FLAD

FLAD analyzes massively parallel sequencing (MPS) data to perform STR and SNP allele calling, assess locus performance, and distinguish true alleles from sequencing errors for forensic DNA profiling.

Key Features:

• MySQL Reference Allele Database: Uses a MySQL reference allele database with automatically determined regions of interest (ROIs) based on a generic maximal flanking algorithm to accommodate any STR or SNP forensic locus.
• Automated Allele Calling: Executes allele calling directly from raw MPS data using Python scripts.
• Forensic Locus Performance Assessment: Includes methods to evaluate the usefulness and overall performance of forensic loci with respect to MPS.
• Unknown Allele Identification: Provides procedures to determine whether an allele absent from the MySQL database represents a novel allele or a sequencing error.
• MPS-specific Capabilities: Supports integration of STR and SNP loci, analysis of small amplicons without size-separation constraints, high multiplexing, deep mixture resolution, and efficient sample multiplexing.

Scientific Applications:

• Forensic MPS profiling (Illumina MiSeq): Applied to an Illumina MiSeq dataset generated from an Illumina amplicon library produced by multilocus STR PCR on single-contributor and multi-person DNA mixtures.
• Mixture analysis: Resolves complex DNA mixtures and enables detection of low-level contributors through deep sequencing and accurate allele calls.
• Molecular autopsy and mitochondrial DNA research: Applicable for routine implementation in legal medicine molecular autopsy workflows and mitochondrial DNA research.

Methodology:

Utilizes a MySQL reference allele database with dynamically determined ROIs via a maximal flanking algorithm; automates allele calling from raw MPS data using Python scripts; assesses forensic locus performance and applies procedures to classify unknown alleles as novel variants or sequencing errors.