Whole-genome DNA methylome analysis of different developmental stages of the entomopathogenic fungus Beauveria bassiana NCHU-157 by nanopore sequencing.

The entomopathogenic fungus (EPF), Beauveria bassiana, is an important and commonly used EPF for microbial control. However, the role of DNA methylation has not been thoroughly studied. Therefore, the whole genomic DNA methylome of one promising EPF isolate, B. bassiana NCHU-157 (Bb-NCHU-157), was investigated by Oxford Nanopore Technologies (ONT). First, the whole genome of Bb-NCHU-157 was sequenced by next-generation sequencing (NGS) and ONT. The genome of Bb-NCHU-157 contains 16 contigs with 34.19 Mb and 50% GC content, which are composed of 10,848 putative protein-coding genes. Two putative DNA methyltransferases (DNMTs) were found, including Dim-2 and C-5 cytosine-specific DNA methylases. Both DNMTs showed higher expression levels in the mycelium stage than in the conidia stage, indicating that development of DNA methylation in Bb-NCHU-157 might occur in the mycelium stage. The global methylation level of the mycelium stage (5 mC = 4.56%, CG = 3.33%, CHG = 0.74%, CHH = 0.49%) was higher than that of the conidial stage (5 mC = 2.99%, CG = 1.99%, CHG = 0.63%, CHH = 0.37%) in both the gene and transposable element (TE) regions. Furthermore, the TE regions showed higher methylation frequencies than the gene regions, especially for CHH site methylation, suggesting regulation of genomic stabilization during mycelium development. In the gene regions, high methylation frequencies were found around the transcription start site (TSS) and transcription end site (TES). Moreover, CG and CHG methylation mainly occur in the promoter and intergenic regions, while CHH methylation occurs in the TE region. Among the methylated regions, 371, 661, and 756 differentially DNA methylated regions (DMRs) were hypermethylated in the mycelium in CG, CHG, and CHH, while only 13 and 7 DMRs were hypomethylated in the mycelium in CHG, and CHH, respectively. Genes located in the DMR shared the GO terms, DNA binding (GO: 0003677), and sequence-specific DNA binding (GO: 0043565) for hypermethylation in the mycelium, suggesting that methylation might regulate gene expression from the initial process. Evaluation of the DNA methylome in Bb-NCHU-157 by ONT provided new insight into this field. These data will be further validated, and epigenetic regulation during the development of B. bassiana will be explored.

Methylome Imputation by Methylation Patterns.

DNA methylation is studied extensively for its relations with several biological processes such as transcriptional regulation. While methylation levels are usually estimated per cytosine or genomic region, additional information on methylation heterogeneity can be obtained when considering stretches of successive cytosines on the same reads; however, the majority of methylomes suffer from low coverage of genomic regions with sequencing depths enough for accurate estimation of methylation heterogeneity using existing methods. Here we describe a probabilistic-based imputation method that makes use of methylation information from neighboring sites to recover partially observed methylation patterns. Our method and software are proven to be faster and more accurate among all evaluated. Ultimately, our method allows for a more streamlined monitoring of epigenetic changes within cellular populations and their putative role in disease.

MethylC-analyzer: a comprehensive downstream pipeline for the analysis of genome-wide DNA methylation.

DNA methylation is a crucial epigenetic modification involved in multiple biological processes and diseases. Current approaches for measuring genome-wide DNA methylation via bisulfite sequencing (BS-seq) include whole-genome bisulfite sequencing (WGBS), reduced representation bisulfite sequencing (RRBS), and enzymatic methyl-seq (EM-seq). The computational analysis tools available for BS-seq data include customized aligners for mapping bisulfite-converted reads and computational pipelines for downstream data analysis. Current post-alignment methylation tools are specialized for the interpretation of CG methylation, which is known to dominate mammalian genomes, however, non-CG methylation (CHG and CHH, where H refers to A, C, or T) is commonly observed in plants and fungi and is closely associated with gene regulation, transposon silencing, and plant development. Thus, we have developed a MethylC-analyzer to analyze and visualize post-alignment WGBS, RRBS, and EM-seq data focusing on CG. The tool is able to also analyze non-CG sites to enhance deciphering genomes of plants and fungi. By processing aligned data and gene location files, MethylC-analyzer generates a genome-wide view of methylation levels and methylation in user-specified genomic regions. The meta-plot, for example, allows the investigation of DNA methylation within specific genomic elements. Moreover, our tool identifies differentially methylated regions (DMRs) and investigates the enrichment of genomic features associated with variable methylation. MethylC-analyzer functionality is not limited to specific genomes, and we demonstrated its performance on both plant and human BS-seq data. MethylC-analyzer is a Python- and R-based program designed to perform comprehensive downstream analyses of methylation data, providing an intuitive analysis platform for scientists unfamiliar with DNA methylation analysis. It is available as either a standalone version for command-line uses or a graphical user interface (GUI) and is publicly accessible at https://github.com/RitataLU/MethylC-analyzer .

