Tree House Explorer: A Novel Genome Browser for Phylogenomics.

Tree House Explorer (THEx) is a genome browser that integrates phylogenomic data and genomic annotations into a single interactive platform for combined analysis. THEx allows users to visualize genome-wide variation in evolutionary histories and genetic divergence on a chromosome-by-chromosome basis, with continuous sliding window comparisons to gene annotations (GFF/GTF), recombination rates, and other user-specified, highly customizable feature annotations. THEx provides a new platform for interactive phylogenomic data visualization to analyze and interpret the diverse evolutionary histories woven throughout genomes. Hosted on Conda, THEx integrates seamlessly into new or pre-existing workflows.

Fast algorithms for computing phylogenetic divergence time.

BACKGROUND: The inference of species divergence time is a key step in most phylogenetic studies. Methods have been available for the last ten years to perform the inference, but the performance of the methods does not yet scale well to studies with hundreds of taxa and thousands of DNA base pairs. For example a study of 349 primate taxa was estimated to require over 9 months of processing time. In this work, we present a new algorithm, AncestralAge, that significantly improves the performance of the divergence time process. RESULTS: As part of AncestralAge, we demonstrate a new method for the computation of phylogenetic likelihood and our experiments show a 90% improvement in likelihood computation time on the aforementioned dataset of 349 primates taxa with over 60,000 DNA base pairs. Additionally, we show that our new method for the computation of the Bayesian prior on node ages reduces the running time for this computation on the 349 taxa dataset by 99%. CONCLUSION: Through the use of these new algorithms we open up the ability to perform divergence time inference on large phylogenetic studies.

An efficient and extensible approach for compressing phylogenetic trees.

BACKGROUND: Biologists require new algorithms to efficiently compress and store their large collections of phylogenetic trees. Our previous work showed that TreeZip is a promising approach for compressing phylogenetic trees. In this paper, we extend our TreeZip algorithm by handling trees with weighted branches. Furthermore, by using the compressed TreeZip file as input, we have designed an extensible decompressor that can extract subcollections of trees, compute majority and strict consensus trees, and merge tree collections using set operations such as union, intersection, and set difference. RESULTS: On unweighted phylogenetic trees, TreeZip is able to compress Newick files in excess of 98%. On weighted phylogenetic trees, TreeZip is able to compress a Newick file by at least 73%. TreeZip can be combined with 7zip with little overhead, allowing space savings in excess of 99% (unweighted) and 92%(weighted). Unlike TreeZip, 7zip is not immune to branch rotations, and performs worse as the level of variability in the Newick string representation increases. Finally, since the TreeZip compressed text (TRZ) file contains all the semantic information in a collection of trees, we can easily filter and decompress a subset of trees of interest (such as the set of unique trees), or build the resulting consensus tree in a matter of seconds. We also show the ease of which set operations can be performed on TRZ files, at speeds quicker than those performed on Newick or 7zip compressed Newick files, and without loss of space savings. CONCLUSIONS: TreeZip is an efficient approach for compressing large collections of phylogenetic trees. The semantic and compact nature of the TRZ file allow it to be operated upon directly and quickly, without a need to decompress the original Newick file. We believe that TreeZip will be vital for compressing and archiving trees in the biological community.

A new support measure to quantify the impact of local optima in phylogenetic analyses.

Phylogentic analyses are often incorrectly assumed to have stabilized to a single optimum. However, a set of trees from a phylogenetic analysis may contain multiple distinct local optima with each optimum providing different levels of support for each clade. For situations with multiple local optima, we propose p-support which is a clade support measure that shows the impact optima have on a final consensus tree. Our p-support measure is implemented in our PeakMapper software package. We study our approach on two published, large-scale biological tree collections. PeakMapper shows that each data set contains multiple local optima. p-support shows that both datasets contain clades in the majority consensus tree that are only supported by a subset of the local optima. Clades with low p-support are most likely to benefit from further investigation. These tools provide researchers with new information regarding phylogenetic analyses beyond what is provided by other support measures alone.

Impacts of the Cretaceous Terrestrial Revolution and KPg extinction on mammal diversification.

Previous analyses of relations, divergence times, and diversification patterns among extant mammalian families have relied on supertree methods and local molecular clocks. We constructed a molecular supermatrix for mammalian families and analyzed these data with likelihood-based methods and relaxed molecular clocks. Phylogenetic analyses resulted in a robust phylogeny with better resolution than phylogenies from supertree methods. Relaxed clock analyses support the long-fuse model of diversification and highlight the importance of including multiple fossil calibrations that are spread across the tree. Molecular time trees and diversification analyses suggest important roles for the Cretaceous Terrestrial Revolution and Cretaceous-Paleogene (KPg) mass extinction in opening up ecospace that promoted interordinal and intraordinal diversification, respectively. By contrast, diversification analyses provide no support for the hypothesis concerning the delayed rise of present-day mammals during the Eocene Period.

Big cat phylogenies, consensus trees, and computational thinking.

