Exome-wide benchmark of difficult-to-sequence regions using short-read next-generation DNA sequencing.

Area of Science:

• Genomics
• Bioinformatics
• Clinical Genetics

Background:

• Next-generation DNA sequencing (NGS) is vital for clinical genetic testing.
• Ensuring NGS data accuracy is critical, with existing quality control focusing on read accuracy.
• Variant calling errors at the single-nucleotide level remain a significant, unexplored challenge.

Purpose of the Study:

• To develop a method for benchmarking difficult-to-sequence regions in the human genome at nucleotide resolution.
• To introduce a novel metric, the UNMET score, for assessing sequencing challenges in exonic regions.
• To enhance the accuracy of clinical genetic testing by addressing potential NGS errors.

Main Methods:

• Utilized a machine-learning approach with 10 genome sequence features.
• Analyzed real-world NGS data from The Genome Aggregation Database (gnomAD).
• Focused on the human reference genome sequence (GRCh38/hg38) at nucleotide-residue resolution.

Main Results:

• Established an exome-wide benchmark of difficult-to-sequence regions.
• Developed the UNMET score to quantify sequencing challenges.
• Demonstrated the UNMET score's utility in assessing exonic regions using short-read NGS.

Conclusions:

• The UNMET score provides a novel way to assess sequencing difficulties in protein-coding exons.
• This metric can help address potential sequencing errors in clinical genetic testing.
• The findings contribute to improving the reliability of genetic testing using NGS data.