My-Forensic-Loci-queries (MyFLq) framework for analysis of forensic STR data generated by massive parallel sequencing

Area of Science:

• Forensic Genomics
• Bioinformatics
• Molecular Biology

Background:

• Capillary electrophoresis (CE) is the current standard for forensic DNA analysis.
• Massive parallel sequencing (MPS) offers significant advantages over CE, including higher multiplexing capacity and improved mixture resolution.
• Transitioning to MPS requires robust bioinformatic tools for data analysis.

Purpose of the Study:

• To present a novel bioinformatic framework, My-Forensic-Loci-queries (MyFLq), for analyzing forensic DNA data generated by MPS.
• To develop methods for accurate allele calling, locus performance assessment, and distinguishing novel alleles from sequencing errors.
• To demonstrate the utility of MyFLq in analyzing forensic DNA profiles from single-source and mixed samples.

Main Methods:

• Development of the MyFLq bioinformatic framework using Python and a MySQL reference allele database.
• Implementation of a generic maximal flanking algorithm for automatic determination of regions of interest (ROIs).
• Application of MyFLq to analyze raw MPS data from an Illumina MiSeq platform using forensic amplicon libraries.

Main Results:

• The MyFLq framework demonstrated excellent performance in allele calling for most loci, even with unoptimized PCR conditions.
• High signal-to-noise ratios, accurate allele identification, and a low limit of detection for minor contributors in DNA mixtures were achieved.
• The framework successfully identified novel alleles and distinguished them from sequencing errors.

Conclusions:

• The MyFLq framework provides a robust and versatile solution for analyzing forensic MPS data.
• Forensic MPS, supported by effective bioinformatic tools like MyFLq, holds significant promise for routine implementation in forensic genomics.
• The developed methods are also applicable to related fields such as molecular autopsy and mitochondrial DNA research.