PlantExp: a platform for exploration of gene expression and alternative splicing based on public plant RNA-seq samples

Abstract

Over the last decade, RNA-seq has produced a massive amount of plant transcriptomic sequencing data deposited in public databases. Reanalysis of these public datasets can generate additional novel hypotheses not included in original studies. However, the large data volume and the requirement for specialized computational resources and expertise present a barrier for experimental biologists to explore public repositories. Here, we introduce PlantExp (https://biotec.njau.edu.cn/plantExp), a database platform for exploration of plant gene expression and alternative splicing profiles based on 131 423 uniformly processed publicly available RNA-seq samples from 85 species in 24 plant orders. In addition to two common retrieval accesses to gene expression and alternative splicing profiles by functional terms and sequence similarity, PlantExp is equipped with four online analysis tools, including differential expression analysis, specific expression analysis, co-expression network analysis and cross-species expression conservation analysis. With these online analysis tools, users can flexibly customize sample groups to reanalyze public RNA-seq datasets and obtain new insights. Furthermore, it offers a wide range of visualization tools to help users intuitively understand analysis results. In conclusion, PlantExp provides a valuable data resource and analysis platform for plant biologists to utilize public RNA-seq. datasets.

Comparative analysis of tissue-specific genes in maize based on machine learning models: CNN performs technically best, LightGBM performs biologically soundest

“…Using high-throughput sequencing techniques, diverse databases of plant transcriptomes comprising integrated sequencing data have been created. The plant databases were created based on a variety of criteria, such as a focus on certain sequencing techniques [PlantExp ( Liu et al, 2022 ), PlantscRNAdb ( Chen et al, 2021 ), etc.] or a focus on individual plant species [Wildsoydb ( Xiao et al, 2022 ), CottonMD ( Yang et al, 2022 ), etc.].…”

Integrating omics databases for enhanced crop breeding

“…By applying single-cell transcriptomics to crop plants, researchers can gain a more comprehensive understanding of gene expression patterns in different cell types and tissues, and the molecular mechanisms that govern crop growth and development. To access bulk transcriptomic data for crop plants, researchers can use established databases such as PlantExp [ 46 ], PPRD [ 25 ], Genevestigator [ 47 ], ePlant [ 48 ], and PlantGenIE [ 49 ], which provide a variety of transcriptomic data sets for different crop species ( Table 2 ) Besides, with the widespread application of single-cell transcriptomic technology in plants, databases focused on plant single-cell transcriptomics, such as PsctH [ 50 ], PlantscRNAdb [ 51 ], and PCMDB [ 52 ] have been established successively ( Table 2 ). These databases are critical resources that enable researchers to explore gene expression patterns across different tissues and under varying conditions.…”

CoSIA: an R Bioconductor package for CrOss Species Investigation and Analysis

“…However, cross-species gene expression comparison is challenging due to biological and technical factors affecting the measurements (Conesa et al 2016;Chung et al 2021). Previous studies have implemented a variety of comparison methods (Zhu et al 2014;Sudmant, Alexis and Burge 2015;Söllner et al 2017;Panahi et al 2019;Wang et al 2020;Bastian et al 2021;García de la Torre et al 2021;Liu et al 2023) that involve either taking into account the evolutionary relationships between the species or rigorous statistical assumptions to account for species-level differences in gene expression (Fisher and Others 1948;Stuart et al 2003;Hu, Greenwood and Beyene 2006;Lu, Rosenfeld and Bar-Joseph 2006;Lu et al 2007Lu et al , 2010Campain and Yang 2010;Le, Oltvai and Bar-Joseph 2010;Tseng, Ghosh and Feingold 2012;Kristiansson et al 2013). We developed CoSIA (Cross-Species Investigation and Analysis), an R package and associated Shiny app, which provides an alternative framework for cross-species RNA expression visualization and comparison across tissues and species using variability metrics.…”