NIPTviewer Technical Manual

Non-Invasive Prenatal Testing Data Visualization and Analysis Platform

Version 2.0 • February 2026 • Independent Re-implementation

⚠️ Important Disclaimer

This application is an independent re-implementation of NIPTviewer for educational and demonstration purposes only. It is NOT validated for clinical diagnostic use. All clinical decisions must be based on validated laboratory procedures and professional medical judgment.

Table of Contents

1. Introduction to NIPT Analysis

1.1 Background

Non-Invasive Prenatal Testing (NIPT) is a screening method that analyzes cell-free DNA (cfDNA) in maternal plasma to detect fetal chromosomal aneuploidies. The cfDNA comprises both maternal and fetal (placental) DNA fragments, with the fetal fraction (FF) typically ranging from 4-30% depending on gestational age, maternal weight, and other factors.

This platform provides comprehensive visualization of NIPT sequencing results, enabling laboratory personnel and clinicians to assess sample quality, interpret screening results, and identify samples requiring further investigation.

1.2 Key Metrics

Normalized Chromosome Value (NCV) / Z-Score

The NCV represents the number of standard deviations a sample's chromosome representation deviates from the reference population mean. It is calculated as:

NCV = (Sample_Ratio - Reference_Mean) / Reference_StdDev

A positive NCV indicates over-representation (potential trisomy), while a negative NCV indicates under-representation (potential monosomy).

Fetal Fraction (FF)

The proportion of cfDNA originating from the fetal-placental unit. Adequate FF (typically ≥4%) is essential for reliable NIPT results. Low FF may lead to false-negative results.

Coverage Ratio

The normalized read depth for each chromosome, expressed as a ratio to the expected proportion based on chromosome size. Values deviating significantly from 1.0 indicate potential copy number abnormalities.

2. Data Input Specification

2.1 CSV File Format

The application accepts CSV files containing NIPT analysis results. Each row represents a sample, with the following key columns:

Column Name Description Example Value
SampleID Unique sample identifier NIPT-2024-001
SampleType Test or Control Test
Flowcell Sequencing flowcell ID FC001
NCV_13, NCV_18, NCV_21 Z-scores for target chromosomes 0.15, -0.08, 5.82
NCV_X, NCV_Y Z-scores for sex chromosomes -0.25, 0.12
Ratio_13, Ratio_18, Ratio_21 Normalized chromosome ratios 1.0015, 1.0008, 1.0582
Ratio_X, Ratio_Y Sex chromosome ratios 0.98, 0.25
Chr1_Coverage ... Chr22_Coverage Per-chromosome coverage depth 8.2, 7.9, 6.8...
ChrX_Coverage, ChrY_Coverage Sex chromosome coverage 5.1, 0.1
Chr1 ... Chr22, ChrX, ChrY Raw chromosome read percentages 0.082, 0.079...
FF_Formatted Fetal fraction percentage 13%
QCFlag Quality control status PASS / FAIL
GCBias GC content bias metric 0.95
Median_13, Stdev_13 etc. Reference population statistics 1.0001, 0.0010

2.2 Clinical Thresholds

NCV Range Classification Clinical Action
|NCV| < 2.0 Normal Screen negative
2.0 ≤ |NCV| < 3.0 Borderline Consider repeat testing or extended analysis
3.0 ≤ |NCV| < 4.0 Elevated (Gray Zone) Genetic counseling; consider diagnostic testing
|NCV| ≥ 4.0 High Risk Recommend diagnostic testing (CVS/amniocentesis)

3. Quality Control Visualizations

The QC tab provides visualizations to assess sample and run quality before interpreting screening results.

3.1 QC Metrics Summary Card

📊 Data Source

CSV columns: QCFlag, QCFailure, QCWarning, GCBias, GCR2, Tags, NonExcludedSites, PerfectMatchTags2Tags

Purpose: Provides an at-a-glance summary of key quality metrics for the selected sample, enabling rapid identification of samples with compromised data quality.

Metrics Displayed:

3.2 Fetal Fraction Over Time

📊 Data Source

CSV column: FF_Formatted (parsed as percentage value)

Purpose: Tracks fetal fraction values across samples in the batch, enabling identification of samples with potentially insufficient FF for reliable screening.

Clinical Significance:

Visualization: Line chart showing FF values for each sample, with reference lines at 4% (minimum threshold) and 10% (optimal threshold).

3.3 Reads Distribution

📊 Data Source

CSV columns: Clusters, IndexedReads, Tags, NonExcludedSites

Purpose: Visualizes the sequencing read processing pipeline, showing the progression from raw clusters to filtered, analysis-ready reads.

Interpretation: A healthy sample shows progressive reduction through filtering stages while maintaining adequate final read counts. Excessive loss at any stage may indicate sample or library preparation issues.

