Cargando…

iHAT: interactive Hierarchical Aggregation Table for Genetic Association Data

In the search for single-nucleotide polymorphisms which influence the observable phenotype, genome wide association studies have become an important technique for the identification of associations between genotype and phenotype of a diverse set of sequence-based data. We present a methodology for t...

Descripción completa

Detalles Bibliográficos
Autores principales: Heinrich, Julian, Vehlow, Corinna, Battke, Florian, Jäger, Günter, Weiskopf, Daniel, Nieselt, Kay
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3355335/
https://www.ncbi.nlm.nih.gov/pubmed/22607364
http://dx.doi.org/10.1186/1471-2105-13-S8-S2
_version_ 1782233354818551808
author Heinrich, Julian
Vehlow, Corinna
Battke, Florian
Jäger, Günter
Weiskopf, Daniel
Nieselt, Kay
author_facet Heinrich, Julian
Vehlow, Corinna
Battke, Florian
Jäger, Günter
Weiskopf, Daniel
Nieselt, Kay
author_sort Heinrich, Julian
collection PubMed
description In the search for single-nucleotide polymorphisms which influence the observable phenotype, genome wide association studies have become an important technique for the identification of associations between genotype and phenotype of a diverse set of sequence-based data. We present a methodology for the visual assessment of single-nucleotide polymorphisms using interactive hierarchical aggregation techniques combined with methods known from traditional sequence browsers and cluster heatmaps. Our tool, the interactive Hierarchical Aggregation Table (iHAT), facilitates the visualization of multiple sequence alignments, associated metadata, and hierarchical clusterings. Different color maps and aggregation strategies as well as filtering options support the user in finding correlations between sequences and metadata. Similar to other visualizations such as parallel coordinates or heatmaps, iHAT relies on the human pattern-recognition ability for spotting patterns that might indicate correlation or anticorrelation. We demonstrate iHAT using artificial and real-world datasets for DNA and protein association studies as well as expression Quantitative Trait Locus data.
format Online
Article
Text
id pubmed-3355335
institution National Center for Biotechnology Information
language English
publishDate 2012
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-33553352012-05-18 iHAT: interactive Hierarchical Aggregation Table for Genetic Association Data Heinrich, Julian Vehlow, Corinna Battke, Florian Jäger, Günter Weiskopf, Daniel Nieselt, Kay BMC Bioinformatics Research In the search for single-nucleotide polymorphisms which influence the observable phenotype, genome wide association studies have become an important technique for the identification of associations between genotype and phenotype of a diverse set of sequence-based data. We present a methodology for the visual assessment of single-nucleotide polymorphisms using interactive hierarchical aggregation techniques combined with methods known from traditional sequence browsers and cluster heatmaps. Our tool, the interactive Hierarchical Aggregation Table (iHAT), facilitates the visualization of multiple sequence alignments, associated metadata, and hierarchical clusterings. Different color maps and aggregation strategies as well as filtering options support the user in finding correlations between sequences and metadata. Similar to other visualizations such as parallel coordinates or heatmaps, iHAT relies on the human pattern-recognition ability for spotting patterns that might indicate correlation or anticorrelation. We demonstrate iHAT using artificial and real-world datasets for DNA and protein association studies as well as expression Quantitative Trait Locus data. BioMed Central 2012-05-18 /pmc/articles/PMC3355335/ /pubmed/22607364 http://dx.doi.org/10.1186/1471-2105-13-S8-S2 Text en Copyright ©2012 Heinrich et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Heinrich, Julian
Vehlow, Corinna
Battke, Florian
Jäger, Günter
Weiskopf, Daniel
Nieselt, Kay
iHAT: interactive Hierarchical Aggregation Table for Genetic Association Data
title iHAT: interactive Hierarchical Aggregation Table for Genetic Association Data
title_full iHAT: interactive Hierarchical Aggregation Table for Genetic Association Data
title_fullStr iHAT: interactive Hierarchical Aggregation Table for Genetic Association Data
title_full_unstemmed iHAT: interactive Hierarchical Aggregation Table for Genetic Association Data
title_short iHAT: interactive Hierarchical Aggregation Table for Genetic Association Data
title_sort ihat: interactive hierarchical aggregation table for genetic association data
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3355335/
https://www.ncbi.nlm.nih.gov/pubmed/22607364
http://dx.doi.org/10.1186/1471-2105-13-S8-S2
work_keys_str_mv AT heinrichjulian ihatinteractivehierarchicalaggregationtableforgeneticassociationdata
AT vehlowcorinna ihatinteractivehierarchicalaggregationtableforgeneticassociationdata
AT battkeflorian ihatinteractivehierarchicalaggregationtableforgeneticassociationdata
AT jagergunter ihatinteractivehierarchicalaggregationtableforgeneticassociationdata
AT weiskopfdaniel ihatinteractivehierarchicalaggregationtableforgeneticassociationdata
AT nieseltkay ihatinteractivehierarchicalaggregationtableforgeneticassociationdata