NiPTUNE: an automated pipeline for noninvasive prenatal testing in an accurate, integrative and flexible framework

Abstract

Noninvasive prenatal testing (NIPT) consists of determining fetal aneuploidies by quantifying copy number alteration from the sequencing of cell-free DNA (cfDNA) from maternal blood. Due to the presence of cfDNA of fetal origin in maternal blood, in silico approaches have been developed to accurately predict fetal aneuploidies. Although NIPT is becoming a new standard in prenatal screening of chromosomal abnormalities, there are no integrated pipelines available to allow rapid, accurate and standardized data analysis in any clinical setting. Several tools have been developed, however often optimized only for research purposes or requiring enormous amount of retrospective data, making hard their implementation in a clinical context. Furthermore, no guidelines have been provided on how to accomplish each step of the data analysis to achieve reliable results. Finally, there is no integrated pipeline to perform all steps of NIPT analysis. To address these needs, we tested several tools for performing NIPT data analysis. We provide extensive benchmark of tools performances but also guidelines for running them. We selected the best performing tools that we benchmarked and gathered them in a computational pipeline. NiPTUNE is an open source python package that includes methods for fetal fraction estimation, a novel method for accurate gender prediction, a principal component analysis based strategy for quality control and fetal aneuploidies prediction. NiPTUNE is constituted by seven modules allowing the user to run the entire pipeline or each module independently. Using two cohorts composed by 1439 samples with 31 confirmed aneuploidies, we demonstrated that NiPTUNE is a valuable resource for NIPT analysis.

GenomeMixer and TRUST: Novel bioinformatics tools to improve reliability of Non-Invasive Prenatal Testing (NIPT) for fetal aneuploidies

“…A sufficient ff is needed to insure the sensibility and specificity of NIPT. In our previous work [11] , we have shown that bioinformatics tools to estimate ff provide very different results. ff values in different clinical laboratories are therefore not comparable and a gold standard threshold of ff to validate NIPT results cannot be determined.…”

“…Thus, from 23 native aneuploid (NA) samples with fetal T21 and 7 NA samples with T18, we generated respectively 437 and 133 synthetic aneuploidies (SA). We calculated the fetal fraction with two tools, Seqff and Defrag_a because previous benchmark showed that these tools are the best performing [11] . Seqff estimated ff for all SA obtaining ranges from 0.88 to 35.5.…”

“…Patient cohort was presented in Duboc et al [11] . Briefly, NIPT was performed on 377 samples from pregnant women at Nice university hospital from January 2017 to September 2018 (cohort 1) and 1062 samples at Marseille university hospital from January 2017 to December 2018 (cohort 2) after informed consent.…”

“…NIPT was performed on patient cohort sequencing using NiPTUNE with default configuration [11] . To verify that samples do not have aberrant read count distributions, we applied the principal component analysis (PCA), as part of the NiPTUNE pipeline, on binned count of normalized reads for samples belonging to each cohort and the two non-pregnant samples ( Fig.…”

“…To verify that samples do not have aberrant read count distributions, we applied the principal component analysis (PCA), as part of the NiPTUNE pipeline, on binned count of normalized reads for samples belonging to each cohort and the two non-pregnant samples ( Fig. 2 of [11] ). NiPTUNE yields the fetal fraction calculated by Seqff [12] and Defrag_a [13] , the aneuploidy detection based on the Z-score calculation with WisecondorX [7] .…”