EXPath 2.0: An Updated Database for Integrating High-Throughput Gene Expression Data with Biological Pathways.

Co-expressed genes tend to have regulatory relationships and participate in similar biological processes. Construction of gene correlation networks from microarray or RNA-seq expression data has been widely applied to study transcriptional regulatory mechanisms and metabolic pathways under specific conditions. Furthermore, since transcription factors (TFs) are critical regulators of gene expression, it is worth investigating TFs on the promoters of co-expressed genes. Although co-expressed genes and their related metabolic pathways can be easily identified from previous resources, such as EXPath and EXPath Tool, this information is not simultaneously available to identify their regulatory TFs. EXPath 2.0 is an updated database for the investigation of regulatory mechanisms in various plant metabolic pathways with 1,881 microarray and 978 RNA-seq samples. There are six significant improvements in EXPath 2.0: (i) the number of species has been extended from three to six to include Arabidopsis, rice, maize, Medicago, soybean and tomato; (ii) gene expression at various developmental stages have been added; (iii) construction of correlation networks according to a group of genes is available; (iv) hierarchical figures of the enriched Gene Ontology (GO) terms are accessible; (v) promoter analysis of genes in a metabolic pathway or correlation network is provided; and (vi) user's gene expression data can be uploaded and analyzed. Thus, EXPath 2.0 is an updated platform for investigating gene expression profiles and metabolic pathways under specific conditions. It facilitates users to access the regulatory mechanisms of plant biological processes. The new version is available at http://EXPath.itps.ncku.edu.tw.

PlantPAN3.0: a new and updated resource for reconstructing transcriptional regulatory networks from ChIP-seq experiments in plants.

The Plant Promoter Analysis Navigator (PlantPAN; http://PlantPAN.itps.ncku.edu.tw/) is an effective resource for predicting regulatory elements and reconstructing transcriptional regulatory networks for plant genes. In this release (PlantPAN 3.0), 17 230 TFs were collected from 78 plant species. To explore regulatory landscapes, genomic locations of TFBSs have been captured from 662 public ChIP-seq samples using standard data processing. A total of 1 233 999 regulatory linkages were identified from 99 regulatory factors (TFs, histones and other DNA-binding proteins) and their target genes across seven species. Additionally, this new version added 2449 matrices extracted from ChIP-seq peaks for cis-regulatory element prediction. In addition to integrated ChIP-seq data, four major improvements were provided for more comprehensive information of TF binding events, including (i) 1107 experimentally verified TF matrices from the literature, (ii) gene regulation network comparison between two species, (iii) 3D structures of TFs and TF-DNA complexes and (iv) condition-specific co-expression networks of TFs and their target genes extended to four species. The PlantPAN 3.0 can not only be efficiently used to investigate critical cis- and trans-regulatory elements in plant promoters, but also to reconstruct high-confidence relationships among TF-targets under specific conditions.

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.

EXPath tool-a system for comprehensively analyzing regulatory pathways and coexpression networks from high-throughput transcriptome data.

Next generation sequencing (NGS) has become the mainstream approach for monitoring gene expression levels in parallel with various experimental treatments. Unfortunately, there is no systematical webserver to comprehensively perform further analysis based on the huge amount of preliminary data that is obtained after finishing the process of gene annotation. Therefore, a user-friendly and effective system is required to mine important genes and regulatory pathways under specific conditions from high-throughput transcriptome data. EXPath Tool (available at: http://expathtool.itps.ncku.edu.tw/) was developed for the pathway annotation and comparative analysis of user-customized gene expression profiles derived from microarray or NGS platforms under various conditions to infer metabolic pathways for all organisms in the KEGG database. EXPath Tool contains several functions: access the gene expression patterns and the candidates of co-expression genes; dissect differentially expressed genes (DEGs) between two conditions (DEGs search), functional grouping with pathway and GO (Pathway/GO enrichment analysis), and correlation networks (co-expression analysis), and view the expression patterns of genes involved in specific pathways to infer the effects of the treatment. Additionally, the effectively of EXPath Tool has been performed by a case study on IAA-responsive genes. The results demonstrated that critical hub genes under IAA treatment could be efficiently identified.