BSImp: Imputing Partially Observed Methylation Patterns for Evaluating Methylation Heterogeneity.

DNA methylation is one of the most studied epigenetic modifications that has applications ranging from transcriptional regulation to aging, and can be assessed by bisulfite sequencing (BS-seq) or enzymatic methyl sequencing (EM-seq) at single base-pair resolution. The permutations of methylation statuses given by aligned reads reflect the methylation patterns of individual cells. These patterns at specific genomic locations are sought to be indicative of cellular heterogeneity within a cellular population, which are predictive of developments and diseases; therefore, methylation heterogeneity has potentials in early detection of these changes. Computational methods have been developed to assess methylation heterogeneity using methylation patterns formed by four consecutive CpGs, but the nature of shotgun sequencing often give partially observed patterns, which makes very limited data available for downstream analysis. While many programs are developed to impute genome-wide methylation levels, currently there is only one method developed for recovering partially observed methylation patterns; however, the program needs lots of data to train and cannot be used directly; therefore, we developed a probabilistic-based imputation method that uses information from neighbouring sites to recover partially observed methylation patterns speedily. It is demonstrated to allow for the evaluation of methylation heterogeneity at 15% more regions genome-wide with high accuracy for data with moderate depth. To make it more user-friendly we also provide a computational pipeline for genome-screening, which can be used in both evaluating methylation levels and profiling methylation patterns genomewide for all cytosine contexts, which is the first of its kind. Our method allows for accurate estimation of methylation levels and makes evaluating methylation heterogeneity available for much more data with reasonable coverage, which has important implications in using methylation heterogeneity for monitoring changes within the cellular populations that were impossible to detect for the assessment of development and diseases.

Transcriptome of Nosema ceranae and Upregulated Microsporidia Genes during Its Infection of Western Honey Bee (Apis mellifera).

Nosema ceranae is one of the fungal parasites of Apis mellifera. It causes physical and behavioral effects in honey bees. However, only a few studies have reported on gene expression profiling during A. mellifera infection. In this study, the transcriptome profile of mature spores at each time point of infection (5, 10, and 20 days post-infection, d.p.i.) were investigated. Based on the transcriptome and expression profile analysis, a total of 878, 952, and 981 differentially expressed genes (DEGs) (fold change ≥ 2 or ≤ -2) were identified in N. ceranae spores (NcSp) at 5 d.p.i., 10 d.p.i., and 20 d.p.i., respectively. Moreover, 70 upregulated genes and 340 downregulated genes among common DEGs (so-called common DEGs) and 166 stage-specific genes at each stage of infection were identified. The Gene Ontology (GO) analysis indicated that the DEGs and corresponding common DEGs are involved in the functions of cytosol (GO:0005829), cytoplasm (GO:0005737), and ATP binding (GO:0005524). Furthermore, the pathway analysis found that the DEGs and common DEGs are involved in metabolism, environmental information processing, and organismal systems. Four upregulated common DEGs with higher fold-change values, highly associated with spore proteins and transcription factors, were selected for validation. In addition, the stage-specific genes are highly involved in the mechanism of pre-mRNA splicing according to GO enrichment analysis; thus, three of them showed high expression at each d.p.i. and were also subjected to validation. The relative gene expression levels showed a similar tendency as the transcriptome predictions at different d.p.i., revealing that the gene expression of N. ceranae during infection may be related to the mechanism of gene transcription, protein synthesis, and structural proteins. Our data suggest that the gene expression profiling of N. ceranae at the transcriptomic level could be a reference for the monitoring of nosemosis at the genetic level.

