SUMMARY: We present GWASpro, a high-performance web server for the analyses of large-scale genome-wide association studies (GWAS). GWASpro was developed to provide data analyses for large-scale molecular genetic data, coupled with complex replicated experimental designs such as found in plant science investigations and to overcome the steep learning curves of existing GWAS software tools. GWASpro supports building complex design matrices, by which complex experimental designs that may include replications, treatments, locations and times, can be accounted for in the linear mixed model. GWASpro is optimized to handle GWAS data that may consist of up to 10 million markers and 10 000 samples from replicable lines or hybrids. GWASpro provides an interface that significantly reduces the learning curve for new GWAS investigators. AVAILABILITY AND IMPLEMENTATION: GWASpro is freely available at https://bioinfo.noble.org/GWASPRO. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Motivation: Joint reconstruction of multiple gene regulatory networks (GRNs) using gene expression data from multiple tissues/conditions is very important for understanding common and tissue/condition-specific regulation. However, there are currently no computational models and methods available for directly constructing such multiple GRNs that not only share some common hub genes but also possess tissue/condition-specific regulatory edges. Results: In this paper, we proposed a new graphic Gaussian model for joint reconstruction of multiple gene regulatory networks (JRmGRN), which highlighted hub genes, using gene expression data from several tissues/conditions. Under the framework of Gaussian graphical model, JRmGRN method constructs the GRNs through maximizing a penalized log likelihood function. We formulated it as a convex optimization problem, and then solved it with an alternating direction method of multipliers (ADMM) algorithm. The performance of JRmGRN was first evaluated with synthetic data and the results showed that JRmGRN outperformed several other methods for reconstruction of GRNs. We also applied our method to real Arabidopsis thaliana RNA-seq data from two light regime conditions in comparison with other methods, and both common hub genes and some conditions-specific hub genes were identified with higher accuracy and precision. Availability and implementation: JRmGRN is available as a R program from: https://github.com/wenpingd. Supplementary information: Supplementary data are available at Bioinformatics online.

The term epistasis refers to interactions between multiple genetic loci. Genetic epistasis is important in regulating biological function and is considered to explain part of the 'missing heritability,' which involves marginal genetic effects that cannot be accounted for in genome-wide association studies. Thus, the study of epistasis is of great interest to geneticists. However, estimating epistatic effects for quantitative traits is challenging due to the large number of interaction effects that must be estimated, thus significantly increasing computing demands. Here, we present a new web server-based tool, the Pipeline for estimating EPIStatic genetic effects (PEPIS), for analyzing polygenic epistatic effects. The PEPIS software package is based on a new linear mixed model that has been used to predict the performance of hybrid rice. The PEPIS includes two main sub-pipelines: the first for kinship matrix calculation, and the second for polygenic component analyses and genome scanning for main and epistatic effects. To accommodate the demand for high-performance computation, the PEPIS utilizes C/C++ for mathematical matrix computing. In addition, the modules for kinship matrix calculations and main and epistatic-effect genome scanning employ parallel computing technology that effectively utilizes multiple computer nodes across our networked cluster, thus significantly improving the computational speed. For example, when analyzing the same immortalized F2 rice population genotypic data examined in a previous study, the PEPIS returned identical results at each analysis step with the original prototype R code, but the computational time was reduced from more than one month to about five minutes. These advances will help overcome the bottleneck frequently encountered in genome wide epistatic genetic effect analysis and enable accommodation of the high computational demand. The PEPIS is publically available at http://bioinfo.noble.org/PolyGenic_QTL/.

BACKGROUND: Extracted ion chromatogram (EIC) extraction and chromatographic peak detection are two important processing procedures in liquid chromatography/mass spectrometry (LC/MS)-based metabolomics data analysis. Most commonly, the LC/MS technique employs electrospray ionization as the ionization method. The EICs from LC/MS data are often noisy and contain high background signals. Furthermore, the chromatographic peak quality varies with respect to its location in the chromatogram and most peaks have zigzag shapes. Therefore, there is a critical need to develop effective metrics for quality evaluation of EICs and chromatographic peaks in LC/MS based metabolomics data analysis. RESULTS: We investigated a comprehensive set of potential quality evaluation metrics for extracted EICs and detected chromatographic peaks. Specifically, for EIC quality evaluation, we analyzed the mass chromatographic quality index (MCQ index) and propose a novel quality evaluation metric, the EIC-related global zigzag index, which is based on an EIC's first order derivatives. For chromatographic peak quality evaluation, we analyzed and compared six metrics: sharpness, Gaussian similarity, signal-to-noise ratio, peak significance level, triangle peak area similarity ratio and the local peak-related local zigzag index. CONCLUSIONS: Although the MCQ index is suited for selecting and aligning analyte components, it cannot fairly evaluate EICs with high background signals or those containing only a single peak. Our proposed EIC related global zigzag index is robust enough to evaluate EIC qualities in both scenarios. Of the six peak quality evaluation metrics, the sharpness, peak significance level, and zigzag index outperform the others due to the zigzag nature of LC/MS chromatographic peaks. Furthermore, using several peak quality metrics in combination is more efficient than individual metrics in peak quality evaluation.

