Mobster

Mobster detects novel mobile element insertion (MEI) events from next-generation sequencing BAM files to identify non-reference LINE-1 (L1) and Alu insertions that alter genomic architecture.

Key Features:

• MEI detection from BAM files: Detects novel mobile element insertion events directly from next-generation sequencing BAM files.
• Discordant read-pair analysis: Leverages discordant read pair methods to identify candidate insertion loci.
• Split-read (clipped-read) detection: Uses split-read (clipped reads) techniques to refine insertion breakpoints.
• Consensus sequence integration: Integrates consensus sequences of known active mobile elements, including LINE-1 (L1) and Alu, for identification of non-reference insertions.
• Performance metrics: Demonstrates a low false discovery rate and high recall for L1 and Alu detection.
• Applicability to WGS/WES: Applicable to whole genome and whole exome sequencing studies for MEI discovery.
• Handling repetitive sequence mappability: Addresses challenges of low mappability associated with highly repetitive mobile element sequences.

Scientific Applications:

• Evolutionary biology: Enables analysis of mobile element dynamics and their contribution to genome evolution.
• Genomics: Supports discovery of non-reference MEIs in genomic studies to characterize structural variation.
• Medical genetics: Facilitates detection of MEIs implicated in disease by identifying insertion events that alter genomic architecture.
• Genomic diversity and disease studies: Provides insights into how L1 and Alu insertions contribute to genomic diversity and disease processes.

Methodology:

Mobster applies an algorithm that combines discordant read-pair analysis and split-read (clipped-read) detection and integrates consensus sequences of known active mobile elements (LINE-1 and Alu) on BAM-formatted next-generation sequencing data.