Application ServerspsRNATarget: A Plant Small RNA Target Analysis Server
Plant endogenous non-coding short small RNAs (20-24 nt), including microRNAs (miRNAs) and a subset of small interfering RNAs (ta-siRNAs), play important role in gene expression regulatory networks (GRNs), for example, many transcription factors and development related genes have been reported as targets of these regulatory small RNAs. Although a number of miRNA target prediction algorithms and programs have been developed, most of them were designed for animal miRNAs which are significantly different from plant miRNAs in the target recognition process. These differences demand the development of separate plant miRNA (and ta-siRNA) target analysis tool(s).
We present psRNATarget, a plant small RNA target analysis server, which features two important analysis functions: 1) reverse complementary matching between miRNA and target transcript using a proven scoring schema, and 2) target site accessibility evaluation by calculating unpaired energy (UPE) required to "open" secondary structure around miRNA’s target site on mRNA. PsRNATarget incorporates recent discoveries in plant miRNA target recognition, e.g. it distinguishes translational and post-transcriptional inhibition, and it reports the number of miRNA/target site pairs that may affect miRNA binding activity to target transcript.
PsRNATarget is designed for high-throughput analysis of next-generation data with an efficient distributed computing back-end pipeline that runs on a Linux cluster. The server front-end integrates three simplified user-friendly interfaces to accept user-submitted or preloaded miRNAs and transcript sequences; and outputs a comprehensive list of miRNA / target pairs along with the online tools for batch downloading, key word searching and results sorting.» Visit psRNATarget
pssRNAMiner: A plant short small RNA regulatory cascade analysis server
In plants, short RNAs including ~21-nt microRNA (miRNA) and 21-nt trans-acting siRNA (ta-siRNA) compose a "miRNA -> ta-siRNA -> target gene" cascade pathway that regulates gene expression at the post-transcriptional level. In this cascade, biogenesis of ta-siRNA clusters requires 21-nt intervals (i.e. phasing) and miRNA (phase-initiator) cleavage sites on its TAS transcript. Here we report a novel web server, pssRNAMiner, which is developed to identify both the clusters of phased small RNAs as well as the potential phase-initiator.
- To detect phased small RNA clusters, the pssRNAMiner maps input small RNAs against user-specified transcript/genomic sequences, and then identifies phased small RNA clusters by evaluating P values of hypergeometric distribution.
- To identify potential phase-initiators, pssRNAMiner aligns input phase-initiators with transcripts of TAS candidates using the Smith-Waterman algorithm. Potential cleavage sites on TAS candidates are further identified from complementary regions by weighting the alignment expectation and its distance to detected phased small RNA clusters.
pssRNAit: Designing Effective and Specific Plant RNAi siRNAs with Genome-wide Off-target Gene Assessment
We present pssRNAit, a web server tool to design effective, specific and non-toxic siRNAs for plant RNAi. This tool implemented several innovative approaches based upon recent understanding of biological mechanism of RNAi pathways gain through our findings and literatures. For this, we have developed reliable computational models specific to each step of RNAi pathways and integrated these models which works like pathways to design siRNAs.
pssRNAit integrated several models and cDNA transcripts library.
- A new SVM model is use to design highly effective siRNA
- Remove siRNA which contains toxic and non-specific sequence motifs
- Our RISCbinder model is use to select those effective siRNA (antisense:sense) whose antisense strand have binding affinity with RISC machinery to execute gene silencing
- Our psRNATarget tool is used to predict off-target genes of design siRNAs and to select more specific siRNAs
- Our recent finding using high-throughput data of miRNAs showed several isomiRs are also generating along with canonical miRNA in order to increase the target specific gene silencing
Therefore, we are intelligently select pool of siRNAs to further increase the specificity of gene silencing. The basic principle is to select bunch of siRNAs which have common target gene but different off-targets.
