IC4R017-RNA-Seq-2014-24518221

Project Title

• Time-series RNA-seq analysis package (TRAP) and its application to the analysis of rice, Oryza sativa L. ssp. Japonica, upon drought stress

The Background of This Project

• The high throughput RNA sequencing(RNA-seq) technique has advantages over microarray technique such as the decreased noise level and more replicable results ,compared to microarrays. For this reason, the number of time-series RNA-seq data sets in the public domain has increased dramatically over the past few years.
• The main issue with RNA-seq is to handle sequencing data that is much bigger than microarray data.The researchers developed a comprehensive package, Time-series RNA-seq Analysis Package (TRAP), for analyzing time series transcriptome data. There have been many packages developed for analyzing time-series gene expression data. The most widely used technique is to identify DEGs. Packages for finding DEGs from time-series data include SAM , LIMMA ,EDGE , maSigPro and BETR .However, most of them are developed for microarray data and they assume that gene expression follows normal distribution. Users with RNA-seq data,therefore, should perform additional conversion process or use the certain type of distribution (e.g., Poisson or Binomial) which needs further validation.
• Finding DEGs and clustering is the first step for identifying genes that may have an important role in relation to phenotypes. However, the analysis needs to go at least one step further to extract biological implication from the gene list. The most widely used method for this additional analysis step is pathway analysis.
• This article describes a Time-series RNA-seq Analysis Package(TRAP), integrating all necessary tasks such as finding DEGs, clustering and pathway analysis for time-series data.

Plant Culture & Treatment

• The analysis starts with selecting DEGs from the genes. The researchers define DEGs as genes which have significantly different gene expression value in the control and treated samples. For gene expression value XðgÞ and YðgÞ of gene g in two different samples X and Y, we define the log fold change of FPKM values as DEðgÞ ¼ logðYðgÞ=XðgÞÞ. DEGs are chosen by Cuffdiff that tests the log fold change against the null hypothesis or by picking up genes above the cutoff value defined by the user.
• Given a set of gene expression values from Lt time points, TRAP extends two pathway analysis algorithms, time-series ORA and time-series SPIA, for the analysis of time series RNA-seq data. The output is a list of DEGs with annotations and a list of pathways with P-values from two analysis methods.

Labs working on this Project

• 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
• Department of Biomedical Sciences, Sunmoon University, Asan 336-708, Republic of Korea

Corresponding Author

• Sun Kim:sunkim.bioinfo@snu.ac.kr