Poster Open Access

TBro - a transcriptome browser for de novo RNA-sequencing experiments

Ankenbrand, Markus J.; Weber, Lorenz; Becker, Dirk; Förster, Frank; Bemm, Felix


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        <foaf:name>Ankenbrand, Markus J.</foaf:name>
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            <foaf:name>Max-Planck-Institute for Developmental Biology, Department Molecular Biology (Detlef Weigel), Tübingen, Germany</foaf:name>
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    <dct:title>TBro - a transcriptome browser for de novo RNA-sequencing experiments</dct:title>
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    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2016</dct:issued>
    <dcat:keyword>Transcriptome</dcat:keyword>
    <dcat:keyword>Browser</dcat:keyword>
    <dcat:keyword>Expression</dcat:keyword>
    <dcat:keyword>Visualization</dcat:keyword>
    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2016-09-13</dct:issued>
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    <dct:relation rdf:resource="https://doi.org/10.1093/bioinformatics/btm189"/>
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    <dct:description>&lt;p&gt;Nowadays, information about the identity and expression levels of genes are often retrieved by RNA-sequencing (RNA-seq). One of its major challenges is gaining biologically meaningful information from the vast amount of short read data. Therefore, tools for assembly and quantification have been developed. Still, the problem remains, that their results are often huge text files which are difficult to handle and interpret. Here we present TBro, a flexible denovo transcriptome browser, tackling this challenge. It aggregates data from different sources and allows interactive exploration of transcriptomes.&lt;br&gt; This comprises assembled sequences as well as multiple annotation information. Moreover, expression level analyses and differential expression analyses can be visualized. Furthermore, TBro supports collaborative workflows. User created subsets of transcripts can be shared with other researchers for further investigation. TBro can be easily integrated into existing workflows and is also easily extensible due to its modular design. Therefore, TBro is well suited to assist in unravelling the biological story hidden underneath the wealth of RNA-seq data.&lt;/p&gt;</dct:description>
    <dct:description>Presented at the GCB 2016 in Berlin (poster 66, session 2).</dct:description>
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