LargePanelLiquidCgpRunChecker

Large Panel Liquid CGP Run

Liquid Biopsy жидкостная биопсия cfDNA ctDNA CTC exosomes NGS qPCR

Utility description: Large Panel Liquid CGP Run

Large Panel Liquid CGP Run Checker — Comprehensive QC for Large-Panel Liquid Biopsy CGP Sequencing Runs

ℹ️ Utility performs end-to-end quality verification of large-panel (>300 genes) liquid biopsy CGP sequencing runs according to CAP/CLIA, FDA NGS guidance and SEQC2 standards:

SEQUENCING:
• Q30 Score: Base quality — foundation for accurate rare variant calling.
• Cluster Density: Cluster density on patterned flow cells.

ENRICHMENT:
• On-Target Rate: Target capture efficiency (≥60% for large panels).
• Uniformity: Coverage evenness — critical for avoiding blind spots.
• Mean/Median Target Depth: Total sequencing depth (≥10,000× for cfDNA).

UMI & ERROR SUPPRESSION:
• UMI Deduplication Rate: Fraction of collapsed reads — library complexity indicator.
• Unique Molecular Depth: Post-deduplication depth — determines real sensitivity.
• UMI Collision Rate: UMI collision frequency — tag diversity exhaustion marker.

BACKGROUND & CONTROLS:
• Background Noise VAF: Median noise in NTC — determines achievable LOD.
• NTC Pass Rate: All negative controls must be clean.
• Positive Control Recovery: Recovery of control variant with known VAF.

DATA INTEGRITY:
• Inter-Replicate CV: Reproducibility between technical replicates.
• Index Hopping Rate: Cross-sample contamination.
• UDI Usage: Unique dual index usage.

⚠️ IMPORTANT:
• For liquid biopsy, unique molecular depth (post-UMI) matters more than total depth.
• Background >0.1% VAF makes reliable detection at VAF <0.5% impossible.
• Any NTC failure = automatic REJECT of entire run.
• Non-UDI indexes on patterned flow cells are unacceptable for clinical liquid biopsy.

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

📍 Scope of Application (Usage Where):
• Clinical Liquid Biopsy Labs: Acceptance of every CGP run.
• Pharmaceutical Companies: QC of stratification biomarkers in trials.
• Panel Developers: Validation of new assay versions.
• Regulatory Inspections: Documentation of QC system for FDA/CE-IVDR.

— WHY IS THIS NEEDED?
Large-panel liquid biopsy operates at sensitivity limit (VAF 0.1–0.5%).
Every QC parameter directly impacts ability to detect rare tumor variants.
Systematic error at any stage (enrichment, UMI, background) leads to false-negative results.
Automated end-to-end verification eliminates subjectivity and ensures reproducibility.

⚠️ CRITICAL:
• Mean Target Depth ≥ 10,000×: Baseline threshold for panels >300 genes.
• Unique Molecular Depth ≥ 1,000×: Determines real sensitivity after error suppression.
• Uniformity ≥ 80%: Below = unacceptable blind spots in large panels.
• Background Noise ≤ 0.1% VAF: Exceedance = LOD unachievable.
• NTC Pass = 100%: Any failure = contamination = REJECT.
• Positive Control Recovery ≥ 70%: Below = sensitivity degradation.
• Index Hopping ≤ 0.5%: Exceedance = cross-contamination = unreliable results.
• UDI Mandatory: For clinical liquid biopsy on patterned FC.

Key features:
• 15+ QC parameters in single check
• Three-tier classification (Accepted / Conditional / Rejected)
• Integration of physical, bioinformatic, and control metrics
• Liquid biopsy-specific thresholds (not tissue!)
• Compliance with CAP/CLIA, FDA NGS guidance, SEQC2

💡 Usage tips:
1. UMI Design: Use UMI ≥8 nt for panels >1 Mb to avoid collisions.
2. NTC Strategy: Minimum 3-4 NTCs per run, distributed across plate.
3. Positive Control: Use cfDNA standard with known variants at multiple VAF levels.
4. Trend Monitoring: Track drift of each metric over time for predictive maintenance.
5. Conditional Protocol: Pre-define release conditions for conditionally accepted runs.

