NucMap: a database of genome-wide nucleosome positioning map across species

Adenovirus small E1A directs activation of Alu transcription at YAP/TEAD- and AP-1-bound enhancers through interactions with the EP400 chromatin remodeler

Alu retrotransposons, which form the largest family of mobile DNA elements in the human genome, have recently come to attention as a potential source of regulatory novelties, most notably by participating in enhancer function. Even though Alu transcription by RNA polymerase III is subjected to tight epigenetic silencing, their expression has long been known to increase in response to various types of stress, including viral infection. Here we show that, in primary human fibroblasts, adenovirus small e1a triggered derepression of hundreds of individual Alus by promoting TFIIIB recruitment by Alu-bound TFIIIC. Epigenome profiling revealed an e1a-induced decrease of H3K27 acetylation and increase of H3K4 monomethylation at derepressed Alus, making them resemble poised enhancers. The enhancer nature of e1a-targeted Alus was confirmed by the enrichment, in their upstream regions, of the EP300/CBP acetyltransferase, EP400 chromatin remodeler and YAP1 and FOS transcription factors. The physical interaction of e1a with EP400 was critical for Alu derepression, which was abrogated upon EP400 ablation. Our data suggest that e1a targets a subset of enhancer Alus whose transcriptional activation, which requires EP400 and is mediated by the e1a-EP400 interaction, may participate in the manipulation of enhancer activity by adenoviruses.

NucMap 2.0: An Updated Database of Genome-wide Nucleosome Positioning Maps Across Species

Nucleosome dynamics plays important roles in many biological processes, such as DNA replication and gene expression. NucMap (https://ngdc.cncb.ac.cn/nucmap) is the first database of genome-wide nucleosome positioning maps across species. Here, we present an updated version, NucMap 2.0, by incorporating more species and MNase-seq samples. In addition, we integrate other related omics data for each MNase-seq sample to provide a comprehensive view of nucleosome positioning, such as gene expression, transcription factor binding sites, histone modifications and DNA methylation. In particular, NucMap 2.0 integrates and pre-analyzes RNA-seq data and ChIP-seq data of human-related samples, which facilitates the interpretation of nucleosome positioning in humans. All processed data are integrated into an in-built genome browser, and users can make comprehensive side-by-side analyses. In addition, more online analytical functions are developed, which allows researchers to identify differential nucleosome regions and explore potential gene regulatory regions. All resources are open access with a user-friendly web interface.

Shared genetic links between hypothyroidism and psychiatric disorders: evidence from a comprehensive genetic analysis

Background

Epidemiologic studies have suggested co-morbidity between hypothyroidism and psychiatric disorders. However, the shared genetic etiology and causal relationship between them remain currently unclear.

Methods

We assessed the genetic correlations between hypothyroidism and psychiatric disorders [anxiety disorders (ANX), schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BIP)] using summary association statistics from genome-wide association studies (GWAS). Two disease-associated pleiotropic risk loci and genes were identified, and pathway enrichment, tissue enrichment, and other analyses were performed to determine their specific functions. Furthermore, we explored the causal relationship between them through Mendelian randomization (MR) analysis.

Results

We found significant genetic correlations between hypothyroidism with ANX, SCZ, and MDD, both in the Linkage disequilibrium score regression (LDSC) approach and the high-definition likelihood (HDL) approach. Meanwhile, the strongest correlation was observed between hypothyroidism and MDD (LDSC: rg=0.264, P=7.35×10-12; HDL: rg=0.304, P=4.14×10-17). We also determined a significant genetic correlation between MDD with free thyroxine (FT4) and thyroid-stimulating hormone (TSH) levels. A total of 30 pleiotropic risk loci were identified between hypothyroidism and psychiatric disorders, of which the 15q14 locus was identified in both ANX and SCZ (P values are 6.59×10-11 and 2.10×10-12, respectively) and the 6p22.1 locus was identified in both MDD and SCZ (P values are 1.05×10-8 and 5.75×10-14, respectively). Sixteen pleiotropic risk loci were identified between MDD and indicators of thyroid function, of which, four loci associated with MDD (1p32.3, 6p22.1, 10q21.1, 11q13.4) were identified in both FT4 normal level and Hypothyroidism. Further, 79 pleiotropic genes were identified using Magma gene analysis (P<0.05/18776 = 2.66×10-6). Tissue-specific enrichment analysis revealed that these genes were highly enriched into six brain-related tissues. The pathway analysis mainly involved nucleosome assembly and lipoprotein particles. Finally, our two-sample MR analysis showed a significant causal effect of MDD on the increased risk of hypothyroidism, and BIP may reduce TSH normal levels.

