Title
Synthesizing Species Trees from Unrooted Gene Trees: A Parameterized Approach
Abstract
Synthesizing species trees from a collection of smaller gene trees is a widely used approach for inferring credible species tree estimates. While corresponding computational problems are typically NP-hard, several of these problems have been effectively addressed by using the parameterized Strict Consensus Approach. This approach is limited to gene trees that are rooted. In practice, however, most gene trees are unrooted, and it is often difficult, if not impossible, to identify accurate rootings. Here, we address this stringent limitation by proposing efficient algorithms that adopt the parameterized Strict Consensus Approach to handle unrooted gene trees. Finally, we demonstrate the performance of our algorithms in a comparative study using empirical and simulated data sets.
Year
DOI
Venue
2017
10.1145/3107411.3107450
BCB
Keywords
Field
DocType
Guidance Tree based Unrooted Tree Fill-in,Guidance Tree based Tree Rooting,Pareto for Clusters,Strict Consensus Approach,Gene Duplication
Parameterized complexity,Computational problem,Data set,Tree rearrangement,Computer science,Artificial intelligence,Weight-balanced tree,Bioinformatics,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-4503-4722-8
1
0.37
References 
Authors
14
2
Name
Order
Citations
PageRank
Jucheol Moon1153.63
Oliver Eulenstein250552.71