Correction to: SpectralTAD: an R package for defining a hierarchy of topologically associated domains using spectral clustering.

An amendment to this paper has been published and can be accessed via the original article.

SpectralTAD: an R package for defining a hierarchy of topologically associated domains using spectral clustering.

BACKGROUND: The three-dimensional (3D) structure of the genome plays a crucial role in gene expression regulation. Chromatin conformation capture technologies (Hi-C) have revealed that the genome is organized in a hierarchy of topologically associated domains (TADs), sub-TADs, and chromatin loops. Identifying such hierarchical structures is a critical step in understanding genome regulation. Existing tools for TAD calling are frequently sensitive to biases in Hi-C data, depend on tunable parameters, and are computationally inefficient. METHODS: To address these challenges, we developed a novel sliding window-based spectral clustering framework that uses gaps between consecutive eigenvectors for TAD boundary identification. RESULTS: Our method, implemented in an R package, SpectralTAD, detects hierarchical, biologically relevant TADs, has automatic parameter selection, is robust to sequencing depth, resolution, and sparsity of Hi-C data. SpectralTAD outperforms four state-of-the-art TAD callers in simulated and experimental settings. We demonstrate that TAD boundaries shared among multiple levels of the TAD hierarchy were more enriched in classical boundary marks and more conserved across cell lines and tissues. In contrast, boundaries of TADs that cannot be split into sub-TADs showed less enrichment and conservation, suggesting their more dynamic role in genome regulation. CONCLUSION: SpectralTAD is available on Bioconductor, http://bioconductor.org/packages/SpectralTAD/ .

A method for estimating coherence of molecular mechanisms in major human disease and traits.

BACKGROUND: Phenotypes such as height and intelligence, are thought to be a product of the collective effects of multiple phenotype-associated genes and interactions among their protein products. High/low degree of interactions is suggestive of coherent/random molecular mechanisms, respectively. Comparing the degree of interactions may help to better understand the coherence of phenotype-specific molecular mechanisms and the potential for therapeutic intervention. However, direct comparison of the degree of interactions is difficult due to different sizes and configurations of phenotype-associated gene networks. METHODS: We introduce a metric for measuring coherence of molecular-interaction networks as a slope of internal versus external distributions of the degree of interactions. The internal degree distribution is defined by interaction counts within a phenotype-specific gene network, while the external degree distribution counts interactions with other genes in the whole protein-protein interaction (PPI) network. We present a novel method for normalizing the coherence estimates, making them directly comparable. RESULTS: Using STRING and BioGrid PPI databases, we compared the coherence of 116 phenotype-associated gene sets from GWAScatalog against size-matched KEGG pathways (the reference for high coherence) and random networks (the lower limit of coherence). We observed a range of coherence estimates for each category of phenotypes. Metabolic traits and diseases were the most coherent, while psychiatric disorders and intelligence-related traits were the least coherent. We demonstrate that coherence and modularity measures capture distinct network properties. CONCLUSIONS: We present a general-purpose method for estimating and comparing the coherence of molecular-interaction gene networks that accounts for the network size and shape differences. Our results highlight gaps in our current knowledge of genetics and molecular mechanisms of complex phenotypes and suggest priorities for future GWASs.

multiHiCcompare: joint normalization and comparative analysis of complex Hi-C experiments.

MOTIVATION: With the development of chromatin conformation capture technology and its high-throughput derivative Hi-C sequencing, studies of the three-dimensional interactome of the genome that involve multiple Hi-C datasets are becoming available. To account for the technology-driven biases unique to each dataset, there is a distinct need for methods to jointly normalize multiple Hi-C datasets. Previous attempts at removing biases from Hi-C data have made use of techniques which normalize individual Hi-C datasets, or, at best, jointly normalize two datasets. RESULTS: Here, we present multiHiCcompare, a cyclic loess regression-based joint normalization technique for removing biases across multiple Hi-C datasets. In contrast to other normalization techniques, it properly handles the Hi-C-specific decay of chromatin interaction frequencies with the increasing distance between interacting regions. multiHiCcompare uses the general linear model framework for comparative analysis of multiple Hi-C datasets, adapted for the Hi-C-specific decay of chromatin interaction frequencies. multiHiCcompare outperforms other methods when detecting a priori known chromatin interaction differences from jointly normalized datasets. Applied to the analysis of auxin-treated versus untreated experiments, and CTCF depletion experiments, multiHiCcompare was able to recover the expected epigenetic and gene expression signatures of loss of chromatin interactions and reveal novel insights. AVAILABILITY AND IMPLEMENTATION: multiHiCcompare is freely available on GitHub and as a Bioconductor R package https://bioconductor.org/packages/multiHiCcompare. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

HiCcompare: an R-package for joint normalization and comparison of HI-C datasets.

