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)

The triplet distance is a distance measure that compares two rooted trees on the same set of leaves by enumerating all sub-sets of three leaves and counting how often the induced topologies of the tree are equal or different. We present an algorithm that computes the triplet distance between two rooted binary trees in time O (n log2 n). The algorithm is related to an algorithm for computing the quartet distance between two unrooted binary trees in time O (n log n). While the quartet distance algorithm has a very severe overhead in the asymptotic time complexity that makes it impractical compared to O (n2) time algorithms, we show through experiments that the triplet distance algorithm can be implemented to give a competitive wall-time running time.

http://www.cs.au.dk/~gerth/papers/apbc13.pdf

A practical O(n log2 n) time algorithm for computing the triplet distance on binary trees.

Given a dictionary S of n binary strings each of length m , we consider the problem of designing a data structure for S that supports d -queries; given a binary query string q of length m , a d -query reports if there exists a string in S within Hamming distance d of q . We construct a data structure for the case d=1 , that requires space O (n log m) and has query time O (1) in a cell probe model with word size m . This generalizes and improves the previous bounds of Yao and Yao for the problem in the bit probe model. The data structure can be constructed in randomized expected time O (nm) .

/pdf/improved-bounds-for-dictionary-look-up-with-one-error-2rejootp1z.pdf

Improved Bounds for Dictionary Look-up with One Error

In the field of online algorithms paging is one of the most studied problems. For randomized paging algorithms a tight bound of H k on the competitive ratio has been known for decades, yet existing algorithms matching this bound have high running times. We present a new randomized paging algorithm OnlineMin that has optimal competitiveness and allows fast implementations. In fact, if k pages fit in internal memory the best previous solution required O(k 2) time per request and O(k) space. We present two implementations of OnlineMin which use O(k) space, but only O(logk) worst case time and O(logk/loglogk) worst case time per page request respectively.

/pdf/onlinemin-a-fast-strongly-competitive-randomized-paging-crwv2bmvj7.pdf

OnlineMin: A Fast Strongly Competitive Randomized Paging Algorithm

The skyline of a set of points in the plane is the subset of maximal points, where a point (x,y) is maximal if no other point (x′,y′) satisfies x′ ≥ x and y′ ≥ y. We consider the problem of preprocessing a set P of n points into a space efficient static data structure supporting orthogonal skyline counting queries, i.e. given a query rectangle R to report the size of the skyline of P ∩ R. We present a data structure for storing n points with integer coordinates having query time \(O(\lg n/\lg\lg n)\) and space usage O(n) words. The model of computation is a unit cost RAM with logarithmic word size. We prove that these bounds are the best possible by presenting a matching lower bound in the cell probe model with logarithmic word size: Space usage \(n\lg^{O(1)} n\) implies worst case query time \(\Omega(\lg n/\lg\lg n)\).

/pdf/optimal-planar-orthogonal-skyline-counting-queries-sz6x3o88a4.pdf

Optimal Planar Orthogonal Skyline Counting Queries

We present algorithms for finding a longest common increasing subsequence of two or more input sequences. For two sequences of lengths m and n , where m >= n, we present an algorithm with an output-dependent expected running time of O((m + n l) log log sigma + Sort) and O(m) space, where l is the length of an LCIS, sigma is the size of the alphabet, and 'Sort' is the time to sort each input sequence. For k >= 3 length-n sequences we present an algorithm running time O(min{k r^2, r log^{k-1} r} + Sort), which improves the previous best bound by more than a factor k for many inputs. In both cases, our algorithms are conceptually quite simple but rely on existing sophisticated data structures. Finally, we introduce the problem of longest common weakly-increasing (or non-decreasing) subsequences (LCWIS), for which we present an O(m + n log n) time algorithm for the 3-letter alphabet case. For the extensively studied Longest Common Subsequence problem, comparable speedups have not been achieved for small alphabets.

/pdf/faster-algorithms-for-computing-longest-common-increasing-45zphev8go.pdf

Gerth Stølting Brodal

Papers

A practical O(n log2 n) time algorithm for computing the triplet distance on binary trees.

Improved Bounds for Dictionary Look-up with One Error

OnlineMin: A Fast Strongly Competitive Randomized Paging Algorithm

Optimal Planar Orthogonal Skyline Counting Queries

Faster Algorithms for Computing Longest Common Increasing Subsequences