Conclusions

Our findings not only provided evidence of a shared genetic etiology between hypothyroidism and psychiatric disorders, but also provided insights into the causal relationships and biological mechanisms that underlie their relationship. These findings contribute to a better understanding of the pleiotropy between hypothyroidism and psychiatric disorders, while having important implications for intervention and treatment goals for these disorders.

Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2024

The National Genomics Data Center (NGDC), which is a part of the China National Center for Bioinformation (CNCB), provides a family of database resources to support the global academic and industrial communities. With the rapid accumulation of multi-omics data at an unprecedented pace, CNCB-NGDC continuously expands and updates core database resources through big data archiving, integrative analysis and value-added curation. Importantly, NGDC collaborates closely with major international databases and initiatives to ensure seamless data exchange and interoperability. Over the past year, significant efforts have been dedicated to integrating diverse omics data, synthesizing expanding knowledge, developing new resources, and upgrading major existing resources. Particularly, several database resources are newly developed for the biodiversity of protists (P10K), bacteria (NTM-DB, MPA) as well as plant (PPGR, SoyOmics, PlantPan) and disease/trait association (CROST, HervD Atlas, HALL, MACdb, BioKA, BioKA, RePoS, PGG.SV, NAFLDkb). All the resources and services are publicly accessible at https://ngdc.cncb.ac.cn.

Plasma cell-free DNA 5-hydroxymethylcytosine and whole-genome sequencing signatures for early detection of esophageal cancer

Esophageal cancer is a highly incidence and deadly disease with a poor prognosis, especially in developing countries. Owing to the lack of specific symptoms and early diagnostic biomarkers, most patients are diagnosed with advanced disease, leading to a 5-year survival rate of less than 15%. Early (n = 50) and middle-advanced (n = 50) esophageal squamous cell carcinoma (ESCC) patients, as well as 71 healthy individuals, underwent 5-hydroxymethylcytosine (5hmC) sequencing on their plasma cell-free DNA (cfDNA). A Northern Chinese cohort of cfDNA 5hmC dataset of 150 ESCC patients and 183 healthy individuals were downloaded for validation. A diagnostic model was developed using cfDNA 5hmC signatures and then improved by low-pass whole genome sequencing (WGS) features of cfDNA. Conserved cfDNA 5hmC modification motifs were observed in the two independent ESCC cohorts. The diagnostic model with 5hmC features achieved an AUC of 0.810 and 0.862 in the Southern and Northern cohorts, respectively, with sensitivities of 69.3-74.3% and specificities of 82.4-90.7%. The performance was well maintained in Stage I to Stage IV, with accuracy of 70-100%, but low in Stage 0, 33.3%. Low-pass WGS of cfDNA improved the AUC to 0.934 with a sensitivity of 82.4%, a specificity of 88.2%, and an accuracy of 84.3%, particularly significantly in Stage 0, with an accuracy up to 80%. 5hmC and WGS could efficiently differentiate very early ESCC from healthy individuals. These findings imply a non-invasive and convenient method for ESCC detection when clinical treatments are available and may eventually prolong survival.

