Transcription factors (TFs) are sequence-specific DNA-binding proteins acting as critical regulators of gene expression. The Plant Promoter Analysis Navigator (PlantPAN; http://PlantPAN2.itps.ncku.edu.tw) provides an informative resource for detecting transcription factor binding sites (TFBSs), corresponding TFs, and other important regulatory elements (CpG islands and tandem repeats) in a promoter or a set of plant promoters. Additionally, TFBSs, CpG islands, and tandem repeats in the conserve regions between similar gene promoters are also identified. The current PlantPAN release (version 2.0) contains 16 960 TFs and 1143 TF binding site matrices among 76 plant species. In addition to updating of the annotation information, adding experimentally verified TF matrices, and making improvements in the visualization of transcriptional regulatory networks, several new features and functions are incorporated. These features include: (i) comprehensive curation of TF information (response conditions, target genes, and sequence logos of binding motifs, etc.), (ii) co-expression profiles of TFs and their target genes under various conditions, (iii) protein-protein interactions among TFs and their co-factors, (iv) TF-target networks, and (v) downstream promoter elements. Furthermore, a dynamic transcriptional regulatory network under various conditions is provided in PlantPAN 2.0. The PlantPAN 2.0 is a systematic platform for plant promoter analysis and reconstructing transcriptional regulatory networks.

BackgroundIn general, the expression of gene alters conditionally to catalyze a specific metabolic pathway. Microarray-based datasets have been massively produced to monitor gene expression levels in parallel with numerous experimental treatments. Although several studies facilitated the linkage of gene expression data and metabolic pathways, none of them are amassed for plants. Moreover, advanced analysis such as pathways enrichment or how genes express under different conditions is not rendered.DescriptionTherefore, EXPath was developed to not only comprehensively congregate the public microarray expression data from over 1000 samples in biotic stress, abiotic stress, and hormone secretion but also allow the usage of this abundant resource for coexpression analysis and differentially expression genes (DEGs) identification, finally inferring the enriched KEGG pathways and gene ontology (GO) terms of three model plants: Arabidopsis thaliana, Oryza sativa, and Zea mays. Users can access the gene expression patterns of interest under various conditions via five main functions (Gene Search, Pathway Search, DEGs Search, Pathways/GO Enrichment, and Coexpression analysis) in EXPath, which are presented by a user-friendly interface and valuable for further research.ConclusionsIn conclusion, EXPath, freely available at http://expath.itps.ncku.edu.tw, is a database resource that collects and utilizes gene expression profiles derived from microarray platforms under various conditions to infer metabolic pathways for plants.

Diverse soil microbial community is determinant for sustainable agriculture. Rich microbial diversity has presumably improved soil health for economic crops to grow. In this work, the benefits of paddy-upland rotation on soil microbial diversity and specific microbes are thus intensively explored. The microbiome from multiple factor experiment (three fertilizations coupled with two rotation systems) were investigated by novel enrichment and co-occurrence analysis in a field well maintained for 25 years. Using next-generation sequencing technique, we firstly present explicit evidence that different rotation systems rather than fertilizations mightily governed the soil microbiome. Paddy-upland rotation (R1) obviously increase more microbial diversity than upland rotation (R2) whether organic (OF), chemical (CF) or integrated fertilizers (IF) were concomitantly applied. Besides, the specific bacterial composition dominated in OF soil is more similar to that of R1 than to CF, suggesting that paddy-upland rotation might be the best option for sustainable agriculture if chemical fertilizer is still required. Interestingly, the pot bioassay verified clearly the novel analysis prediction, illustrating that greater microbial diversity and specific microbial composition correlated significantly with disease resistance. This finding highlights the eminence of paddy-upland rotation in promoting microbial diversity and specific microbial compositions, preserving soil health for sustainable agriculture.

BackgroundTranscription factors (TFs) play essential roles during plant development and response to environmental stresses. However, the relationships among transcription factors, cis-acting elements and target gene expression under endo- and exogenous stimuli have not been systematically characterized.ResultsHere, we developed a series of bioinformatics analysis methods to infer transcriptional regulation by using numerous gene expression data from abiotic stresses and hormones treatments. After filtering the expression profiles of TF-encoding genes, 291 condition specific transcription factors (CsTFs) were obtained. Differentially expressed genes were then classified into various co-expressed gene groups based on each CsTFs. In the case studies of heat stress and ABA treatment, several known and novel cis-acting elements were identified following our bioinformatics approach. Significantly, a palindromic sequence of heat-responsive elements is recognized, and also obtained from a 3D protein structure of heat-shock protein-DNA complex. Notably, overrepresented 3- and 4-mer motifs in an enriched 8-mer motif could be a core cis-element for a CsTF. In addition, the results suggest DNA binding preferences of the same CsTFs are different according to various conditions.ConclusionsThe overall results illustrate this study may be useful in identifying condition specific cis- and trans- regulatory elements and facilitate our understanding of the relationships among TFs, cis-acting elements and target gene expression.Electronic supplementary materialThe online version of this article (10.1186/s12864-018-4469-4) contains supplementary material, which is available to authorized users.

Supplementary data are available at Bioinformatics online.