NIPTeR: an R package for fast and accurate trisomy prediction in non-invasive prenatal testing.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Accurate prediction of fetal trisomies in non-invasive prenatal testing (NIPT) relies on robust control groups.
• Variability in cell-free DNA (cfDNA) data can impact prediction accuracy.
• Existing methods require careful selection and processing of control samples.

Purpose of the Study:

• To introduce NIPTeR, an open-source R package designed for efficient NIPT data analysis.
• To provide tools for reducing data variability and enhancing trisomy prediction.
• To facilitate quality control and statistical analysis of NIPT data.

Main Methods:

• Development of an R package (NIPTeR) integrating data preprocessing, variation reduction, and trisomy prediction algorithms.
• Implementation of quality control metrics for NIPT data.
• Facilitation of control group statistics calculation.

Main Results:

• NIPTeR enables fast and flexible NIPT analysis workflows.
• The package accounts for data variability and improves prediction accuracy.
• NIPTeR supports the calculation of essential control group statistics.

Conclusions:

• NIPTeR assists laboratories in assessing NIPT data quality and identifying fetal trisomies.
• The package supports next-generation sequencing data processing for NIPT.
• NIPTeR is freely available under the GNU LGPL v3 license.