sRIS: A Small RNA Illustration System for Plant Next-Generation Sequencing Data Analysis.

Small RNA (sRNA), such as microRNA (miRNA) and short interfering RNA, are well-known to control gene expression based on degradation of target mRNA in plants. A considerable amount of research has applied next-generation sequencing (NGS) to reveal the regulatory pathways of plant sRNAs. Consequently, numerous bioinformatics tools have been developed for the purpose of analyzing sRNA NGS data. However, most methods focus on the study of sRNA expression profiles or novel miRNAs predictions. The analysis of sRNA target genes is usually not integrated into their pipelines. As a result, there is still no means available for identifying the interaction mechanisms between host and virus or the synergistic effects between two viruses. For the present study, a comprehensive system, called the Small RNA Illustration System (sRIS), has been developed. This system contains two main components. The first is for sRNA overview analysis and can be used not only to identify miRNA but also to investigate virus-derived small interfering RNA. The second component is for sRNA target prediction, and it employs both bioinformatics calculations and degradome sequencing data to enhance the accuracy of target prediction. In addition, this system has been designed so that figures and tables for the outputs of each analysis can be easily retrieved and accessed, making it easier for users to quickly identify and quantify their results. sRIS is available at http://sris.itps.ncku.edu.tw/.

microRPM: a microRNA prediction model based only on plant small RNA sequencing data.

Motivation: MicroRNAs (miRNAs) are endogenous non-coding small RNAs (of about 22 nucleotides), which play an important role in the post-transcriptional regulation of gene expression via either mRNA cleavage or translation inhibition. Several machine learning-based approaches have been developed to identify novel miRNAs from next generation sequencing (NGS) data. Typically, precursor/genomic sequences are required as references for most methods. However, the non-availability of genomic sequences is often a limitation in miRNA discovery in non-model plants. A systematic approach to determine novel miRNAs without reference sequences is thus necessary. Results: In this study, an effective method was developed to identify miRNAs from non-model plants based only on NGS datasets. The miRNA prediction model was trained with several duplex structure-related features of mature miRNAs and their passenger strands using a support vector machine algorithm. The accuracy of the independent test reached 96.61% and 93.04% for dicots (Arabidopsis) and monocots (rice), respectively. Furthermore, true small RNA sequencing data from orchids was tested in this study. Twenty-one predicted orchid miRNAs were selected and experimentally validated. Significantly, 18 of them were confirmed in the qRT-PCR experiment. This novel approach was also compiled as a user-friendly program called microRPM (miRNA Prediction Model). Availability and implementation: This resource is freely available at http://microRPM.itps.ncku.edu.tw. Contact: nslin@sinica.edu.tw or sarah321@mail.ncku.edu.tw. Supplementary information: Supplementary data are available at Bioinformatics online.

Delineation of condition specific Cis- and Trans-acting elements in plant promoters under various Endo- and exogenous stimuli.

BACKGROUND: Transcription 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. RESULTS: Here, 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. CONCLUSIONS: The 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.

PlantPAN 2.0: an update of plant promoter analysis navigator for reconstructing transcriptional regulatory networks in plants.

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.

EXPath: a database of comparative expression analysis inferring metabolic pathways for plants.

BACKGROUND: In 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. DESCRIPTION: Therefore, 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. CONCLUSIONS: In 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.

AlgaePath: comprehensive analysis of metabolic pathways using transcript abundance data from next-generation sequencing in green algae.

