Purpose

Define user requirements for a standalone FUZKK utility that accepts laboratory CSV data, evaluates the records using limits embedded in code, and produces LabWare-compatible JSON.

Scope

The utility is intended for preliminary QC/QA review, integration testing, LIMS/LabWare flow and evidence-trail preparation. Final release decisions remain under the laboratory's validated procedure and responsible personnel.

Users

QC analyst, QA reviewer, CSV/validation engineer, LIMS/LabWare integration engineer, responsible laboratory specialist.

User requirements

1. The utility shall run without arguments and print its self-description, a built-in input.csv example from GetDemoData(), and demo evaluation for the embedded records.
2. The utility shall run with two arguments: input.csv output.json.
3. The utility shall not read input.csv and shall not write output.json when started without arguments.
4. CSV numeric values shall be parsed using CultureInfo.InvariantCulture.
5. Output shall be generated as LabWare-compatible JSON with Header, Samples, Results, Status, StatusCode, ErrorMessage, Description and DescriptionEN.
6. For PASS records, ErrorMessage shall be an empty string.
7. Embedded limits shall follow this priority: Ph. Eur. → British Pharmacopoeia / UK implementation → EAEU / regional requirements → EMA/ICH/EU guidance → USP fallback.
8. If an exact monograph is not known, strict standard API limits are used where applicable: assay 98–102%, total impurities ≤1.0%, individual impurity ≤0.5%.
9. For biologics and mAb-like products, aggregation, sterility and endotoxin checks shall be included where relevant to the utility purpose.
10. If a parameter may arrive in different units, the unit shall be represented as a separate input field or explicitly reflected in the input.csv field name.

Input CSV

BatchNumber,ProductName,Total_Reads,Mean_Coverage,Percent_Coverage_95Pct,Sequence_Identity_Percent,Structural_Variants_Count,Major_Deletion_Percent,ITR_Coverage,Promoter_Coverage,PolyA_Coverage
CGT-NGS-2026-001,AAV9-CNS-Gene,5000000,1500.0,98.0,99.8,0,1.5,800.0,1400.0,1350.0
CGT-NGS-2026-002,Lenti-CAR-T,8000000,2000.0,99.0,99.9,0,0.5,0.0,1900.0,1850.0
CGT-NGS-2026-003,Adeno-Onco,3000000,800.0,95.0,98.5,2,8.0,0.0,750.0,700.0

input.csv fields

Utility description

Vector Identity NGS Profiler — Vector Identity NGS Profiler

Vector Identity NGS Profiler — Vector Identity NGS Profiler

ℹ️ Utility analyzes Next Generation Sequencing data:
• Sequence identity (≥99%)
• Coverage depth (≥1000X)
• Structural variants (deletions/insertions)
• Regulatory element integrity (ITR, Promoter)

⚠️ CRITICAL: Deletions in transgene lead to loss of function!
NGS is the only method to detect complex rearrangements.

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

Input format:
BatchNumber,ProductName,Total_Reads,Mean_Coverage,Percent_Coverage_95Pct,Sequence_Identity_Percent,Structural_Variants_Count,Major_Deletion_Percent,ITR_Coverage,Promoter_Coverage,PolyA_Coverage

Example:
CGT-NGS-2026-001,AAV9-CNS-Gene,5000000,1500.0,98.0,99.8,0,1.5,800.0,1400.0,1350.0

— WHY IS THIS NEEDED?
Identity control is critical for gene therapy (Novartis, BioNTech, Oxford Biomedica):
• Viral vectors may undergo recombination, deletions, and rearrangements during production
• Standard PCR cannot detect large structural changes or population heterogeneity
• NGS allows confirmation of presence of all regulatory elements (promoter, enhancer, polyA)
• For AAV, integrity of ITR (Inverted Terminal Repeats) required for packaging is critical

⚠️ CRITICAL:
• Sequence identity ≥99% — no unexpected mutations
• Coverage depth ≥1000X — statistical significance for rare variant detection
• No structural variants (SV) — transgene integrity guarantee
• Low level of major deletions (<5%) — packaging quality control
• ITR/Promoter/PolyA coverage — confirmation of all functional parts presence

