AbSim

AbSim simulates time-resolved antibody repertoire evolution to generate realistic B-cell lineage sequences for evaluating and comparing phylogenetic reconstruction methods.


Key Features:

  • Time-resolved simulation: Simulates both single lineages using specific sets of V-, D-, and J-genes and full antibody repertoires across evolutionary time.
  • Intermediate-stage modeling: Generates intermediate antibody sequence states to represent evolutionary progression over time.
  • Immunologically relevant parameters: Incorporates parameters such as duration of repertoire evolution and mutation methods and frequency.
  • Topology replication: Produces simulated trees that replicate topological similarities observed in experimental sequencing data.
  • Phylogenetic assessment: Enables demonstration and analysis of cases where existing phylogenetic methods fail to recover true evolutionary trees.

Scientific Applications:

  • Phylogenetic method benchmarking: Tests the accuracy of phylogenetic reconstruction methods for B-cell lineages and antibody molecular evolution.
  • Validation against experimental data: Provides simulated datasets for comparing inferred trees and sequences to experimental sequencing observations.
  • Guideline development: Supports formulation of simulation-validated guidelines for interpreting antibody evolution and improving phylogenetic approaches.

Methodology:

Computationally generates antibody sequences and phylogenies calibrated to replicate topological patterns from experimental sequencing data and uses these simulations to validate phylogenetic inference accuracy.

Topics

Details

License:
GPL-2.0
Tool Type:
library
Operating Systems:
Linux, Windows, Mac
Programming Languages:
R
Added:
6/18/2018
Last Updated:
12/10/2018

Operations

Publications

Yermanos A, Greiff V, Krautler NJ, Menzel U, Dounas A, Miho E, Oxenius A, Stadler T, Reddy ST. Comparison of methods for phylogenetic B-cell lineage inference using time-resolved antibody repertoire simulations (AbSim). Bioinformatics. 2017;33(24):3938-3946. doi:10.1093/bioinformatics/btx533.

Funding: - Swiss National Science Foundation: 31003A_143869, 31003A_170110

Documentation