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Plant genomes encode a complex and evolutionary diverse regulatory grammar that forms the basis for most life on earth. A wealth of regulome and epigenome data have been generated in various plant species, but no common, standardized resource is available so far for biologists. Here we present ChIP-Hub, an integrative web-based platform in the ENCODE standards that bundles > 10000 publicly available datasets reanalyzed from > 40 plant species, allowing visualization and meta-analysis. We manually curate the datasets through assessing ~ 540 original publications and comprehensively evaluate their data quality. As a proof of concept, we extensively survey the co-association of different regulators and construct a hierarchical regulatory network under a broad developmental context. Furthermore, we show how our annotation allows to investigate the dynamic activity of tissue-specific regulatory elements (promoters and enhancers) and their underlying sequence grammar. Finally, we analyze the function and conservation of tissue-specific chromatin states based on comparative genomics. Taken together, the ChIP-Hub platform and the analysis results provide rich resources for deep exploration of plant ENCODE.

The wave of high-throughput technologies in genomics and phenomics are enabling data to be generated on an unprecedented scale and at a reasonable cost. Exploring the large-scale data sets generated by these technologies to derive biological insights requires efficient bioinformatic tools. Here we introduce an interactive, open-source web application (HTPmod) for high-throughput biological data modeling and visualization. HTPmod is implemented with the Shiny framework by integrating the computational power and professional visualization of R and including various machine-learning approaches. We demonstrate that HTPmod can be used for modeling and visualizing large-scale, high-dimensional data sets (such as multiple omics data) under a broad context. By reinvestigating example data sets from recent studies, we find not only that HTPmod can reproduce results from the original studies in a straightforward fashion and within a reasonable time, but also that novel insights may be gained from fast reinvestigation of existing data by HTPmod.

Plant genomes encode a complex and evolutionary diverse regulatory grammar that forms the basis for most life on earth. A wealth of regulome and epigenome data have been generated in various plant species, but no common, standardized resource is available so far for biologists. Here we present ChIP-Hub, an integrative web-based platform in the ENCODE standards that bundles publicly available datasets reanalyzed from >40 plant species, allowing visualization and meta-analysis.

Deoxyribonuclease I (DNase I)-hypersensitive site sequencing (DNase-seq) has been widely used to determine chromatin accessibility and its underlying regulatory lexicon. However, exploring DNase-seq data requires sophisticated downstream bioinformatics analyses. In this study, we first review computational methods for all of the major steps in DNase-seq data analysis, including experimental design, quality control, read alignment, peak calling, annotation of cis-regulatory elements, genomic footprinting and visualization. The challenges associated with each step are highlighted. Next, we provide a practical guideline and a computational pipeline for DNase-seq data analysis by integrating some of these tools. We also discuss the competing techniques and the potential applications of this pipeline for the analysis of analogous experimental data. Finally, we discuss the integration of DNase-seq with other functional genomics techniques.

Our knowledge of the role of higher-order chromatin structures in transcription of microRNA genes (MIRs) is evolving rapidly. Here we investigate the effect of 3D architecture of chromatin on the transcriptional regulation of MIRs. We demonstrate that MIRs have transcriptional features that are similar to protein-coding genes. RNA polymerase II–associated ChIA-PET data reveal that many groups of MIRs and protein-coding genes are organized into functionally compartmentalized chromatin communities and undergo coordinated expression when their genomic loci are spatially colocated. We observe that MIRs display widespread communication in those transcriptionally active communities. Moreover, miRNA–target interactions are significantly enriched among communities with functional homogeneity while depleted from the same community from which they originated, suggesting MIRs coordinating function-related pathways at posttranscriptional level. Further investigation demonstrates the existence of spatial MIR–MIR chromatin interacting networks. We show that groups of spatially coordinated MIRs are frequently from the same family and involved in the same disease category. The spatial interaction network possesses both common and cell-specific subnetwork modules that result from the spatial organization of chromatin within different cell types. Together, our study unveils an entirely unexplored layer of MIR regulation throughout the human genome that links the spatial coordination of MIRs to their co-expression and function.