BACKGROUND: Changes in spatial chromatin interactions are now emerging as a unifying mechanism orchestrating the regulation of gene expression. Hi-C sequencing technology allows insight into chromatin interactions on a genome-wide scale. However, Hi-C data contains many DNA sequence- and technology-driven biases. These biases prevent effective comparison of chromatin interactions aimed at identifying genomic regions differentially interacting between, e.g., disease-normal states or different cell types. Several methods have been developed for normalizing individual Hi-C datasets. However, they fail to account for biases between two or more Hi-C datasets, hindering comparative analysis of chromatin interactions. RESULTS: We developed a simple and effective method, HiCcompare, for the joint normalization and differential analysis of multiple Hi-C datasets. The method introduces a distance-centric analysis and visualization of the differences between two Hi-C datasets on a single plot that allows for a data-driven normalization of biases using locally weighted linear regression (loess). HiCcompare outperforms methods for normalizing individual Hi-C datasets and methods for differential analysis (diffHiC, FIND) in detecting a priori known chromatin interaction differences while preserving the detection of genomic structures, such as A/B compartments. CONCLUSIONS: HiCcompare is able to remove between-dataset bias present in Hi-C matrices. It also provides a user-friendly tool to allow the scientific community to perform direct comparisons between the growing number of pre-processed Hi-C datasets available at online repositories. HiCcompare is freely available as a Bioconductor R package https://bioconductor.org/packages/HiCcompare/ .

TADCompare: An R Package for Differential and Temporal Analysis of Topologically Associated Domains.

Recent research using chromatin conformation capture technologies, such as Hi-C, has demonstrated the importance of topologically associated domains (TADs) and smaller chromatin loops, collectively referred hereafter as "interacting domains." Many such domains change during development or disease, and exhibit cell- and condition-specific differences. Quantification of the dynamic behavior of interacting domains will help to better understand genome regulation. Methods for comparing interacting domains between cells and conditions are highly limited. We developed TADCompare, a method for differential analysis of boundaries of interacting domains between two or more Hi-C datasets. TADCompare is based on a spectral clustering-derived measure called the eigenvector gap, which enables a loci-by-loci comparison of boundary differences. Using this measure, we introduce methods for identifying differential and consensus boundaries of interacting domains and tracking boundary changes over time. We further propose a novel framework for the systematic classification of boundary changes. Colocalization- and gene enrichment analysis of different types of boundary changes demonstrated distinct biological functionality associated with them. TADCompare is available on https://github.com/dozmorovlab/TADCompare and Bioconductor (submitted).

Prevalence of hematologic toxicity from copperhead envenomation: an observational study.

Background: Copperhead snakes (Agkistrodon contortrix) are considered as the least toxic of the North American pit vipers. The reported incidence of coagulopathy from copperhead envenomation is variable, possibly secondary to regional variation in subspecies and venom potency. Coagulation studies are often obtained when evaluating for the coagulopathic effects of copperhead venom, but the clinical utility of these indices is unclear. The aim of this study was to determine the prevalence of hematologic toxicity due to copperhead envenomation in hospitalized patients.Methods: This was a multi-center retrospective chart review study using electronic hospital data between January 1, 2006 and December 31, 2016 evaluating prevalence of coagulopathy following copperhead envenomation. Patients presenting to one of three major academic tertiary care centers in Virginia with suspected copperhead envenomation were identified using medical billing codes. The primary outcome was to summarize the prevalence of hematologic toxicity including thrombocytopenia, elevated prothrombin or partial thromboplastin times, or hypofibrinogenemia.Results: There were 244 cases used for final analysis. Hematologic toxicity occurred in 14% (95% CI 10-18%) of patients. Specific indices included thrombocytopenia in 1.2% (95% CI 0.4-3.6%), hypofibrinogenemia in 0.7% (95% CI 0.0-3.8%), elevated PT in 10.0% (95% CI 6.8-14.5%), and aPTT in 3.9% (95% CI 2.1-7.2%) of patients. There was no clinically significant bleeding reported in any case.Conclusions: Subtle hematologic abnormalities due to copperhead envenomation in patients treated in the Commonwealth of Virginia were relatively common, but do not appear to be clinically significant in this study population.

Making Football Safer: Assessing the Current National Football League Policy on the Type of Helmets Allowed on the Playing Field.

In an effort to reduce concussions in football, a helmet safety-rating system was developed in 2011 that rated helmets based on their ability to reduce g-forces experienced by the head across a range of impact forces measured on the playing field. Although this was considered a major step in making the game safer, the National Football League (NFL) continues to allow players the right to choose what helmet to wear during play. This prompted us to ask: What helmets do NFL players wear and does this helmet policy make the game safer? Accordingly, we identified the helmets worn by nearly 1000 players on Week 13 of the 2015-2016 season and Week 1 of the 2016-2017 season. Using stop-motion footage, we found that players wore a wide range of helmets with varying safety ratings influenced in part by the player's position and age. Moreover, players wearing lower safety-rated helmets were more likely to receive a concussion than those wearing higher safety-rated helmets. Interestingly, many players suffering a concussion in 2015 did not switch to a higher safety-rated helmet in 2016. Using a helmet-to-helmet impactor, we found that the g-forces experienced in the highest safety-rated helmets were roughly 30% less than that for the lowest safety-rated helmets. These results suggest that the current NFL helmet policy puts players at increased risk of receiving a concussion as many players are wearing low safety-rated helmets, which transmits more energy to the brain than higher safety-rated helmets, following collision. Thus, to reduce concussions, the NFL should mandate that players only wear helmets that receive the highest safety rating.