Introduction

Phenomena such as incomplete lineage sorting, horizontal gene transfer, gene duplication and subsequent sub- and neo-functionalisation can result in distinct local phylogenetic relationships that are discordant with species phylogeny. In order to assess the possible biological roles for these subdivisions, they must first be identified and characterised, preferably on a large scale and in an automated fashion.We developed Saguaro, a combination of a Hidden Markov Model (HMM) and a Self Organising Map (SOM), to characterise local phylogenetic relationships among aligned sequences using cacti, matrices of pair-wise distance measures. While the HMM determines the genomic boundaries from aligned sequences, the SOM hypothesises new cacti in an unsupervised and iterative fashion based on the regions that were modelled least well by existing cacti. After testing the software on simulated data, we demonstrate the utility of Saguaro by testing two different data sets: (i) 181 Dengue virus strains, and (ii) 5 primate genomes. Saguaro identifies regions under lineage-specific constraint for the first set, and genomic segments that we attribute to incomplete lineage sorting in the second dataset. Intriguingly for the primate data, Saguaro also classified an additional ~3% of the genome as most incompatible with the expected species phylogeny. A substantial fraction of these regions was found to overlap genes associated with both the innate and adaptive immune systems.Saguaro detects distinct cacti describing local phylogenetic relationships without requiring any a priori hypotheses. We have successfully demonstrated Saguaro's utility with two contrasting data sets, one containing many members with short sequences (Dengue viral strains: n = 181, genome size = 10,700 nt), and the other with few members but complex genomes (related primate species: n = 5, genome size = 3 Gb), suggesting that the software is applicable to a wide variety of experimental populations. Saguaro is written in C++, runs on the Linux operating system, and can be downloaded from http://saguarogw.sourceforge.net/.

Publications

  1. Unsupervised genome-wide recognition of local relationship patterns.
    Cite this
    Zamani N, Russell P, Lantz H, Hoeppner MP, Meadows JR, Vijay N, Mauceli E, di Palma F, Lindblad-Toh K, Jern P, Grabherr MG, 2013-05-01 - BMC genomics

Credits

  1. Neda Zamani
    Developer

    Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Sweden

  2. Pamela Russell
    Developer

  3. Henrik Lantz
    Developer

  4. Marc P Hoeppner
    Developer

  5. Jennifer Rs Meadows
    Developer

  6. Nagarjun Vijay
    Developer

  7. Evan Mauceli
    Developer

  8. Federica di Palma
    Developer

  9. Kerstin Lindblad-Toh
    Developer

  10. Patric Jern
    Developer

  11. Manfred G Grabherr
    Investigator

Community Ratings

UsabilityEfficiencyReliabilityRated By
0 user
Sign in to rate
Summary
AccessionBT000499
Tool TypeApplication
Category
PlatformsLinux/Unix
TechnologiesC++, Perl
User InterfaceTerminal Command Line
Download Count0
Submitted ByManfred G Grabherr