Search

Non-invasive prenatal testing (NIPT) is a powerful screening method for fetal aneuploidy detection, relying on laboratory and computational analysis of cell-free DNA. Although several published computational NIPT analysis tools are available, no prior comprehensive, head-to-head accuracy comparison of the various tools has been published. Here, we compared the outcome accuracies obtained for clinically validated samples with five commonly used computational NIPT aneuploidy analysis tools (WisecondorX, NIPTeR, NIPTmer, RAPIDR, and GIPseq) across various sequencing depths (coverage) and fetal DNA fractions. The sample set included cases of fetal trisomy 21 (Down syndrome), trisomy 18 (Edwards syndrome), and trisomy 13 (Patau syndrome). We determined that all of the compared tools were considerably affected by lower sequencing depths, such that increasing proportions of undetected trisomy cases (false negatives) were observed as the sequencing depth decreased. We summarised our benchmarking results and highlighted the advantages and disadvantages of each computational NIPT software. To conclude, trisomy detection for lower coverage NIPT samples (e.g. 2.5M reads per sample) is technically possible but can, with some NIPT tools, produce troubling rates of inaccurate trisomy detection, especially in low-FF samples. Author summary: Non-invasive prenatal testing analysis relies on computational algorithms that are used for inferring chromosomal aneuploidies, such as chromosome 21 triploidy in the case of Down syndrome. However, the performance of these algorithms has not been compared on the same clinically validated data. Here we conducted a head-to-head comparison of WGS-based NIPT aneuploidy detection tools. Our findings indicate that at and below 2.5M reads per sample, the least accurate algorithm would miss detection of almost a third of trisomy cases. Furthermore, we describe and quantify a previously undocumented aneuploidy risk uncertainty that is mainly relevant in cases of very low sequencing coverage (at and below 1.25M reads per sample) and could, in the worst-case scenario, lead to a false negative rate of 245 undetected trisomies per 1,000 trisomy cases. Our findings underscore the importance of the informed selection of NIPT software tools in combination with sequencing coverage, which directly impacts NIPT sequencing cost and accuracy. [ABSTRACT FROM AUTHOR]

Objective The study aimed to validate a whole‐genome sequencing‐based NIPT laboratory method and our recently developed NIPTmer aneuploidy detection software with the potential to integrate the pipeline into prenatal clinical care in Estonia. Method: In total, 424 maternal blood samples were included. Analysis pipeline involved cell‐free DNA extraction, library preparation and massively parallel sequencing on Illumina platform. Aneuploidies were determined with NIPTmer software, which is based on counting pre‐defined per‐chromosome sets of unique k‐mers from sequencing raw data. SeqFF was implemented to estimate cell‐free fetal DNA (cffDNA) fraction. Results: NIPTmer identified correctly all samples of non‐mosaic trisomy 21 (T21, 15/15), T18 (9/9), T13 (4/4) and monosomy X (4/4) cases, with the 100% sensitivity. However, one mosaic T18 remained undetected. Six false‐positive (FP) results were observed (FP rate of 1.5%, 6/398), including three for T18 (specificity 99.3%) and three for T13 (specificity 99.3%). The level of cffDNA of <4% was estimated in eight samples, including one sample with T13 and T18. Despite low cffDNA level, these two samples were determined as aneuploid. Conclusion: We believe that the developed NIPT method can successfully be used as a universal primary screening test in combination with ultrasound scan for the first trimester fetal examination. What is already known about this topic? The developed NIPT approaches are different in terms of the genetic and bioinformatic technologies used, computational resources required, data analysis speed, and finally even in robustness and reliability. What does this study add? Novel in‐house developed NIPTmer program counts pre‐defined per‐chromosome sets of unique k‐mers from sequencing data.NIPTmer uses less computer resources and is faster than available NIPT‐tools.NIPTmer was validated for detection of fetal aneuploidies, demonstrating its readiness for use in prenatal testing. [ABSTRACT FROM AUTHOR]

