Grene, R. | bioRxiv Science

bioRxiv2019

Comparing Time Series Transcriptome Data Between Plants Using A Network Module Finding Algorithm

Comparative transcriptome analysis is the comparison of expression patterns between homologous genes in different species. Since most molecular mechanistic studies in plants have been performed in model species including Arabidopsis and rice, comparative transcriptome analysis is particularly important for functional annotation of genes in other plant species. Many biological processes, such as embryo development, are highly conserved between different plant species. The challenge is to establish one-to-one mapping of the developmental stages between two species. In this protocol, we solve this problem by converting the gene expression patterns into a co-expression network and then apply network module-finding algorithms to the cross-species co-expression network. We describe how to perform such analysis using bash scripts for preliminary data processing and R programming language, which implemented simulated annealing method for module finding. We also provide instructions on how to visualize the resulting co-expression networks across species.

bioinformatics↗

bioRxiv2019

Prediction of condition-specific regulatory maps in Arabidopsis using integrated genomic data

Recent advances in genomic technologies have generated large-scale protein-DNA interaction data and open chromatic regions for multiple plant species. To predict condition specific gene regulatory networks using these data, we developed the Condition Specific Regulatory network inference engine (ConSReg), which combines heterogeneous genomic data using sparse linear model followed by feature selection and stability selection to select key regulatory genes. Using Arabidopsis as a model system, we constructed maps of gene regulation under more than 50 experimental conditions including abiotic stresses, cell type-specific expression, and stress responses in individual cell types. Our results show that ConSReg accurately predicted gene expressions (average auROC of 0.84) across multiple testing datasets. We found that, (1) including open chromatin information from ATAC-seq data significantly improves the performance of ConSReg across all tested datasets; (2) choice of negative training samples and length of promoter regions are two key factors that affect model performance. We applied ConSReg to Arabidopsis single cell RNA-seq data of two root cell types (endodermis and cortex) and identified five regulators in two root cell types. Four out of the five regulators have additional experimental evidence to support their roles in regulating gene expression in Arabidopsis roots. By comparing regulatory maps in abiotic stress responses and cell type-specific experiments, we revealed that transcription factors that regulate tissue levels abiotic stresses tend to also regulate stress responses in individual cell types in plants.

bioinformatics↗