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ObjectivesTo gain insight into the experiences and concerns of front-line National Health Service (NHS) workers while caring for patients with COVID-19.DesignQualitative analysis of data collected through an anonymous website (www.covidconfidential) provided a repository of uncensored COVID-19 experiences of front-line NHS workers, accessed via a link advertised on the Twitter feed of two high profile medical tweeters and their retweets.SettingCommunity of NHS workers who accessed this social media.Participants54 healthcare workers, including doctors, nurses and physiotherapists, accessed the website and left a ‘story’.ResultsStories ranged from 1 word to 10 min in length. Thematic analysis identified common themes, with a central aspect being the experience and psychological consequence of trauma. Specific themes were: (1) the shock of the virus, (2) staff sacrifice and dedication, (3) collateral damage ranging from personal health concerns to the long-term impact on, and care of, discharged patients and (4) a hierarchy of power and inequality within the healthcare system.ConclusionsCOVID-19 confidential gave an outlet for unprompted and uncensored stories of healthcare workers in the context of COVID-19. In addition to personal experiences of trauma, there were perceptions that many operational difficulties stemmed from inequalities of power between management and front-line workers. Learning from these experiences will reduce staff distress and improve patient care in the face of further waves of the pandemic.

The rapidly increasing quantity of genome-wide chromosome conformation capture data presents great opportunities and challenges in the computational modeling and interpretation of the three-dimensional genome. In particular, with recent trends towards higher-resolution high-throughput chromosome conformation capture (Hi-C) data, the diversity and complexity of biological hypotheses that can be tested necessitates rigorous computational and statistical methods as well as scalable pipelines to interpret these datasets. Here we review computational tools to interpret Hi-C data, including pipelines for mapping, filtering, and normalization, and methods for confidence estimation, domain calling, visualization, and three-dimensional modeling.

Venous thromboembolism (VTE) is a common complication in patients with malignant disease. First recognised by Bouillard in 1823 and later described by Trousseau in 1844, multiple studies have since provided considerable evidence for a clinical association between VTE and cancer. Across all cancers, the risk for VTE is elevated 7-fold; in certain malignancies, the risk for VTE may be increased up to 28-fold. Venous thromboembolism is the second leading cause of death in patients with cancer; among survivors, complications commonly include recurrent VTE and post-thrombotic syndrome, and (more rarely) chronic thromboembolic pulmonary hypertension, which are costly, and have a profound impact on the patient's quality of life. Tumour cells can activate blood coagulation through multiple mechanisms, including production of procoagulant, fibrinolytic, and proaggregating activities, release of proinflammatory and proangiogenic cytokines, and interacting directly with host vascular and blood cells (e.g., endothelial cells, leukocytes, and platelets) through adhesion molecules. Increasing evidence suggests that elements of the haemostatic system also have a direct role in eliciting or enhancing angiogenesis, cell survival, and metastasis. Despite the problem posed by VTE in the setting of cancer, it is evident that a significant number of oncologists do not recognise the link between cancer, its treatment, and thrombogenesis. On 22 May 2009, a group of UK-based physicians met in London, UK, to evaluate recent data on cancer thrombosis. This article (1 of 4) briefly reviews key data on the epidemiology and pathophysiology of VTE as a context for a discussion and consensus statement developed by meeting attendees, on the implications of this information for UK clinical practice.

Percolator is a widely used software tool that increases yield in shotgun proteomics experiments and assigns reliable statistical confidence measures, such as q values and posterior error probabilities, to peptides and peptide-spectrum matches (PSMs) from such experiments. Percolator’s processing speed has been sufficient for typical data sets consisting of hundreds of thousands of PSMs. With our new scalable approach, we can now also analyze millions of PSMs in a matter of minutes on a commodity computer. Furthermore, with the increasing awareness for the need for reliable statistics on the protein level, we compared several easy-to-understand protein inference methods and implemented the best-performing method—grouping proteins by their corresponding sets of theoretical peptides and then considering only the best-scoring peptide for each protein—in the Percolator package. We used Percolator 3.0 to analyze the data from a recent study of the draft human proteome containing 25 million spectra (PM:24870542). The source code and Ubuntu, Windows, MacOS, and Fedora binary packages are available from http://percolator.ms/ under an Apache 2.0 license. Graphical Abstract ᅟ

This protocol enables users to perform de novo motif discovery, motif enrichment analysis, motif location analysis, and motif clustering, providing a comprehensive picture of the DNA or RNA motifs that are enriched in the input sequences. MEME-ChIP is a web-based tool for analyzing motifs in large DNA or RNA data sets. It can analyze peak regions identified by ChIP-seq, cross-linking sites identified by CLIP-seq and related assays, as well as sets of genomic regions selected using other criteria. MEME-ChIP performs de novo motif discovery, motif enrichment analysis, motif location analysis and motif clustering, providing a comprehensive picture of the DNA or RNA motifs that are enriched in the input sequences. MEME-ChIP performs two complementary types of de novo motif discovery: weight matrix–based discovery for high accuracy; and word-based discovery for high sensitivity. Motif enrichment analysis using DNA or RNA motifs from human, mouse, worm, fly and other model organisms provides even greater sensitivity. MEME-ChIP's interactive HTML output groups and aligns significant motifs to ease interpretation. This protocol takes less than 3 h, and it provides motif discovery approaches that are distinct and complementary to other online methods.

