Tripal: a construction toolkit for online genome databases

Ficklin, Stephen P.; Sanderson, Lacey-Anne; Cheng, Cuixia; Staton, Margaret; Lee, Taein; Cho, Il-Hyung; Jung, Sook; Bett, Kirstin E.; Main, Doreen

doi:10.1093/database/bar044

Cited by 64 publications

(64 citation statements)

References 33 publications

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“…These associations as well as all GILs were input into an online database called GeneNet Engine which is available online at http://sysbio.genome.clemson.edu. The data is housed in a Chado database schema [46] with custom tables and visualized using Tripal [47]. Next all available rice SNPs from NCBI’s dbSNP [48] database were uniquely mapped to the rice genome and loaded into the database so that an end-user can identify proximal biomarkers for genotype-phenotype hypothesis testing.…”

Section: Resultsmentioning

confidence: 99%

A Systems-Genetics Approach and Data Mining Tool to Assist in the Discovery of Genes Underlying Complex Traits in Oryza sativa

Ficklin

Feltus

2013

PLoS ONE

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Many traits of biological and agronomic significance in plants are controlled in a complex manner where multiple genes and environmental signals affect the expression of the phenotype. In Oryza sativa (rice), thousands of quantitative genetic signals have been mapped to the rice genome. In parallel, thousands of gene expression profiles have been generated across many experimental conditions. Through the discovery of networks with real gene co-expression relationships, it is possible to identify co-localized genetic and gene expression signals that implicate complex genotype-phenotype relationships. In this work, we used a knowledge-independent, systems genetics approach, to discover a high-quality set of co-expression networks, termed Gene Interaction Layers (GILs). Twenty-two GILs were constructed from 1,306 Affymetrix microarray rice expression profiles that were pre-clustered to allow for improved capture of gene co-expression relationships. Functional genomic and genetic data, including over 8,000 QTLs and 766 phenotype-tagged SNPs (p-value < = 0.001) from genome-wide association studies, both covering over 230 different rice traits were integrated with the GILs. An online systems genetics data-mining resource, the GeneNet Engine, was constructed to enable dynamic discovery of gene sets (i.e. network modules) that overlap with genetic traits. GeneNet Engine does not provide the exact set of genes underlying a given complex trait, but through the evidence of gene-marker correspondence, co-expression, and functional enrichment, site visitors can identify genes with potential shared causality for a trait which could then be used for experimental validation. A set of 2 million SNPs was incorporated into the database and serve as a potential set of testable biomarkers for genes in modules that overlap with genetic traits. Herein, we describe two modules found using GeneNet Engine, one with significant overlap with the trait amylose content and another with significant overlap with blast disease resistance.

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Section: Resultsmentioning

confidence: 99%

A Systems-Genetics Approach and Data Mining Tool to Assist in the Discovery of Genes Underlying Complex Traits in Oryza sativa

Ficklin

Feltus

2013

PLoS ONE

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“…Standardization around this schema enables seamless integration across the two repositories. For visualizing the data, TreeGenes and HWG use another open-source technology, Tripal (Sanderson et al, 2013;Ficklin et al, 2011), which integrates Chado through specific templates designed in the Drupal content management system.…”

Section: Cartogratree Datamentioning

confidence: 99%

CartograTree: Enabling landscape genomics for forest trees

Herndon

Grau

Batra

et al. 2016

Preprint

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Forest trees cover just over 30% of the earth's surface and are studied by researchers around the world for both their conservation and economic value. With the onset of high throughput technologies, tremendous phenotypic and genomic data sets have been generated for hundreds of species. These long-lived and immobile individuals serve as ideal models to assess population structure and adaptation to environment. Despite the availability of comprehensive data, researchers are challenged to integrate genotype, phenotype, and environment in one place. Towards this goal, CartograTree was designed and implemented as an open repository and open-source analytic framework for genomic, phenotypic, and environmental data for forest trees. One of its key components, the integration of geospatial data, allows the display of environmental layers and acquisition of environmental metrics relative to the positions of georeferenced individuals. Currently, CartograTree uses the Google Maps API to load environmental data. Limitations inherent to this API are driving new development with a focus on functionality to provide efficient queries of numerous environmental metrics.

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“…Most tools are tailored for genome reference based RNA-seq studies [e.g. Tripal (17, 18), Intermine (19), TraV (20), RNASeqExpressionBrowser (21)] or aim for a specific species [e.g. dbWFA (22)] with Intermine and Tripal the most feature rich and best maintained tools available.…”

Section: Introductionmentioning

confidence: 99%

TBro: visualization and management ofde novotranscriptomes

et al. 2016

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RNA sequencing (RNA-seq) has become a powerful tool to understand molecular mechanisms and/or developmental programs. It provides a fast, reliable and cost-effective method to access sets of expressed elements in a qualitative and quantitative manner. Especially for non-model organisms and in absence of a reference genome, RNA-seq data is used to reconstruct and quantify transcriptomes at the same time. Even SNPs, InDels, and alternative splicing events are predicted directly from the data without having a reference genome at hand. A key challenge, especially for non-computational personnal, is the management of the resulting datasets, consisting of different data types and formats. Here, we present TBro, a flexible de novo transcriptome browser, tackling this challenge. TBro aggregates sequences, their annotation, expression levels as well as differential testing results. It provides an easy-to-use interface to mine the aggregated data and generate publication-ready visualizations. Additionally, it supports users with an intuitive cart system, that helps collecting and analysing biological meaningful sets of transcripts. TBro’s modular architecture allows easy extension of its functionalities in the future. Especially, the integration of new data types such as proteomic quantifications or array-based gene expression data is straightforward. Thus, TBro is a fully featured yet flexible transcriptome browser that supports approaching complex biological questions and enhances collaboration of numerous researchers.Database URL: tbro.carnivorom.com

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Tripal: a construction toolkit for online genome databases

Cited by 64 publications

References 33 publications

A Systems-Genetics Approach and Data Mining Tool to Assist in the Discovery of Genes Underlying Complex Traits in Oryza sativa

A Systems-Genetics Approach and Data Mining Tool to Assist in the Discovery of Genes Underlying Complex Traits in Oryza sativa

CartograTree: Enabling landscape genomics for forest trees

TBro: visualization and management ofde novotranscriptomes

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