A comprehensive overview of lncRNA annotation resources.

Long noncoding RNAs (lncRNAs) are emerging as a class of important regulators participating in various biological functions and disease processes. With the widespread application of next-generation sequencing technologies, large numbers of lncRNAs have been identified, producing plenty of lncRNA annotation resources in different contexts. However, at present, we lack a comprehensive overview of these lncRNA annotation resources. In this study, we reviewed 24 currently available lncRNA annotation resources referring to > 205 000 lncRNAs in over 50 tissues and cell lines. We characterized these annotation resources from different aspects, including exon structure, expression, histone modification and function. We found many distinct properties among these annotation resources. Especially, these resources showed diverse chromatin signatures, remarkable tissue and cell type dependence and functional specificity. Our results suggested the incompleteness and complementarity of current lncRNA annotations and the necessity of integration of multiple resources to comprehensively characterize lncRNAs. Finally, we developed 'LNCat' (lncRNA atlas, freely available at http://biocc.hrbmu.edu.cn/LNCat/), a user-friendly database that provides a genome browser of lncRNA structures, visualization of different resources from multiple angles and download of different combinations of lncRNA annotations, and supports rapid exploration, comparison and integration of lncRNA annotation resources. Overall, our study provides a comprehensive comparison of numerous lncRNA annotations, and can facilitate understanding of lncRNAs in human disease.

Portable, intelligent MIECL sensing platform for ciprofloxacin detection using a fast convolutional neural networks-assisted Tb@Lu2O3 nanoemitter.

Environmental pollution caused by ciprofloxacin is a major problem of global public health. A machine learning-assisted portable smartphone-based visualized molecularly imprinted electrochemiluminescence (MIECL) sensor was developed for the highly selective and sensitive detection of ciprofloxacin (CFX) in food. To boost the efficiency of electrochemiluminescence (ECL), oxygen vacancies (OVs) enrichment was introduced into the flower-like Tb@Lu2O3 nanoemitter. With the specific recognition reaction between MIP as capture probes and CFX as detection target, the ECL signal significantly decreased. According to, CFX analysis was determined by traditional ECL analyzer detector in the concentration range from 5 × 10-4 to 5 × 102 µmol L-1 with the detection limit (LOD) of 0.095 nmol L-1 (S/N = 3). Analysis of luminescence images using fast electrochemiluminescence judgment network (FEJ-Net) models, achieving portable and intelligent quick analysis of CFX. The proposed MIECL sensor was used for CFX analysis in real meat samples and satisfactory results, as well as efficient selectivity and good stability.

The facilitating role of phycospheric heterotrophic bacteria in cyanobacterial phosphonate availability and Microcystis bloom maintenance.

BACKGROUND: Phosphonates are the main components in the global phosphorus redox cycle. Little is known about phosphonate metabolism in freshwater ecosystems, although rapid consumption of phosphonates has been observed frequently. Cyanobacteria are often the dominant primary producers in freshwaters; yet, only a few strains of cyanobacteria encode phosphonate-degrading (C-P lyase) gene clusters. The phycosphere is defined as the microenvironment in which extensive phytoplankton and heterotrophic bacteria interactions occur. It has been demonstrated that phytoplankton may recruit phycospheric bacteria based on their own needs. Therefore, the establishment of a phycospheric community rich in phosphonate-degrading-bacteria likely facilitates cyanobacterial proliferation, especially in waters with scarce phosphorus. We characterized the distribution of heterotrophic phosphonate-degrading bacteria in field Microcystis bloom samples and in laboratory cyanobacteria "phycospheres" by qPCR and metagenomic analyses. The role of phosphonate-degrading phycospheric bacteria in cyanobacterial proliferation was determined through coculturing of heterotrophic bacteria with an axenic Microcystis aeruginosa strain and by metatranscriptomic analysis using field Microcystis aggregate samples. RESULTS: Abundant bacteria that carry C-P lyase clusters were identified in plankton samples from freshwater Lakes Dianchi and Taihu during Microcystis bloom periods. Metagenomic analysis of 162 non-axenic laboratory strains of cyanobacteria (consortia cultures containing heterotrophic bacteria) showed that 20% (128/647) of high-quality bins from eighty of these consortia encode intact C-P lyase clusters, with an abundance ranging up to nearly 13%. Phycospheric bacterial phosphonate catabolism genes were expressed continually across bloom seasons, as demonstrated through metatranscriptomic analysis using sixteen field Microcystis aggregate samples. Coculturing experiments revealed that although Microcystis cultures did not catabolize methylphosphonate when axenic, they demonstrated sustained growth when cocultured with phosphonate-utilizing phycospheric bacteria in medium containing methylphosphonate as the sole source of phosphorus. CONCLUSIONS: The recruitment of heterotrophic phosphonate-degrading phycospheric bacteria by cyanobacteria is a hedge against phosphorus scarcity by facilitating phosphonate availability. Cyanobacterial consortia are likely primary contributors to aquatic phosphonate mineralization, thereby facilitating sustained cyanobacterial growth, and even bloom maintenance, in phosphate-deficient waters. Video Abstract.

Predicting the functions of long noncoding RNAs using RNA-seq based on Bayesian network.

