Utility description: Library Prep Batch
Library Prep Batch Checker — NGS Library Preparation Batch Quality Control
ℹ️ Utility performs comprehensive assessment of library preparation quality at batch level according to CAP/CLIA, GATK Best Practices and ISO 15189:
INDIVIDUAL SPECIFICATIONS:
• Concentration: Library concentration within acceptable pooling range.
• Fragment Size: Mean fragment size matches expected for library type.
• Molarity: Sufficient molarity for flow cell clustering.
• Adapter Dimers: Adapter dimer level below contamination threshold.
• PCR Cycles: Amplification cycles within limit (avoiding over-amplification).
• Input DNA: Input DNA amount meets protocol minimum.
BATCH-LEVEL METRICS:
• Pass Rate: Fraction of samples passing all individual specifications (≥90%).
• Concentration CV: Coefficient of variation of concentration within batch (≤30%).
• Fragment Size CV: Coefficient of variation of fragment sizes (≤15%).
• Outlier Detection: Samples beyond 2 SD from batch mean.
• Historical Drift: Z-score of batch mean relative to historical baseline.
• Max Adapter Dimer: Maximum dimer level in any batch sample.
⚠️ IMPORTANT:
• Library prep is a stage where systematic errors affect the ENTIRE batch.
• High concentration CV (>30%) leads to uneven pool representation.
• Adapter dimers >5% compete with library during clustering, reducing data output.
• Historical drift (|Z| > 2) indicates reagent degradation or protocol change.
• Over-amplification (>15 cycles) increases duplicates and coverage bias.
Usage:
LibraryPrepBatchChecker.exe → demo mode (console output)
LibraryPrepBatchChecker.exe input.csv output.json → evaluate your data
📍 Scope of Application (Usage Where):
• NGS Laboratories: Mandatory QC before pooling and sequencing.
• Clinical Diagnostics: Guarantee of inter-batch reproducibility.
• Method Development: Optimization of library preparation protocols.
• CAP/ISO Accreditation: Documentation of sample preparation quality control system.
— WHY IS THIS NEEDED?
Poor-quality library cannot be fixed at sequencing stage.
Bad batch wastes expensive flow cell resources and delays result delivery.
Systematic errors (old reagents, instrument calibration) are invisible when checking individual samples.
Batch-level analysis reveals patterns unavailable through individual QC.
⚠️ CRITICAL:
• Pass Rate ≥ 90%: Below = systematic preparation problem.
• Concentration CV ≤ 30%: Exceedance = uneven pooling = data loss.
• Fragment Size CV ≤ 15%: High CV = size selection or fragmentation issue.
• Adapter Dimers ≤ 5%: Above = SPRI re-cleanup mandatory.
• Historical |Z| ≤ 2.0: Exceedance = drift cause investigation.
• Outliers ≤ 10%: High outlier % = unstable process.
• PCR Cycles ≤ Max: Over-amplification irreversibly degrades quality.
Key features:
• Two-tier verification (individual + batch)
• Automatic CV, Z-score, and outlier calculation
• Integration with historical baselines
• Three-tier classification (Accepted / Review Required / Rejected)
• Per-sample failure reason detail
💡 Usage tips:
1. Baseline Establishment: Calculate Historical Mean/SD from ≥20 validated batches.
2. Trend Charts: Maintain Levey-Jennings control charts for concentration and size.
3. Normalization: Use qPCR (not Qubit) for accurate molarity before pooling.
4. SPRI Cleanup: For dimers >5%, perform double SPRI cleanup at 0.8× ratio.
5. Root Cause: Upon REJECTED batch, conduct investigation (reagents, operator, equipment).
⚠️ Note: This utility assesses PHYSICAL QUALITY of libraries. It does not replace content verification (on-target rate, coverage uniformity) performed post-sequencing (DepthUniformityChecker, LargePanelLiquidCgpRunChecker).