Recent deep learning models that predict the Hi-C contact map from DNA sequence achieve promising accuracy but cannot generalize to new cell types and or even capture differences among training cell types. We propose Epiphany, a neural network to predict cell-type-specific Hi-C contact maps from widely available epigenomic tracks. Epiphany uses bidirectional long short-term memory layers to capture long-range dependencies and optionally a generative adversarial network architecture to encourage contact map realism. Epiphany shows excellent generalization to held-out chromosomes within and across cell types, yields accurate TAD and interaction calls, and predicts structural changes caused by perturbations of epigenomic signals.

Over the past 15 years, a variety of next-generation sequencing assays have been developed for measuring the 3D conformation of DNA in the nucleus. Each of these assays gives, for a particular cell or tissue type, a distinct picture of 3D chromatin architecture. Accordingly, making sense of the relationship between genome structure and function requires teasing apart two closely related questions: how does chromatin 3D structure change from one cell type to the next, and how do different measurements of that structure differ from one another, even when the two assays are carried out in the same cell type? In this work, we assemble a collection of chromatin 3D datasets—each represented as a 2D contact map—spanning multiple assay types and cell types. We then build a machine learning model that predicts missing contact maps in this collection. We use the model to systematically explore how genome 3D architecture changes, at the level of compartments, domains, and loops, between cell type and between assay types.

X chromosome inactivation (XCI) silences most genes on one X chromosome in female mammals, but some genes escape XCI. To identify escape genes in vivo and to explore molecular mechanisms that regulate this process we analyzed the allele-specific expression and chromatin structure of X-linked genes in mouse tissues and cells with skewed XCI and distinguishable alleles based on single nucleotide polymorphisms. Using a binomial model to assess allelic expression, we demonstrate a continuum between complete silencing and expression from the inactive X (Xi). The validity of the RNA-seq approach was verified using RT-PCR with species-specific primers or Sanger sequencing. Both common escape genes and genes with significant differences in XCI status between tissues were identified. Such genes may be candidates for tissue-specific sex differences. Overall, few genes (3-7%) escape XCI in any of the mouse tissues examined, suggesting stringent silencing and escape controls. In contrast, an in vitro system represented by the embryonic-kidney-derived Patski cell line showed a higher density of escape genes (21%), representing both kidney-specific escape genes and cell-line specific escape genes. Allele-specific RNA polymerase II occupancy and DNase I hypersensitivity at the promoter of genes on the Xi correlated well with levels of escape, consistent with an open chromatin structure at escape genes. Allele-specific CTCF binding on the Xi clustered at escape genes and was denser in brain compared to the Patski cell line, possibly contributing to a more compartmentalized structure of the Xi and fewer escape genes in brain compared to the cell line where larger domains of escape were observed.

The ultimate aim of proteomics is to fully identify and quantify the entire complement of proteins and post-translational modifications in biological samples of interest. For the last 15 years, liquid chromatography-tandem mass spectrometry (LC-MS/MS) in data-dependent acquisition (DDA) mode has been the standard for proteomics when sampling breadth and discovery were the main objectives; multiple reaction monitoring (MRM) LC-MS/MS has been the standard for targeted proteomics when precise quantification, reproducibility, and validation were the main objectives. Recently, improvements in mass spectrometer design and bioinformatics algorithms have resulted in the rediscovery and development of another sampling method: data-independent acquisition (DIA). DIA comprehensively and repeatedly samples every peptide in a protein digest, producing a complex set of mass spectra that is difficult to interpret without external spectral libraries. Currently, DIA approaches the identification breadth of DDA while achieving the reproducible quantification characteristic of MRM or its newest version, parallel reaction monitoring (PRM). In comparative de novo identification and quantification studies in human cell lysates, DIA identified up to 89% of the proteins detected in a comparable DDA experiment while providing reproducible quantification of over 85% of them. DIA analysis aided by spectral libraries derived from prior DIA experiments or auxiliary DDA data produces identification and quantification as reproducible and precise as that achieved by MRM/PRM, except on low-abundance peptides that are obscured by stronger signals. DIA is still a work in progress toward the goal of sensitive, reproducible, and precise quantification without external spectral libraries. New software tools applied to DIA analysis have to deal with deconvolution of complex spectra as well as proper filtering of false positives and false negatives. However, the future outlook is positive, and various researchers are working on novel bioinformatics techniques to address these issues and increase the reproducibility, fidelity, and identification breadth of DIA.

Functional changes in spatial genome organization during human development are poorly understood. Here we report a comprehensive profile of nuclear dynamics during human cardiogenesis from pluripotent stem cells by integrating Hi-C, RNA-seq and ATAC-seq. While chromatin accessibility and gene expression show complex on/off dynamics, large-scale genome architecture changes are mostly unidirectional. Many large cardiac genes transition from a repressive to an active compartment during differentiation, coincident with upregulation. We identify a network of such gene loci that increase their association inter-chromosomally, and are targets of the muscle-specific splicing factor RBM20. Genome editing studies show that TTN pre-mRNA, the main RBM20-regulated transcript in the heart, nucleates RBM20 foci that drive spatial proximity between the TTN locus and other inter-chromosomal RBM20 targets such as CACNA1C and CAMK2D . This mechanism promotes RBM20-dependent alternative splicing of the resulting transcripts, indicating the existence of a cardiac-specific trans -interacting chromatin domain (TID) functioning as a splicing factory. The spatial organization of the genome plays an important but unclearly defined role in gene regulation. Here, the authors integrate Hi-C, RNA-seq and ATAC-seq data to map cardiogenesis from pluripotent stem cells and describe an RBM20-dependent splicing factory assembling the TTN locus with other RBM20 targets.