Long noncoding RNAs (lncRNAs) have been shown to play key roles in various biological processes. However, functions of most lncRNAs are poorly characterized. Here, we represent a framework to predict functions of lncRNAs through construction of a regulatory network between lncRNAs and protein-coding genes. Using RNA-seq data, the transcript profiles of lncRNAs and protein-coding genes are constructed. Using the Bayesian network method, a regulatory network, which implies dependency relations between lncRNAs and protein-coding genes, was built. In combining protein interaction network, highly connected coding genes linked by a given lncRNA were subsequently used to predict functions of the lncRNA through functional enrichment. Application of our method to prostate RNA-seq data showed that 762 lncRNAs in the constructed regulatory network were assigned functions. We found that lncRNAs are involved in diverse biological processes, such as tissue development or embryo development (e.g., nervous system development and mesoderm development). By comparison with functions inferred using the neighboring gene-based method and functions determined using lncRNA knockdown experiments, our method can provide comparable predicted functions of lncRNAs. Overall, our method can be applied to emerging RNA-seq data, which will help researchers identify complex relations between lncRNAs and coding genes and reveal important functions of lncRNAs.

Gene Perturbation Atlas (GPA): a single-gene perturbation repository for characterizing functional mechanisms of coding and non-coding genes.

Genome-wide transcriptome profiling after gene perturbation is a powerful means of elucidating gene functional mechanisms in diverse contexts. The comprehensive collection and analysis of the resulting transcriptome profiles would help to systematically characterize context-dependent gene functional mechanisms and conduct experiments in biomedical research. To this end, we collected and curated over 3000 transcriptome profiles in human and mouse from diverse gene perturbation experiments, which involved 1585 different perturbed genes (microRNAs, lncRNAs and protein-coding genes) across 1170 different cell lines/tissues. For each profile, we identified differential genes and their associated functions and pathways, constructed perturbation networks, predicted transcription regulation and cancer/drug associations, and assessed cooperative perturbed genes. Based on these transcriptome analyses, the Gene Perturbation Atlas (GPA) can be used to detect (i) novel or cell-specific functions and pathways affected by perturbed genes, (ii) protein interactions and regulatory cascades affected by perturbed genes, and (iii) perturbed gene-mediated cooperative effects. The GPA is a user-friendly database to support the rapid searching and exploration of gene perturbations. Particularly, we visualized functional effects of perturbed genes from multiple perspectives. In summary, the GPA is a valuable resource for characterizing gene functions and regulatory mechanisms after single-gene perturbations. The GPA is freely accessible at http://biocc.hrbmu.edu.cn/GPA/.

Integration strategy is a key step in network-based analysis and dramatically affects network topological properties and inferring outcomes.

An increasing number of experiments have been designed to detect intracellular and intercellular molecular interactions. Based on these molecular interactions (especially protein interactions), molecular networks have been built for using in several typical applications, such as the discovery of new disease genes and the identification of drug targets and molecular complexes. Because the data are incomplete and a considerable number of false-positive interactions exist, protein interactions from different sources are commonly integrated in network analyses to build a stable molecular network. Although various types of integration strategies are being applied in current studies, the topological properties of the networks from these different integration strategies, especially typical applications based on these network integration strategies, have not been rigorously evaluated. In this paper, systematic analyses were performed to evaluate 11 frequently used methods using two types of integration strategies: empirical and machine learning methods. The topological properties of the networks of these different integration strategies were found to significantly differ. Moreover, these networks were found to dramatically affect the outcomes of typical applications, such as disease gene predictions, drug target detections, and molecular complex identifications. The analysis presented in this paper could provide an important basis for future network-based biological researches.

Stratification of gene coexpression patterns and GO function mining for a RNA-Seq data series.

RNA-Seq is emerging as an increasingly important tool in biological research, and it provides the most direct evidence of the relationship between the physiological state and molecular changes in cells. A large amount of RNA-Seq data across diverse experimental conditions have been generated and deposited in public databases. However, most developed approaches for coexpression analyses focus on the coexpression pattern mining of the transcriptome, thereby ignoring the magnitude of gene differences in one pattern. Furthermore, the functional relationships of genes in one pattern, and notably among patterns, were not always recognized. In this study, we developed an integrated strategy to identify differential coexpression patterns of genes and probed the functional mechanisms of the modules. Two real datasets were used to validate the method and allow comparisons with other methods. One of the datasets was selected to illustrate the flow of a typical analysis. In summary, we present an approach to robustly detect coexpression patterns in transcriptomes and to stratify patterns according to their relative differences. Furthermore, a global relationship between patterns and biological functions was constructed. In addition, a freely accessible web toolkit "coexpression pattern mining and GO functional analysis" (COGO) was developed.

IndividualizedPath: identifying genetic alterations contributing to the dysfunctional pathways in glioblastoma individuals.

Due to the extensive complexity and high genetic heterogeneity of genetic alterations in cancer, comprehensively depicting the molecular mechanisms of cancer remains difficult. Characterizing personalized pathogenesis in cancer individuals can help to reveal new details of the complex mechanisms. In this study, we proposed an integrative method called IndividualizedPath to identify genetic alterations and their downstream risk pathways from the perspective of individuals through combining the DNA copy number, gene expression data and topological structures of biological pathways. By applying the method to TCGA glioblastoma multiforme (GBM) samples, we identified 394 gene-pathway pairs in 252 GBM individuals. We found that genes with copy number alterations showed high heterogeneity across GBM individuals, whereas they affected relatively consistent biological pathways. A global landscape of gene-pathway pairs showed that EGFR linked with multiple cancer-related biological pathways confers the highest risk of GBM. GBM individuals with MET-pathway pairs showed significantly shorter survival times than those with only MET amplification. Importantly, we found that the same risk pathways were affected by different genes in distinct groups of GBM individuals with a significant pattern of mutual exclusivity. Similarly, GBM subtype analysis revealed some subtype-specific gene-pathway pairs. In addition, we found that some rare copy number alterations had a large effect on contribution to numerous cancer-related pathways. In summary, our method offers the possibility to identify personalized cancer mechanisms, which can be applied to other types of cancer through the web server (http://bioinfo.hrbmu.edu.cn/IndividualizedPath/).