ORIO (Online Resource for Integrative Omics): a web-based platform for rapid integration of next generation sequencing data

From multi-omics approaches to personalized medicine in myocardial infarction

Myocardial infarction (MI) is a prevalent cardiovascular disease characterized by myocardial necrosis resulting from coronary artery ischemia and hypoxia, which can lead to severe complications such as arrhythmia, cardiac rupture, heart failure, and sudden death. Despite being a research hotspot, the etiological mechanism of MI remains unclear. The emergence and widespread use of omics technologies, including genomics, transcriptomics, proteomics, metabolomics, and other omics, have provided new opportunities for exploring the molecular mechanism of MI and identifying a large number of disease biomarkers. However, a single-omics approach has limitations in understanding the complex biological pathways of diseases. The multi-omics approach can reveal the interaction network among molecules at various levels and overcome the limitations of the single-omics approaches. This review focuses on the omics studies of MI, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and other omics. The exploration extended into the domain of multi-omics integrative analysis, accompanied by a compilation of diverse online resources, databases, and tools conducive to these investigations. Additionally, we discussed the role and prospects of multi-omics approaches in personalized medicine, highlighting the potential for improving diagnosis, treatment, and prognosis of MI.

MLL::AF9 degradation induces rapid changes in transcriptional elongation and subsequent loss of an active chromatin landscape

MLL rearrangements produce fusion oncoproteins that drive leukemia development, but the direct effects of MLL-fusion inactivation remain poorly defined. We designed models with degradable MLL::AF9 where treatment with small molecules induces rapid degradation. We leveraged the kinetics of this system to identify a core subset of MLL::AF9 target genes where MLL::AF9 degradation induces changes in transcriptional elongation within 15 minutes. MLL::AF9 degradation subsequently causes loss of a transcriptionally active chromatin landscape. We used this insight to assess the effectiveness of small molecules that target members of the MLL::AF9 multiprotein complex, specifically DOT1L and MENIN. Combined DOT1L/MENIN inhibition resembles MLL::AF9 degradation, whereas single-agent treatment has more modest effects on MLL::AF9 occupancy and gene expression. Our data show that MLL::AF9 degradation leads to decreases in transcriptional elongation prior to changes in chromatin landscape at select loci and that combined inhibition of chromatin complexes releases the MLL::AF9 oncoprotein from chromatin globally.

Integration of Multi-Omics Data Using Probabilistic Graph Models and External Knowledge

Background:

High-throughput sequencing technologies have revolutionized the ability to perform systems-level biology and elucidate molecular mechanisms of disease through the comprehensive characterization of different layers of biological information. Integration of these heterogeneous layers can provide insight into the underlying biology but is challenged by modeling complex interactions.

Objective:

We introduce OBaNK: omics integration using Bayesian networks and external knowledge, an algorithm to model interactions between heterogeneous high-dimensional biological data to elucidate complex functional clusters and emergent relationships associated with an observed phenotype.

Method:

Using Bayesian network learning, we modeled the statistical dependencies and interactions between lipidomics, proteomics, and metabolomics data. The strength of a learned interaction between molecules was altered based on external knowledge.

Results :

Networks learned from synthetic datasets based on real pathways achieved an average area under the curve score of ~0.85, an improvement of ~0.23 from baseline methods. When applied to real multi-omics data collected during pregnancy, five distinct functional networks of heterogeneous biological data were identified, and the results were compared to other multi-omics integration approaches.

Conclusion:

OBaNK successfully improved the accuracy of learning interaction networks from data integrating external knowledge, identified heterogeneous functional networks from real data, and suggested potential novel interactions associated with the phenotype. These findings can guide future hypothesis generation. OBaNK source code is available at: https://github.com/bridgettripp/OBaNK.git, and a graphical user interface is available at: http://otulab.unl.edu/OBaNK.

The Integrator Complex Attenuates Promoter-Proximal Transcription at Protein-Coding Genes

The transition of RNA polymerase II (Pol II) from initiation to productive elongation is a central, regulated step in metazoan gene expression. At many genes, Pol II pauses stably in early elongation, remaining engaged with the 25- to 60-nt-long nascent RNA for many minutes while awaiting signals for release into the gene body. However, 15%-20% of genes display highly unstable promoter Pol II, suggesting that paused polymerase might dissociate from template DNA at these promoters and release a short, non-productive mRNA. Here, we report that paused Pol II can be actively destabilized by the Integrator complex. Specifically, we present evidence that Integrator utilizes its RNA endonuclease activity to cleave nascent RNA and drive termination of paused Pol II. These findings uncover a previously unappreciated mechanism of metazoan gene repression, akin to bacterial transcription attenuation, wherein promoter-proximal Pol II is prevented from entering productive elongation through factor-regulated termination.

