Invis: Exploring high-dimensional RNA sequences from in vitro selection

Çaǧatay Demiralp, Eric Hayden, Jeff Hammerbacher, Jeffrey Heer

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

In vitro selection and evolution is a powerful method for discovering RNA molecules based on their binding and catalysis properties. It has important applications to the study of genetic variation and molecular evolution. However, the resulting RNA sequences form a large, high-dimensional space and biologists lack adequate tools to explore and interpret these sequences. We present invis, the first visual analysis tool to facilitate exploration of in vitro selection sequence spaces. invis introduces a novel configuration of coordinated views that enables simultaneous inspection of global projections of sequence data alongside local regions of selected dimensions and sequence clusters. It allows scientists to isolate related sequences for further data analysis, compare sequence populations over varying conditions, filter sequences based on their similarities, and visualize likely pathways of genetic evolution. User feedback indicates that invis enables effective exploration of in vitro RNA selection sequences.

Original languageEnglish
Title of host publicationBioVis 2013 - IEEE Symposium on Biological Data Visualization 2013, Proceedings
PublisherIEEE Computer Society
Pages1-8
Number of pages8
ISBN (Print)9781479916580
DOIs
StatePublished - 2013
Event3rd IEEE Symposium on Biological Data Visualization, BioVis 2013 - Atlanta, GA, United States
Duration: 13 Oct 201314 Oct 2013

Publication series

NameBioVis 2013 - IEEE Symposium on Biological Data Visualization 2013, Proceedings

Conference

Conference3rd IEEE Symposium on Biological Data Visualization, BioVis 2013
Country/TerritoryUnited States
CityAtlanta, GA
Period13/10/1314/10/13

Keywords

  • J.3 [Computer Applications]: Life and Medical Sciences-Biology and Genetics

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