DepthUniformityChecker

Depth Uniformity

Liquid Biopsy жидкостная биопсия cfDNA ctDNA CTC exosomes NGS qPCR

Utility description: Depth Uniformity

Depth Uniformity Checker — NGS Coverage Uniformity Assessment

ℹ️ Utility performs comprehensive assessment of target region coverage uniformity according to CAP/CLIA and NGS platform specifications:
• % Bases Above 0.2× Mean: Fraction of bases with depth ≥20% of mean — primary uniformity metric.
• Fold-80 Base Penalty: Factor by which sequencing must be increased for 80% of bases to reach target depth.
• Regions Below Min Depth: Percentage of target regions not reaching minimum clinical depth.
• Depth CV: Coefficient of variation of depth across regions — measure of coverage spread.
• Median/Mean Ratio: Indicator of depth distribution asymmetry (skew).

⚠️ IMPORTANT:
• High mean depth does NOT compensate for poor uniformity: 1000× mean at 60% uniformity means 40% of panel is insufficiently covered.
• Fold-80 >2.0 indicates significant sequencing efficiency loss.
• Each region below clinical depth is a potential false-negative error.

Usage:
DepthUniformityChecker.exe → demo mode (console output)
DepthUniformityChecker.exe input.csv output.json → evaluate your data

Input format:
SampleID,PanelName,MeanTargetDepth_X,MedianTargetDepth_X,PercentBases_Above_0_2xMean,MinPercentBases_Above_0_2xMean,Fold80_BasePenalty,MaxFold80_BasePenalty,PercentTargets_Below_MinDepth,MaxPercentTargets_Below_MinDepth,MinClinicalDepth_X,DepthCV_Percent,MaxDepthCV_Percent,TotalTargetRegions,RegionsBelowMinDepth

Example:
NGS-001,OncoPanel,850,820,94.5,80,1.3,2.0,1.2,5.0,200,28,50,500,6

📍 Scope of Application (Usage Where):
• Clinical NGS Laboratories: Per-sample QC before variant calling.
• Panel Development: Probe/primer design evaluation and optimization.
• Method Validation: Confirmation of uniformity as part of analytical validation.
• Reagent QC: Detection of enrichment kit degradation.

— WHY IS THIS NEEDED?
Target enrichment (hybridization or amplification) is never perfectly uniform.
GC-rich regions, homologous sequences, and exon edges are systematically under-covered.
Without uniformity monitoring, laboratory may issue results with unknown blind spots.
Automated verification guarantees that every claimed gene is truly covered at clinically meaningful depth.

⚠️ CRITICAL:
• Uniformity ≥80%: Baseline threshold for clinical panels.
• Fold-80 ≤2.0: Exceedance means inefficient sequencing resource utilization.
• Regions Below Min ≤5%: More than 5% uncovered regions requires design review or resequencing.
• Depth CV ≤50%: High CV indicates systematic enrichment issues.
• Median/Mean <0.7: Severe distribution skew, even if other metrics are borderline.

Key features:
• Five-parameter uniformity assessment
• Automatic blind spot detection
• Integration of absolute and relative metrics
• PASS / WARNING / FAIL classification
• Compliance with CAP Molecular Pathology Checklist

Critical parameters:
• % Bases ≥0.2× Mean: ≥ 80%
• Fold-80 Penalty: ≤ 2.0
• Regions Below Min Depth: ≤ 5%
• Depth CV: ≤ 50%
• Median/Mean Ratio: > 0.7

💡 Usage tips:
1. Panel Design: Use in silico prediction during development to minimize non-uniformity.
2. Pool Balancing: Accurate library normalization improves run-level uniformity.
3. Trend Monitoring: Uniformity drift over time indicates enrichment reagent degradation.
4. Region List: Maintain list of problematic regions per panel and check them separately.
5. Resequencing: Upon FAIL, consider increasing depth or repeating enrichment.