Phylogenetics seeks to deduce the pattern of relatedness between organisms by using a phylogeny or evolutionary tree. For a given set of organisms or taxa, there may be many evolutionary trees depicting how these organisms evolved from a common ancestor. As a result, consensus trees are a popular approach for summarizing the shared evolutionary relationships in a group of trees. We examine these consensus techniques by studying how the pantherine lineage of cats (clouded leopard, jaguar, leopard, lion, snow leopard, and tiger) evolved, which is hotly debated. While there are many phylogenetic resources that describe consensus trees, there is very little information, written for biologists, regarding the underlying computational techniques for building them. The pantherine cats provide us with a small, relevant example to explore the computational techniques (such as sorting numbers, hashing functions, and traversing trees) for constructing consensus trees. Our hope is that life scientists enjoy peeking under the computational hood of consensus tree construction and share their positive experiences with others in their community.

Large-scale analysis of phylogenetic search behavior.

Phylogenetic analysis is used in all branches of biology with applications ranging from studies on the origin of human populations to investigations of the transmission patterns of HIV. Most phylogenetic analyses rely on effective heuristics for obtaining accurate trees. However, relatively little work has been done to analyze quantitatively the behavior of phylogenetic heuristics in tree space. A better understanding of local search behavior can facilitate the design of better heuristics, which ultimately lead to more accurate depictions of the true evolutionary relationships. In this paper, we present new and novel insights into local search behavior for maximum parsimony on three biological datasets consisting of 44, 60, and 174 taxa. By analyzing all trees from search, we find that, as the search algorithm climbs the hill to local optima, the trees in the neighborhood surrounding the current solution improve as well. Furthermore, the search is quite robust to a small number of randomly selected neighbors. Thus, our work shows how to gain insights into the behavior of local search algorithm by exploring a large diverse collection of trees.

MrsRF: an efficient MapReduce algorithm for analyzing large collections of evolutionary trees.

BACKGROUND: MapReduce is a parallel framework that has been used effectively to design large-scale parallel applications for large computing clusters. In this paper, we evaluate the viability of the MapReduce framework for designing phylogenetic applications. The problem of interest is generating the all-to-all Robinson-Foulds distance matrix, which has many applications for visualizing and clustering large collections of evolutionary trees. We introduce MrsRF (MapReduce Speeds up RF), a multi-core algorithm to generate a t x t Robinson-Foulds distance matrix between t trees using the MapReduce paradigm. RESULTS: We studied the performance of our MrsRF algorithm on two large biological trees sets consisting of 20,000 trees of 150 taxa each and 33,306 trees of 567 taxa each. Our experiments show that MrsRF is a scalable approach reaching a speedup of over 18 on 32 total cores. Our results also show that achieving top speedup on a multi-core cluster requires different cluster configurations. Finally, we show how to use an RF matrix to summarize collections of phylogenetic trees visually. CONCLUSION: Our results show that MapReduce is a promising paradigm for developing multi-core phylogenetic applications. The results also demonstrate that different multi-core configurations must be tested in order to obtain optimum performance. We conclude that RF matrices play a critical role in developing techniques to summarize large collections of trees.

Using tree diversity to compare phylogenetic heuristics.

BACKGROUND: Evolutionary trees are family trees that represent the relationships between a group of organisms. Phylogenetic heuristics are used to search stochastically for the best-scoring trees in tree space. Given that better tree scores are believed to be better approximations of the true phylogeny, traditional evaluation techniques have used tree scores to determine the heuristics that find the best scores in the fastest time. We develop new techniques to evaluate phylogenetic heuristics based on both tree scores and topologies to compare Pauprat and Rec-I-DCM3, two popular Maximum Parsimony search algorithms. RESULTS: Our results show that although Pauprat and Rec-I-DCM3 find the trees with the same best scores, topologically these trees are quite different. Furthermore, the Rec-I-DCM3 trees cluster distinctly from the Pauprat trees. In addition to our heatmap visualizations of using parsimony scores and the Robinson-Foulds distance to compare best-scoring trees found by the two heuristics, we also develop entropy-based methods to show the diversity of the trees found. Overall, Pauprat identifies more diverse trees than Rec-I-DCM3. CONCLUSION: Overall, our work shows that there is value to comparing heuristics beyond the parsimony scores that they find. Pauprat is a slower heuristic than Rec-I-DCM3. However, our work shows that there is tremendous value in using Pauprat to reconstruct trees-especially since it finds identical scoring but topologically distinct trees. Hence, instead of discounting Pauprat, effort should go in improving its implementation. Ultimately, improved performance measures lead to better phylogenetic heuristics and will result in better approximations of the true evolutionary history of the organisms of interest.

Rec-I-DCM3: a fast algorithmic technique for reconstructing large phylogenetic trees.

Phylogenetic trees are commonly reconstructed based on hard optimization problems such as maximum parsimony (MP) and maximum likelihood (ML). Conventional MP heuristics for producing phylogenetic trees produce good solutions within reasonable time on small datasets (up to a few thousand sequences), while ML heuristics are limited to smaller datasets (up to a few hundred sequences). However, since MP (and presumably ML) is NP-hard, such approaches do not scale when applied to large datasets. In this paper, we present a new technique called Recursive-Iterative-DCM3 (Rec-I-DCM3), which belongs to our family of Disk-Covering Methods (DCMs). We tested this new technique on ten large biological datasets ranging from 1,322 to 13,921 sequences and obtained dramatic speedups as well as significant improvements in accuracy (better than 99.99%) in comparison to existing approaches. Thus, high-quality reconstructions can be obtained for datasets at least ten times larger than was previously possible.