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Articles 1 - 5 of 5
Full-Text Articles in Physical Sciences and Mathematics
On The Use Of Cartographic Projections In Visualizing Phylogenetic Treespace, Mark J. Clement, Quinn O. Snell, Kenneth Sundberg
On The Use Of Cartographic Projections In Visualizing Phylogenetic Treespace, Mark J. Clement, Quinn O. Snell, Kenneth Sundberg
Faculty Publications
Phylogenetic analysis is becoming an increasingly important tool for biological research. Applications include epidemiological studies, drug development, and evolutionary analysis. Phylogenetic search is a known NP-Hard problem. The size of the data sets which can be analyzed is limited by the exponential growth in the number of trees that must be considered as the problem size increases. A better understanding of the problem space could lead to better methods, which in turn could lead to the feasible analysis of more data sets. We present a definition of phylogenetic tree space and a visualization of this space that shows significant exploitable …
Psodascript: Applying Advanced Language Constructs To Open-Source Phylogenetic Search, Hyrum Carroll, Mark J. Clement, Jonathan Krein, Quinn O. Snell, Adam R. Teichert
Psodascript: Applying Advanced Language Constructs To Open-Source Phylogenetic Search, Hyrum Carroll, Mark J. Clement, Jonathan Krein, Quinn O. Snell, Adam R. Teichert
Faculty Publications
Due to the immensity of phylogenetic tree space for large data sets, researches must rely on heuristic searches to infer reasonable phylogenies. By designing meta-searches which appropriately combine a variety of heuristics and parameter settings, researchers can significantly improve the performance of heuristic searches. Advanced language constructs in the open-source PSODA project—including variables, mathematical and logical expressions, conditional statements, and user-defined commands—give researchers a better framework for the exploration and exploitation of phylogenetic meta-search algorithms. PSODA’s approach to scripting meta-search algorithms is unique among open-source packages and addresses several limitations of other phylogenetic applications.
Phylogenetic Analysis Of Large Sequence Data Sets, Hyrum Carroll, Mark J. Clement, Keith Crandall, Quinn O. Snell
Phylogenetic Analysis Of Large Sequence Data Sets, Hyrum Carroll, Mark J. Clement, Keith Crandall, Quinn O. Snell
Faculty Publications
Phylogenetic analysis is an integral part of biological research. As the number of sequenced genomes increases, available data sets are growing in number and size. Several algorithms have been proposed to handle these larger data sets. A family of algorithms known as disc covering methods (DCMs), have been selected by the NSF funded CIPRes project to boost the performance of existing phylogenetic algorithms. Recursive Iterative Disc Covering Method 3 (Rec-I-DCM3), recursively decomposes the guide tree into subtrees, executing a phylogenetic search on the subtree and merging the subtrees, for a set number of iterations. This paper presents a detailed analysis …
Jumpstarting Phylogenetic Analysis, Mark J. Clement, Keith A. Crandall, Kevin Seppi, Quinn O. Snell
Jumpstarting Phylogenetic Analysis, Mark J. Clement, Keith A. Crandall, Kevin Seppi, Quinn O. Snell
Faculty Publications
When a new epidemic strikes, it is often important to determine the relationship between the current organism and others that have been successfully treated previously. The phylogenetic analysis problem generates the most likely family tree for a group of organisms based on DNA sequence data. This process can take a prohibitively long period of time with current algorithms. If trees resulting from prior searches are used to seed the search, correct trees can be found much more quickly. This jumpstarting algorithm can generate superior phylogenetic solutions much more quickly than existing algorithms.
Parallel Phylogenetic Inference, Mark J. Clement, David Mclaughlin, Quinn O. Snell, Michael Whiting
Parallel Phylogenetic Inference, Mark J. Clement, David Mclaughlin, Quinn O. Snell, Michael Whiting
Faculty Publications
Recent advances in DNA sequencing technology have created large data sets upon which phylogenetic inference can be performed. However, current research is limited by the prohibitive time necessary to perform tree search on even a reasonably sized data set. Some parallel algorithms have been developed but the biological research community does not use them because they don’t trust the results from newly developed parallel software. This paper presents a new phylogenetic algorithm that allows existing, trusted phylogenetic software packages to be executed in parallel using the DOGMA parallel processing system. The results presented here indicate that data sets that currently …