PMID- 24518221
OWN - NLM
STAT- MEDLINE
DCOM- 20150114
LR  - 20151119
IS  - 1095-9130 (Electronic)
IS  - 1046-2023 (Linking)
VI  - 67
IP  - 3
DP  - 2014 Jun 1
TI  - Time-series RNA-seq analysis package (TRAP) and its application to the analysis 
      of rice, Oryza sativa L. ssp. Japonica, upon drought stress.
PG  - 364-72
LID - S1046-2023(14)00029-2 [pii]
LID - 10.1016/j.ymeth.2014.02.001 [doi]
AB  - Measuring expression levels of genes at the whole genome level can be useful for 
      many purposes, especially for revealing biological pathways underlying specific 
      phenotype conditions. When gene expression is measured over a time period, we 
      have opportunities to understand how organisms react to stress conditions over 
      time. Thus many biologists routinely measure whole genome level gene expressions 
      at multiple time points. However, there are several technical difficulties for 
      analyzing such whole genome expression data. In addition, these days gene 
      expression data is often measured by using RNA-sequencing rather than microarray 
      technologies and then analysis of expression data is much more complicated since 
      the analysis process should start with mapping short reads and produce 
      differentially activated pathways and also possibly interactions among pathways. 
      In addition, many useful tools for analyzing microarray gene expression data are 
      not applicable for the RNA-seq data. Thus a comprehensive package for analyzing 
      time series transcriptome data is much needed. In this article, we present a 
      comprehensive package, Time-series RNA-seq Analysis Package (TRAP), integrating 
      all necessary tasks such as mapping short reads, measuring gene expression 
      levels, finding differentially expressed genes (DEGs), clustering and pathway 
      analysis for time-series data in a single environment. In addition to 
      implementing useful algorithms that are not available for RNA-seq data, we 
      extended existing pathway analysis methods, ORA and SPIA, for time series 
      analysis and estimates statistical values for combined dataset by an advanced 
      metric. TRAP also produces visual summary of pathway interactions. Gene 
      expression change labeling, a practical clustering method used in TRAP, enables 
      more accurate interpretation of the data when combined with pathway analysis. We 
      applied our methods on a real dataset for the analysis of rice (Oryza sativa L. 
      Japonica nipponbare) upon drought stress. The result showed that TRAP was able to 
      detect pathways more accurately than several existing methods. TRAP is available 
      at http://biohealth.snu.ac.kr/software/TRAP/.
CI  - Copyright (c) 2014 Elsevier Inc. All rights reserved.
FAU - Jo, Kyuri
AU  - Jo K
AD  - Department of Computer Science and Engineering, Seoul National University, Seoul, 
      Republic of Korea.
FAU - Kwon, Hawk-Bin
AU  - Kwon HB
AD  - Department of Biomedical Sciences, Sunmoon University, Asan 336-708, Republic of 
      Korea.
FAU - Kim, Sun
AU  - Kim S
AD  - Department of Computer Science and Engineering, Seoul National University, Seoul, 
      Republic of Korea; Bioinformatics Institute, Seoul National University, Seoul, 
      Republic of Korea; Interdisciplinary Program in Bioinformatics, Seoul National 
      University, Seoul, Republic of Korea. Electronic address: 
      sunkim.bioinfo@snu.ac.kr.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20140208
PL  - United States
TA  - Methods
JT  - Methods (San Diego, Calif.)
JID - 9426302
SB  - IM
MH  - Algorithms
MH  - *Droughts
MH  - Gene Expression Regulation, Plant
MH  - Oryza/*genetics
MH  - Sequence Analysis, RNA/*methods
MH  - Software
MH  - Stress, Psychological/*genetics
OTO - NOTNLM
OT  - Drought
OT  - Drought resistance rice
OT  - RNA-seq
OT  - Time-series
OT  - Time-series gene expression
OT  - Water stress
EDAT- 2014/02/13 06:00
MHDA- 2015/01/15 06:00
CRDT- 2014/02/13 06:00
PHST- 2013/11/18 00:00 [received]
PHST- 2014/01/17 00:00 [revised]
PHST- 2014/02/01 00:00 [accepted]
PHST- 2014/02/13 06:00 [entrez]
PHST- 2014/02/13 06:00 [pubmed]
PHST- 2015/01/15 06:00 [medline]
AID - S1046-2023(14)00029-2 [pii]
AID - 10.1016/j.ymeth.2014.02.001 [doi]
PST - ppublish
SO  - Methods. 2014 Jun 1;67(3):364-72. doi: 10.1016/j.ymeth.2014.02.001. Epub 2014 Feb 
      8.