Methodology article

Visualization of large influenza virus sequence datasets using adaptively aggregated trees with sampling-based subscale representation

Leonid Zaslavsky , Yiming Bao and Tatiana A Tatusova

National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA

author email corresponding author email

BMC Bioinformatics 2008, 9:237doi:10.1186/1471-2105-9-237

Published:	16 May 2008

Abstract

Background

With the amount of influenza genome sequence data growing rapidly, researchers need machine assistance in selecting datasets and exploring the data. Enhanced visualization tools are required to represent results of the exploratory analysis on the web in an easy-to-comprehend form and to facilitate convenient information retrieval.

Results

We developed an approach to visualize large phylogenetic trees in an aggregated form with a special representation of subscale details. The initial aggregated tree representation is built with a level of resolution automatically selected to fit into the available screen space, with terminal groups selected based on sequence similarity. The default aggregated representation can be refined by users interactively.

Structure and data variability within terminal groups are displayed using small trees that have the same vertical size as the text annotation of the group. These subscale representations are calculated using systematic sampling from the corresponding terminal group. The aggregated tree containing terminal groups can be annotated using aggregation of structured metadata, such as seasonal distribution, geographic locations, etc.

Availability

The algorithms are implemented in JavaScript within the NCBI Influenza Virus Resource [1].