DNA methylation analysis explores the molecular basis of plasma cell-free DNA fragmentation

Plasma cell-free DNA (cfDNA) are small molecules generated through a non-random fragmentation procedure. Despite commendable translational values in cancer liquid biopsy, however, the biology of cfDNA, especially the principles of cfDNA fragmentation, remains largely elusive. Through orientation-aware analyses of cfDNA fragmentation patterns against the nucleosome structure and integration with multidimensional functional genomics data, here we report a DNA methylation - nuclease preference - cutting end - size distribution axis, demonstrating the role of DNA methylation as a functional molecular regulator of cfDNA fragmentation. Hence, low-level DNA methylation could increase nucleosome accessibility and alter the cutting activities of nucleases during DNA fragmentation, which further leads to variation in cutting sites and size distribution of cfDNA. We further develop a cfDNA ending preference-based metric for cancer diagnosis, whose performance has been validated by multiple pan-cancer datasets. Our work sheds light on the molecular basis of cfDNA fragmentation towards broader applications in cancer liquid biopsy.

Gene co-expression network analysis revealed novel biomarkers for ovarian cancer

Ovarian cancer is the second most common gynecologic cancer and remains the leading cause of death of all gynecologic oncologic disease. Therefore, understanding the molecular mechanisms underlying the disease, and the identification of effective and predictive biomarkers are invaluable for the development of diagnostic and treatment strategies. In the present study, a differential co-expression network analysis was performed via meta-analysis of three transcriptome datasets of serous ovarian adenocarcinoma to identify novel candidate biomarker signatures, i.e. genes and miRNAs. We identified 439 common differentially expressed genes (DEGs), and reconstructed differential co-expression networks using common DEGs and considering two conditions, i.e. healthy ovarian surface epithelia samples and serous ovarian adenocarcinoma epithelia samples. The modular analyses of the constructed networks indicated a co-expressed gene module consisting of 17 genes. A total of 11 biomarker candidates were determined through receiver operating characteristic (ROC) curves of gene expression of module genes, and miRNAs targeting these genes were identified. As a result, six genes (CDT1, CNIH4, CRLS1, LIMCH1, POC1A, and SNX13), and two miRNAs (mir-147a, and mir-103a-3p) were suggested as novel candidate prognostic biomarkers for ovarian cancer. Further experimental and clinical validation of the proposed biomarkers could help future development of potential diagnostic and therapeutic innovations in ovarian cancer.

Is the mutation rate lower in genomic regions of stronger selective constraints?

A study of the plant Arabidopsis thaliana detected lower mutation rates in genomic regions where mutations are more likely to be deleterious, challenging the principle that mutagenesis is blind to its consequence. To examine the generality of this finding, we analyze large mutational data from baker's yeast and humans. The yeast data do not exhibit this trend, whereas the human data show an opposite trend that disappears upon the control of potential confounders. We find that the Arabidopsis study identified substantially more mutations than reported in the original data-generating studies and expected from Arabidopsis' mutation rate. These extra mutations are enriched in polynucleotide tracts and have relatively low sequencing qualities so are likely sequencing errors. Furthermore, the polynucleotide “mutations” can produce the purported mutational trend in Arabidopsis. Together, our results do not support lower mutagenesis of genomic regions of stronger selective constraints in the plant, fungal, and animal models examined.

Nucleosome-Omics: A Perspective on the Epigenetic Code and 3D Genome Landscape

Genetic information is loaded on chromatin, which involves DNA sequence arrangement and the epigenetic landscape. The epigenetic information including DNA methylation, nucleosome positioning, histone modification, 3D chromatin conformation, and so on, has a crucial impact on gene transcriptional regulation. Out of them, nucleosomes, as basal chromatin structural units, play an important central role in epigenetic code. With the discovery of nucleosomes, various nucleosome-level technologies have been developed and applied, pushing epigenetics to a new climax. As the underlying methodology, next-generation sequencing technology has emerged and allowed scientists to understand the epigenetic landscape at a genome-wide level. Combining with NGS, nucleosome-omics (or nucleosomics) provides a fresh perspective on the epigenetic code and 3D genome landscape. Here, we summarized and discussed research progress in technology development and application of nucleosome-omics. We foresee the future directions of epigenetic development at the nucleosome level.