Non-invasive prenatal testing (NIPT) enables accurate detection of fetal chromosomal trisomies. The majority of publicly available computational methods for sequencing-based NIPT analyses rely on low-coverage whole-genome sequencing (WGS) data and are not applicable for targeted high-coverage sequencing data from cell-free DNA samples. Here, we present a novel computational framework for a targeted high-coverage sequencing-based NIPT analysis. The developed framework uses a hidden Markov model (HMM) in conjunction with a supplemental machine learning model, such as decision tree (DT) or support vector machine (SVM), to detect fetal trisomy and parental origin of additional fetal chromosomes. These models were developed using simulated datasets covering a wide range of biologically relevant scenarios with various chromosomal quantities, parental origins of extra chromosomes, fetal DNA fractions, and sequencing read depths. Developed models were tested on simulated and experimental targeted sequencing datasets. Consequently, we determined the functional feasibility and limitations of each proposed approach and demonstrated that read count-based HMM achieved the best overall classification accuracy of 0.89 for detecting fetal euploidies and trisomies on simulated dataset. Furthermore, we show that by using the DT and SVM on the HMM classification results, it was possible to increase the final trisomy classification accuracy to 0.98 and 0.99, respectively. We demonstrate that read count and allelic ratio-based models can achieve a high accuracy (up to 0.98) for detecting fetal trisomy even if the fetal fraction is as low as 2%. Currently, existing commercial NIPT analysis requires at least 4% of fetal fraction, which can be possibly a challenge in case of early gestational age (<10 weeks) or high maternal body mass index (>35 kg/m2). More accurate detection can be achieved at higher sequencing depth using HMM in conjunction with supplemental models, which significantly improve the trisomy detection especially in borderline scenarios (e.g., very low fetal fraction) and enables to perform NIPT even earlier than 10 weeks of pregnancy. [ABSTRACT FROM AUTHOR]

Background: Benign metastasizing leiomyoma (BML) is an orphan neoplasm commonly characterized by pulmonary metastases consisting of smooth muscle cells. Patients with BML have usually a current or previous uterine leiomyoma, which is therefore suggested to be the most probable source of this tumour. The purpose of this case report was to determine the possible genetic grounds for pulmonary BML. Case presentation: We present a case report in an asymptomatic 44-year-old female patient, who has developed uterine leiomyoma with subsequent pulmonary BML. Whole exome sequencing (WES) was used to detect somatic mutations in BML lesion. Somatic single nucleotide mutations were identified by comparing the WES data between the pulmonary metastasis and blood sample of the same BML patient. One heterozygous somatic mutation was selected for validation by Sanger sequencing. Clonality of the pulmonary metastasis and uterine leiomyoma was assessed by X-chromosome inactivation assay. Conclusions: We describe a potentially deleterious somatic heterozygous mutation in bone morphogenetic protein 8B (BMP8B) gene (c.1139A > G, Tyr380Cys) that was identified in the pulmonary metastasis and was absent from blood and uterine leiomyoma, and may play a facilitating role in the metastasizing of BML. The clonality assay confirmed a skewed pattern of X-chromosome inactivation, suggesting monoclonal origin of the pulmonary metastases. [ABSTRACT FROM AUTHOR]

Objective: Clinically pathogenic chromosomal microdeletions cause severe genetic disorders. Motivated by the absence of reliable screening of microdeletions during the first-trimester screening, we developed BinDel, a software tool to determine the risk of clinically relevant pathogenic fetal microdeletions from low-coverage whole-genome-sequencing (WGS) based NIPT data., Methods: We developed novel computational software that employs a targeted approach with region-specific normalisation and calling procedures to detect microdeletion risk in predefined chromosomal regions. The software was developed using 500 NIPT samples and validated on an additional 84 samples, including 34 rare fetal microdeletions confirmed both pre- and postnatally., Results: BinDel correctly identified 30 out of 34 samples with microdeletions, with only three false-positive calls among 50 euploid samples, all latter originating from the Williams-Beuren and Prader-Willi/Angelman syndrome-associated microdeletion regions., Conclusions: We confirmed BinDel's feasibility for integrating microdeletion analysis into routine NIPT protocol. This work stands as a unique contribution to prenatal microdeletion screening, providing a novel and readily available software tool that was validated with a large set of actual microdeletion samples, positioning it as the first of its kind in the field. BinDel is available at https://github.com/seqinfo/BinDel., (© 2025 The Author(s). Prenatal Diagnosis published by John Wiley & Sons Ltd.)

Non-invasive prenatal testing (NIPT) is a recent and rapidly evolving method for detecting genetic lesions, such as aneuploidies, of a fetus. However, there is a need for faster and cheaper laboratory and analysis methods to make NIPT more widely accessible. We have developed a novel software package for detection of fetal aneuploidies from next-generation low-coverage whole genome sequencing data. Our tool - NIPTmer - is based on counting pre-defined per-chromosome sets of unique k-mers from raw sequencing data, and applying linear regression model on the counts. Additionally, the filtering process used for k-mer list creation allows one to take into account the genetic variance in a specific sample, thus reducing the source of uncertainty. The processing time of one sample is less than 10 CPU-minutes on a high-end workstation. NIPTmer was validated on a cohort of 583 NIPT samples and it correctly predicted 37 non-mosaic fetal aneuploidies. NIPTmer has the potential to reduce significantly the time and complexity of NIPT post-sequencing analysis compared to mapping-based methods. For non-commercial users the software package is freely available at http://bioinfo.ut.ee/NIPTMer/ .