Assexon

Assexon performs de novo assembly and targeted exon reconstruction to produce multi-locus datasets for exon-capture phylogenomic analyses, including paralog resolution and recovery of flanking sequences.


Key Features:

  • De Novo Assembly: Optimized for de novo assembly, enabling recovery of target loci even at low read depth for phylogenomic studies.
  • Efficiency and Speed: Assexon assembled more loci and ran at least twice as fast than PHYLUCE and HybPiper on datasets from Lepisosteus osseus (4.37 Gb) and Boleophthalmus pectinirostris (2.43 Gb).
  • Handling Large Files: Manages large exon-capture datasets typical of high-throughput sequencing experiments.
  • Paralog Resolution: Implements procedures to detect and exclude paralogous sequences during assembly.
  • Automation: Provides a fully automated workflow for assembly and locus processing.
  • Additional Functionalities: Includes scripts to filter poorly aligned coding regions and flanking sequences, calculate locus summary statistics, and select loci with reliable phylogenetic signal.

Scientific Applications:

  • Phylogenomics of non-model organisms: Generation of multi-locus datasets for reconstructing evolutionary relationships across broad phylogenetic scales without relying on reference genomes.
  • Exon-capture analyses: Recovery and curation of targeted exons and flanking regions from exon-capture sequencing data for downstream phylogenetic and genetic diversity studies.

Methodology:

Assexon processes raw sequencing reads via de novo assembly to reconstruct targeted exons and flanking sequences, applies algorithms for paralog resolution and data management, and provides scripts to filter poorly aligned coding regions, calculate locus summary statistics, and select loci with reliable phylogenetic signal.

Topics

Details

Programming Languages:
Perl
Added:
11/14/2019
Last Updated:
12/3/2020

Operations

Publications

Yuan H, Atta C, Tornabene L, Li C. Assexon: Assembling Exon Using Gene Capture Data. Evolutionary Bioinformatics. 2019;15. doi:10.1177/1176934319874792. PMID:31523128. PMCID:PMC6732846.