ArticlePLoS computational biology2021
Systematic evaluation of NIPT aneuploidy detection software tools with clinically validated NIPT samples.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed, 17 citations in OpenAlex.
- Recurrent Hodgkin's Lymphoma Detected Using Abnormal NIPT in Pregnancy: A Case Report and Literature Review.Diagnostics (Basel, Switzerland) · 2026Article
- Clinical performance of expanded non-invasive prenatal testing for aneuploidies: a real-world experience in Thailand.BMC pregnancy and childbirth · 2025Article
- BinDel: Detecting Clinically Relevant Fetal Genomic Microdeletions Using Low-Coverage Whole-Genome Sequencing-Based NIPT.Prenatal diagnosis · 2025Article
- Artificial intelligence and machine learning in cell-free-DNA-based diagnostics.Genome research · 2025Review
- Association of maternal risk factors with fetal aneuploidy and the accuracy of prenatal aneuploidy screening: a correlation analysis based on 12,186 karyotype reports.BMC pregnancy and childbirth · 2023Article
- False-positives and false-negatives in non-invasive prenatal testing (NIPT): what can we learn from a meta-analyses on > 750,000 tests?Molecular cytogenetics · 2022Article
- Article
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
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