Sequence Bundles

https://www.ebi.ac.uk/goldman-srv/sequencebundles/

Methods

linear single view single scale single focus contiguous no abstraction no arrangement no interconnection point contiguous type

Tool

Access Format web application
Supported Files fasta
License r GNU Affero General Public License version 3
Tool name

Sequence Bundles
Tool Link https://www.science-practice.com/projects/sequence-bundles/
Documentation https://www.ebi.ac.uk/goldman-srv/sequencebundles/

Paper

Sequence Bundles: a novel method for visualising, discovering and exploring sequence motifs

Kultys M, Nicholas L, Schwarz R, Goldman N, King J. Sequence Bundles: a novel method for visualising, discovering and exploring sequence motifs. BMC Proc. bmcproc.biomedcentral.com; 2014;8: S8.

Abstract

Background: We introduce Sequence Bundles--a novel data visualisation method for representing multiple sequence alignments (MSAs). We identify and address key limitations of the existing bioinformatics data visualisation methods (i.e. the Sequence Logo) by enabling Sequence Bundles to give salient visual expression to sequence motifs and other data features, which would otherwise remain hidden. Methods: For the development of Sequence Bundles we employed research-led information design methodologies. Sequences are encoded as uninterrupted, semi-opaque lines plotted on a 2-dimensional reconfigurable grid. Each line represents a single sequence. The thickness and opacity of the stack at each residue in each position indicates the level of conservation and the lines' curved paths expose patterns in correlation and functionality. Several MSAs can be visualised in a composite image. The Sequence Bundles method is designed to favour a tangible, continuous and intuitive display of information. Results: We have developed a software demonstration application for generating a Sequence Bundles visualisation of MSAs provided for the BioVis 2013 redesign contest. A subsequent exploration of the visualised line patterns allowed for the discovery of a number of interesting features in the dataset. Reported features include the extreme conservation of sequences displaying a specific residue and bifurcations of the consensus sequence., Conclusions: Sequence Bundles is a novel method for visualisation of MSAs and the discovery of sequence motifs. It can aid in generating new insight and hypothesis making. Sequence Bundles is well disposed for future implementation as an interactive visual analytics software, which can complement existing visualisation tools.