input.csv
BatchID,SampleID,LibraryType,PrepKit,Concentration_ng_uL,MinConcentration,MaxConcentration,MeanFragmentSize_bp,MinFragmentSize,MaxFragmentSize,Molarity_nM,MinMolarity,AdapterDimer_Percent,MaxAdapterDimer_Percent,PCRCycles,MaxPCRCycles,InputDNA_ng,MinInputDNA_ng,HistoricalMean_Concentration,HistoricalSD_Concentration
BATCH-2026-045,S001,WES,KAPA HyperPrep,28.5,2.0,100.0,380,200,700,12.5,2.0,0.8,5.0,8,15,50,10,27.0,7.0
BATCH-2026-045,S002,WES,KAPA HyperPrep,32.1,2.0,100.0,395,200,700,14.2,2.0,0.5,5.0,8,15,50,10,27.0,7.0
BATCH-2026-045,S003,WES,KAPA HyperPrep,25.8,2.0,100.0,370,200,700,11.8,2.0,1.2,5.0,8,15,50,10,27.0,7.0
BATCH-2026-045,S004,WES,KAPA HyperPrep,30.2,2.0,100.0,388,200,700,13.5,2.0,0.6,5.0,8,15,50,10,27.0,7.0
BATCH-2026-046,S005,Targeted,Agilent SureSelect,5.2,2.0,100.0,280,200,700,3.1,2.0,12.5,5.0,14,15,20,10,35.0,8.0
BATCH-2026-046,S006,Targeted,Agilent SureSelect,42.0,2.0,100.0,420,200,700,18.5,2.0,8.2,5.0,14,15,50,10,35.0,8.0
BATCH-2026-046,S007,Targeted,Agilent SureSelect,1.5,2.0,100.0,250,200,700,1.0,2.0,18.0,5.0,14,15,10,10,35.0,8.0
URS & FS — User Requirements and Functional Specification
This document describes the controlled interface and behaviour of LibraryPrepBatchChecker for Library Prep Batch Checker.
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 comprehensive assessment of library preparation quality at batch level according to CAP/CLIA, GATK Best Practices and ISO 15189:
- • PCR Cycles: Amplification cycles within limit (avoiding over-amplification).
- • Pass Rate: Fraction of samples passing all individual specifications (≥90%).
- • Concentration CV: Coefficient of variation of concentration within batch (≤30%).
- • Fragment Size CV: Coefficient of variation of fragment sizes (≤15%).
- • High concentration CV (>30%) leads to uneven pool representation.
- • Adapter dimers >5% compete with library during clustering, reducing data output.
- • Historical drift (|Z| > 2) indicates reagent degradation or protocol change.
- • Over-amplification (>15 cycles) increases duplicates and coverage bias.
- • CAP/ISO Accreditation: Documentation of sample preparation quality control system.
- ⚠️ CRITICAL:
- • Pass Rate ≥ 90%: Below = systematic preparation problem.
- • Concentration CV ≤ 30%: Exceedance = uneven pooling = data loss.
- • Fragment Size CV ≤ 15%: High CV = size selection or fragmentation issue.
- • Adapter Dimers ≤ 5%: Above = SPRI re-cleanup mandatory.
- • Historical |Z| ≤ 2.0: Exceedance = drift cause investigation.
- • Outliers ≤ 10%: High outlier % = unstable process.
- • PCR Cycles ≤ Max: Over-amplification irreversibly degrades quality.
