KF-NIPT: K-mer and fetal fraction-based estimation of chromosomal anomaly from NIPT data

• Published in: BMC bioinformatics Vol. 26; no. 1; pp. 133 - 7
• Main Authors: Kim, Dongin, Sohn, Ji Yeon, Cho, Jin Hee, Choi, Ji-Hye, Oh, Gwi-young, Woo, Hyun Goo
• Format: Journal Article
• Language: English
• Published: London BioMed Central 22.05.2025; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Abnormalities; Accuracy; Algorithm; Algorithms; Amniocentesis; Bioinformatics; Biomedical and Life Sciences; Cell-Free Nucleic Acids - genetics; cfDNA; Chromosome Aberrations; Chromosome abnormalities; Chromosomes; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Data analysis; Datasets; Diagnosis; DNA sequencing; Error reduction; Female; Fetus - metabolism; Fetuses; Gene mapping; Gene sequencing; Genomes; Genomics; Humans; KF-score; Life Sciences; Linux; Machine learning; Methods; Microarrays; NIPT; Noninvasive Prenatal Testing - methods; Nucleotide sequence; Nucleotide sequencing; Pipeline; Pregnancy; Pregnant women; Prenatal diagnosis; Prenatal Diagnosis - methods; Python; Software; Source code; Standard scores; Subsystems; Villus; WGS; Whole Genome Sequencing; South Korea; KF-score; WGS; NIPT; Algorithm; cfDNA; Pipeline
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-025-06127-y

Background Non-Invasive Prenatal Testing (NIPT) is a technique that allows pregnant women to screen for chromosomal abnormalities in their developing fetus without the need for invasive procedures like amniocentesis or chorionic villus sampling. However, current methods to detect anomaly from maternal cell-free DNAs (cfDNAs) that are based on the sequence read counts calculating z-scores face challenges with false positives and negatives. To address these challenges, we aimed to develop a novel NIPT algorithm named KF-NIPT, which is derived from the initials of k-mer and fetal fraction used in its development with the goal of significantly improving accuracy. Results We developed a KF-NIPT, a new algorithm that estimate chromosomal anomaly by calculating K-mer-based sequence depth and fetal fraction from the whole genome sequencing (WGS) data. Moreover, we implemented a modified preprocessing pipeline for the WGS data, correcting the biases of the genomic mapping quality and the GC contents. The performance of our method was evaluated using publicly available NIPT data. We could demonstrate that our method has better accuracy and sensitivity compared to those of the previous methods. Conclusions We found that using k-mer and fetal fraction reduces errors in NIPT and have integrated this into a pipeline, showing that the traditional read count-based z-score method can be improved. KF-NIPT is implemented in the R and Python environment. The source code is available at https://github.com/eastbrain/KF-NIPT . KF-NIPT has been tested on Ubuntu Linux-64 server and Linux-64 on Windows using a WSL (Windows Subsystem for Linux).