Plant Omics Data Center: An Integrated Web Repository for Interspecies Gene Expression Networks with NLP-Based Curation

Abstract

Comprehensive integration of large-scale omics resources such as genomes, transcriptomes and metabolomes will provide deeper insights into broader aspects of molecular biology. For better understanding of plant biology, we aim to construct a next-generation sequencing (NGS)-derived gene expression network (GEN) repository for a broad range of plant species. So far we have incorporated information about 745 high-quality mRNA sequencing (mRNA-Seq) samples from eight plant species (Arabidopsis thaliana, Oryza sativa, Solanum lycopersicum, Sorghum bicolor, Vitis vinifera, Solanum tuberosum, Medicago truncatula and Glycine max) from the public short read archive, digitally profiled the entire set of gene expression profiles, and drawn GENs by using correspondence analysis (CA) to take advantage of gene expression similarities. In order to understand the evolutionary significance of the GENs from multiple species, they were linked according to the orthology of each node (gene) among species. In addition to other gene expression information, functional annotation of the genes will facilitate biological comprehension. Currently we are improving the given gene annotations with natural language processing (NLP) techniques and manual curation. Here we introduce the current status of our analyses and the web database, PODC (Plant Omics Data Center; http://bioinf.mind.meiji.ac.jp/podc/), now open to the public, providing GENs, functional annotations and additional comprehensive omics resources.

NorWood: a gene expression resource for evo‐devo studies of conifer wood development

“…For example, the PLAZA (Proost et al, 2015) and Phytozome (Goodstein et al, 2012) resources provide extensive coverage of both angiosperm and gymnosperm (in the case of PLAZA) species, with cross-species gene family information allowing exploration of protein coding gene conservation. Similarly, a number of resources integrate gene expression and proteinprotein interaction data, including PlaNet, PODC, CORNET and ATTED-II (Mutwil et al, 2011;De Bodt et al, 2012;Ohyanagi et al, 2015;Aoki et al, 2016). However, the majority of these are focused towards agricultural food crop species and A. thaliana.…”

Identification of reference genes for quantitative expression analysis using large-scale RNA-seq data of <i>Arabidopsis thaliana</i> and model crop plants

“…Thus, one of the CK2A genes may also be suitable as a reference gene in these plants, depending on experimental conditions, suggesting the potency of ortholog-based selection. While a number of plant databases, such as PODC (Ohyanagi et al, 2015), ATTED-II (Obayashi et al, 2014), OryzaExpress (Hamada et al, 2011), eFP Browsers (Winter et al, 2007), UniVIO (Kudo et al, 2013) and qTeller (http:// www.qteller.com/), are dealing with plant transcriptomic data, direct information on reference gene candidates has not been provided in the databases. A database implemented with a search function for reference genes, combined with information on orthologs and experimental conditions, would be helpful to explore reference gene candidates in species with insufficient accumulation of transcriptomic data.…”

“…Referring to experimental descriptions available in the SRA database, RNA-seq data obtained from technically biased RNA and small RNA libraries were roughly excluded manually. Quality control and mapping of reads, and calculation of gene expression levels, were carried out as previously described (Ohyanagi et al, 2015) with some version updates of tools and genome files: cutadapt (version 1.4.1) (Martin, 2011), tophat2 (version 2.0.12) ) applying the '--no-novel-juncs' option, bowtie2 (version 2.2.3) (Langmead and Salzberg, 2012), and cufflinks software (version 2.2.1) applying the '-u' option; and potato reference genome and genome annotation (version 4.03) (The Potato Genome Sequencing Consortium, 2011; Sharma et al, 2013). The RNA-seq data were further filtered by mapping rate after the quality control.…”

“…For instance, a web-based omics database, the Plant Omics Data Center (PODC, http://bioinf.mind.meiji. ac.jp/podc/), has been established with a search function for gene expression network constructed by correspondence analysis (Yano et al, 2006) using the global RNA-seq data of eight plant species: Arabidopsis, Glycine max (soybean), Medicago truncatula (medicago), Oryza sativa (rice), Solanum lycopersicum (tomato), S. tuberosum (potato), Sorghum bicolor (sorghum) and Vitis vinifera (grapevine) (Ohyanagi et al, 2015). When one attempts to perform RT-qPCR analysis to validate the expression pattern found in such global profiling, a reference gene(s) that is usable in broad experimental conditions will be required.…”

Integrated omics analysis of specialized metabolism in medicinal plants

“…Pathway‐based omics data integration is another popular approach used by plant biologists to get a broad over‐view of metabolic pathways under a given set of conditions. There are several web‐based or standalone tools that allow pathway‐based integration of omics data including PathVisio (van Iersel et al ., ), Paintomics (Garcia‐Alcalde et al ., ), MapMan (Thimm et al ., ), INMEX (Xia et al ., ), IMPaLA (Kamburov et al ., ; Eichner et al ., ), SAMNetWeb (Gosline et al ., ), pwOmics (Wachter and Beissbarth, ), GabiPD (Riano‐Pachon et al ., ), PODC (Ohyanagi et al ., ), MONGKIE (Jang et al ., ), MetaMapR (Grapov et al ., ), KEGG Mapper (Kanehisa et al ., ), Marvis‐pathways (Kaever et al ., ), 3Omics (Kuo et al ., ) and mixOmics (http://mixomics.org/). Many of these tools have been reviewed in great detail (Higashi and Saito, ; Fondi and Lio, ).…”