Paper Info
Title
Prediction Of Transposable Elements Evolution Using Tabu Search
Abstract
Transposable elements (TEs) are DNA sequences that can move or copy to new positions within a genome. Due to their abundance in many species, predicting the evolution of these TEs within a genome is a major component of understanding the evolution of the genome generally. The sequential interruption model is defined between TEs that occur in a single genome, which has been shown to be useful in previous literature in predicting TE ages and periods of activity throughout evolution. This model is closely related to a classic matrix optimization problem: the linear ordering problem (LOP). By applying a well-studied method of solving the LOP, tabu search, to the sequential interruption model, a relative age order of all TEs in the human genome is predicted in only 38 seconds. A comparison of the TE ordering between tabu search and the previously existing method shows that tabu search solves the TE problem exceedingly more efficiently, while it still achieves a more accurate result. The speed improvements allow a complete prediction of human TEs to be made, whereas previously, ordering of only a small portion of human TEs could be predicted. A simulation of TE transpositions throughout evolution is then developed and used as a form of in silico verification to the sequential interruption model. By feeding the simulated TE remnants and activity data into the model, a relative age order is predicted using the sequential interruption model, and a quantified correlation between this predicted order and the input (true) age order in the simulation can be calculated. An average correlation over ten simulations is calculated as 0.738 with the correct simulated answer.
Year
DOI
Venue
2018
10.1109/BIBM.2018.8621478
PROCEEDINGS 2018 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM)
Keywords
Field
DocType
transposable elements, the human genome, evolution, interruptional analysis, tabu search, linear ordering problem
Genome,Computer science,Transposable element,Matrix (mathematics),Algorithm,Artificial intelligence,DNA sequencing,Human genome,Optimization problem,Tabu search,Machine learning,In silico
Conference
ISSN
Citations 
PageRank 
2156-1125
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Lingling Jin100.34
Ian McQuillan29724.72