Several (1) studies have revealed that the reactive oxygen species (ROS) induced by antibacterial stimulation accelerates the evolution of antibiotic resistance, which uncovered new links between oxygen rise and evolution and inspired new strategies to prevent antibiotic resistance. Considering many other mechanisms cause DNA mutations aside from ROS damage, evaluating the significance of oxidative DNA damage in the development of antibiotic resistance is of great interest. In this study, we examined the ratio of G:C > T:A transversion to G:C > A:T transition in drug-resistant Escherichia coli and Mycobacterium tuberculosis and found that it is significantly higher than the background values. This finding strongly suggests that ROS damage plays a critical role in the development of antibacterial resistance. Considering the long-term co-evolution between host organisms and pathogenic bacteria, we speculate that the hosts may have evolved strategies for combating antibiotic resistance by controlling DNA damage in bacteria. Analysis of the global transcriptional profiles of Staphylococcus aureus treated with berberine (derived from Berberis, a traditional antibacterial medicine) revealed that the transcription of DNA repair enzymes was markedly upregulated, whereas the antioxidant enzymes were significantly downregulated. Thus, we propose that consolidating the DNA repair systems of bacteria may be a viable strategy for preventing antibiotic resistance. (1)These authors contributed equally to this work.

Recently, numerous genome analyses revealed the existence of a universal G:C→A:T mutation bias in bacteria, fungi, plants and animals. To explore the molecular basis for this mutation bias, we examined the three well-known DNA mutation models, i.e., oxidative damage model, UV-radiation damage model and CpG hypermutation model. It was revealed that these models cannot provide a sufficient explanation to the universal mutation bias. Therefore, we resorted to a DNA mutation model proposed by Löwdin 40 years ago, which was based on inter-base double proton transfers (DPT). Since DPT is a fundamental and spontaneous chemical process and occurs much more frequently within GC pairs than AT pairs, Löwdin model offers a common explanation for the observed universal mutation bias and thus has broad biological implications.

New strategies for target identification are urgently needed to tackle the current productivity challenges in drug discovery. By examining successful human drug targets, it can be seen that approximately 50% are associated with genetic disorders. Further analysis shows that these successfully targeted genes share some common evolutionary features, which strongly suggests that evolutionary information can help identify drug targets with the greatest potential for therapeutic development.

Mutations in drug targets can alter the therapeutic effects of drugs. Therefore, evaluating the effects of single-nucleotide polymorphisms (SNPs) on drug-target binding is of significant interest. This study focuses on the analysis of the structural and energy properties of SNPs in successful drug targets by using the data derived from HapMap and the Therapeutic Target Database. The results show the following: (i) Drug targets undergo strong purifying selection, and the majority (92.4%) of the SNPs are located far from the drug-binding sites (>12 A). (ii) For SNPs near the drug-binding pocket (≤12 A), nearly half of the drugs are weakly affected by the SNPs, and only a few drugs are significantly affected by the target mutations. These results have direct implications for population-based drug therapy and for chemical treatment of genetic diseases as well.

The 3D chromatin structure modeling by chromatin interactions derived from Hi-C experiments is significantly challenged by the intrinsic sequencing biases in these experiments. Conventional modeling methods only focus on the bias among different chromatin regions within the same experiment but neglect the bias arising from different experimental sequencing depth. We now show that the regional interaction bias is tightly coupled with the sequencing depth, and we further identify a chromatin structure parameter as the inherent characteristics of Hi-C derived data for chromatin regions. Then we present an approach for chromatin structure prediction capable of relaxing both kinds of sequencing biases by using this identified parameter. This method is validated by intra and inter cell-line comparisons among various chromatin regions for four human cell-lines (K562, GM12878, IMR90 and H1hESC), which shows that the openness of chromatin region is well correlated with chromatin function. This method has been executed by an automatic pipeline (AutoChrom3D) and thus can be conveniently used.