Inter-nucleosomal potentials from nucleosomal positioning data

No systematic method exists to derive inter-nucleosomal potentials between nucleosomes along a chromosome consistently across a given genome. Such potentials can yield information on nucleosomal ordering, thermal as well as mechanical properties of chromosomes. Thus, indirectly, they shed light on a possible mechanical genomic code along a chromosome. To develop a method yielding effective inter-nucleosomal potentials between nucleosomes, a generalized Lennard-Jones potential for the parameterization is developed based on nucleosomal positioning data. This approach eliminates some of the problems that the underlying nucleosomal positioning data have, rendering the extraction difficult on the individual nucleosomal level. Furthermore, patterns on which to base a classification along a chromosome appear on larger domains, such as hetero- and euchromatin. An intuitive selection strategy for the noisy optimization problem is employed to derive effective exponents for the generalized potential. The method is tested on the Candida albicans genome. Applying k-means clustering based on potential parameters and thermodynamic compressibilities, a genome-wide clustering of nucleosome sequences is obtained for C. albicans. This clustering shows that a chromosome beyond the classical dichotomic categories of hetero- and euchromatin is more feature-rich.

Superstructure Detection in Nucleosome Distribution Shows Common Pattern within a Chromosome and within the Genome

Nucleosome positioning plays an important role in crucial biological processes such as replication, transcription, and gene regulation. It has been widely used to predict the genome’s function and chromatin organisation. So far, the studies of patterns in nucleosome positioning have been limited to transcription start sites, CTCFs binding sites, and some promoter and loci regions. The genome-wide organisational pattern remains unknown. We have developed a theoretical model to coarse-grain nucleosome positioning data in order to obtain patterns in their distribution. Using hierarchical clustering on the auto-correlation function of this coarse-grained nucleosome positioning data, a genome-wide clustering is obtained for Candida albicans. The clustering shows the existence beyond hetero- and eu-chromatin inside the chromosomes. These non-trivial clusterings correspond to different nucleosome distributions and gene densities governing differential gene expression patterns. Moreover, these distribution patterns inside the chromosome appeared to be conserved throughout the genome and within species. The pipeline of the coarse grain nucleosome positioning sequence to identify underlying genomic organisation used in our study is novel, and the classifications obtained are unique and consistent.

Prediction and comparative analysis of CTCF binding sites based on a first principle approach

We calculated the patterns for the CCCTC transcription factor (CTCF) binding sites across many genomes on a first principle approach. The validation of the first principle method was done on the human as well as on the mouse genome. The predicted human CTCF binding sites are consistent with the consensus sequence, ChIP-seq data for the K562 cell, nucleosome positions for IMR90 cell as well as the CTCF binding sites in the mouse HOXA gene. The analysis of Homo sapiens, Mus musculus, Sus scrofa, Capra hircus and Drosophila melanogaster whole genomes shows: binding sites are organized in cluster-like groups, where two consecutive sites obey a power-law with coefficient ranging from to 0.3292 0.0068 to 0.5409 0.0064; the distance between these groups varies from 18.08 0.52kbp to 42.1 2.0kbp. The genome of Aedes aegypti does not show a power law, but 19.9% of binding sites are 144 4 and 287 5bp distant of each other. We run negative tests, confirming the under-representation of CTCF binding sites in Caenorhabditis elegans, Plasmodium falciparum and Arabidopsis thaliana complete genomes.

Gene Expression Nebulas (GEN): a comprehensive data portal integrating transcriptomic profiles across multiple species at both bulk and single-cell levels