3.4 Chromosome Coverage Distribution

📊 Data Source

CSV columns: Chr1_Coverage through Chr22_Coverage, ChrX_Coverage, ChrY_Coverage

Purpose: Displays the sequencing coverage depth for each chromosome, enabling assessment of coverage uniformity across the genome.

Expected Pattern: Coverage values correlate with chromosome size (Chr1 highest, Chr21/22 lowest). The pattern should be consistent across samples within a batch. Deviations may indicate library bias or true biological variation.

Visual Encoding: Target chromosomes (13, 18, 21, X, Y) are highlighted in teal for easy identification.

4. Results Visualizations

The Results tab provides visualizations for interpreting aneuploidy screening outcomes.

4.1 Chromosome Z-Score Bar Chart

📊 Data Source

CSV columns: NCV_13, NCV_18, NCV_21, NCV_X, NCV_Y

Purpose: Displays the Normalized Chromosome Values (Z-scores) for the five clinically relevant chromosomes, enabling rapid identification of potential aneuploidies.

View Modes:

Color Coding:

ColorNCV RangeInterpretation
Green|NCV| < 2Normal range
Yellow2 ≤ |NCV| < 3Borderline
Orange3 ≤ |NCV| < 4Elevated
Red|NCV| ≥ 4High risk

Clinical Associations:

4.2 Normalized Coverage Ratios

📊 Data Source

CSV columns: Ratio_13, Ratio_18, Ratio_21, Ratio_X, Ratio_Y

Purpose: Displays the normalized chromosome representation ratios for target chromosomes. Unlike Z-scores which are standardized to a reference population, ratios show the direct proportional representation.

Interpretation:

Ratio ≈ 1.0 → Normal (euploid)
Ratio > 1.05 → Potential trisomy (over-representation)
Ratio < 0.95 → Potential monosomy (under-representation)

Example: A Ratio_21 of 1.0582 indicates ~5.8% over-representation of chromosome 21, consistent with a fetal trisomy 21 at the observed fetal fraction.

4.3 Genome-Wide CNV Profile

📊 Data Source

CSV columns: Ratio_13, Ratio_18, Ratio_21, Ratio_X, Ratio_Y (converted to log₂ scale)

Purpose: Visualizes copy number variations using log₂ ratio transformation, a standard representation in genomic analysis. This approach is inspired by WisecondorX visualization methodology.

log₂(Ratio) = 0 → Normal copy number
log₂(Ratio) > 0 → Copy number gain
log₂(Ratio) < 0 → Copy number loss

Color Encoding: Bar colors reflect NCV significance levels, providing integrated visualization of both ratio magnitude and statistical confidence.

4.4 Aberration Highlight Plot

📊 Data Source

CSV columns: NCV_13, NCV_18, NCV_21, NCV_X, NCV_Y, Ratio_13, Ratio_18, Ratio_21, Ratio_X, Ratio_Y

Purpose: Provides a focused view of chromosomal aberrations with configurable Z-score thresholds and clear gain/loss classification.

Features:

4.5 NCD Over Time

📊 Data Source

CSV columns: NCD_13, NCD_18, NCD_21, NCD_X, NCD_Y

Purpose: Tracks Normalized Chromosome Deviation values across samples in the batch, showing temporal consistency and enabling identification of batch effects.

Clinical Use: Useful for monitoring run-to-run consistency and identifying systematic shifts that may indicate assay drift or batch-specific issues.

4.6 Sex Chromosome Analysis

📊 Data Source

CSV columns: NCV_X, NCV_Y, FF_Formatted

Purpose: Scatter plot visualization of sex chromosome NCV values for fetal sex determination and sex chromosome aneuploidy detection.

Expected Patterns:

4.7 Y-Fraction Histogram

📊 Data Source

CSV columns: ChrY_Coverage, Chr1_Coverage through ChrX_Coverage (for total coverage calculation)

Purpose: Histogram with Gaussian mixture model overlay for fetal sex determination, inspired by WisecondorX methodology.

Y-Fraction = ChrY_Coverage / Total_Coverage

Interpretation:

The bimodal distribution with overlaid Gaussian fits allows visualization of the separation between female and male samples, with the valley representing the classification threshold.

5. Investigator Tools

Advanced analysis tools for detailed investigation of screening results and assessment of result confidence.

5.1 FF vs NCV Correlation Scatter

📊 Data Source

CSV columns: FF_Formatted, NCV_13, NCV_18, NCV_21

Purpose: Visualizes the relationship between fetal fraction and NCV to assess result confidence and identify potential false positives/negatives.