BACKGROUND: Algae are important non-vascular plants that have many research applications, including high species diversity, biofuel sources, and adsorption of heavy metals and, following processing, are used as ingredients in health supplements. The increasing availability of next-generation sequencing (NGS) data for algae genomes and transcriptomes has made the development of an integrated resource for retrieving gene expression data and metabolic pathway essential for functional analysis and systems biology. In a currently available resource, gene expression profiles and biological pathways are displayed separately, making it impossible to easily search current databases to identify the cellular response mechanisms. Therefore, in this work the novel AlgaePath database was developed to retrieve transcript abundance profiles efficiently under various conditions in numerous metabolic pathways. DESCRIPTION: AlgaePath is a web-based database that integrates gene information, biological pathways, and NGS datasets for the green algae Chlamydomonas reinhardtii and Neodesmus sp. UTEX 2219-4. Users can search this database to identify transcript abundance profiles and pathway information using five query pages (Gene Search, Pathway Search, Differentially Expressed Genes (DEGs) Search, Gene Group Analysis, and Co-expression Analysis). The transcript abundance data of 45 and four samples from C. reinhardtii and Neodesmus sp. UTEX 2219-4, respectively, can be obtained directly on pathway maps. Genes that are differentially expressed between two conditions can be identified using Folds Search. The Gene Group Analysis page includes a pathway enrichment analysis, and can be used to easily compare the transcript abundance profiles of functionally related genes on a map. Finally, the Co-expression Analysis page can be used to search for co-expressed transcripts of a target gene. The results of the searches will provide a valuable reference for designing further experiments and for elucidating critical mechanisms from high-throughput data. CONCLUSIONS: AlgaePath is an effective interface that can be used to clarify the transcript response mechanisms in different metabolic pathways under various conditions. Importantly, AlgaePath can be mined to identify critical mechanisms based on high-throughput sequencing. To our knowledge, AlgaePath is the most comprehensive resource for integrating numerous databases and analysis tools in algae. The system can be accessed freely online at http://algaepath.itps.ncku.edu.tw.

Paddy-upland rotation for sustainable agriculture with regards to diverse soil microbial community.

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.

AtmiRNET: a web-based resource for reconstructing regulatory networks of Arabidopsis microRNAs.

Compared with animal microRNAs (miRNAs), our limited knowledge of how miRNAs involve in significant biological processes in plants is still unclear. AtmiRNET is a novel resource geared toward plant scientists for reconstructing regulatory networks of Arabidopsis miRNAs. By means of highlighted miRNA studies in target recognition, functional enrichment of target genes, promoter identification and detection of cis- and trans-elements, AtmiRNET allows users to explore mechanisms of transcriptional regulation and miRNA functions in Arabidopsis thaliana, which are rarely investigated so far. High-throughput next-generation sequencing datasets from transcriptional start sites (TSSs)-relevant experiments as well as five core promoter elements were collected to establish the support vector machine-based prediction model for Arabidopsis miRNA TSSs. Then, high-confidence transcription factors participate in transcriptional regulation of Arabidopsis miRNAs are provided based on statistical approach. Furthermore, both experimentally verified and putative miRNA-target interactions, whose validity was supported by the correlations between the expression levels of miRNAs and their targets, are elucidated for functional enrichment analysis. The inferred regulatory networks give users an intuitive insight into the pivotal roles of Arabidopsis miRNAs through the crosstalk between miRNA transcriptional regulation (upstream) and miRNA-mediate (downstream) gene circuits. The valuable information that is visually oriented in AtmiRNET recruits the scant understanding of plant miRNAs and will be useful (e.g. ABA-miR167c-auxin signaling pathway) for further research. Database URL: http://AtmiRNET.itps.ncku.edu.tw/

Large-scale investigation of human TF-miRNA relations based on coexpression profiles.

Noncoding, endogenous microRNAs (miRNAs) are fairly well known for regulating gene expression rather than protein coding. Dysregulation of miRNA gene, either upregulated or downregulated, may lead to severe diseases or oncogenesis, especially when the miRNA disorder involves significant bioreactions or pathways. Thus, how miRNA genes are transcriptionally regulated has been highlighted as well as target recognition in recent years. In this study, a large-scale investigation of novel cis- and trans-elements was undertaken to further determine TF-miRNA regulatory relations, which are necessary to unravel the transcriptional regulation of miRNA genes. Based on miRNA and annotated gene expression profiles, the term "coTFBS" was introduced to detect common transcription factors and the corresponding binding sites within the promoter regions of each miRNA and its coexpressed annotated genes. The computational pipeline was successfully established to filter redundancy due to short sequence motifs for TFBS pattern search. Eventually, we identified more convinced TF-miRNA regulatory relations for 225 human miRNAs. This valuable information is helpful in understanding miRNA functions and provides knowledge to evaluate the therapeutic potential in clinical research. Once most expression profiles of miRNAs in the latest database are completed, TF candidates of more miRNAs can be explored by this filtering approach in the future.