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Background: Bisulfite sequencing (BS-seq) has become a standard technology to profile genome-wide DNA methylation at single-base resolution. It allows researchers to conduct genome-wise cytosine methylation analyses on issues about genomic imprinting, transcriptional regulation, cellular development and differentiation. One single data from a BS-Seq experiment is resolved into many features according to the sequence contexts, making methylome data analysis and data visualization a complex task. Results: We developed a streamlined platform, TEA, for analyzing and visualizing data from whole-genome BS-Seq (WGBS) experiments conducted in the model plant Arabidopsis thaliana. To capture the essence of the genome methylation level and to meet the efficiency for running online, we introduce a straightforward method for measuring genome methylation in each sequence context by gene. The method is scripted in Java to process BSSeq mapping results. Through a simple data uploading process, the TEA server deploys a web-based platform for deep analysis by linking data to an updated Arabidopsis annotation database and toolkits. Conclusions: TEA is an intuitive and efficient online platform for analyzing the Arabidopsis genomic DNA methylation landscape. It provides several ways to help users exploit WGBS data. TEA is freely accessible for academic users at: http://tea.iis.sinica.edu.tw. [ABSTRACT FROM AUTHOR]

Background: Recent progress in next-generation sequencing technology has afforded several improvements such as ultra-high throughput at low cost, very high read quality, and substantially increased sequencing depth. State-ofthe- art high-throughput sequencers, such as the Illumina MiSeq system, can generate ~15 Gbp sequencing data per run, with >80% bases above Q30 and a sequencing depth of up to several 1000x for small genomes. Illumina HiSeq 2500 is capable of generating up to 1 Tbp per run, with >80% bases above Q30 and often >100x sequencing depth for large genomes. To speed up otherwise time-consuming genome assembly and/or to obtain a skeleton of the assembly quickly for scaffolding or progressive assembly, methods for noise removal and reduction of redundancy in the original data, with almost equal or better assembly results, are worth studying. Results: We developed two subset selection methods for single-end reads and a method for paired-end reads based on base quality scores and other read analytic tools using the MapReduce framework. We proposed two strategies to select reads: MinimalQ and ProductQ. MinimalQ selects reads with minimal base-quality above a threshold. ProductQ selects reads with probability of no incorrect base above a threshold. In the single-end experiments, we used Escherichia coli and Bacillus cereus datasets of MiSeq, Velvet assembler for genome assembly, and GAGE benchmark tools for result evaluation. In the paired-end experiments, we used the giant grouper (Epinephelus lanceolatus) dataset of HiSeq, ALLPATHS-LG genome assembler, and QUAST quality assessment tool for comparing genome assemblies of the original set and the subset. The results show that subset selection not only can speed up the genome assembly but also can produce substantially longer scaffolds. Availability: The software is freely available at https://github.com/moneycat/QReadSelector. [ABSTRACT FROM AUTHOR]

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Background: Agarwood, a heartwood derived from Aquilaria trees, is a valuable commodity that has seen prevalent use among many cultures. In particular, it is widely used in herbal medicine and many compounds in agarwood are known to exhibit medicinal properties. Although there exists much research into medicinal herbs and extraction of high value compounds, few have focused on increasing the quantity of target compounds through stimulation of its related pathways in this species. Results: In this study, we observed that cucurbitacin yield can be increased through the use of different light conditions to stimulate related pathways and conducted three types of high-throughput sequencing experiments in order to study the effect of light conditions on secondary metabolism in agarwood. We constructed genome-wide profiles of RNA expression, small RNA, and DNA methylation under red light and far-red light conditions. With these profiles, we identified a set of small RNA which potentially regulates gene expression via the RNA-directed DNA methylation pathway. Conclusions: We demonstrate that light conditions can be used to stimulate pathways related to secondary metabolism, increasing the yield of cucurbitacins. The genome-wide expression and methylation profiles from our study provide insight into the effect of light on gene expression for secondary metabolism in agarwood and provide compelling new candidates towards the study of functional secondary metabolic components. [ABSTRACT FROM AUTHOR]

Background: Agarwood, a heartwood derived from Aquilaria trees, is a valuable commodity that has seen prevalent use among many cultures. In particular, it is widely used in herbal medicine and many compounds in agarwood are known to exhibit medicinal properties. Although there exists much research into medicinal herbs and extraction of high value compounds, few have focused on increasing the quantity of target compounds through stimulation of its related pathways in this species. Results: In this study, we observed that cucurbitacin yield can be increased through the use of different light conditions to stimulate related pathways and conducted three types of high-throughput sequencing experiments in order to study the effect of light conditions on secondary metabolism in agarwood. We constructed genome-wide profiles of RNA expression, small RNA, and DNA methylation under red light and far-red light conditions. With these profiles, we identified a set of small RNA which potentially regulates gene expression via the RNA-directed DNA methylation pathway. Conclusions: We demonstrate that light conditions can be used to stimulate pathways related to secondary metabolism, increasing the yield of cucurbitacins. The genome-wide expression and methylation profiles from our study provide insight into the effect of light on gene expression for secondary metabolism in agarwood and provide compelling new candidates towards the study of functional secondary metabolic components. [ABSTRACT FROM AUTHOR]