⚠️ Note: This utility assesses RUN QUALITY, not clinical validity of individual results. Variant interpretation requires additional stages (VariantCallAcceptanceChecker, VariantClassificationGateChecker, VariantReportabilityRulesChecker).

input.csv

RunID,PanelName,Platform,TargetRegionSize_Mb,PercentQ30,MinQ30,ClusterDensity_K_mm2,MinClusterDensity,MaxClusterDensity,OnTargetRate_Percent,MinOnTargetRate,Uniformity_Percent,MinUniformity,MeanTargetDepth_X,MinMeanTargetDepth,MedianTargetDepth_X,UMIDeduplication_Rate,MaxUMIDeduplication,UniqueMolecularDepth_X,MinUniqueMolecularDepth,UMICollisionRate_Percent,MaxUMICollisionRate,BackgroundNoise_VAF,MaxBackgroundNoise_VAF,NTCSamples_Count,NTCPassCount,PositiveControl_Detected,PositiveControl_VAF_Observed,PositiveControl_VAF_Expected,PositiveControl_Recovery_Percent,MinPositiveControl_Recovery,InterReplicate_CV_Percent,MaxInterReplicate_CV,IndexHoppingRate_Percent,MaxIndexHoppingRate,UDI_Used
LB-CGP-2026-R001,OncoLiquid_500,NovaSeq 6000,1.8,88.5,75,950,800,1200,78.5,60,91.2,80,18500,10000,17200,0.72,0.85,5200,1000,1.8,5.0,0.0003,0.001,4,4,true,0.48,0.50,96.0,70,12.5,25,0.08,0.5,true
LB-CGP-2026-R002,OncoLiquid_500,NovaSeq 6000,1.8,72.0,75,1350,800,1200,52.0,60,68.5,80,8200,10000,5100,0.91,0.85,750,1000,8.5,5.0,0.0025,0.001,4,2,true,0.32,0.50,64.0,70,35.0,25,1.2,0.5,false

URS & FS — User Requirements and Functional Specification

This document describes the controlled interface and behaviour of LargePanelLiquidCgpRunChecker for Large Panel Liquid CGP Run 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.

ℹ️ Utility performs end-to-end quality verification of large-panel (>300 genes) liquid biopsy CGP sequencing runs according to CAP/CLIA, FDA NGS guidance and SEQC2 standards:
ENRICHMENT:
• On-Target Rate: Target capture efficiency (≥60% for large panels).
• Uniformity: Coverage evenness — critical for avoiding blind spots.
• Mean/Median Target Depth: Total sequencing depth (≥10,000× for cfDNA).
• NTC Pass Rate: All negative controls must be clean.
• Background >0.1% VAF makes reliable detection at VAF <0.5% impossible.
Large-panel liquid biopsy operates at sensitivity limit (VAF 0.1–0.5%).
Systematic error at any stage (enrichment, UMI, background) leads to false-negative results.
⚠️ CRITICAL:
• Mean Target Depth ≥ 10,000×: Baseline threshold for panels >300 genes.
• Unique Molecular Depth ≥ 1,000×: Determines real sensitivity after error suppression.
• Uniformity ≥ 80%: Below = unacceptable blind spots in large panels.
• Background Noise ≤ 0.1% VAF: Exceedance = LOD unachievable.
• Positive Control Recovery ≥ 70%: Below = sensitivity degradation.
• Index Hopping ≤ 0.5%: Exceedance = cross-contamination = unreliable results.
1. UMI Design: Use UMI ≥8 nt for panels >1 Mb to avoid collisions.