URS — User Requirements Specification
| ID | Requirement | Criticality | Acceptance criterion |
|---|
| URS-001 | The utility shall accept an input.csv file for Library Prep Batch 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 | BatchID | string / controlled vocabulary | BATCH-2026-045 | Batch or lot identifier used for traceability. |
| 2 | SampleID | string / controlled vocabulary | S001 | Sample or laboratory specimen identifier. |
| 3 | LibraryType | string / controlled vocabulary | WES | Controlled input parameter for deterministic QC rules. |
| 4 | PrepKit | string / controlled vocabulary | KAPA HyperPrep | Controlled input parameter for deterministic QC rules. |
| 5 | Concentration_ng_uL | decimal | 28.5 | Component ratio; structural or formulation CQA. |
| 6 | MinConcentration | integer / decimal | 2.0 | Component ratio; structural or formulation CQA. |
| 7 | MaxConcentration | integer / decimal | 100.0 | Component ratio; structural or formulation CQA. |
| 8 | MeanFragmentSize_bp | decimal | 380 | Controlled input parameter for deterministic QC rules. |
| 9 | MinFragmentSize | decimal | 200 | Controlled input parameter for deterministic QC rules. |
| 10 | MaxFragmentSize | decimal | 700 | Controlled input parameter for deterministic QC rules. |
| 11 | Molarity_nM | decimal | 12.5 | Controlled input parameter for deterministic QC rules. |
| 12 | MinMolarity | decimal | 2.0 | Controlled input parameter for deterministic QC rules. |
| 13 | AdapterDimer_Percent | decimal | 0.8 | Controlled input parameter for deterministic QC rules. |
| 14 | MaxAdapterDimer_Percent | decimal | 5.0 | Controlled input parameter for deterministic QC rules. |
| 15 | PCRCycles | integer / decimal | 8 | Controlled input parameter for deterministic QC rules. |
| 16 | MaxPCRCycles | integer / decimal | 15 | Controlled input parameter for deterministic QC rules. |
| 17 | InputDNA_ng | decimal | 50 | Biological/molecular component controlled as a CQA. |
| 18 | MinInputDNA_ng | decimal | 10 | Biological/molecular component controlled as a CQA. |
| 19 | HistoricalMean_Concentration | integer / decimal | 27.0 | Component ratio; structural or formulation CQA. |
| 20 | HistoricalSD_Concentration | integer / decimal | 7.0 | Component ratio; structural or formulation CQA. |
BatchID,SampleID,LibraryType,PrepKit,Concentration_ng_uL,MinConcentration,MaxConcentration,MeanFragmentSize_bp,MinFragmentSize,MaxFragmentSize,Molarity_nM,MinMolarity,AdapterDimer_Percent,MaxAdapterDimer_Percent,PCRCycles,MaxPCRCycles,InputDNA_ng,MinInputDNA_ng,HistoricalMean_Concentration,HistoricalSD_Concentration
BATCH-2026-045,S001,WES,KAPA HyperPrep,28.5,2.0,100.0,380,200,700,12.5,2.0,0.8,5.0,8,15,50,10,27.0,7.0
BATCH-2026-045,S002,WES,KAPA HyperPrep,32.1,2.0,100.0,395,200,700,14.2,2.0,0.5,5.0,8,15,50,10,27.0,7.0
BATCH-2026-045,S003,WES,KAPA HyperPrep,25.8,2.0,100.0,370,200,700,11.8,2.0,1.2,5.0,8,15,50,10,27.0,7.0
Input validation rules
| ID | Field | Rule | Criticality |
|---|
| VR-001 | BatchID | The field shall match an approved dictionary or accepted string representation. | High |
| VR-002 | SampleID | The field shall match an approved dictionary or accepted string representation. | High |
| VR-003 | LibraryType | The field shall match an approved dictionary or accepted string representation. | High |
| VR-004 | PrepKit | The field shall match an approved dictionary or accepted string representation. | Medium |
| VR-005 | Concentration_ng_uL | The field shall match an approved dictionary or accepted string representation. | Medium |
| VR-006 | MinConcentration | The field shall match an approved dictionary or accepted string representation. | Medium |
| VR-007 | MaxConcentration | The field shall match an approved dictionary or accepted string representation. | Medium |