HISTONE DEACETYLASE 15 and MOS4-associated complex subunits 3A/3B coregulate intron retention of ABA-responsive genes.

Histone deacetylases (HDAs) play an important role in transcriptional regulation of multiple biological processes. In this study, we investigated the function of HDA15 in abscisic acid (ABA) responses. We used immunopurification coupled with mass spectrometry-based proteomics to identify proteins interacting with HDA15 in Arabidopsis (Arabidopsis thaliana). HDA15 interacted with the core subunits of the MOS4-associated complex (MAC), MAC3A and MAC3B, with interaction between HDA15 and MAC3B enhanced by ABA. hda15 and mac3a/mac3b mutants were ABA-insensitive during seed germination and hyposensitive to salinity. RNA sequencing analysis demonstrated that HDA15 and MAC3A/MAC3B co-regulate ABA-responsive intron retention (IR). Furthermore, HDA15 reduced the histone acetylation level of genomic regions near ABA-responsive IR sites and the association of MAC3B with ABA-responsive pre-mRNA was dependent on HDA15. Our results indicate that HDA15 is involved in ABA responses by interacting with MAC3A/MAC3B to mediate splicing of introns.

EED is required for mouse primordial germ cell differentiation in the embryonic gonad.

Development of primordial germ cells (PGCs) is required for reproduction. During PGC development in mammals, major epigenetic remodeling occurs, which is hypothesized to establish an epigenetic landscape for sex-specific germ cell differentiation and gametogenesis. In order to address the role of embryonic ectoderm development (EED) and histone 3 lysine 27 trimethylation (H3K27me3) in this process, we created an EED conditional knockout mouse and show that EED is essential for regulating the timing of sex-specific PGC differentiation in both ovaries and testes, as well as X chromosome dosage decompensation in testes. Integrating chromatin and whole genome bisulfite sequencing of epiblast and PGCs, we identified a poised repressive signature of H3K27me3/DNA methylation that we propose is established in the epiblast where EED and DNMT1 interact. Thus, EED joins DNMT1 in regulating the timing of sex-specific PGC differentiation during the critical window when the gonadal niche cells specialize into an ovary or testis.

Aberrant overexpression of HOTAIR inhibits abdominal adipogenesis through remodelling of genome-wide DNA methylation and transcription.

OBJECTIVE: Abdominal adiposity is strongly associated with diabetic and cardiovascular comorbidities. The long noncoding RNA HOTAIR (HOX Transcript Antisense Intergenic RNA) is an important epigenetic regulator with fat depot-specific expression. Its functional roles and epigenetic regulation in abdominal adipogenesis remain uncertain. METHODS: We collected different fat depots from healthy, severely obese, and uraemic subjects to measure fat-depot specific gene expression and quantify regional adiposity via dual-energy X-ray absorptiometry (DXA). HOTAIR was overexpressed to evaluate its functional roles. Reduced representation bisulfite sequencing (RRBS), RNA-sequencing, real-time qPCR and RNA/chromatin immunoprecipitation were performed to analyse HOTAIR-mediated epigenetic regulation. RESULTS: A negative correlation between adipose tissue HOTAIR expression (arm or abdominal subcutaneous fat depots) and regional adiposity under the status of severe obesity or uraemia was observed. HOTAIR overexpression using human immortalized abdominal preadipocytes further revealed its anti-adipogenic effects. Integrative analysis of genome-wide DNA methylation by reduced representation bisulfite sequencing (RRBS) and gene expression was performed. Overall, the differentially methylated genes were functionally enriched for nervous system development, suggesting that HOTAIR may be epigenetically associated with cell lineage commitment. We specifically found that HOTAIR-mediated genes showed strong changes in both DNA methylation and gene expression during abdominal adipogenesis. We observed that two HOTAIR-repressed genes, SLITRK4 and PITPNC1, present an obesity-driven fat-depot specific expression pattern that is positively correlated with the central body fat distribution. CONCLUSIONS: Our study indicated that HOTAIR is a key regulator of abdominal adipogenesis via intricate DNA methylation and is likely to be associated with the transcriptional regulation of genes involved in nervous system development and lipid metabolism, such as SLITRK4 and PITPNC1.