⚠️ Note: This utility assesses technical coverage quality. It does not replace verification of specific clinically significant variants but guarantees that the panel as a whole is suitable for reliable variant calling.

input.csv

SampleID,PanelName,MeanTargetDepth_X,MedianTargetDepth_X,PercentBases_Above_0_2xMean,MinPercentBases_Above_0_2xMean,Fold80_BasePenalty,MaxFold80_BasePenalty,PercentTargets_Below_MinDepth,MaxPercentTargets_Below_MinDepth,MinClinicalDepth_X,DepthCV_Percent,MaxDepthCV_Percent,TotalTargetRegions,RegionsBelowMinDepth
NGS-2026-UNI-001,OncoPanel_500,850,820,94.5,80.0,1.3,2.0,1.2,5.0,200,28.0,50.0,500,6
NGS-2026-UNI-002,OncoPanel_500,620,380,68.0,80.0,3.8,2.0,12.5,5.0,200,72.0,50.0,500,63
NGS-2026-UNI-003,HemePanel_300,450,410,82.0,80.0,1.9,2.0,4.8,5.0,150,45.0,50.0,300,14

URS & FS — User Requirements and Functional Specification

This document describes the controlled interface and behaviour of DepthUniformityChecker for Depth Uniformity Checker.

Domain limits URS input.csv FS IQ/OQ/PQ

Domain limits and critical parameters

Key fragments from the source description are shown below. Before production use, limits must be verified against the approved specification, registration dossier and local SOPs.

• % Bases Above 0.2× Mean: Fraction of bases with depth ≥20% of mean — primary uniformity metric.
• Fold-80 Base Penalty: Factor by which sequencing must be increased for 80% of bases to reach target depth.
• Fold-80 >2.0 indicates significant sequencing efficiency loss.
• Reagent QC: Detection of enrichment kit degradation.
Target enrichment (hybridization or amplification) is never perfectly uniform.
GC-rich regions, homologous sequences, and exon edges are systematically under-covered.
⚠️ CRITICAL:
• Uniformity ≥80%: Baseline threshold for clinical panels.
• Fold-80 ≤2.0: Exceedance means inefficient sequencing resource utilization.
• Regions Below Min ≤5%: More than 5% uncovered regions requires design review or resequencing.
• Depth CV ≤50%: High CV indicates systematic enrichment issues.
• Median/Mean <0.7: Severe distribution skew, even if other metrics are borderline.
Critical parameters:
• % Bases ≥0.2× Mean: ≥ 80%
• Fold-80 Penalty: ≤ 2.0
• Regions Below Min Depth: ≤ 5%
• Depth CV: ≤ 50%
• Median/Mean Ratio: > 0.7

URS — User Requirements Specification

ID	Requirement	Criticality	Acceptance criterion
URS-001	The utility shall accept an input.csv file for Depth Uniformity Checker with headers defined in the data contract.	High	The file is processed without manual header editing.
URS-002	The utility shall perform deterministic QC evaluation without machine learning and without probabilistic conformance decisions.	High	Identical input data, rule version and configuration produce reproducible results.
URS-003	The utility shall validate mandatory fields, data types, ranges, units and domain plausibility.	High	Schema, conversion and range errors are explicitly reported.
URS-004	The utility shall apply domain limits and rules from the description, approved specification, registration dossier and local SOPs.	High	Each check has PASS/WARNING/FAIL and a clear message.
URS-005	The utility shall generate output.json with machine-readable results, source values, warnings, failures and critical findings.	High	JSON is suitable for LIMS/ELN/MES integration and QA/QC review.
URS-006	The utility shall preserve traceability between batch/sample, input file, applied rules and final status.	High	Output contains identifiers, checked parameters and audit metadata.
URS-007	The documentation shall support IQ/OQ/PQ, CSV/CSA and review by internal QA or inspectors.	Medium	URS, FS, input/output contract and test scenarios are supplied with the utility.
URS-008	The utility shall be used as a QC decision-support tool and not as a substitute for approved specifications and QA/QP release decision.	Medium	Documentation states change control and limit-verification expectations.