ORSO (Online Resource for Social Omics): A data-driven social network connecting scientists to genomics datasets

High-throughput sequencing has become ubiquitous in biomedical sciences. As new technologies emerge and sequencing costs decline, the diversity and volume of available data increases exponentially, and successfully navigating the data becomes more challenging. Though datasets are often hosted by public repositories, scientists must rely on inconsistent annotation to identify and interpret meaningful data. Moreover, the experimental heterogeneity and wide-ranging quality of high-throughput biological data means that even data with desired cell lines, tissue types, or molecular targets may not be readily interpretable or integrated. We have developed ORSO (Online Resource for Social Omics) as an easy-to-use web application to connect life scientists with genomics data. In ORSO, users interact within a data-driven social network, where they can favorite datasets and follow other users. In addition to more than 30,000 datasets hosted from major biomedical consortia, users may contribute their own data to ORSO, facilitating its discovery by other users. Leveraging user interactions, ORSO provides a novel recommendation system to automatically connect users with hosted data. In addition to social interactions, the recommendation system considers primary read coverage information and annotated metadata. Similarities used by the recommendation system are presented by ORSO in a graph display, allowing exploration of dataset associations. The topology of the network graph reflects established biology, with samples from related systems grouped together. We tested the recommendation system using an RNA-seq time course dataset from differentiation of embryonic stem cells to cardiomyocytes. The ORSO recommendation system correctly predicted early data point sources as embryonic stem cells and late data point sources as heart and muscle samples, resulting in recommendation of related datasets. By connecting scientists with relevant data, ORSO provides a critical new service that facilitates wide-ranging research interests.

New sequencing technologies have rapidly transformed biomedical research. Public data repositories now contain millions of datasets, which have the potential to accelerate and bolster research projects. However, the sheer magnitude of available data makes navigation difficult. We created ORSO (Online Resource for Social Omics) to address these challenges. ORSO is a social network where entries are not status updates or tweets, but biological datasets. Users may add their own data to ORSO, joining 30,000 validated datasets that are already hosted, and other users may find these data through intuitive search functions and informative analytics. Users can then favorite datasets relevant to their interests or follow contributing users. ORSO also uses a recommendation system like those used on commercial websites to automatically recommend data to users based on user interactions and dataset similarities. By making data more accessible and by connecting users to relevant data, we anticipate that ORSO will be an important resource for scientists. ORSO may be the first of many applications that use methods originating in social media and ecommerce to enhance and further research projects in the life sciences.

The Integrator Complex Attenuates Promoter-Proximal Transcription at Protein-Coding Genes

The transition of RNA polymerase II (Pol II) from initiation to productive elongation is a central, regulated step in metazoan gene expression. At many genes, Pol II pauses stably in early elongation, remaining engaged with the 25- to 60-nt-long nascent RNA for many minutes while awaiting signals for release into the gene body. However, 15%-20% of genes display highly unstable promoter Pol II, suggesting that paused polymerase might dissociate from template DNA at these promoters and release a short, non-productive mRNA. Here, we report that paused Pol II can be actively destabilized by the Integrator complex. Specifically, we present evidence that Integrator utilizes its RNA endonuclease activity to cleave nascent RNA and drive termination of paused Pol II. These findings uncover a previously unappreciated mechanism of metazoan gene repression, akin to bacterial transcription attenuation, wherein promoter-proximal Pol II is prevented from entering productive elongation through factor-regulated termination.

Going low to reach high: Small-scale ChIP-seq maps new terrain

Chromatin immunoprecipitation (ChIP) enables mapping of specific histone modifications or chromatin-associated factors in the genome and represents a powerful tool in the study of chromatin and genome regulation. Importantly, recent technological advances that couple ChIP with whole-genome high-throughput sequencing (ChIP-seq) now allow the mapping of chromatin factors throughout the genome. However, the requirement for large amounts of ChIP-seq input material has long made it challenging to assess chromatin profiles of cell types only available in limited numbers. For many cell types, it is not feasible to reach high numbers when collecting them as homogeneous cell populations in vivo. Nonetheless, it is an advantage to work with pure cell populations to reach robust biological conclusions. Here, we review (a) how ChIP protocols have been scaled down for use with as little as a few hundred cells; (b) which considerations to be aware of when preparing small-scale ChIP-seq and analyzing data; and (c) the potential of small-scale ChIP-seq datasets for elucidating chromatin dynamics in various biological systems, including some examples such as oocyte maturation and preimplantation embryo development. This article is categorized under: Laboratory Methods and Technologies > Genetic/Genomic Methods Developmental Biology > Developmental Processes in Health and Disease Biological Mechanisms > Cell Fates.