Transcriptome-level assessment of the impact of deformed wing virus on honey bee larvae.

Deformed wing virus (DWV) prevalence is high in honey bee (Apis mellifera) populations. The virus infects honey bees through vertical and horizontal transmission, leading to behavioural changes, wing deformity, and early mortality. To better understand the impacts of viral infection in the larval stage of honey bees, artificially reared honey bee larvae were infected with DWV (1.55 × 1010 copies/per larva). No significant mortality occurred in infected honey bee larvae, while the survival rates decreased significantly at the pupal stage. Examination of DWV replication revealed that viral replication began at 2 days post inoculation (d.p.i.), increased dramatically to 4 d.p.i., and then continuously increased in the pupal stage. To better understand the impact of DWV on the larval stage, DWV-infected and control groups were subjected to transcriptomic analysis at 4 d.p.i. Two hundred fifty-five differentially expressed genes (DEGs) (fold change ≥ 2 or ≤ -2) were identified. Of these DEGs, 168 genes were downregulated, and 87 genes were upregulated. Gene Ontology (GO) analysis showed that 141 DEGs (55.3%) were categorized into molecular functions, cellular components and biological processes. One hundred eleven genes (38 upregulated and 73 downregulated) were annotated by KO (KEGG Orthology) pathway mapping and involved metabolic pathways, biosynthesis of secondary metabolites and glycine, serine and threonine metabolism pathways. Validation of DEGs was performed, and the related gene expression levels showed a similar tendency to the DEG predictions at 4 d.p.i.; cell wall integrity and stress response component 1 (wsc1), cuticular protein and myo-inositol 2-dehydrogenase (iolG) were significantly upregulated, and small conductance calcium-activated potassium channel protein (SK) was significantly downregulated at 4 d.p.i. Related gene expression levels at different d.p.i. revealed that these DEGs were significantly regulated from the larval stage to the pupal stage, indicating the potential impacts of gene expression levels from the larval to the pupal stages. Taken together, DWV infection in the honey bee larval stage potentially influences the gene expression levels from larvae to pupae and reduces the survival rate of the pupal stage. This information emphasizes the consequences of DWV prevalence in honey bee larvae for apiculture.

MethGET: web-based bioinformatics software for correlating genome-wide DNA methylation and gene expression.

BACKGROUND: DNA methylation is a major epigenetic modification involved in regulating gene expression. The effects of DNA methylation on gene expression differ by genomic location and vary across kingdoms, species and environmental conditions. To identify the functional regulatory roles of DNA methylation, the correlation between DNA methylation changes and alterations in gene expression is crucial. With the advance of next-generation sequencing, genome-wide methylation and gene expression profiling have become feasible. Current bioinformatics tools for investigating such correlation are designed to the assessment of DNA methylation at CG sites. The correlation of non-CG methylation and gene expression is very limited. Some bioinformatics databases allow correlation analysis, but they are limited to specific genomes such as that of humans and do not allow user-provided data. RESULTS: Here, we developed a bioinformatics web tool, MethGET (Methylation and Gene Expression Teller), that is specialized to analyse the association between genome-wide DNA methylation and gene expression. MethGET is the first web tool to which users can supply their own data from any genome. It is also the tool that correlates gene expression with CG, CHG, and CHH methylation based on whole-genome bisulfite sequencing data. MethGET not only reveals the correlation within an individual sample (single-methylome) but also performs comparisons between two groups of samples (multiple-methylomes). For single-methylome analyses, MethGET provides Pearson correlations and ordinal associations to investigate the relationship between DNA methylation and gene expression. It also groups genes with different gene expression levels to view the methylation distribution at specific genomic regions. Multiple-methylome analyses include comparative analyses and heatmap representations between two groups. These functions enable the detailed investigation of the role of DNA methylation in gene regulation. Additionally, we applied MethGET to rice regeneration data and discovered that CHH methylation in the gene body region may play a role in the tissue culture process, which demonstrates the capability of MethGET for use in epigenomic research. CONCLUSIONS: MethGET is a Python software that correlates DNA methylation and gene expression. Its web interface is publicly available at https://paoyang.ipmb.sinica.edu.tw/Software.html. The stand-alone version and source codes are available on GitHub at https://github.com/Jason-Teng/MethGET.