Confidence Zone Assessment:

ZoneCriteriaInterpretation
High ConfidenceFF ≥ 10% AND |NCV| ≥ 4Strong signal with adequate FF
Moderate ConfidenceFF 4-10% AND |NCV| ≥ 3Signal present but FF suboptimal
Low ConfidenceFF < 4% OR borderline NCVConsider repeat testing

Clinical Rationale: For a true fetal trisomy, the expected NCV magnitude is proportional to fetal fraction. Low FF with high NCV may indicate confined placental mosaicism, while high FF with borderline NCV warrants careful interpretation.

5.2 Confidence Score Panel

📊 Data Source

CSV columns: FF_Formatted, NCV_13, NCV_18, NCV_21, QCFlag, GCBias, Tags, NCV_X, NCV_Y

Purpose: Provides a composite confidence score (0-100) integrating multiple quality metrics, presented as a traffic-light gauge.

Component Metrics and Weights:

Score Interpretation:

5.3 Maternal-Fetal Signal Estimator

📊 Data Source

CSV columns: FF_Formatted, NCV_13, NCV_18, NCV_21

Purpose: Assists in distinguishing whether an elevated NCV signal originates from the fetus, mother, or represents a technical artifact.

Expected NCV Estimation:

For a true fetal trisomy, the expected NCV is proportional to fetal fraction. The algorithm compares observed NCV to estimated expected values:

Expected_NCV ≈ FF × Constant_Factor

Signal Origin Classification:

Clinical Note: Maternal copy number variants, while rare, can cause false-positive NIPT results. Maternal karyotyping or microarray may be indicated in discordant cases.

5.4 Confined Placental Mosaicism (CPM) Indicator

📊 Data Source

CSV columns: FF_Formatted, NCV_13, NCV_18, NCV_21

Purpose: Assesses the likelihood of confined placental mosaicism as an explanation for positive NIPT results.

Background: CPM occurs when chromosomal abnormality is present in the placenta but not in the fetus. Since NIPT analyzes placental cfDNA, CPM is a recognized cause of false-positive results.

CPM Risk by Chromosome:

ChromosomeCPM Rate Among NIPT Positives
Trisomy 13~20-30%
Trisomy 18~10-20%
Trisomy 21~2-5%

NCV Zone Assessment:

6. Data Tables

The Tables tab provides direct access to raw and processed sample data for detailed examination.

6.1 Sample Data Table

📊 Data Source

All columns from input CSV, organized in structured table format with search and sort capabilities.

Displayed Fields:

Features:

7. Technical Reference

7.1 Data Processing Pipeline

  1. CSV Parsing: Input files are parsed using a type-safe parser that validates column presence and data types
  2. Data Normalization: String values are converted to appropriate numeric types; percentage strings parsed to decimal values
  3. Database Storage: Processed samples are stored in PostgreSQL with relational structure (Batch → Samples)
  4. Visualization Rendering: React components query data via REST API and render using Recharts library

7.2 Key Calculations

Fetal Fraction Parsing

Input: "13%" → Output: 13 (numeric)
FF_Formatted field is parsed to extract numeric percentage value

Y-Fraction Calculation

Y_Fraction = ChrY_Coverage / Σ(Chr1_Coverage ... ChrY_Coverage)

Log₂ Ratio Transformation

Log2_Ratio = log₂(Ratio_XX)
Where Ratio_XX is the normalized chromosome ratio from CSV

7.3 Technology Stack

ComponentTechnologyPurpose
Frontend FrameworkNext.js 14 / React 18Server-side rendering, routing
VisualizationRechartsSVG-based charting library
StylingTailwind CSSUtility-first CSS framework
DatabasePostgreSQL / Prisma ORMData persistence and querying
Type SystemTypeScriptStatic type checking

8. Attribution and Credits

8.1 Original Software Attribution

NIPTviewer

This application is an independent re-implementation inspired by the architecture and concepts of NIPTviewer, developed by Clinical Genomics Gothenburg.

Repository: https://github.com/ClinicalGenomicsGBG/NIPTviewer

License: MIT License

WisecondorX

Visualization concepts, particularly genome-wide CNV profiles and Y-fraction histograms, are inspired by WisecondorX.

Repository: https://github.com/CenterForMedicalGeneticsGhent/WisecondorX

License: CC BY-NC-SA

Citation: Raman L, Dheedene A, De Smet M, et al. WisecondorX: improved copy number detection for routine shallow whole-genome sequencing. Nucleic Acids Research. 2019;47(4):1605-1614.

8.2 Disclaimer

⚠️ Not For Clinical Use

This software is provided for educational and demonstration purposes only. It has NOT been validated for clinical diagnostic use and should NOT be used for making clinical decisions.

All NIPT screening results must be interpreted by qualified healthcare professionals using validated laboratory information systems. Positive screening results require confirmatory diagnostic testing.

8.3 Version Information

Application Version2.0
Manual Version1.0
Last UpdatedFebruary 2026
PlatformWeb Application (Next.js)