| VR-008 | MeanFragmentSize_bp | The field shall match an approved dictionary or accepted string representation. | Medium |
| VR-009 | MinFragmentSize | The field shall match an approved dictionary or accepted string representation. | Medium |
| VR-010 | MaxFragmentSize | The field shall match an approved dictionary or accepted string representation. | Medium |
| VR-011 | Molarity_nM | The field shall match an approved dictionary or accepted string representation. | Medium |
| VR-012 | MinMolarity | The field shall match an approved dictionary or accepted string representation. | Medium |
| VR-013 | AdapterDimer_Percent | The field shall match an approved dictionary or accepted string representation. | Medium |
| VR-014 | MaxAdapterDimer_Percent | The field shall match an approved dictionary or accepted string representation. | Medium |
| VR-015 | PCRCycles | The field shall match an approved dictionary or accepted string representation. | Medium |
| VR-016 | MaxPCRCycles | The field shall match an approved dictionary or accepted string representation. | Medium |
| VR-017 | InputDNA_ng | The field shall match an approved dictionary or accepted string representation. | Medium |
| VR-018 | MinInputDNA_ng | The field shall match an approved dictionary or accepted string representation. | Medium |
| VR-019 | HistoricalMean_Concentration | The field shall match an approved dictionary or accepted string representation. | Medium |
| VR-020 | HistoricalSD_Concentration | 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 Library Prep Batch 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": "libraryprepbatchchecker",
"utilityFolder": "LibraryPrepBatchChecker",
"package": "LiquidBiopsy",
"overallStatus": "PASS|WARNING|FAIL",
"sourceFile": "input.csv",
"processedAtUtc": "2026-06-10T00:00:00Z",
"checks": [
{
"parameter": "BatchID",
"value": "BATCH-2026-045",
"status": "PASS|WARNING|FAIL",
"message": "Deterministic rule-based check result",
"ruleReference": "FS-RULE-001"
},
{
"parameter": "SampleID",
"value": "S001",
"status": "PASS|WARNING|FAIL",
"message": "Deterministic rule-based check result",
"ruleReference": "FS-RULE-002"
},
{
"parameter": "LibraryType",
"value": "WES",
"status": "PASS|WARNING|FAIL",
"message": "Deterministic rule-based check result",
"ruleReference": "FS-RULE-003"
},
{
"parameter": "PrepKit",
"value": "KAPA HyperPrep",
"status": "PASS|WARNING|FAIL",
"message": "Deterministic rule-based check result",
"ruleReference": "FS-RULE-004"
},
{
"parameter": "Concentration_ng_uL",
"value": "28.5",
"status": "PASS|WARNING|FAIL",
"message": "Deterministic rule-based check result",
"ruleReference": "FS-RULE-005"
},
{
"parameter": "MinConcentration",
"value": "2.0",
"status": "PASS|WARNING|FAIL",
"message": "Deterministic rule-based check result",
"ruleReference": "FS-RULE-006"
},
{
"parameter": "MaxConcentration",
"value": "100.0",
"status": "PASS|WARNING|FAIL",
"message": "Deterministic rule-based check result",
"ruleReference": "FS-RULE-007"
},
{
"parameter": "MeanFragmentSize_bp",
"value": "380",
"status": "PASS|WARNING|FAIL",
"message": "Deterministic rule-based check result",
"ruleReference": "FS-RULE-008"
},
{
"parameter": "MinFragmentSize",
"value": "200",
"status": "PASS|WARNING|FAIL",
"message": "Deterministic rule-based check result",
"ruleReference": "FS-RULE-009"
},
{
"parameter": "MaxFragmentSize",
"value": "700",
"status": "PASS|WARNING|FAIL",
"message": "Deterministic rule-based check result",
"ruleReference": "FS-RULE-010"
},
{
"parameter": "Molarity_nM",
"value": "12.5",
"status": "PASS|WARNING|FAIL",
"message": "Deterministic rule-based check result",
"ruleReference": "FS-RULE-011"
},
{
"parameter": "MinMolarity",
"value": "2.0",
"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": "LibraryPrepBatchChecker.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.