Epigenomic regulation of OTU5 in Arabidopsis thaliana.

Epigenetic regulation by DNA methylation and histone marks is crucial to plant development. In Arabidopsis, the otu5 mutant exhibited altered root phenotypes resembling those of phosphate-deficient plants. In low phosphate (Pi) conditions, altered H3K4 and H3K27 trimethylation were associated with the expression of Pi homeostasis-related genes. However, the genetic effect of OTU5 on the epigenomes was left unexplored. We assessed genome-wide DNA methylation, gene expression and histone modifications of roots from both Col-0 and otu5 mutants. We found that OTU5 altered DNA methylation profile with a context-specific effect through targeting local genomic regions. Our analysis showed that in otu5 the abundance of H3K4me3 was clearly associated with the changes of DNA methylation, leading to the transcriptional difference from wildtype. We concluded that OTU5 induced cross-talks among epigenomes that altogether impacted the regulation of approximately 7060 genes. Of which 186 genes associated with root development were likely to be epigenetically regulated.

Screening of Differentially Expressed Microsporidia Genes from Nosema ceranae Infected Honey Bees by Suppression Subtractive Hybridization.

The microsporidium Nosema ceranae is a high prevalent parasite of the European honey bee (Apis mellifera). This parasite is spreading across the world into its novel host. The developmental process, and some mechanisms of N. ceranae-infected honey bees, has been studied thoroughly; however, few studies have been carried out in the mechanism of gene expression in N. ceranae during the infection process. We therefore performed the suppressive subtractive hybridization (SSH) approach to investigate the candidate genes of N. ceranae during its infection process. All 96 clones of infected (forward) and non-infected (reverse) library were dipped onto the membrane for hybridization. A total of 112 differentially expressed sequence tags (ESTs) had been sequenced. For the host responses, 20% of ESTs (13 ESTs, 10 genes, and 1 non-coding RNA) from the forward library and 93.6% of ESTs (44 ESTs, 28 genes) from the reverse library were identified as differentially expressed genes (DEGs) of the hosts. A high percentage of DEGs involved in catalytic activity and metabolic processes revealed that the host gene expression change after N. ceranae infection might lead to an unbalance of physiological mechanism. Among the ESTs from the forward library, 75.4% ESTs (49 ESTs belonged to 24 genes) were identified as N. ceranae genes. Out of 24 N. ceranae genes, nine DEGs were subject to real-time quantitative reverse transcription PCR (real-time qRT-PCR) for validation. The results indicated that these genes were highly expressed during N. ceranae infection. Among nine N. ceranae genes, one N. ceranae gene (AAJ76_1600052943) showed the highest expression level after infection. These identified differentially expressed genes from this SSH could provide information about the pathological effects of N. ceranae. Validation of nine up-regulated N. ceranae genes reveal high potential for the detection of early nosemosis in the field and provide insight for further applications.

The expression of long non-coding RNAs is associated with H3Ac and H3K4me2 changes regulated by the HDA6-LDL1/2 histone modification complex in Arabidopsis.

