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chevron_left Gene co-expression prediction Gene expression clustering Differential expression Bioinformatics workflows Read alignment Normalization Known transcript quantification Spliced read alignment Time course chevron_right
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TRAP specifications

Information


Unique identifier OMICS_02590
Name TRAP
Alternative name Time-series RNA-seq Analysis Package
Software type Pipeline/Workflow
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Computer skills Advanced
Version 2.2
Stability Stable
Maintained Yes

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Maintainer


  • person_outline Sun Kim <>

Information


Unique identifier OMICS_02590
Name TRAP
Alternative name Time-series RNA-seq Analysis Package
Interface Web user interface
Restrictions to use None
Computer skills Basic
Stability Stable
Maintained Yes

Maintainer


  • person_outline Sun Kim <>

Publication for Time-series RNA-seq Analysis Package

TRAP in publications

 (2)
PMCID: 5259824
PMID: 28155707
DOI: 10.1186/s12859-016-1335-8

[…] producing time-series gene expression data has increased dramatically []. thus, several pathway analysis methods for time-series gene expression data have also been developed recently. for example, time-series rna-seq analysis package (trap) analyzes time-series gene expression data and identifies significant pathways with regard to the propagation difference of gene expression between two […]

PMCID: 4457141
PMID: 26097463
DOI: 10.3389/fneur.2015.00100

[…] the expression data were expressed in fragments per kilobase of transcript per million fragments mapped (fpkm)., to compare gene expression between e and l time-series samples, we employed the time-series rna-seq analysis package (trap) (). to identify time-series degs, as well as relevant biological pathways, trap implements the over-representation analysis (ora) (), and pathway topology […]


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TRAP institution(s)
Department of Computer Science and Engineering, Seoul National University, Seoul, South Korea; Bioinformatics Institute, Seoul National University, Seoul, South Korea; Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South Korea; Department of Biomedical Sciences, Sunmoon University, Asan, South Korea
TRAP funding source(s)
Supported by a grant from the Next-Generation BioGreen 21 Program (No. PJ009037022012), Rural Development Administration, Republic of Korea; the Bio & Medical Technology Development Program of the National Research Foundation (NRF) funded by the Ministry of Science, ICT & Future Planning(2012M3A9D1054622).

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