Case study: visualizing sets of evolutionary trees

Amenta, Nina; Klingner, Jeff

doi:10.1109/infvis.2002.1173150

Cited by 56 publications

(63 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We have implemented our algorithm as a Java class that can be used as one of many distance functions from within popular packages such as TreeViz [14,15] and Mesquite [8]. Our source code can be obtained from http://compbio.unm.edu.…”

Section: Methodsmentioning

confidence: 99%

A Sublinear-Time Randomized Approximation Scheme for the Robinson-Foulds Metric

Pattengale

Moret

2006

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. The Robinson-Foulds (RF) metric is the measure most widely used in comparing phylogenetic trees; it can be computed in linear time using Day's algorithm. When faced with the need to compare large numbers of large trees, however, even linear time becomes prohibitive. We present a randomized approximation scheme that provides, with high probability, a (1 + ε) approximation of the true RF metric for all pairs of trees in a given collection. Our approach is to use a sublinear-space embedding of the trees, combined with an application of the Johnson-Lindenstrauss lemma to approximate vector norms very rapidly. We discuss the consequences of various parameter choices (in the embedding and in the approximation requirements). We also implemented our algorithm as a Java class that can easily be combined with popular packages such as Mesquite; in consequence, we present experimental results illustrating the precision and running-time tradeoffs as well as demonstrating the speed of our approach.

show abstract

Section: Methodsmentioning

confidence: 99%

A Sublinear-Time Randomized Approximation Scheme for the Robinson-Foulds Metric

Pattengale

Moret

2006

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…Our visualization system has been implemented in such a module, TreeSetVisualization, the first published version of which can be downloaded from our webpage [1]. The module was introduced last summer at Evolution 2002 meeting and has since been downloaded by hundreds of researchers.…”

Section: Methodsmentioning

confidence: 99%

“…Using this universal hash function, the probability that any two bipartitions B 1 and B 2 collide (that is, that h 1 (B 1 ) = h 1 (B 2 )) is 1/m 1 [4], so that if we choose m 1 …”

Section: T1mentioning

confidence: 99%

“…We were motivated to find an efficient algorithm for the majority rule tree, because we wanted to compute it on-the-fly in an interactive visualization application [1]. The goal of the visualization system is to give the user a more sensitive description of the distribution of a set of trees than can be presented with a single consensus tree.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Linear-Time Majority Tree Algorithm

Amenta

Clarke

John

2003

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. We give a randomized linear-time algorithm for computing the majority rule consensus tree. The majority rule tree is widely used for summarizing a set of phylogenetic trees, which is usually a postprocessing step in constructing a phylogeny. We are implementing the algorithm as part of an interactive visualization system for exploring distributions of trees, where speed is a serious concern for real-time interaction. The linear running time is achieved by using succinct representation of the subtrees and efficient methods for the final tree reconstruction.

show abstract

“…At the heart of many techniques for information visualisation is the requirement to construct a two-dimensional representation of a multidimensional abstract data set (recently, for example [Andrews et al 2002], [Rodden et al 2001], [Amenta and Klinger 2002]). As these data sets will often have no inherent two-dimensional mapping, an optimal configuration of objects is sought that preserves the intrinsic structure of the data.…”

Section: Introductionmentioning

confidence: 99%

Improving hybrid MDS with pivot-based searching

Morrison

Chalmers

2003

IEEE Symposium on Information Visualization 2003 (IEEE Cat. No.03TH8714)

View full text Add to dashboard Cite

An algorithm is presented for the visualisation of multidimensional abstract data, building on a hybrid model introduced at InfoVis 2002. The most computationally complex stage of the original model involved performing a nearestneighbour search for every data item. The complexity of this phase has been reduced by treating all high-dimensional relationships as a set of discretised distances to a constant number of randomly selected pivot items. In improving this computational bottleneck, the complexity is reduced from). As well as documenting this improvement, the paper describes evaluation with a data set of 108000 14-dimensional items; a considerable increase on the size of data previously tested. Results illustrate that the reduction in complexity is reflected in significantly improved run times and that no negative impact is made upon the quality of layout produced.

show abstract

Case study: visualizing sets of evolutionary trees

Cited by 56 publications

References 13 publications

A Sublinear-Time Randomized Approximation Scheme for the Robinson-Foulds Metric

A Sublinear-Time Randomized Approximation Scheme for the Robinson-Foulds Metric

A Linear-Time Majority Tree Algorithm

Improving hybrid MDS with pivot-based searching

Contact Info

Product

Resources

About