FQStat: a parallel architecture for very high-speed assessment of sequencing quality metrics

Background

High throughput DNA/RNA sequencing has revolutionized biological and clinical research. Sequencing is widely used, and generates very large amounts of data, mainly due to reduced cost and advanced technologies. Quickly assessing the quality of giga-to-tera base levels of sequencing data has become a routine but important task. Identification and elimination of low-quality sequence data is crucial for reliability of downstream analysis results. There is a need for a high-speed tool that uses optimized parallel programming for batch processing and simply gauges the quality of sequencing data from multiple datasets independent of any other processing steps.

Results

FQStat is a stand-alone, platform-independent software tool that assesses the quality of FASTQ files using parallel programming. Based on the machine architecture and input data, FQStat automatically determines the number of cores and the amount of memory to be allocated per file for optimum performance. Our results indicate that in a core-limited case, core assignment overhead exceeds the benefit of additional cores. In a core-unlimited case, there is a saturation point reached in performance by increasingly assigning additional cores per file. We also show that memory allocation per file has a lower priority in performance when compared to the allocation of cores. FQStat’s output is summarized in HTML web page, tab-delimited text file, and high-resolution image formats. FQStat calculates and plots read count, read length, quality score, and high-quality base statistics. FQStat identifies and marks low-quality sequencing data to suggest removal from downstream analysis. We applied FQStat on real sequencing data to optimize performance and to demonstrate its capabilities. We also compared FQStat’s performance to similar quality control (QC) tools that utilize parallel programming and attained improvements in run time.

Conclusions

FQStat is a user-friendly tool with a graphical interface that employs a parallel programming architecture and automatically optimizes its performance to generate quality control statistics for sequencing data. Unlike existing tools, these statistics are calculated for multiple datasets and separately at the “lane,” “sample,” and “experiment” level to identify subsets of the samples with low quality, thereby preventing the loss of complete samples when reliable data can still be obtained.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-3015-y) contains supplementary material, which is available to authorized users.

Revealing a human p53 universe

p53 transcriptional networks are well-characterized in many organisms. However, a global understanding of requirements for in vivo p53 interactions with DNA and relationships with transcription across human biological systems in response to various p53 activating situations remains limited. Using a common analysis pipeline, we analyzed 41 data sets from genome-wide ChIP-seq studies of which 16 have associated gene expression data, including our recent primary data with normal human lymphocytes. The resulting extensive analysis, accessible at p53 BAER hub via the UCSC browser, provides a robust platform to characterize p53 binding throughout the human genome including direct influence on gene expression and underlying mechanisms. We establish the impact of spacers and mismatches from consensus on p53 binding in vivo and propose that once bound, neither significantly influences the likelihood of expression. Our rigorous approach revealed a large p53 genome-wide cistrome composed of >900 genes directly targeted by p53. Importantly, we identify a core cistrome signature composed of genes appearing in over half the data sets, and we identify signatures that are treatment- or cell-specific, demonstrating new functions for p53 in cell biology. Our analysis reveals a broad homeostatic role for human p53 that is relevant to both basic and translational studies.

Widespread transcriptional pausing and elongation control at enhancers

In this study, Henriques et al. demonstrate that transcription is a nearly universal feature of enhancers in Drosophila and mammalian cells and that nascent RNA sequencing strategies are optimal for identification of both enhancers and superenhancers. Their findings provide insights into the unique characteristics of superenhancers, which stimulate high-level gene expression through rapid pause release; interestingly, this property renders associated genes resistant to loss of factors that stabilize paused RNAPII.

Regulation by gene-distal enhancers is critical for cell type-specific and condition-specific patterns of gene expression. Thus, to understand the basis of gene activity in a given cell type or tissue, we must identify the precise locations of enhancers and functionally characterize their behaviors. Here, we demonstrate that transcription is a nearly universal feature of enhancers in Drosophila and mammalian cells and that nascent RNA sequencing strategies are optimal for identification of both enhancers and superenhancers. We dissect the mechanisms governing enhancer transcription and discover remarkable similarities to transcription at protein-coding genes. We show that RNA polymerase II (RNAPII) undergoes regulated pausing and release at enhancers. However, as compared with mRNA genes, RNAPII at enhancers is less stable and more prone to early termination. Furthermore, we found that the level of histone H3 Lys4 (H3K4) methylation at enhancers corresponds to transcriptional activity such that highly active enhancers display H3K4 trimethylation rather than the H3K4 monomethylation considered a hallmark of enhancers. Finally, our work provides insights into the unique characteristics of superenhancers, which stimulate high-level gene expression through rapid pause release; interestingly, this property renders associated genes resistant to the loss of factors that stabilize paused RNAPII.