ARTra: Additive and Replacing Transfer Inference
ARTra (short for “Additive and Replacing Transfer Inference”) is a program for inferring and distinguishing between additive and replacing horizontal gene transfer events. ARTra uses Duplication-Transfer-Loss (DTL) reconciliation to infer transfer events and then uses a trained machine learning classifier to classify the inferred transfers as additive or replacing. It also implements three simple non-machine-learning-based classification heuristics, including the “gene-frequency” heuristic described in the paper available from https://doi.org/10.1145/3307339.3342168 and implemented in the RANGER-DTL-RT tool. The other two heuristics, lost-gene heuristic and mapping-count heuristic, are described in the paper cited below. The machine learning classifier uses the classifications generated by these heuristics, along with some additional features, to generate an improved ensemble classification. Further technical details appear in the paper cited below.
ARTra was implemented by Abhijit Mondal and is available open-source under GNU GPL. Precompiled executables, source code, and a user manual are available below.
- Software: ARTra.zip
- User manual: ARTra_manual.pdf
- Source code: ARTra_SourceCode.zip (available under GNU GPL)
This software can be cited as follows:
- A Supervised Machine Learning Approach for Distinguishing Between Additive and Replacing Horizontal Gene Transfers
Abhijit Mondal, Misagh Kordi, Mukul S. Bansal
ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) 2020: to appear.
Funding: Development of the software resource(s) available from this webpage was funded in part by U.S. National Science Foundation award MCB 1616514.
Contact: Please feel free to contact Abhijit Mondal (abhijit.mondal@uconn.edu) or Mukul Bansal (mukul.bansal@uconn.edu) if you have any questions, concerns, or suggestions.