AraQTL - Workbench and Archive for systems genetics in Arabidopsis thaliana

Harm Nijveen, Wilco Ligterink, Joost J.B. Keurentjes, Olivier Loudet, Jiao Long, Mark G. Sterken, Pjotr Prins, Henk W. Hilhorst, Dick De Ridder, Jan E. Kammenga, Basten L. Snoek

Research output: Contribution to journalArticleAcademicpeer-review

4 Citations (Scopus)

Abstract

Genetical genomics studies uncover genome-wide genetic interactions between genes and their transcriptional regulators. High-throughput measurement of gene expression in recombinant inbred line populations enabled the investigation of the genetic architecture of gene expression variation. This has the potential to enrich the understanding of the molecular mechanisms affected by and underlying natural variation. Moreover, it contributes to the systems biology of natural variation, as a substantial number of experiments have resulted in a valuable amount of interconnectable phenotypic, molecular and genotypic data. For Arabidopsis thaliana a number of genetical genomics studies have been published, uncovering many expression quantitative trait loci (eQTLs). Yet, this complex data is not easily accessible to the plant research community, leaving most of the valuable genetic interactions unexplored as cross-analysis of these studies is a major effort. We address this with AraQTL www.bioinformatics.nl/AraQTL/, an easily accessible workbench and database for comparative analysis and meta-analysis of all published Arabidopsis eQTL datasets. AraQTL provides a workbench for comparing, re-using and extending upon the results of these experiments. For example, one can easily screen a physical region for specific local-eQTLs that could harbour candidate genes for phenotypic QTL, or detect gene-by-environment interactions by comparing eQTLs under different conditions.
Original languageEnglish
Pages (from-to)1225-1235
JournalThe Plant Journal
Volume89
Issue number6
DOIs
Publication statusPublished - 2017

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Quantitative Trait Loci
Arabidopsis
quantitative trait loci
Arabidopsis thaliana
Genomics
Gene Expression
Gene-Environment Interaction
Systems Biology
genomics
Computational Biology
gene expression
genes
Genes
Meta-Analysis
meta-analysis
bioinformatics
inbred lines
Genome
Databases
transcription factors

Cite this

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title = "AraQTL - Workbench and Archive for systems genetics in Arabidopsis thaliana",
abstract = "Genetical genomics studies uncover genome-wide genetic interactions between genes and their transcriptional regulators. High-throughput measurement of gene expression in recombinant inbred line populations enabled the investigation of the genetic architecture of gene expression variation. This has the potential to enrich the understanding of the molecular mechanisms affected by and underlying natural variation. Moreover, it contributes to the systems biology of natural variation, as a substantial number of experiments have resulted in a valuable amount of interconnectable phenotypic, molecular and genotypic data. For Arabidopsis thaliana a number of genetical genomics studies have been published, uncovering many expression quantitative trait loci (eQTLs). Yet, this complex data is not easily accessible to the plant research community, leaving most of the valuable genetic interactions unexplored as cross-analysis of these studies is a major effort. We address this with AraQTL www.bioinformatics.nl/AraQTL/, an easily accessible workbench and database for comparative analysis and meta-analysis of all published Arabidopsis eQTL datasets. AraQTL provides a workbench for comparing, re-using and extending upon the results of these experiments. For example, one can easily screen a physical region for specific local-eQTLs that could harbour candidate genes for phenotypic QTL, or detect gene-by-environment interactions by comparing eQTLs under different conditions.",
author = "Harm Nijveen and Wilco Ligterink and Keurentjes, {Joost J.B.} and Olivier Loudet and Jiao Long and Sterken, {Mark G.} and Pjotr Prins and Hilhorst, {Henk W.} and {De Ridder}, Dick and Kammenga, {Jan E.} and Snoek, {Basten L.}",
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AraQTL - Workbench and Archive for systems genetics in Arabidopsis thaliana. / Nijveen, Harm; Ligterink, Wilco; Keurentjes, Joost J.B.; Loudet, Olivier; Long, Jiao; Sterken, Mark G.; Prins, Pjotr; Hilhorst, Henk W.; De Ridder, Dick; Kammenga, Jan E.; Snoek, Basten L.

In: The Plant Journal, Vol. 89, No. 6, 2017, p. 1225-1235.

Research output: Contribution to journalArticleAcademicpeer-review

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AU - Nijveen, Harm

AU - Ligterink, Wilco

AU - Keurentjes, Joost J.B.

AU - Loudet, Olivier

AU - Long, Jiao

AU - Sterken, Mark G.

AU - Prins, Pjotr

AU - Hilhorst, Henk W.

AU - De Ridder, Dick

AU - Kammenga, Jan E.

AU - Snoek, Basten L.

PY - 2017

Y1 - 2017

N2 - Genetical genomics studies uncover genome-wide genetic interactions between genes and their transcriptional regulators. High-throughput measurement of gene expression in recombinant inbred line populations enabled the investigation of the genetic architecture of gene expression variation. This has the potential to enrich the understanding of the molecular mechanisms affected by and underlying natural variation. Moreover, it contributes to the systems biology of natural variation, as a substantial number of experiments have resulted in a valuable amount of interconnectable phenotypic, molecular and genotypic data. For Arabidopsis thaliana a number of genetical genomics studies have been published, uncovering many expression quantitative trait loci (eQTLs). Yet, this complex data is not easily accessible to the plant research community, leaving most of the valuable genetic interactions unexplored as cross-analysis of these studies is a major effort. We address this with AraQTL www.bioinformatics.nl/AraQTL/, an easily accessible workbench and database for comparative analysis and meta-analysis of all published Arabidopsis eQTL datasets. AraQTL provides a workbench for comparing, re-using and extending upon the results of these experiments. For example, one can easily screen a physical region for specific local-eQTLs that could harbour candidate genes for phenotypic QTL, or detect gene-by-environment interactions by comparing eQTLs under different conditions.

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