URS — User Requirements Specification

ID	Requirement	Criticality	Acceptance criterion
URS-001	The utility shall accept an input.csv file for Large Panel Liquid CGP Run 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	`RunID`	string / controlled vocabulary	`LB-CGP-2026-R001`	Controlled input parameter for deterministic QC rules.
2	`PanelName`	string / controlled vocabulary	`OncoLiquid_500`	Controlled input parameter for deterministic QC rules.
3	`Platform`	string / controlled vocabulary	`NovaSeq 6000`	Controlled input parameter for deterministic QC rules.
4	`TargetRegionSize_Mb`	string / controlled vocabulary	`1.8`	Controlled input parameter for deterministic QC rules.
5	`PercentQ30`	decimal	`88.5`	Controlled input parameter for deterministic QC rules.
6	`MinQ30`	decimal	`75`	Controlled input parameter for deterministic QC rules.
7	`ClusterDensity_K_mm2`	decimal	`950`	Controlled input parameter for deterministic QC rules.
8	`MinClusterDensity`	decimal	`800`	Controlled input parameter for deterministic QC rules.
9	`MaxClusterDensity`	decimal	`1200`	Controlled input parameter for deterministic QC rules.
10	`OnTargetRate_Percent`	decimal	`78.5`	Controlled input parameter for deterministic QC rules.
11	`MinOnTargetRate`	string / controlled vocabulary	`60`	Controlled input parameter for deterministic QC rules.
12	`Uniformity_Percent`	decimal	`91.2`	Controlled input parameter for deterministic QC rules.
13	`MinUniformity`	decimal	`80`	Controlled input parameter for deterministic QC rules.
14	`MeanTargetDepth_X`	string / controlled vocabulary	`18500`	Controlled input parameter for deterministic QC rules.
15	`MinMeanTargetDepth`	string / controlled vocabulary	`10000`	Controlled input parameter for deterministic QC rules.
16	`MedianTargetDepth_X`	string / controlled vocabulary	`17200`	Controlled input parameter for deterministic QC rules.
17	`UMIDeduplication_Rate`	string / controlled vocabulary	`0.72`	Controlled input parameter for deterministic QC rules.
18	`MaxUMIDeduplication`	string / controlled vocabulary	`0.85`	Controlled input parameter for deterministic QC rules.
19	`UniqueMolecularDepth_X`	decimal	`5200`	Controlled input parameter for deterministic QC rules.
20	`MinUniqueMolecularDepth`	decimal	`1000`	Controlled input parameter for deterministic QC rules.
21	`UMICollisionRate_Percent`	decimal	`1.8`	Controlled input parameter for deterministic QC rules.
22	`MaxUMICollisionRate`	decimal	`5.0`	Controlled input parameter for deterministic QC rules.
23	`BackgroundNoise_VAF`	decimal	`0.0003`	Controlled input parameter for deterministic QC rules.
24	`MaxBackgroundNoise_VAF`	decimal	`0.001`	Controlled input parameter for deterministic QC rules.
25	`NTCSamples_Count`	integer / decimal	`4`	Sample or laboratory specimen identifier.
26	`NTCPassCount`	integer / decimal	`4`	Count parameter used for microbiological, particulate or cellular control.
27	`PositiveControl_Detected`	string / controlled vocabulary	`true`	Control point/control sample for assay run validity.
28	`PositiveControl_VAF_Observed`	string / controlled vocabulary	`0.48`	Control point/control sample for assay run validity.
29	`PositiveControl_VAF_Expected`	decimal	`0.50`	Control point/control sample for assay run validity.
30	`PositiveControl_Recovery_Percent`	decimal	`96.0`	Recovery; indicator of analytical validity.
31	`MinPositiveControl_Recovery`	decimal	`70`	Recovery; indicator of analytical validity.
32	`InterReplicate_CV_Percent`	decimal	`12.5`	Controlled input parameter for deterministic QC rules.
33	`MaxInterReplicate_CV`	decimal	`25`	Controlled input parameter for deterministic QC rules.
34	`IndexHoppingRate_Percent`	decimal	`0.08`	Controlled input parameter for deterministic QC rules.
35	`MaxIndexHoppingRate`	decimal	`0.5`	Controlled input parameter for deterministic QC rules.
36	`UDI_Used`	string / controlled vocabulary	`true`	Controlled input parameter for deterministic QC rules.