Transcriptomic profiling is critical to uncovering functional elements from transcriptional and post-transcriptional aspects. Here, we present Gene Expression Nebulas (GEN, https://ngdc.cncb.ac.cn/gen/), an open-access data portal integrating transcriptomic profiles under various biological contexts. GEN features a curated collection of high-quality bulk and single-cell RNA sequencing datasets by using standardized data processing pipelines and a structured curation model. Currently, GEN houses a large number of gene expression profiles from 323 datasets (157 bulk and 166 single-cell), covering 50 500 samples and 15 540 169 cells across 30 species, which are further categorized into six biological contexts. Moreover, GEN integrates a full range of transcriptomic profiles on expression, RNA editing and alternative splicing for 10 bulk datasets, providing opportunities for users to conduct integrative analysis at both transcriptional and post-transcriptional levels. In addition, GEN provides abundant gene annotations based on value-added curation of transcriptomic profiles and delivers online services for data analysis and visualization. Collectively, GEN presents a comprehensive collection of transcriptomic profiles across multiple species, thus serving as a fundamental resource for better understanding genetic regulatory architecture and functional mechanisms from tissues to cells.

A set of accessible enhancers enables the initial response of breast cancer cells to physiological progestin concentrations

Here, we report that in T47D breast cancer cells 50 pM progestin is sufficient to activate cell cycle entry and the progesterone gene expression program. At this concentration, equivalent to the progesterone blood levels found around the menopause, progesterone receptor (PR) binds only to 2800 genomic sites, which are accessible to ATAC cleavage prior to hormone exposure. These highly accessible sites (HAs) are surrounded by well-organized nucleosomes and exhibit breast enhancer features, including estrogen receptor alpha (ERα), higher FOXA1 and BRD4 (bromodomain containing 4) occupancy. Although HAs are enriched in RAD21 and CTCF, PR binding is the driving force for the most robust interactions with hormone-regulated genes. HAs show higher frequency of 3D contacts among themselves than with other PR binding sites, indicating colocalization in similar compartments. Gene regulation via HAs is independent of classical coregulators and ATP-activated remodelers, relying mainly on MAP kinase activation that enables PR nuclear engagement. HAs are also preferentially occupied by PR and ERα in breast cancer xenografts derived from MCF-7 cells as well as from patients, indicating their potential usefulness as targets for therapeutic intervention.

iNucs: Inter-Nucleosome Interactions

MOTIVATION

Deciphering nucleosome-nucleosome interactions is an important step towards mesoscale description of chromatin organization but computational tools to perform such analyses are not publicly available.

RESULTS

We developed iNucs, a user-friendly and efficient Python-based bioinformatics tool to compute and visualize nucleosome resolved interactions using standard pairs format input generated from pairtools.

AVAILABILITY

https://github.com/Karimi-Lab/inucs/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

NUCOME: A comprehensive database of nucleosome organization referenced landscapes in mammalian genomes

Background

Nucleosome organization is involved in many regulatory activities in various organisms. However, studies integrating nucleosome organization in mammalian genomes are very limited mainly due to the lack of comprehensive data quality control (QC) assessment and uneven data quality of public data sets.

Results

The NUCOME is a database focused on filtering qualified nucleosome organization referenced landscapes covering various cell types in human and mouse based on QC metrics. The filtering strategy guarantees the quality of nucleosome organization referenced landscapes and exempts users from redundant data set selection and processing. The NUCOME database provides standardized, qualified data source and informative nucleosome organization features at a whole-genome scale and on the level of individual loci.

Conclusions

The NUCOME provides valuable data resources for integrative analyses focus on nucleosome organization. The NUCOME is freely available at http://compbio-zhanglab.org/NUCOME.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04239-9.

TP53 Targeted Deep Sequencing of Cell-Free DNA in Esophageal Squamous Cell Carcinoma Using Low-Quality Serum: Concordance with Tumor Mutation

