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Database (Oxford). 2016 Dec 26;2016. pii: baw146. doi: 10.1093/database/baw146. Print 2016.

TBro: visualization and management of de novo transcriptomes.

Author information

1
Department of Animal Ecology and Tropical Biology, Biocenter, Am Hubland, 97074 Würzburg, Germany.
2
Department of Bioinformatics, Biocenter, Am Hubland, 97074 Würzburg, Germany.
3
Center for Computational and Theoretical Biology, University of Würzburg, 97074 Würzburg, Germany.
4
Institute for Molecular Plant Physiology and Biophysics, University of Würzburg, 97082 Würzburg, Germany.
5
Department of Bioinformatics, Biocenter, Am Hubland, 97074 Würzburg, Germany felix.bemm@tuebingen.mpg.de.
6
Department Molecular Biology (Detlef Weigel), Max-Planck-Institute for Developmental Biology, 72076 Tübingen, Germany.

Abstract

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.

PMID:
28025338
PMCID:
PMC5199188
DOI:
10.1093/database/baw146
[Indexed for MEDLINE]
Free PMC Article

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