BACKGROUND: Plants regulate intrinsic gene expression through transcription factors (TFs), transcriptional regulators (TRs), chromatin regulators (CRs), and the basal transcription machinery. An understanding of plant gene regulatory mechanisms at a systems level requires the identification of these regulatory elements on a genomic scale. RESULTS: Here, we present PlantTFcat, a high-performance web-based analysis tool that is designed to identify and categorize plant TF/TR/CR genes from genome-scale protein and nucleic acid sequences by systematically analyzing InterProScan domain patterns in protein sequences. The comprehensive prediction logics that are included in PlantTFcat are based on relationships between gene families and conserved domains from 108 published plant TF/TR/CR families. These prediction logics effectively distinguish TF/TR/CR families with common conserved domains. Our systematic performance evaluations indicate that PlantTFcat annotates known TF/TR/CR families with high coverage and sensitivity. CONCLUSIONS: PlantTFcat provides an analysis tool to identify and categorize plant TF/TR/CR genes on a genomic scale. PlantTFcat is freely available to the public at http://plantgrn.noble.org/PlantTFcat/.

Plant microRNAs (miRNA) target recognition mechanism was once thought to be simple and straightforward, i.e. through perfect reverse complementary matching; therefore, very few target prediction tools and algorithms were developed for plants as compared to those for animals. However, the discovery of transcription suppression and the more recent observation of widespread translational regulation by miRNAs highlight the enormous diversity and complexity of gene regulation in plant systems. This, in turn, necessitates the need for advanced computational tools/algorithms for comprehensive miRNA target analysis to help understand miRNA regulatory mechanisms. Yet, advanced/comprehensive plant miRNA target analysis tools are still lacking despite the desirability and importance of such tools, especially the ability of predicting translational inhibition and integrating transcriptome data. This review focuses on recent progress in plant miRNA target recognition mechanism, principles of target prediction based on these understandings, comparison of current prediction tools and algorithms for plant miRNA target analysis and the outlook for future directions in the development of plant miRNA target tools and algorithms.

BACKGROUND: Legumes (Leguminosae or Fabaceae) play a major role in agriculture. Transcriptomics studies in the model legume species, Medicago truncatula, are instrumental in helping to formulate hypotheses about the role of legume genes. With the rapid growth of publically available Affymetrix GeneChip Medicago Genome Array GeneChip data from a great range of tissues, cell types, growth conditions, and stress treatments, the legume research community desires an effective bioinformatics system to aid efforts to interpret the Medicago genome through functional genomics. We developed the Medicago truncatula Gene Expression Atlas (MtGEA) web server for this purpose. DESCRIPTION: The Medicago truncatula Gene Expression Atlas (MtGEA) web server is a centralized platform for analyzing the Medicago transcriptome. Currently, the web server hosts gene expression data from 156 Affymetrix GeneChip(R) Medicago genome arrays in 64 different experiments, covering a broad range of developmental and environmental conditions. The server enables flexible, multifaceted analyses of transcript data and provides a range of additional information about genes, including different types of annotation and links to the genome sequence, which help users formulate hypotheses about gene function. Transcript data can be accessed using Affymetrix probe identification number, DNA sequence, gene name, functional description in natural language, GO and KEGG annotation terms, and InterPro domain number. Transcripts can also be discovered through co-expression or differential expression analysis. Flexible tools to select a subset of experiments and to visualize and compare expression profiles of multiple genes have been implemented. Data can be downloaded, in part or full, in a tabular form compatible with common analytical and visualization software. The web server will be updated on a regular basis to incorporate new gene expression data and genome annotation, and is accessible at: http://bioinfo.noble.org/gene-atlas/. CONCLUSIONS: The MtGEA web server has a well managed rich data set, and offers data retrieval and analysis tools provided in the web platform. It's proven to be a powerful resource for plant biologists to effectively and efficiently identify Medicago transcripts of interest from a multitude of aspects, formulate hypothesis about gene function, and overall interpret the Medicago genome from a systematic point of view.