Key features:
• Whole-genome or targeted vector sequencing data analysis
• Structural variant (SV) and indel detection
• Coverage uniformity assessment across vector genome
• Support for various platforms (Illumina, Nanopore, PacBio)

Critical parameters:
• Identity: ≥99%
• Coverage: ≥1000X
• Structural variants: 0
• Major deletions: ≤5%
• ITR coverage: ≥500X

💡 Usage tips:
1. Use long-read sequencing for better resolution of repetitive regions (ITR)
2. Apply specialized bioinformatics pipelines for viral vectors
3. Compare results with reference plasmid used for production
4. Pay attention to coverage "drop-outs" — they may indicate secondary structures
5. For AAV, must check integrity of both ITRs (5' and 3')

⚠️ Note: Unlike small molecules where identity is determined by mass and spectrum, for biological vectors identity is a complex structural characteristic. NGS is the "gold standard" for confirming that you are producing exactly the vector declared in the dossier.

Traceability and limitations

• The URS is used as the source document for functional specification, CSV review and later validation work.
• This document does not replace an approved pharmacopoeial monograph, validated analytical method or internal product specification.
• For product-specific limits, the approved customer specification takes priority.

The functional specification describes the behaviour of the standalone FUZKK console utility, input-data format, evaluation algorithm and output JSON structure.

Functional flow

1. Main() checks the number of arguments.
2. If no arguments are provided: PrintHello() prints the description and built-in input.csv example, then RunDemoEvaluation() executes Evaluate() over GetDemoData() and prints demo JSON.
3. If two arguments are provided: RunWithFiles(input.csv, output.json) reads CSV, evaluates each record and writes LabWare-compatible JSON.
4. LoadData() uses CultureInfo.InvariantCulture and shall not be called in no-arguments mode.
5. Evaluate() returns a named tuple with BatchNumber, ProductName, Parameters, CriticalFailCount, WarningCount, Recommendation and RecommendationEN.
6. GetIssues() builds messages for ErrorMessage in WARNING/FAIL cases.

Evaluation rules

• PASS: CriticalFailCount = 0 and WarningCount = 0.
• WARNING: CriticalFailCount = 0 and WarningCount > 0.
• FAIL: CriticalFailCount > 0.
• ERROR: exception during reading or processing.
• ErrorMessage remains empty for PASS.
• Limits are embedded in Program.cs; no external limit configuration is required.

Input and fields

BatchNumber,ProductName,Total_Reads,Mean_Coverage,Percent_Coverage_95Pct,Sequence_Identity_Percent,Structural_Variants_Count,Major_Deletion_Percent,ITR_Coverage,Promoter_Coverage,PolyA_Coverage
CGT-NGS-2026-001,AAV9-CNS-Gene,5000000,1500.0,98.0,99.8,0,1.5,800.0,1400.0,1350.0
CGT-NGS-2026-002,Lenti-CAR-T,8000000,2000.0,99.0,99.9,0,0.5,0.0,1900.0,1850.0
CGT-NGS-2026-003,Adeno-Onco,3000000,800.0,95.0,98.5,2,8.0,0.0,750.0,700.0

Output JSON

{
  "Header": {
    "UtilityName": "Vector_Identity_NGS_Profiler",
    "Version": "1.0.0",
    "Timestamp": "UTC",
    "InstrumentID": "FUZKK-QC-WORKSTATION",
    "OperatorID": "Admin"
  },
  "Samples": [
    {
      "SampleID": "from BatchNumber",
      "BatchNumber": "from CSV",
      "ProductName": "from CSV",
      "TestName": "utility-specific test",
      "AnalysisCode": "utility-specific code",
      "Status": "PASS | WARNING | FAIL | ERROR",
      "StatusCode": "1 | 2 | 0 | -1",
      "ErrorMessage": "",
      "Description": "Russian recommendation",
      "DescriptionEN": "English recommendation",
      "Results": [
        {
          "ParameterName": "parameter",
          "ResultValue": 0.0,
          "UnitOfMeasure": "unit",
          "SpecificationLimit": "limit",
          "IsWithinSpec": true
        }
      ]
    }
  ]
}