RunID,PanelName,Platform,TargetRegionSize_Mb,PercentQ30,MinQ30,ClusterDensity_K_mm2,MinClusterDensity,MaxClusterDensity,OnTargetRate_Percent,MinOnTargetRate,Uniformity_Percent,MinUniformity,MeanTargetDepth_X,MinMeanTargetDepth,MedianTargetDepth_X,UMIDeduplication_Rate,MaxUMIDeduplication,UniqueMolecularDepth_X,MinUniqueMolecularDepth,UMICollisionRate_Percent,MaxUMICollisionRate,BackgroundNoise_VAF,MaxBackgroundNoise_VAF,NTCSamples_Count,NTCPassCount,PositiveControl_Detected,PositiveControl_VAF_Observed,PositiveControl_VAF_Expected,PositiveControl_Recovery_Percent,MinPositiveControl_Recovery,InterReplicate_CV_Percent,MaxInterReplicate_CV,IndexHoppingRate_Percent,MaxIndexHoppingRate,UDI_Used
LB-CGP-2026-R001,OncoLiquid_500,NovaSeq 6000,1.8,88.5,75,950,800,1200,78.5,60,91.2,80,18500,10000,17200,0.72,0.85,5200,1000,1.8,5.0,0.0003,0.001,4,4,true,0.48,0.50,96.0,70,12.5,25,0.08,0.5,true
LB-CGP-2026-R002,OncoLiquid_500,NovaSeq 6000,1.8,72.0,75,1350,800,1200,52.0,60,68.5,80,8200,10000,5100,0.91,0.85,750,1000,8.5,5.0,0.0025,0.001,4,2,true,0.32,0.50,64.0,70,35.0,25,1.2,0.5,false

Input validation rules

ID	Field	Rule	Criticality
VR-001	`RunID`	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	`Platform`	The field shall match an approved dictionary or accepted string representation.	High
VR-004	`TargetRegionSize_Mb`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-005	`PercentQ30`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-006	`MinQ30`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-007	`ClusterDensity_K_mm2`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-008	`MinClusterDensity`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-009	`MaxClusterDensity`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-010	`OnTargetRate_Percent`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-011	`MinOnTargetRate`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-012	`Uniformity_Percent`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-013	`MinUniformity`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-014	`MeanTargetDepth_X`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-015	`MinMeanTargetDepth`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-016	`MedianTargetDepth_X`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-017	`UMIDeduplication_Rate`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-018	`MaxUMIDeduplication`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-019	`UniqueMolecularDepth_X`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-020	`MinUniqueMolecularDepth`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-021	`UMICollisionRate_Percent`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-022	`MaxUMICollisionRate`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-023	`BackgroundNoise_VAF`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-024	`MaxBackgroundNoise_VAF`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-025	`NTCSamples_Count`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-026	`NTCPassCount`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-027	`PositiveControl_Detected`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-028	`PositiveControl_VAF_Observed`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-029	`PositiveControl_VAF_Expected`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-030	`PositiveControl_Recovery_Percent`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-031	`MinPositiveControl_Recovery`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-032	`InterReplicate_CV_Percent`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-033	`MaxInterReplicate_CV`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-034	`IndexHoppingRate_Percent`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-035	`MaxIndexHoppingRate`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-036	`UDI_Used`	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 Large Panel Liquid CGP Run 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": "largepanelliquidcgprunchecker",
  "utilityFolder": "LargePanelLiquidCgpRunChecker",
  "package": "LiquidBiopsy",
  "overallStatus": "PASS|WARNING|FAIL",
  "sourceFile": "input.csv",
  "processedAtUtc": "2026-06-10T00:00:00Z",
  "checks": [
    {
      "parameter": "RunID",
      "value": "LB-CGP-2026-R001",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-001"
    },
    {
      "parameter": "PanelName",
      "value": "OncoLiquid_500",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-002"
    },
    {
      "parameter": "Platform",
      "value": "NovaSeq 6000",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-003"
    },
    {
      "parameter": "TargetRegionSize_Mb",
      "value": "1.8",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-004"
    },
    {
      "parameter": "PercentQ30",
      "value": "88.5",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-005"
    },
    {
      "parameter": "MinQ30",
      "value": "75",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-006"
    },
    {
      "parameter": "ClusterDensity_K_mm2",
      "value": "950",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-007"
    },
    {
      "parameter": "MinClusterDensity",
      "value": "800",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-008"
    },
    {
      "parameter": "MaxClusterDensity",
      "value": "1200",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-009"
    },
    {
      "parameter": "OnTargetRate_Percent",
      "value": "78.5",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-010"
    },
    {
      "parameter": "MinOnTargetRate",
      "value": "60",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-011"
    },
    {
      "parameter": "Uniformity_Percent",
      "value": "91.2",
      "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": "LargePanelLiquidCgpRunChecker.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