The evolutionary history of a set of taxa is usually represented by a phylogenetic tree, and this model has greatly facilitated the discussion and testing of hypotheses. However, it is well known that more complex evolutionary scenarios are poorly described by such models. Further, even when evolution proceeds in a tree-like manner, analysis of the data may not be best served by using methods that enforce a tree structure but rather by a richer visualization of the data to evaluate its properties, at least as an essential first step. Thus, phylogenetic networks should be employed when reticulate events such as hybridization, horizontal gene transfer, recombination, or gene duplication and loss are believed to be involved, and, even in the absence of such events, phylogenetic networks have a useful role to play. This article reviews the terminology used for phylogenetic networks and covers both split networks and reticulate networks, how they are defined, and how they can be interpreted. Additionally, the article outlines the beginnings of a comprehensive statistical framework for applying split network methods. We show how split networks can represent confidence sets of trees and introduce a conservative statistical test for whether the conflicting signal in a network is treelike. Finally, this article describes a new program, SplitsTree4, an interactive and comprehensive tool for inferring different types of phylogenetic networks from sequences, distances, and trees.

https://bioinformatics.cs.vt.edu/~easychair/HusonBryant_MBE_2006.pdf

Application of Phylogenetic Networks in Evolutionary Studies

BOLD: The Barcode of Life Data System: Barcoding

Gene families are growing rapidly, but standard methods for inferring phylogenies do not scale to alignments with over 10,000 sequences. We present FastTree, a method for constructing large phylogenies and for estimating their reliability. Instead of storing a distance matrix, FastTree stores sequence profiles of internal nodes in the tree. FastTree uses these profiles to implement Neighbor-Joining and uses heuristics to quickly identify candidate joins. FastTree then uses nearest neighbor interchanges to reduce the length of the tree. For an alignment with N sequences, L sites, and a different characters, a distance matrix requires O(N2) space and O(N2L) time, but FastTree requires just O(NLa + N) memory and O(Nlog (N)La) time. To estimate the tree's reliability, FastTree uses local bootstrapping, which gives another 100-fold speedup over a distance matrix. For example, FastTree computed a tree and support values for 158,022 distinct 16S ribosomal RNAs in 17 h and 2.4 GB of memory. Just computing pairwise Jukes–Cantor distances and storing them, without inferring a tree or bootstrapping, would require 17 h and 50 GB of memory. In simulations, FastTree was slightly more accurate than Neighbor-Joining, BIONJ, or FastME; on genuine alignments, FastTree's topologies had higher likelihoods. FastTree is available at http://microbesonline.org/fasttree.

/pdf/fasttree-computing-large-minimum-evolution-trees-with-eo2tre5l8r.pdf

FastTree: Computing Large Minimum Evolution Trees with Profiles instead of a Distance Matrix

Gene families are growing rapidly, but standard methods for inferring phylogenies do not scale to alignments with over 10,000 sequences. We present FastTree, a method for constructing large phylogenies and for estimating their reliability. Instead of storing a distance matrix, FastTree stores sequence profiles of internal nodes in the tree. FastTree uses these profiles to implement neighbor-joining and uses heuristics to quickly identify candidate joins. FastTree then uses nearest-neighbor interchanges to reduce the length of the tree. For an alignment with N sequences, L sites, and a different characters, a distance matrix requires O(N^2) space and O(N^2 L) time, but FastTree requires just O( NLa + N sqrt(N) ) memory and O( N sqrt(N) log(N) L a ) time. To estimate the tree's reliability, FastTree uses local bootstrapping, which gives another 100-fold speedup over a distance matrix. For example, FastTree computed a tree and support values for 158,022 distinct 16S ribosomal RNAs in 17 hours and 2.4 gigabytes of memory. Just computing pairwise Jukes-Cantor distances and storing them, without inferring a tree or bootstrapping, would require 17 hours and 50 gigabytes of memory. In simulations, FastTree was slightly more accurate than neighbor joining, BIONJ, or FastME; on genuine alignments, FastTree's topologies had higher likelihoods. FastTree is available at http://microbesonline.org/fasttree.

/pdf/fast-tree-computing-large-minimum-evolution-trees-with-4grq0gwfpy.pdf

Fast Tree: Computing Large Minimum-Evolution Trees with Profiles instead of a Distance Matrix

The Barcode of Life Data System ( BOLD ) is an informatics workbench aiding the acquisition, storage, analysis and publication of DNA barcode records. By assembling molecular, morphological and distributional data, it bridges a traditional bioinformatics chasm. BOLD is freely available to any researcher with interests in DNA barcoding. By providing specialized services, it aids the assembly of records that meet the standards needed to gain BARCODE designation in the global sequence databases. Because of its web-based delivery and flexible data security model, it is also well positioned to support projects that involve broad research alliances. This paper provides a brief introduction to the key elements of BOLD , discusses their functional capabilities, and concludes by examining computational resources and future prospects.