In recent years, eukaryotic long non-coding RNAs (lncRNAs) have been identified as important factors involved in a wide variety of biological processes, including histone modification, alternative splicing and transcription enhancement. The expression of lncRNAs is highly tissue-specific and is regulated by environmental stresses. Recently, a large number of plant lncRNAs have been identified, but very few of them have been studied in detail. Furthermore, the mechanism of lncRNA expression regulation remains largely unknown. Arabidopsis HISTONE DEACETYLASE 6 (HDA6) and LSD1-LIKE 1/2 (LDL1/2) can repress gene expression synergistically by regulating H3Ac/H3K4me. In this research, we performed RNA-seq and ChIP-seq analyses to further clarify the function of HDA6-LDL1/2. Our results indicated that the global expression of lncRNAs is increased in hda6/ldl1/2 and that this increased lncRNA expression is particularly associated with H3Ac/H3K4me2 changes. In addition, we found that HDA6-LDL1/2 is important for repressing lncRNAs that are non-expressed or show low-expression, which may be strongly associated with plant development. GO-enrichment analysis also revealed that the neighboring genes of the lncRNAs that are upregulated in hda6/ldl1/2 are associated with various developmental processes. Collectively, our results revealed that the expression of lncRNAs is associated with H3Ac/H3K4me2 changes regulated by the HDA6-LDL1/2 histone modification complex.

ATACgraph: Profiling Genome-Wide Chromatin Accessibility From ATAC-seq.

Assay for transposase-accessible chromatin using sequencing data (ATAC-seq) is an efficient and precise method for revealing chromatin accessibility across the genome. Most of the current ATAC-seq tools follow chromatin immunoprecipitation sequencing (ChIP-seq) strategies that do not consider ATAC-seq-specific properties. To incorporate specific ATAC-seq quality control and the underlying biology of chromatin accessibility, we developed a bioinformatics software named ATACgraph for analyzing and visualizing ATAC-seq data. ATACgraph profiles accessible chromatin regions and provides ATAC-seq-specific information including definitions of nucleosome-free regions (NFRs) and nucleosome-occupied regions. ATACgraph also allows identification of differentially accessible regions between two ATAC-seq datasets. ATACgraph incorporates the docker image with the Galaxy platform to provide an intuitive user experience via the graphical interface. Without tedious installation processes on a local machine or cloud, users can analyze data through activated websites using pre-designed workflows or customized pipelines composed of ATACgraph modules. Overall, ATACgraph is an effective tool designed for ATAC-seq for biologists with minimal bioinformatics knowledge to analyze chromatin accessibility. ATACgraph can be run on any ATAC-seq data with no limit to specific genomes. As validation, we demonstrated ATACgraph on human genome to showcase its functions for ATAC-seq interpretation. This software is publicly accessible and can be downloaded at https://github.com/RitataLU/ATACgraph.

Diversity of Fungal DNA Methyltransferases and Their Association With DNA Methylation Patterns.

DNA methyltransferases (DNMTs) are a group of proteins that catalyze DNA methylation by transferring a methyl group to DNA. The genetic variation in DNMTs results in differential DNA methylation patterns associated with various biological processes. In fungal species, DNMTs and their DNA methylation profiles were found to be very diverse and have gained many research interests. We reviewed fungal DNMTs in terms of their biological functions, protein domain structures, and their associated epigenetic regulations compared to those known in plant and animal systems. In addition, we summarized recent reports on potential RNA-directed DNA methylation (RdDM) related to DNMT5 in fungi. We surveyed up to 40 fungal species with published genome-wide DNA methylation profiles (methylomes) and presented the associations between the specific patterns of fungal DNA methylation and their DNMTs based on a phylogenetic tree of protein domain structures. For example, the main DNMTs in Basidiomycota, DNMT1 with RFD domain + DNMT5, contributing to CG methylation preference, were distinct from RID + Dim-2 in Ascomycota, resulting in a non-CG methylation preference. Lastly, we revealed that the dynamic methylation involved in fungal life stage changes was particularly low in mycelium and DNA methylation was preferentially located in transposable elements (TEs). This review comprehensively discussed fungal DNMTs and methylomes and their connection with fungal development and taxonomy to present the diverse usages of DNA methylation in fungal genomes.

SWI3B and HDA6 interact and are required for transposon silencing in Arabidopsis.