input.csv contract

#	Field	Type	Sample	Purpose
1	`SampleID`	string / controlled vocabulary	`NGS-2026-UNI-001`	Sample or laboratory specimen identifier.
2	`PanelName`	string / controlled vocabulary	`OncoPanel_500`	Controlled input parameter for deterministic QC rules.
3	`MeanTargetDepth_X`	string / controlled vocabulary	`850`	Controlled input parameter for deterministic QC rules.
4	`MedianTargetDepth_X`	string / controlled vocabulary	`820`	Controlled input parameter for deterministic QC rules.
5	`PercentBases_Above_0_2xMean`	integer / decimal	`94.5`	Controlled input parameter for deterministic QC rules.
6	`MinPercentBases_Above_0_2xMean`	integer / decimal	`80.0`	Controlled input parameter for deterministic QC rules.
7	`Fold80_BasePenalty`	decimal	`1.3`	Controlled input parameter for deterministic QC rules.
8	`MaxFold80_BasePenalty`	decimal	`2.0`	Controlled input parameter for deterministic QC rules.
9	`PercentTargets_Below_MinDepth`	decimal	`1.2`	Controlled input parameter for deterministic QC rules.
10	`MaxPercentTargets_Below_MinDepth`	decimal	`5.0`	Controlled input parameter for deterministic QC rules.
11	`MinClinicalDepth_X`	decimal	`200`	Controlled input parameter for deterministic QC rules.
12	`DepthCV_Percent`	decimal	`28.0`	Controlled input parameter for deterministic QC rules.
13	`MaxDepthCV_Percent`	decimal	`50.0`	Controlled input parameter for deterministic QC rules.
14	`TotalTargetRegions`	string / controlled vocabulary	`500`	Controlled input parameter for deterministic QC rules.
15	`RegionsBelowMinDepth`	decimal	`6`	Controlled input parameter for deterministic QC rules.

SampleID,PanelName,MeanTargetDepth_X,MedianTargetDepth_X,PercentBases_Above_0_2xMean,MinPercentBases_Above_0_2xMean,Fold80_BasePenalty,MaxFold80_BasePenalty,PercentTargets_Below_MinDepth,MaxPercentTargets_Below_MinDepth,MinClinicalDepth_X,DepthCV_Percent,MaxDepthCV_Percent,TotalTargetRegions,RegionsBelowMinDepth
NGS-2026-UNI-001,OncoPanel_500,850,820,94.5,80.0,1.3,2.0,1.2,5.0,200,28.0,50.0,500,6
NGS-2026-UNI-002,OncoPanel_500,620,380,68.0,80.0,3.8,2.0,12.5,5.0,200,72.0,50.0,500,63
NGS-2026-UNI-003,HemePanel_300,450,410,82.0,80.0,1.9,2.0,4.8,5.0,150,45.0,50.0,300,14

Input validation rules

ID	Field	Rule	Criticality
VR-001	`SampleID`	The field shall match an approved dictionary or accepted string representation.	High
VR-002	`PanelName`	The field shall match an approved dictionary or accepted string representation.	High
VR-003	`MeanTargetDepth_X`	The field shall match an approved dictionary or accepted string representation.	High
VR-004	`MedianTargetDepth_X`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-005	`PercentBases_Above_0_2xMean`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-006	`MinPercentBases_Above_0_2xMean`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-007	`Fold80_BasePenalty`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-008	`MaxFold80_BasePenalty`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-009	`PercentTargets_Below_MinDepth`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-010	`MaxPercentTargets_Below_MinDepth`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-011	`MinClinicalDepth_X`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-012	`DepthCV_Percent`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-013	`MaxDepthCV_Percent`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-014	`TotalTargetRegions`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-015	`RegionsBelowMinDepth`	The field shall match an approved dictionary or accepted string representation.	Medium

FS — Functional Specification

ID	Function	Implementation
FS-001	CLI execution	Support execution modes: demo mode without arguments and production mode input.csv output.json.
FS-002	CSV import	Read input.csv in UTF-8/CSV-compatible format and validate header and expected columns.
FS-003	Schema validation	Check mandatory fields, column count, unknown key fields and empty mandatory values.
FS-004	Type conversion	Convert numeric, flag and text values; invalid format is recorded as a row-level error.
FS-005	Domain rule engine	Apply rules for Depth Uniformity Checker, including critical limits from the description and approved specification.
FS-006	Status aggregation	Produce final status: FAIL for critical failure, WARNING for non-critical deviation, PASS for conformance.
FS-007	JSON export	Write output.json with detailed checks, source values, warnings, failures and critical findings.
FS-008	Audit support	Keep result structure suitable for review, deviation investigation and calculation reproduction.
FS-009	Integration contract	Support the scenario LIMS/ELN/MES → input.csv → utility → output.json → portal/admin review.
FS-010	Error handling	Return explicit messages for missing file, empty CSV, invalid schema, output write failure and invalid format.

