PLUTON-CLI

Non-invasive prenatal testing (NIPT) is a rapidly evolving and non-invasive method for fetal aneuploidy detection through screening of circulating cell-free DNA (cfDNA) in maternal blood. Advancement in massively parallel sequencing techniques of cfDNA make this type of NIPT more robust and significant. Despite high accuracy and significantly better sensitivity of this sequencing-based technique in comparison to other existing non-invasive methods for detecting fetal chromosomal abnormalities, lack of automated methodologies and workflow complexity with existing computational NIPT and sequence analysis tools are limiting its adoption in the clinical setting. An automated and open-access pipeline integrating best performing NIPT tools may enable standard and less time-consuming analysis without requirements of extensive expertise in bioinformatics. In this study, individual discrete tools and bioinformatics approaches that are available for performing each step of the NIPT, including sequence quality check, alignment with indexed human reference genome (GRCh38), pre-processing the aligned reads, fetal aneuploidies and gender prediction were evaluated and then integrated comprehensively using clinically validated samples. A control group of selected 23 non-trisomy samples were used to create reference for prediction. Using a query cohort of 10 samples with one confirmed aneuploidy, we achieved reliable results. Based on the assessment, we developed a fully automated, flexible and user-friendly framework for NIPT analysis- “PLUTON” command-line interface (CLI) utilizing the most efficient tools for manageable and reproducible interpretation of fetal chromosomal state through NIPT. This automated pipeline is an open-source shell package including several selected and benchmarked tools such as MultiQC, FastQC, SAMtools, Picard, Bowtie2 and WisecondorX which will allow execution of pre-processing of aligned reads and prediction of fetal gender and chromosomal aneuploidies (if any) with simple initial commands. We provide optimized reference data, sample data and also user-guidelines to execute this novel method in the Linux environment. This open-source pipeline may contribute to wider acceptability and implementation of NIPT in clinical context to end-users. It may also reduce the complexity of handling laboratory processing next-generation sequencing data, time-consumption to perform NIPT and the demand of risky invasive procedures of prenatal testing. The automated tool “PLUTON” is freely available under the GNU LGPL license for non-commercial use (https://github.com/epigen-bioinfolab/PLUTON).

PLUTON-GUL

Noninvasive prenatal testing is used to detect chromosomal abnormalities of a fetus. In the undecannial anniversary of noninvasive prenatal testing we present PLUTON GUI, a cross-Platform aUtomated TOol for Noninvasive prenatal testing (PLUTON). Due to lack of automated easy to use NIPT pipeline, adoption of NIPT in clinical settings have been discouraged in poor countries, whereas several commercial solutions are present in developed countries. In this project we present PLUTON GUI which is web based accurate cross platform chromosomal number variation prediction tool. PLUTON GUI is developed using multiple different frameworks, which are divided into backbone, database and a graphic user interface connected by JavaScript. Most accurate and efficient modules were used while developing this tool to ensure reliability and efficiency. A novel incorporation of database management system and webserver along with the flexibility of the framework allows accurate reliable prediction of trisomy 13, 18 and 21 with intuitive easy-to-use UI. The tool can generate report for patients and detailed report for doctors with chromosomal read plots and chromosomal aberration statistics. Despite of being highly flexible the program is computationally intensive, highly dependent on reference file and struggles to detect CNV if the sequencing technology is older or not compatible with the trained dataset. But this limitation can be overcome due to flexibility of our program by training a new dataset and incorporating more efficient modules released in near future.