NIPTmer

NIPTmer detects fetal aneuploidies from raw sequencing data by counting predefined chromosome-specific unique k-mers and applying a linear regression model.

Key Features:

• K-mer Counting Methodology: Counts predefined sets of unique k-mers for each chromosome from raw sequencing data.
• Linear Regression Model Application: Applies a linear regression model to k-mer counts to predict fetal aneuploidies.
• Genetic Variance Consideration: Incorporates filtering during k-mer list creation to account for genetic variance within samples.
• High Processing Efficiency: Processes a single sample in less than 10 CPU-minutes on a high-end workstation.

Scientific Applications:

• Fetal aneuploidy detection (NIPT): Detects fetal aneuploidies from raw NIPT sequencing data.
• Validation: Validated on a cohort of 583 NIPT samples, predicting 37 non-mosaic fetal aneuploidies.

Methodology:

Computational steps include counting chromosome-specific unique k-mers from raw sequencing data, creating and filtering k-mer lists to account for genetic variance, and applying a linear regression model to the k-mer counts.