Circulating cell-free DNA (cfDNA) is emerging as a potential tumor biomarker. CfDNA-based biomarkers may be applicable in tumors without an available non-invasive screening method among at-risk populations. Esophageal squamous cell carcinoma (ESCC) and residents of the Asian cancer belt are examples of those malignancies and populations. Previous epidemiological studies using cfDNA have pointed to the need for high volumes of good quality plasma (i.e., >1 mL plasma with 0 or 1 cycles of freeze-thaw) rather than archival serum, which is often the main available source of cfDNA in retrospective studies. Here, we have investigated the concordance of TP53 mutations in tumor tissue and cfDNA extracted from archival serum left-over from 42 cases and 39 matched controls (age, gender, residence) in a high-risk area of Northern Iran (Golestan). Deep sequencing of TP53 coding regions was complemented with a specialized variant caller (Needlestack). Overall, 23% to 31% of mutations were concordantly detected in tumor and serum cfDNA (based on two false discovery rate thresholds). Concordance was positively correlated with high cfDNA concentration, smoking history (p-value = 0.02) and mutations with a high potential of neoantigen formation (OR; 95%CI = 1.9 (1.11-3.29)), suggesting that tumor DNA release in the bloodstream might reflect the effects of immune and inflammatory context on tumor cell turnover. We identified TP53 mutations in five controls, one of whom was subsequently diagnosed with ESCC. Overall, the results showed that cfDNA mutations can be reliably identified by deep sequencing of archival serum, with a rate of success comparable to plasma. Nonetheless, 70% non-identifiable mutations among cancer patients and 12% mutation detection in controls are the main challenges in applying cfDNA to detect tumor-related variants when blindly targeting whole coding regions of the TP53 gene in ESCC.

ΔNp63 is a pioneer factor that binds inaccessible chromatin and elicits chromatin remodeling

BACKGROUND

ΔNp63 is a master transcriptional regulator playing critical roles in epidermal development and other cellular processes. Recent studies suggest that ΔNp63 functions as a pioneer factor that can target its binding sites within inaccessible chromatin and induce chromatin remodeling.

METHODS

In order to examine if ΔNp63 can bind to inaccessible chromatin and to determine if specific histone modifications are required for binding, we induced ΔNp63 expression in two p63-naïve cell lines. ΔNp63 binding was then examined by ChIP-seq and the chromatin at ΔNp63 targets sites was examined before and after binding. Further analysis with competitive nucleosome binding assays was used to determine how ΔNp63 directly interacts with nucleosomes.

RESULTS

Our results show that before ΔNp63 binding, targeted sites lack histone modifications, indicating ΔNp63's capability to bind at unmodified chromatin. Moreover, the majority of the sites that are bound by ectopic ΔNp63 expression exist in an inaccessible state. Once bound, ΔNp63 induces acetylation of the histone and the repositioning of nucleosomes at its binding sites. Further analysis with competitive nucleosome binding assays reveal that ΔNp63 can bind directly to nucleosome edges with significant binding inhibition occurring within 50 bp of the nucleosome dyad.

CONCLUSION

Overall, our results demonstrate that ΔNp63 is a pioneer factor that binds nucleosome edges at inaccessible and unmodified chromatin sites and induces histone acetylation and nucleosome repositioning.

Mesoscale Modeling and Single-Nucleosome Tracking Reveal Remodeling of Clutch Folding and Dynamics in Stem Cell Differentiation

Nucleosomes form heterogeneous groups in vivo, named clutches. Clutches are smaller and less dense in mouse embryonic stem cells (ESCs) compared to neural progenitor cells (NPCs). Using coarse-grained modeling of the pluripotency Pou5f1 gene, we show that the genome-wide clutch differences between ESCs and NPCs can be reproduced at a single gene locus. Larger clutch formation in NPCs is associated with changes in the compaction and internucleosome contact probability of the Pou5f1 fiber. Using single-molecule tracking (SMT), we further show that the core histone protein H2B is dynamic, and its local mobility relates to the structural features of the chromatin fiber. H2B is less stable and explores larger areas in ESCs compared to NPCs. The amount of linker histone H1 critically affects local H2B dynamics. Our results have important implications for how nucleosome organization and H2B dynamics contribute to regulate gene activity and cell identity.

