L1EM

L1EM quantifies locus-specific LINE-1 (Long Interspersed Nuclear Element-1) RNA using an expectation-maximization algorithm to distinguish protein-coding, retrotransposition-competent transcripts from other sources of LINE-1 RNA.

Key Features:

• Locus-Specific Quantification: Provides per-locus quantification of LINE-1 RNA across the genome.
• Transcript Differentiation: Separates transcripts with protein-coding capability (retrotransposition-competent) from other sources of LINE-1 RNA.
• Expectation-Maximization Algorithm: Uses an expectation-maximization statistical approach to iteratively refine abundance estimates for repetitive LINE-1 loci.

Scientific Applications:

• Disease Research: Enables investigation of locus-specific LINE-1 expression in studies of disease progression and cellular damage.
• Genomic Studies: Supports analyses of retrotransposon activity and dynamics within the genome.

Methodology:

Implemented in Python and employing an expectation-maximization algorithm to analyze sequencing data; validated on simulated datasets and long-read sequencing data from HEK cells; requires samtools version 1.0 or higher and bwa version 0.7.17 due to XA tag handling.