Abstract
BackgroundVarious algorithms have been developed to predict fetal trisomies using cell-free DNA in non-invasive prenatal testing (NIPT). As basis for prediction, a control group of non-trisomy samples is needed. Prediction accuracy is dependent on the characteristics of this group and can be improved by reducing variability between samples and by ensuring the control group is representative for the sample analyzed.ResultsNIPTeR is an open-source R Package that enables fast NIPT analysis and simple but flexible workflow creation, including variation reduction, trisomy prediction algorithms and quality control. This broad range of functions allows users to account for variability in NIPT data, calculate control group statistics and predict the presence of trisomies.ConclusionNIPTeR supports laboratories processing next-generation sequencing data for NIPT in assessing data quality and determining whether a fetal trisomy is present. NIPTeR is available under the GNU LGPL v3 license and can be freely downloaded from https://github.com/molgenis/NIPTeR or CRAN.
Highlights
Various algorithms have been developed to predict fetal trisomies using cell-free DNA in non-invasive prenatal testing (NIPT)
Johansson et al BMC Bioinformatics (2018) 19:531 we report NIPTeR, an R package that provides fast NIPT analysis for research and diagnostics and provides users with multiple methods for variation reduction, prediction and quality control based upon comparison of a sample with a set of negative control samples
Workflow All these NIPTeR building blocks can be combined into an analysis workflow
Summary
Workflow All these NIPTeR building blocks can be combined into an analysis workflow. For example, the NIPTeR workflow for the Fan & Quake analysis [10], using a weighted bin GC correction and a standard Z-score prediction for trisomy 21, and given a GC-corrected control group is: In addition, control group statistics and the match control of the sample to the control group can be performed: Prediction and control group statistics The output formats of the calculate_z_score and calculate_ncv_score functions are similar. NCV analysis was performed in an additional 1 to 6 min using a maximum number of 6 to 9 chromosomes as denominator This example shows that, for many chromosomes in sample 21 one or both of the strands have a Z-score higher than 3. When looking at the individual Match QC scores of the GC corrected NIPTSample compared to the GC corrected NIPTControlGroup, the list of sum of squares of differences in chromosomal fractions of the test sample compared to each control sample is shown: Conclusion NIPTeR allows for fast NIPT analysis and flexible workflow creation and includes variation correction and prediction algorithms as well as QC control. Additional file 4: Supplemental information comparing the manual calculations for the standard Z-score, Normalized Chromosome Value, Regression-based Z-score and the Match QC with NIPTeR calculations.
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