Chromosome Structural Mechanics Dictates the Local Spreading of Epigenetic Marks

We present a theoretical model that demonstrates the integral role chromosome organization and structural mechanics play in the spreading of histone modifications involved in epigenetic regulation. Our model shows that heterogeneous nucleosome positioning, and the resulting position-dependent mechanical properties, must be included to reproduce several qualitative features of experimental data of histone methylation spreading around an artificially induced "nucleation site." We show that our model recreates both the extent of spreading and the presence of a subdominant peak upstream of the transcription start site. Our model indicates that the spreading of epigenetic modifications is sensitive to heterogeneity in chromatin organization and the resulting variability in the chromatin's mechanical properties, suggesting that nucleosome spacing can directly control the conferral of epigenetic marks by modifying the structural mechanics of the chromosome. It further illustrates how the physical organization of the DNA polymer may play a significant role in re-establishing the epigenetic code upon cell division.

CORENup: a combination of convolutional and recurrent deep neural networks for nucleosome positioning identification

BACKGROUND

Nucleosomes wrap the DNA into the nucleus of the Eukaryote cell and regulate its transcription phase. Several studies indicate that nucleosomes are determined by the combined effects of several factors, including DNA sequence organization. Interestingly, the identification of nucleosomes on a genomic scale has been successfully performed by computational methods using DNA sequence as input data.

RESULTS

In this work, we propose CORENup, a deep learning model for nucleosome identification. CORENup processes a DNA sequence as input using one-hot representation and combines in a parallel fashion a fully convolutional neural network and a recurrent layer. These two parallel levels are devoted to catching both non periodic and periodic DNA string features. A dense layer is devoted to their combination to give a final classification.

CONCLUSIONS

Results computed on public data sets of different organisms show that CORENup is a state of the art methodology for nucleosome positioning identification based on a Deep Neural Network architecture. The comparisons have been carried out using two groups of datasets, currently adopted by the best performing methods, and CORENup has shown top performance both in terms of classification metrics and elapsed computation time.

Genome Dashboards: Framework and Examples

Genomics is a sequence-based informatics science and a three-dimensional-structure-based material science. However, in practice, most genomics researchers utilize sequence-based informatics approaches or three-dimensional-structure-based material science techniques, not both. This division is, at least in part, the result of historical developments rather than a fundamental necessity. The underlying computational tools, experimental techniques, and theoretical models were developed independently. The primary result presented here is a framework for the unification of informatics- and physics-based data associated with DNA, nucleosomes, and chromatin. The framework is based on the mathematical representation of geometrically exact rods and the generalization of DNA basepair step parameters. Data unification enables researchers to integrate computational, experimental, and theoretical approaches for the study of chromatin biology. The framework can be implemented using model-view-controller design principles, existing genome browsers, and existing molecular visualization tools. We developed a minimal, web-based genome dashboard, G-Dash-min, and applied it to two simple examples to demonstrate the usefulness of data unification and proof of concept. Genome dashboards developed using the framework and design principles presented here are extensible and customizable and are therefore more broadly applicable than the examples presented. We expect a number of purpose-specific genome dashboards to emerge as a novel means of investigating structure-function relationships for genomes that range from basepairs to entire chromosomes and for generating, validating, and testing mechanistic hypotheses.

TFIIIC Binding to Alu Elements Controls Gene Expression via Chromatin Looping and Histone Acetylation

How repetitive elements, epigenetic modifications, and architectural proteins interact ensuring proper genome expression remains poorly understood. Here, we report regulatory mechanisms unveiling a central role of Alu elements (AEs) and RNA polymerase III transcription factor C (TFIIIC) in structurally and functionally modulating the genome via chromatin looping and histone acetylation. Upon serum deprivation, a subset of AEs pre-marked by the activity-dependent neuroprotector homeobox Protein (ADNP) and located near cell-cycle genes recruits TFIIIC, which alters their chromatin accessibility by direct acetylation of histone H3 lysine-18 (H3K18). This facilitates the contacts of AEs with distant CTCF sites near promoter of other cell-cycle genes, which also become hyperacetylated at H3K18. These changes ensure basal transcription of cell-cycle genes and are critical for their re-activation upon serum re-exposure. Our study reveals how direct manipulation of the epigenetic state of AEs by a general transcription factor regulates 3D genome folding and expression.