/pdf/bold-the-barcode-of-life-data-system-www-barcodinglife-org-11xa2bq3ju.pdf

BOLD : The Barcode of Life Data System (www.barcodinglife.org)

We propose several modifications to the binary buddy system for managing dynamic allocation of memory blocks whose sizes are powers of two. The standard buddy system allocates and deallocates blocks in $\Theta(\lg n)$ time in the worst case (and on an amortized basis), where n is the size of the memory. We present three schemes that improve the running time to O(1) time, where the time bound for deallocation is amortized for the first two schemes. The first scheme uses just one more word of memory than the standard buddy system, but may result in greater fragmentation than necessary. The second and third schemes have essentially the same fragmentation as the standard buddy system, and use $O(2^{(1 + \sqrt{\lg n}) \lg \lg n})$ bits of auxiliary storage, which is $\omega(\lg^k n)$ but $o(n^\varepsilon)$ for all $k \geq 1$ and $\varepsilon > 0$ . Finally, we present simulation results estimating the effect of the excess fragmentation in the first scheme.

Fast allocation and deallocation with an improved buddy system

We consider the problem of implementing finger search trees on the pointer machine, i.e., how to maintain a sorted list such that searching for an element 2, starting the search at any arbitrary element f in the list, only requires logarithmic time in the distance between 2 and f in the list. We present the first pointer-based implementation of finger search trees allowing new elements to be inserted at any arbitrary position in the list in worst case constant time. Previously, the best known insertion time on the pointer machine was O(log* n), where n is the total length of the list. On a unit-cost RAM, a constant insertion time has been achieved by Dietz and Raman by using standard techniques of packing small problem sizes into a constant number of machine words. Deletion of a list element is supported in O(log* n) time, which matches the previous best bounds. Our data structure requires linear space.

Finger search trees with constant insertion time

http://www.cs.au.dk/~gerth/papers/orl08.pdf

An O(nlogn) version of the Averbakh-Berman algorithm for the robust median of a tree

Presents a parallel priority data structure that improves the running time of certain algorithms for problems that lack a fast and work-efficient parallel solution. As a main application, we give a parallel implementation of Dijkstra's (1959) algorithm which runs in O(n) time while performing O(m log n) work on a CREW PRAM. This is a logarithmic factor improvement for the running time compared with previous approaches. The main feature of our data structure is that the operations needed in each iteration of Dijkstra's algorithm can be supported in O(1) time.

https://www.ceid.upatras.gr/webpages/faculty/zaro/pub/conf/C20-IPPS97-ppq.pdf

A parallel priority data structure with applications

We present I/O-efficient fully persistent B-Trees that support range searches at any version in O(logBn + t/B) I/Os and updates at any version in O(logBn + log2B) amortized I/Os, using space O(m/B) disk blocks. By n we denote the number of elements in the accessed version, by m the total number of updates, by t the size of the query's output, and by B the disk block size. The result improves the previous fully persistent B-Trees of Lanka and Mays by a factor of O(logBm) for the range query complexity and O(logBn) for the update complexity. To achieve the result, we first present a new B-Tree implementation that supports searches and updates in O(logBn) I/Os, using O(n/B) blocks of space. Moreover, every update makes in the worst case a constant number of modifications to the data structure. We make these B-Trees fully persistent using an I/O-efficient method for full persistence that is inspired by the node-splitting method of Driscoll et al. The method we present is interesting in its own right and can be applied to any external memory pointer based data structure with maximum in-degree din bounded by a constant and out-degree bounded by O(B), where every node occupies a constant number of blocks on disk. The I/O-overhead per modification to the ephemeral structure is O(din log2B) amortized I/Os, and the space overhead is O(din/B) amortized blocks. Access to a field of an ephemeral block is supported in O(log2din) worst case I/Os.

https://epubs.siam.org/doi/pdf/10.1137/1.9781611973099.51

Gerth Stølting Brodal

Papers

Fast allocation and deallocation with an improved buddy system

Finger search trees with constant insertion time

An O(nlogn) version of the Averbakh-Berman algorithm for the robust median of a tree

A parallel priority data structure with applications

Fully persistent B-trees