Although the interplay of covalent histone acetylation/deacetylation and ATP-dependent chromatin remodelling is crucial for the regulation of chromatin structure and gene expression in eukaryotes, the underlying molecular mechanism in plants remains largely unclear. Here we show a direct interaction between Arabidopsis SWI3B, an essential subunit of the SWI/SNF chromatin-remodelling complex, and the RPD3/HDA1-type histone deacetylase HDA6 both in vitro and in vivo. Furthermore, SWI3B and HDA6 co-repress the transcription of a subset of transposons. Both SWI3B and HDA6 maintain transposon silencing by decreasing histone H3 lysine 9 acetylation, but increasing histone H3 lysine 9 di-methylation, DNA methylation and nucleosome occupancy. Our findings reveal that SWI3B and HDA6 may act in the same co-repressor complex to maintain transposon silencing in Arabidopsis.

Detecting and prioritizing biosynthetic gene clusters for bioactive compounds in bacteria and fungi.

Secondary metabolites (SM) produced by fungi and bacteria have long been of exceptional interest owing to their unique biomedical ramifications. The traditional discovery of new natural products that was mainly driven by bioactivity screening has now experienced a fresh new approach in the form of genome mining. Several bioinformatics tools have been continuously developed to detect potential biosynthetic gene clusters (BGCs) that are responsible for the production of SM. Although the principles underlying the computation of these tools have been discussed, the biological background is left underrated and ambiguous. In this review, we emphasize the biological hypotheses in BGC formation driven from the observations across genomes in bacteria and fungi, and provide a comprehensive list of updated algorithms/tools exclusively for BGC detection. Our review points to a direction that the biological hypotheses should be systematically incorporated into the BGC prediction and assist the prioritization of candidate BGC.

Global impacts of chromosomal imbalance on gene expression in Arabidopsis and other taxa.

Changes in dosage of part of the genome (aneuploidy) have long been known to produce much more severe phenotypic consequences than changes in the number of whole genomes (ploidy). To examine the basis of these differences, global gene expression in mature leaf tissue for all five trisomies and in diploids, triploids, and tetraploids of Arabidopsis thaliana was studied. The trisomies displayed a greater spread of expression modulation than the ploidy series. In general, expression of genes on the varied chromosome ranged from compensation to dosage effect, whereas genes from the remainder of the genome ranged from no effect to reduced expression approaching the inverse level of chromosomal imbalance (2/3). Genome-wide DNA methylation was examined in each genotype and found to shift most prominently with trisomy 4 but otherwise exhibited little change, indicating that genetic imbalance is generally mechanistically unrelated to DNA methylation. Independent analysis of gene functional classes demonstrated that ribosomal, proteasomal, and gene body methylated genes were less modulated compared with all classes of genes, whereas transcription factors, signal transduction components, and organelle-targeted protein genes were more tightly inversely affected. Comparing transcription factors and their targets in the trisomies and in expression networks revealed considerable discordance, illustrating that altered regulatory stoichiometry is a major contributor to genetic imbalance. Reanalysis of published data on gene expression in disomic yeast and trisomic mouse cells detected similar stoichiometric effects across broad phylogenetic taxa, and indicated that these effects reflect normal gene regulatory processes.

Computational Methods for Assessing Chromatin Hierarchy.

The hierarchical organization of chromatin is known to associate with diverse cellular functions; however, the precise mechanisms and the 3D structure remain to be determined. With recent advances in high-throughput next generation sequencing (NGS) techniques, genome-wide profiling of chromatin structures is made possible. Here, we provide a comprehensive overview of NGS-based methods for profiling "higher-order" and "primary-order" chromatin structures from both experimental and computational aspects. Experimental requirements and considerations specific for each method were highlighted. For computational analysis, we summarized a common analysis strategy for both levels of chromatin assessment, focusing on the characteristic computing steps and the tools. The recently developed single-cell level techniques based on Hi-C and ATAC-seq present great potential to reveal cell-to-cell variability in chromosome architecture. A brief discussion on these methods in terms of experimental and data analysis features is included. We also touch upon the biological relevance of chromatin organization and how the combination with other techniques uncovers the underlying mechanisms. We conclude with a summary and our prospects on necessary improvements of currently available methods in order to advance understanding of chromatin hierarchy. Our review brings together the analyses of both higher- and primary-order chromatin structures, and serves as a roadmap when choosing appropriate experimental and computational methods for assessing chromatin hierarchy.