LEMON

LEMON reconstructs local strains at horizontal gene transfer (HGT) sites within gut metagenomic shotgun next-generation sequencing (NGS) datasets to identify transferred genome segments and analyze resulting structural variation.

Key Features:

• Graph-Based Reconstruction: Employs a graph-based method to identify and reconstruct local genome structures at HGT sites within gut microbiomes.
• Detection of Transferred Segments: Uses shotgun NGS datasets to detect breakpoints associated with HGT events and thereby identify transferred genomic segments.
• Simulation and Validation: Demonstrates high accuracy on simulated results and reference sequences from microbiome data to validate recovered transferred segments and local strains.
• Gene Fusion Detection: Recovers local strains that exhibit complex structural variations and detects gene fusion points proximal to HGT breakpoints in real datasets.
• Reliability Assessment: Assesses reconstructed strains via replication time profile analysis, reporting lower standard error as an indicator of reconstruction reliability.
• Breakpoint Processing Script: Includes processing and analysis scripts such as get_raw_bkp.py for detecting HGT breakpoints.

Scientific Applications:

• HGT identification: Identification and characterization of horizontal gene transfer events in gut microbiome shotgun NGS data.
• Local strain reconstruction: Reconstruction of sequence structure for bacteria harboring HGT events to resolve strain-level variation.
• Structural variation analysis: Analysis of complex structural variants and gene fusions resulting from HGT.
• Microbial gene flow studies: Investigation of gene flow among microbial species within the gut microbiome and its potential impacts on genomic diversity and human health.

Methodology:

LEMON applies a graph-based reconstruction approach on shotgun NGS metagenomic data to detect HGT breakpoints and transferred segments, validates recovery accuracy using simulated results and reference sequences, detects gene fusion points near breakpoints, and evaluates reconstructed strains using replication time profile standard error; processing scripts such as get_raw_bkp.py are used for breakpoint analysis.