Customisation of the exome data analysis pipeline using a combinatorial approach.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Next-generation sequencing (NGS) technologies have transformed biological data production and analysis.
• NGS offers cost-effective genome-wide variant discovery but requires validation due to inherent error rates.
• Whole exome sequencing is an affordable alternative to whole genome sequencing, yet novel variants still need validation.

Purpose of the Study:

• To address the challenges of high error rates and data analysis complexity in NGS.
• To develop a customized NGS data analysis pipeline for improved true variant retention and reduced false positives.
• To simplify the selection of appropriate analytical tools for researchers with limited bioinformatics expertise.

Main Methods:

• Evaluation of various freely available bioinformatics tools for NGS data alignment.
• Assessment of different post-alignment variant detection algorithms.
• Development of a framework using pre-existing metrics to identify optimal tool combinations for variant analysis.

Main Results:

• Identification of specific tool combinations that enhance the accuracy of variant detection from NGS data.
• Demonstration of a method to minimize false positives in NGS variant datasets.
• A framework is proposed to guide researchers in selecting suitable analytical tools based on defined metrics.

Conclusions:

• Customizing NGS data analysis pipelines is crucial for reliable variant discovery.
• The proposed framework aids in selecting appropriate bioinformatics tools, improving the quality of significant datasets.
• This approach facilitates more accurate and efficient genomic variant analysis for individual investigators.