Splice Delta Effect Rank

Rank caller-supplied splice delta effect scores without predicting clinical pathogenicity.

Description

Splice effect predictors can supply several delta scores for one variant.

Splice effect predictors can supply several delta scores for one variant. Ranking makes the largest supplied score visible without making a clinical conclusion.

Rank caller-supplied splice delta effect scores without predicting clinical pathogenicity.

When to use Splice Delta Effect Rank

Use this for the following task: Rank caller-supplied splice delta effect scores without predicting clinical pathogenicity.

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

How to use Splice Delta Effect Rank 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 effectLabels, deltaScores using the field table, then run the tool.
Read rankedEffects, rankedScores, topEffect. 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
effectLabels	Effect labels	list of string; minimum entries 1; maximum entries 100000; each entry: string; minimum text length 1; maximum text length 1000000; pattern \S	Required
deltaScores	Delta scores	list of number; minimum entries 1; maximum entries 100000; each entry: number; minimum 0; maximum 1	Required

Align distinct effect labels with scores in [0,1]. Sort indices stably, return ranked labels/scores and identify the first label as topEffect.^[1]

rank by decreasing deltaScore, ties by original index

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

Output field	Meaning	Value type
rankedEffects	Descending scores; ties retain input order.	list of string
rankedScores	Ranked scores	list of number
topEffect	Top effect	string

json

{
  "input": {
    "effectLabels": [
      "acceptor-gain",
      "acceptor-loss",
      "donor-gain",
      "donor-loss"
    ],
    "deltaScores": [
      0.1,
      0.8,
      0.2,
      0.3
    ]
  },
  "output": {
    "rankedEffects": [
      "acceptor-loss",
      "donor-loss",
      "donor-gain",
      "acceptor-gain"
    ],
    "rankedScores": [
      0.8,
      0.3,
      0.2,
      0.1
    ],
    "topEffect": "acceptor-loss"
  }
}

Limitations and assumptions

No splice score is predicted or calibrated. Ranking is not a pathogenicity classification.

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 Length Normalized PSI for this operation: Quantify PSI from inclusion and skipping counts normalized by their effective lengths. For a complementary task, use Isoform Domain Loss for this operation: Identify annotated domains lost in a supplied isoform switch.

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

References

Splice Delta Effect Rank — tool.io

Similar or alternative tools

Length Normalized PSI
Quantify PSI from inclusion and skipping counts normalized by their effective lengths.
Isoform Domain Loss
Identify annotated domains lost in a supplied isoform switch.