Issue No. 04 - July-Aug. (2014 vol. 11)
Fei Hu , Tianjin Key Lab. of Cognitive Comput. & Applic., Tianjin Univ., Tianjin, China
Jun Zhou , Dept. of Comput. Sci. & Eng., Univ. of South Carolina, Columbia, SC, USA
Lingxi Zhou , Dept. of Comput. Sci. & Eng., Univ. of South Carolina, Columbia, SC, USA
Jijun Tang , Tianjin Key Lab. of Cognitive Comput. & Applic., Tianjin Univ., Tianjin, China
Changes of gene orderings have been extensively used as a signal to reconstruct phylogenies and ancestral genomes. Inferring the gene order of an extinct species has a wide range of applications, including the potential to reveal more detailed evolutionary histories, to determine gene content and ordering, and to understand the consequences of structural changes for organismal function and species divergence. In this study, we propose a new adjacency-based method, PMAG + , to infer ancestral genomes under a more general model of gene evolution involving gene insertions and deletions (indels), in addition to gene rearrangements. PMAG + improves on our previous method PMAG by developing a new approach to infer ancestral gene contents and reducing the adjacency assembly problem to an instance of TSP. We designed a series of experiments to extensively validate PMAG + and compared the results with the most recent and comparable method GapAdj. According to the results, ancestral gene contents predicted by PMAG + coincides highly with the actual contents with error rates less than 1 percent. Under various degrees of indels, PMAG + consistently achieves more accurate prediction of ancestral gene orders and at the same time, produces contigs very close to the actual chromosomes.
Genomics, Biological cells, Phylogeny, Error analysis, Probability, Bioinformatics, Computational biology,gene deletion, Ancestral genome, gene order, genome rearrangement, gene insertion
Fei Hu, Jun Zhou, Lingxi Zhou, Jijun Tang, "Probabilistic Reconstruction of Ancestral Gene Orders with Insertions and Deletions", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 11, no. , pp. 667-672, July-Aug. 2014, doi:10.1109/TCBB.2014.2309602