Background: Network is a useful way for presenting many types of biological data including protein-protein interactions, gene regulations, cellular pathways, and signal transductions. We can measure nodes by their network features to infer their importance in the network, and it can help us identify central elements of biological networks. Results: We introduce a novel Cytoscape plugin cytoHubba for ranking nodes in a network by their network features. CytoHubba provides 11 topological analysis methods including Degree, Edge Percolated Component, Maximum Neighborhood Component, Density of Maximum Neighborhood Component, Maximal Clique Centrality and six centralities (Bottleneck, EcCentricity, Closeness, Radiality, Betweenness, and Stress) based on shortest paths. Among the eleven methods, the new proposed method, MCC, has a better performance on the precision of predicting essential proteins from the yeast PPI network. Conclusions: CytoHubba provide a user-friendly interface to explore important nodes in biological networks. It computes all eleven methods in one stop shopping way. Besides, researchers are able to combine cytoHubba with and other plugins into a novel analysis scheme. The network and sub-networks caught by this topological analysis strategy will lead to new insights on essential regulatory networks and protein drug targets for experimental biologists. According to cytoscape plugin download statistics, the accumulated number of cytoHubba is around 6,700 times since 2010. [ABSTRACT FROM AUTHOR]

Background: Animals co-evolve with their gut microbiota; the latter can perform complex metabolic reactions that cannot be done independently by the host. Although the importance of gut microbiota has been well demonstrated, there is a paucity of research regarding its role in foliage-foraging mammals with a specialized digestive system. Results: In this study, a 16S rRNA gene survey and metagenomic sequencing were used to characterize genetic diversity and functional capability of cecal microbiota of the folivorous flying squirrel (Petaurista alborufus lena). Phylogenetic compositions of the cecal microbiota derived from 3 flying squirrels were dominated by Firmicutes. Based on end-sequences of fosmid clones from 1 flying squirrel, we inferred that microbial metabolism greatly contributed to intestinal functions, including degradation of carbohydrates, metabolism of proteins, and synthesis of vitamins. Moreover, 33 polysaccharide-degrading enzymes and 2 large genomic fragments containing a series of carbohydrate-associated genes were identified. Conclusions: Cecal microbiota of the leaf-eating flying squirrel have great metabolic potential for converting diverse plant materials into absorbable nutrients. The present study should serve as the basis for future investigations, using metagenomic approaches to elucidate the intricate mechanisms and interactions between host and gut microbiota of the flying squirrel digestive system, as well as other mammals with similar adaptations. [ABSTRACT FROM AUTHOR]

Background: Nucleic acid hybridization is an extensively adopted principle in biomedical research, in which the performance of any hybridization-based method depends on the specificity of probes to their targets. To determine the optimal probe(s) for detecting target(s) from a sample cocktail, we developed a novel algorithm, which has been implemented into a web platform for probe designing. This probe design workflow is now upgraded to satisfy experiments that require a probe designing tool to take the increasing volume of sequence datasets. Results: Algorithms and probe parameters applied in UPS 2.0 include GC content, the secondary structure, melting temperature (Tm), the stability of the probe-target duplex estimated by the thermodynamic model, sequence complexity, similarity of probes to non-target sequences, and other empirical parameters used in the laboratory. Several probe background options,Unique probe within a group,Unique probe in a specific Unigene set,Unique probe based onthe pangenomic level, and Unique Probe in the user-defined genome/transcriptome, are available to meet the scenarios that the experiments will be conducted. Parameters, such as salt concentration and the lower-bound Tm of probes, are available for users to optimize their probe design query. Output files are available for download on the result page. Probes designed by the UPS algorithm are suitable for generating microarrays, and the performance of UPS-designed probes has been validated by experiments. Conclusions: The UPS 2.0 evaluates probe-to-target hybridization under a user-defined condition to ensure high-performance hybridization with minimal chance of non-specific binding at the pangenomic and genomic levels. The UPS algorithm mimics the target/non-target mixture in an experiment and is very useful in developing diagnostic kits and microarrays. The UPS 2.0 website has had more than 1,300 visits and 360,000 sequences performed the probe designing task in the last 30 months. It is freely accessible at http://array.iis.sinica.edu.tw/ups/. Screen cast: http://array.iis.sinica.edu.tw/ups/demo/demo.htm [ABSTRACT FROM AUTHOR]

