A web-based tool for the prediction of rice transcription factor function

Chandran, Anil Nalini; Moon, Sunok; Yoo, Yo-Han; Gho, Yoon-Shil; Cao, Peijian; Sharma, Rita; Sharma, Manoj K.; Ronald, Pamela C.; Jung, Ki-Hong

doi:10.1093/database/baz061

A web-based tool for the prediction of rice transcription factor function

Journal Article · Thu Jun 06 00:00:00 EDT 2019 · Database

DOI:https://doi.org/10.1093/database/baz061· OSTI ID:2469652

Chandran, Anil Nalini ^[1]; Moon, Sunok ^[1]; Yoo, Yo-Han ^[1]; Gho, Yoon-Shil ^[1]; Cao, Peijian ^[2]; Sharma, Rita ^[3]; Sharma, Manoj K. ^[3]; Ronald, Pamela C. ^[4]; Jung, Ki-Hong ^[1]

Kyung Hee Univ. (Korea, Republic of)
Zhengzhou Tobacco Research Institute (China)
Jawaharlal Nehru University, New Delhi (India)
Univ. of California, Davis, CA (United States); Joint BioEnergy Institute (JBEI), Emeryville, CA (United States)

Transcription factors (TFs) are an important class of regulatory molecules. Despite their importance, only a small number of genes encoding TFs have been characterized in Oryza sativa (rice), often because gene duplication and functional redundancy complicate their analysis. To address this challenge, we developed a web-based tool called the Rice Transcription Factor Phylogenomics Database (RTFDB) and demonstrate its application for predicting TF function. The RTFDB hosts transcriptome and co-expression analyses. Sources include high-throughput data from oligonucleotide microarray (Affymetrix and Agilent) as well as RNA-Seq-based expression profiles. We used the RTFDB to identify tissue-specific and stress-related gene expression. Subsequently, 273 genes preferentially expressed in specific tissues or organs, 455 genes showing a differential expression pattern in response to 4 abiotic stresses, 179 genes responsive to infection of various pathogens and 512 genes showing differential accumulation in response to various hormone treatments were identified through the meta-expression analysis. Pairwise Pearson correlation coefficient analysis between paralogous genes in a phylogenetic tree was used to assess their expression collinearity and thereby provides a hint on their genetic redundancy. Integrating transcriptome with the gene evolutionary information reveals the possible functional redundancy or dominance played by paralog genes in a highly duplicated genome such as rice. With this method, we estimated a predominant role for 83.3% (65/78) of the TF or transcriptional regulator genes that had been characterized via loss-of-function studies. In this regard, the proposed method is applicable for functional studies of other plant species with annotated genome.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 2469652

Journal Information:: Database, Journal Name: Database Vol. 2019; ISSN 1758-0463

Publisher:: Oxford University Press - International Society for BiocurationCopyright Statement

Country of Publication:: United States

Language:: English

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