. Return FPKM in input order and its sum. All-zero feature counts are accepted.

FPKM_i=10⁹ fragmentCount_i/(mappedFragments×effectiveLengthBp_i)

Units, indexing, boundary conditions and exceptional cases are defined in the calculation rules above.

Output field	Meaning	Value type
fpkm	Fpkm	list of number
sumFpkm	Sum fpkm	number

json

{
  "input": {
    "fragmentCounts": [
      10,
      20
    ],
    "effectiveLengthsBp": [
      1000,
      2000
    ],
    "mappedFragments": 1000000
  },
  "output": {
    "fpkm": [
      10,
      10
    ],
    "sumFpkm": 20
  }
}

Limitations and assumptions

No fragment assignment or effective-length estimation is performed. The sum need not be one million, and this normalization alone is not a differential-expression model.

Numeric inputs must be finite. Follow the calculation-specific rules for overflow, underflow and exceptional cases. Lists are limited to 100,000 entries; the rules above may require smaller limits. Missing required fields, extra fields, wrong value types and violations of the calculation rules are input errors.

Alternative or Complementary methods

Use Transcripts Per Million: Normalize transcript length-adjusted abundances to one million TPM. For a complementary task, use Reads Per Kilobase Million: Calculate reads per kilobase of gene per million mapped reads.

References

Fragments Per Kilobase Million — tool.io

Similar or alternative tools

Transcripts Per Million
Normalize transcript length-adjusted abundances to one million TPM.
Reads Per Kilobase Million
Calculate reads per kilobase of gene per million mapped reads.