BACKGROUND: Membrane transporters play crucial roles in living cells. Experimental characterization of transporters is costly and time-consuming. Current computational methods for transporter characterization still require extensive curation efforts, especially for eukaryotic organisms. We developed a novel genome-scale transporter prediction and characterization system called TransportTP that combined homology-based and machine learning methods in a two-phase classification approach. First, traditional homology methods were employed to predict novel transporters based on sequence similarity to known classified proteins in the Transporter Classification Database (TCDB). Second, machine learning methods were used to integrate a variety of features to refine the initial predictions. A set of rules based on transporter features was developed by machine learning using well-curated proteomes as guides. RESULTS: In a cross-validation using the yeast proteome for training and the proteomes of ten other organisms for testing, TransportTP achieved an equivalent recall and precision of 81.8%, based on TransportDB, a manually annotated transporter database. In an independent test using the Arabidopsis proteome for training and four recently sequenced plant proteomes for testing, it achieved a recall of 74.6% and a precision of 73.4%, according to our manual curation. CONCLUSIONS: TransportTP is the most effective tool for eukaryotic transporter characterization up to date.

MOTIVATION: Membrane transport proteins play a crucial role in the import and export of ions, small molecules or macromolecules across biological membranes. Currently, there are a limited number of published computational tools which enable the systematic discovery and categorization of transporters prior to costly experimental validation. To approach this problem, we utilized a nearest neighbor method which seamlessly integrates homologous search and topological analysis into a machine-learning framework. RESULTS: Our approach satisfactorily distinguished 484 transporter families in the Transporter Classification Database, a curated and representative database for transporters. A five-fold cross-validation on the database achieved a positive classification rate of 72.3% on average. Furthermore, this method successfully detected transporters in seven model and four non-model organisms, ranging from archaean to mammalian species. A preliminary literature-based validation has cross-validated 65.8% of our predictions on the 11 organisms, including 55.9% of our predictions overlapping with 83.6% of the predicted transporters in TransportDB.

The advance of high-throughput experimental technologies poses continuous challenges to computational data analysis in functional and comparative genomics studies. Gene ontology (GO) annotation and transcriptional profiling using gene expression array have been two of the major approaches for system-wide analysis of gene functions and gene interactions. In the literature, extensive studies have been reported in each aspect. Yet there is a lack of efficient algorithm that discover associative patterns across these two data domains. We proposed a mixture model associative artificial neural network to tackle this deficiency. The algorithm inherits the theoretical foundation of adaptive resonance associative map (ARAM), with essential redefinition of pattern similarity measures and learning functions. The proposed algorithm is capable of clustering data based on both GO semantic similarity and expressional correlation, for the purpose of systematically discovering genome-wide, highly correlated gene groups, which in turn suggest similar or closely related functions. We applied the proposed algorithm to the analysis of the Saccharomyces cerevisiae (yeast) dataset and obtained satisfactory results.

BACKGROUND: BLAST searches are widely used for sequence alignment. The search results are commonly adopted for various functional and comparative genomics tasks such as annotating unknown sequences, investigating gene models and comparing two sequence sets. Advances in sequencing technologies pose challenges for high-throughput analysis of large-scale sequence data. A number of programs and hardware solutions exist for efficient BLAST searching, but there is a lack of generic software solutions for mining and personalized management of the results. Systematically reviewing the results and identifying information of interest remains tedious and time-consuming. RESULTS: Personal BLAST Navigator (PLAN) is a versatile web platform that helps users to carry out various personalized pre- and post-BLAST tasks, including: (1) query and target sequence database management, (2) automated high-throughput BLAST searching, (3) indexing and searching of results, (4) filtering results online, (5) managing results of personal interest in favorite categories, (6) automated sequence annotation (such as NCBI NR and ontology-based annotation). PLAN integrates, by default, the Decypher hardware-based BLAST solution provided by Active Motif Inc. with a greatly improved efficiency over conventional BLAST software. BLAST results are visualized by spreadsheets and graphs and are full-text searchable. BLAST results and sequence annotations can be exported, in part or in full, in various formats including Microsoft Excel and FASTA. Sequences and BLAST results are organized in projects, the data publication levels of which are controlled by the registered project owners. In addition, all analytical functions are provided to public users without registration. CONCLUSION: PLAN has proved a valuable addition to the community for automated high-throughput BLAST searches, and, more importantly, for knowledge discovery, management and sharing based on sequence alignment results. The PLAN web interface is platform-independent, easily configurable and capable of comprehensive expansion, and user-intuitive. PLAN is freely available to academic users at http://bioinfo.noble.org/plan/. The source code for local deployment is provided under free license. Full support on system utilization, installation, configuration and customization are provided to academic users.

Computational prediction of transcription factor's binding sites and regulatory target genes has great value to the biological studies of cellular process. Existing practices either look into first-hand gene expression data which could be costly for large scale analysis, or apply statistical or heuristic learning methods to discover potential binding sites which have limited accuracy due to the complexity of the data. Based on well-studied information retrieval theories, this paper proposes a novel systematic approach for transcription factor target gene prediction. The key of the approach is to model the prediction problem as a classification task by representing the features of the sequential data into vector data points in a higher-order domain. The proposed approach has produced satisfactory results in our controlled experiment on Auxin Response Factor (ARF) target gene prediction in Arabidopsis