Example output.json

{
  "utilityId": "depthuniformitychecker",
  "utilityFolder": "DepthUniformityChecker",
  "package": "LiquidBiopsy",
  "overallStatus": "PASS|WARNING|FAIL",
  "sourceFile": "input.csv",
  "processedAtUtc": "2026-06-10T00:00:00Z",
  "checks": [
    {
      "parameter": "SampleID",
      "value": "NGS-2026-UNI-001",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-001"
    },
    {
      "parameter": "PanelName",
      "value": "OncoPanel_500",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-002"
    },
    {
      "parameter": "MeanTargetDepth_X",
      "value": "850",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-003"
    },
    {
      "parameter": "MedianTargetDepth_X",
      "value": "820",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-004"
    },
    {
      "parameter": "PercentBases_Above_0_2xMean",
      "value": "94.5",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-005"
    },
    {
      "parameter": "MinPercentBases_Above_0_2xMean",
      "value": "80.0",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-006"
    },
    {
      "parameter": "Fold80_BasePenalty",
      "value": "1.3",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-007"
    },
    {
      "parameter": "MaxFold80_BasePenalty",
      "value": "2.0",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-008"
    },
    {
      "parameter": "PercentTargets_Below_MinDepth",
      "value": "1.2",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-009"
    },
    {
      "parameter": "MaxPercentTargets_Below_MinDepth",
      "value": "5.0",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-010"
    },
    {
      "parameter": "MinClinicalDepth_X",
      "value": "200",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-011"
    },
    {
      "parameter": "DepthCV_Percent",
      "value": "28.0",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-012"
    }
  ],
  "criticalFindings": [],
  "warnings": [],
  "audit": {
    "inputHash": "sha256:<calculated at runtime>",
    "rulesVersion": "<utility executable version>",
    "documentation": "DepthUniformityChecker.documentation.html"
  }
}

Traceability matrix

URS	FS	Test	Evidence
URS-001	FS-001, FS-002	OQ-001	Verify execution and import of valid input.csv.
URS-002	FS-005, FS-006	OQ-004	Repeat the same dataset and compare output.json.
URS-003	FS-003, FS-004, FS-010	OQ-002, OQ-003	Verify missing columns and invalid types.
URS-004	FS-005, FS-006	OQ-004, PQ-001	Verify critical deviations on real/boundary data.
URS-005	FS-007, FS-009	OQ-005	Verify JSON schema and downstream-system suitability.
URS-006	FS-008	OQ-006	Verify identifiers and audit metadata.
URS-007	FS-008, FS-010	IQ-001, OQ-007	Verify documentation completeness and control evidence.
URS-008	FS-005, FS-008	PQ-002	Verify review workflow and no replacement of QA decision.

IQ/OQ/PQ test scenarios

ID	Scenario	Expected result
IQ-001	Verify executable, input.csv, documentation and checksum availability.	Delivery set is complete; version is recorded.
OQ-001	Valid sample row from input.csv.	PASS or acceptable WARNING according to rules.
OQ-002	Remove a mandatory CSV column.	Schema error or FAIL with missing-column reference.
OQ-003	Place a non-numeric value into a numeric field.	Type-conversion error with row/field reference.
OQ-004	Set a critical parameter outside the limit.	FAIL and critical finding.
OQ-005	Verify output.json structure.	All mandatory sections are present and JSON is valid.
OQ-006	Verify batch/sample traceability.	Input and result identifiers match.
PQ-001	Verify 3–5 real user batches/samples.	Result is confirmed by QC/QA review.
PQ-002	Verify deviation workflow and manual QA decision.	Utility supports review but does not replace approved decision.

QA/QC and change control

Do not rename columns without updating validator, documentation and test set.
Retain input.csv, output.json, executable version and checksum.
Before production use, perform IQ/OQ/PQ or equivalent CSV/CSA verification.
Critical limits shall be verified against the approved specification, registration dossier and local SOPs.
The utility provides structured QC decision support; final release decision remains with QA/QP and approved procedures.

Included in packages

Liquid Biopsy QC Suite

QC and pre-analytical control package for liquid biopsy workflows: cfDNA/ctDNA, CTC, EV/exosomes, methylation, NGS/qPCR/ddPCR, sample quality, contamination, sensitivity and reporting checks.

Open