Background: Many research results show that the biological systems are composed of functional modules. Members in the same module usually have common functions. This is useful information to understand how biological systems work. Therefore, detecting functional modules is an important research topic in the post-genome era. One of functional module detecting methods is to find dense regions in Protein-Protein Interaction (PPI) networks. Most of current methods neglect confidence-scores of interactions, and pay little attention on using gene expression data to improve their results. Results: In this paper, we propose a novel hub-attachment based method to detect functional modules from confidence-scored protein interactions and expression profiles, and we name it HUNTER. Our method not only can extract functional modules from a weighted PPI network, but also use gene expression data as optional input to increase the quality of outcomes. Using HUNTER on yeast data, we found it can discover more novel components related with RNA polymerase complex than those existed methods from yeast interactome. And these new components show the close relationship with polymerase after functional analysis on Gene Ontology. Conclusion: A C++ implementation of our prediction method, dataset and supplementary material are available at http://hub.iis.sinica.edu.tw/Hunter/. Our proposed HUNTER method has been applied on yeast data, and the empirical results show that our method can accurately identify functional modules. Such useful application derived from our algorithm can reconstruct the biological machinery, identify undiscovered components and decipher common sub-modules inside these complexes like RNA polymerases I, II, III. [ABSTRACT FROM AUTHOR]

Background: Nucleic acid hybridization, a fundamental technique in molecular biology, can be modified into very effective and sensitive methods for detecting particular targets mixed with millions of non-target sequences. Therefore, avoiding cross-hybridization is the most crucial issue for developing diagnostic methods based on hybridization. Results: To develop a probe with a high discriminating power, this study constructed a web service, the Unique Probe Selector (UPS), for customized probe design. The UPS service integrates a probe design mechanism and a scoring system for evaluating the performance of probe annealing and the uniqueness of a probe in a user-defined genetic background. Starting from an intuitive web interface, the UPS accepts a query with single or multiple sequences in fasta format. The best probe(s) for each sequence can be downloaded from result pages in a fasta or .csv format with a summary of probe characteristics. The option "Unique probe within group" selects the most unique probe for each target sequence with low probability to hybridize to the other sequences in the same submitted query. The option "Unique probe in the specific organism" devises probes for each submitted sequence to identify its target among selected genetic backgrounds based on Unigene. Conclusion: The UPS evaluates probe-to-target hybridization under a user-defined condition in silico to ensure high-performance hybridization and minimizes the possibility of non-specific reactions. UPS has been applied to design human arrays for gene expression studies and to develop several small arrays of gene families that were inferred as molecular signatures of cancer typing/ staging or pathogen signatures. Notably, UPS is freely accessible at http://array.iis.sinica.edu.tw/ups/. [ABSTRACT FROM AUTHOR]

Background: Proteins control and mediate many biological activities of cells by interacting with other protein partners. This work presents a statistical model to predict protein interaction networks of Drosophila melanogaster based on insight into domain interactions. Results: Three high-throughput yeast two-hybrid experiments and the collection in FlyBase were used as our starting datasets. The co-occurrences of domains in these interactive events are converted into a probability score of domain-domain interaction. These scores are used to infer putative interaction among all available open reading frames (ORFs) of fruit fly. Additionally, the likelihood function is used to estimate all potential protein-protein interactions. All parameters are successfully iterated and MLE is obtained for each pair of domains. Additionally, the maximized likelihood reaches its converged criteria and maintains the probability stable. The hybrid model achieves a high specificity with a loss of sensitivity, suggesting that the model may possess major features of protein-protein interactions. Several putative interactions predicted by the proposed hybrid model are supported by literatures, while experimental data with a low probability score indicate an uncertain reliability and require further proof of interaction. Fly-DPI is the online database used to present this work. It is an integrated proteomics tool with comprehensive protein annotation information from major databases as well as an effective means of predicting protein-protein interactions. As a novel search strategy, the ping-pong search is a naïve path map between two chosen proteins based on pre-computed shortest paths. Adopting effective filtering strategies will facilitate researchers in depicting the bird's eye view of the network of interest. Fly-DPI can be accessed at http://flydpi.nhri.org.tw. Conclusion: This work provides two reference systems, statistical and biological, to evaluate the reliability of protein interaction. First, the hybrid model statistically estimates both experimental and predicted protein interaction relationships. Second, the biological information for filtering and annotation itself is a strong indicator for the reliability of protein-protein interaction. The space-temporal or stage-specific expression patterns of genes are also critical for identifying proteins involved in a particular situation. [ABSTRACT FROM AUTHOR]