HRGRN: A Graph Search-Empowered Integrative Database of Arabidopsis Signaling Transduction, Metabolism and Gene Regulation Networks
we introduce HRGRN, a graph search-empowered integrative database of Arabidopsis signal transduction, metabolism, and gene regulatory networks. HRGRN utilizes the highly scalable graph database, Neo4j, to host large-scale of biological interactions among genes, proteins, compounds and small RNAs that were either validated experimentally or predicted computationally. The associated biological pathway information were also specially marked for the interactions that are involved in the pathway to facilitate the investigation of cross-talks between pathways.GPLEXUS: Genome-scale Gene Association Network Reconstruction and Analysis for very large-scale expression data
We developed a novel online platform GPLEXUS, a publicly and freely available web server that enables and empowers genome-scale GAN Analysis. Key features of GPLEXUS include high performance construction of Gene Association Network (GANs), identification of functional subnetworks, and network analyses for novel biological discovery. Briefly, the GPLEXUS integrates the following key components and functionalities:
- GPLEXUS adopts a construction-followed-by-refinement procedure to build GANs from very large-scale gene expression data
- GPLEXUS adopt a ultrafast Spearman correlation-based transformation to estimate the mutual information; Other two methods B-Spline-based pair-wise MI estimation, and Gaussian kernel-based pair-wise MI estimation are also integrated in our Platform
- OGPLEXUS constructs GANs at high accuracy and sensitivity through efficiently removing potential false positive edges by applying Data Processing Inequality (DPI) filtering
- GPLEXUS integrates the Markov Clustering Algorithm (MCL) for effective subnetwork identification and discovery
- And, it is worth highlighting here that GPLEXUS implements an innovative function to identify experiment-specific conditions that majorly contribute to gene-gene associations in the constructed networks. Such analysis may greatly...
Analysis of genome-scale Gene Networks (GNs) from large-scale gene expression profiles opens the door to uncover new biological knowledge. However, inferring genome-scale Gene Network (GN) from large-scale gene expression data and subsequent functional module mining are very computational intense tasks; therefore it requires both efficient algorithms and parallel computing engineering in order to enable and empower Genome-scale Gene Network analysis. Context likelihood of relatedness (CLR) method  based on the mutual information for scoring the similarity of gene pairs is one of the most accurate methods to infer gene networks. But it is computational unfeasibility to decipher a genome-wide network with large genomes, such as many plant genomes, with large-scale gene expression profiles, on a single computer due to limits on memory and CPU capacity. DeGNServer is a high performance web server that is capable of constructing genome-scale networks and further mining sub-networks from large genome-scale expression profiles for the species with large genomes/large number of genes:
- DeGNServer integrates six proven association methods(Spearman rank correlation,Pearson correlation,Mutual-information, Maximum information coefficient, Kendall rank correlation,Thei-Sen Estimator) for co-expression GN analysis and further utilize Context Likelihood of Relatedness approach for gene network analysis. In order to enable and empower genome-scale GN analysis, all algorithm have been implemented and deployed on our in-house parallel computing platform, namely BioGrid, which has over dedicated 700 CPU cores;
- Subnetwork identification and visualzation based on community structure mining methods
Here, we present PlantTFcat, which is a high-performance web-based plant transcription factor and transcriptional regulator categorization and analysis tool, designed to identify and categorize TFs and TRs in genome-scale protein or nucleic acid sequences. The PlantTFcat’s prediction logic was built upon a comprehensive collection of manually compiled and curated conserved domain patterns found in almost all (108, to be specific) published plant CR, TF and TR families up to date, and thus warrants high quality prediction in terms of both accuracy and coverage.PlantGRN: Modeling and Deciphering Plant Transcriptional Regulatory Networks
We propose a novel systematic computational approach for predicting TFTGs directly on a genome scale. Utilizing gene co-expression data, we modeled the prediction problem as a 'yes' or 'no' classification task by converting biological sequences into novel reverse-complementary position-sensitive n-gram profiles and implemented the classifiers with support vector machines. Our approach does not necessarily predict new DNA binding sites, which other studies have shown to be difficult and inaccurate. We applied the proposed approach to predict auxin-response factor target genes from published Arabidopsis thaliana co-expression data and obtained satisfactory results.An Integrative Platform to Study Gene Function and Genome Evolution in Legumes (Version 2.0)
The LegumeIP 2.0 provide comprehensive search and visualization tools to search, retrieve and visualize large-scale gene sequences, annotations, gene families, macro- and micro- synteny blocks, and transcriptome profiles. LegumeIP3 almost ready...A Common Bean Gene Expression Atlas
This atlas presents the gene expression patterns of 24 unique samples collected from seven distinct tissues of Phaseolus vulgaris cv. negro jamapa; roots, nodules, leaves, stems, flowers, seeds, and pods. Samples were collected at developmentally important time-points spanning the processes of symbiosis, seed and pod development. Plants were either provided with nutrients via fertilizer or inoculated with either effective or ineffective rhizobium. Plants inoculated with ineffective rhizobium formed nodules, but did not fix N2, resulting in N deficient plants.The Alfalfa Gene Index and Expression Atlas Database
This web-accessbile database is intended to provide the Alfalfa researchers with the MSGI 1.2 transcriptome sequences, annotations, expression profiles, and SNPs identified in this study.