Syntenet

Syntenet infers and analyzes synteny networks from whole-genome protein sequence data to detect anchor pairs, construct synteny graphs, perform network clustering and phylogenomic profiling, and reconstruct microsynteny-based phylogenies using maximum likelihood.

Key Features:

• Data Preprocessing: Prepares whole-genome protein sequences for downstream synteny analysis.
• Synteny Detection (MCScanX via Rcpp): Detects anchor pairs using the MCScanX algorithm integrated into R through Rcpp.
• Network Inference: Constructs synteny networks by treating detected anchor pairs as nodes in undirected unweighted graphs.
• Network Clustering: Identifies clusters within synteny networks that may correspond to taxon-specific gene groups.
• Phylogenomic Profiling: Profiles species by determining which synteny clusters are present in each genome.
• Microsynteny-Based Phylogeny Reconstruction: Reconstructs phylogenies from microsynteny data using maximum likelihood methods.
• Visualization: Produces plots to visualize synteny networks and highlight taxon-specific clusters.

Scientific Applications:

• Evolutionary Biology: Identification of taxon-specific gene clusters to study contributions of genes or gene groups to the evolution of biological traits.
• Phylogenetics: Reconstruction of phylogenies from microsynteny data to resolve complex or contentious evolutionary relationships.

Methodology:

Computational steps explicitly include data preprocessing of whole-genome protein sequences; synteny detection using MCScanX (integrated via Rcpp) to identify anchor pairs; construction of synteny networks as undirected unweighted graphs; network clustering and phylogenomic profiling to map clusters to genomes; and microsynteny-based phylogeny reconstruction using maximum likelihood.