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Circular extrachromosomal DNA (ecDNA) in patient tumors is an important driver of oncogenic gene expression, evolution of drug resistance and poor patient outcomes. Applying computational methods for the detection and reconstruction of ecDNA across a retrospective cohort of 481 medulloblastoma tumors from 465 patients, we identify circular ecDNA in 82 patients (18%). Patients with ecDNA-positive medulloblastoma were more than twice as likely to relapse and three times as likely to die within 5 years of diagnosis. A subset of tumors harbored multiple ecDNA lineages, each containing distinct amplified oncogenes. Multimodal sequencing, imaging and CRISPR inhibition experiments in medulloblastoma models reveal intratumoral heterogeneity of ecDNA copy number per cell and frequent putative 'enhancer rewiring' events on ecDNA. This study reveals the frequency and diversity of ecDNA in medulloblastoma, stratified into molecular subgroups, and suggests copy number heterogeneity and enhancer rewiring as oncogenic features of ecDNA.

Ependymoma is a tumor of the brain or spinal cord. The two most common and aggressive molecular groups of ependymoma are the supratentorial ZFTA-fusion associated and the posterior fossa ependymoma group A. In both groups, tumors occur mainly in young children and frequently recur after treatment. Although molecular mechanisms underlying these diseases have recently been uncovered, they remain difficult to target and innovative therapeutic approaches are urgently needed. Here, we use genome-wide chromosome conformation capture (Hi-C), complemented with CTCF and H3K27ac ChIP-seq, as well as gene expression and DNA methylation analysis in primary and relapsed ependymoma tumors, to identify chromosomal conformations and regulatory mechanisms associated with aberrant gene expression. In particular, we observe the formation of new topologically associating domains ('neo-TADs') caused by structural variants, group-specific 3D chromatin loops, and the replacement of CTCF insulators by DNA hyper-methylation. Through inhibition experiments, we validate that genes implicated by these 3D genome conformations are essential for the survival of patient-derived ependymoma models in a group-specific manner. Thus, this study extends our ability to reveal tumor-dependency genes by 3D genome conformations even in tumors that lack targetable genetic alterations.

Summary Copy number variation (CNV) and alteration (CNA) analysis is a crucial component in many genomic studies and its applications span from basic research to clinic diagnostics and personalized medicine. CNVpytor is a tool featuring a read depth-based caller and combined read depth and B-allele frequency (BAF) based 2D caller to find CNVs and CNAs. The tool stores processed intermediate data and CNV/CNA calls in a compact HDF5 file—pytor file. Here, we describe a new track in igv.js that utilizes pytor and whole genome variant files as input for on-the-fly read depth and BAF visualization, CNV/CNA calling and analysis. Embedding into HTML pages and Jupiter Notebooks enables convenient remote data access and visualization simplifying interpretation and analysis of omics data. Availability and implementation The CNVpytor track is integrated with igv.js and available at https://github.com/igvteam/igv.js. The documentation is available at https://github.com/igvteam/igv.js/wiki/cnvpytor. Usage can be tested in the IGV-Web app at https://igv.org/app and also on https://github.com/abyzovlab/CNVpytor. [ABSTRACT FROM AUTHOR]

RNA has the intrinsic property to base pair, forming complex structures fundamental to its diverse functions. Here, we develop PARIS, a method based on reversible psoralen crosslinking for global mapping of RNA duplexes with near base-pair resolution in living cells. PARIS analysis in three human and mouse cell types reveals frequent long-range structures, higher-order architectures, and RNA-RNA interactions in trans across the transcriptome. PARIS determines base-pairing interactions on an individual-molecule level, revealing pervasive alternative conformations. We used PARIS-determined helices to guide phylogenetic analysis of RNA structures and discovered conserved long-range and alternative structures. XIST, a long noncoding RNA (lncRNA) essential for X chromosome inactivation, folds into evolutionarily conserved RNA structural domains that span many kilobases. XIST A-repeat forms complex inter-repeat duplexes that nucleate higher-order assembly of the key epigenetic silencing protein SPEN. PARIS is a generally applicable and versatile method that provides novel insights into the RNA structurome and interactome. VIDEO ABSTRACT.

Complex biomedical analyses require the use of multiple software tools in concert and remain challenging for much of the biomedical research community. We introduce GenomeSpace (http://www.genomespace.org), a cloud-based, cooperative community resource that currently supports the streamlined interaction of 20 bioinformatics tools and data resources. To facilitate integrative analysis by non-programmers, it offers a growing set of 'recipes', short workflows to guide investigators through high-utility analysis tasks.

The Integrative Genomics Viewer (IGV) for iPad, based on the popular IGV application for desktop and laptop computers, supports researchers who wish to take advantage of the mobility of today's tablet computers to view genomic data and present findings to colleagues.

Data visualization is an essential component of genomic data analysis. However, the size and diversity of the data sets produced by today's sequencing and array-based profiling methods present major challenges to visualization tools. The Integrative Genomics Viewer (IGV) is a high-performance viewer that efficiently handles large heterogeneous data sets, while providing a smooth and intuitive user experience at all levels of genome resolution. A key characteristic of IGV is its focus on the integrative nature of genomic studies, with support for both array-based and next-generation sequencing data, and the integration of clinical and phenotypic data. Although IGV is often used to view genomic data from public sources, its primary emphasis is to support researchers who wish to visualize and explore their own data sets or those from colleagues. To that end, IGV supports flexible loading of local and remote data sets, and is optimized to provide high-performance data visualization and exploration on standard desktop systems. IGV is freely available for download from http://www.broadinstitute.org/igv, under a GNU LGPL open-source license.

Although genetic lesions responsible for some mendelian disorders can be rapidly discovered through massively parallel sequencing of whole genomes or exomes, not all diseases readily yield to such efforts. We describe the illustrative case of the simple mendelian disorder medullary cystic kidney disease type 1 (MCKD1), mapped more than a decade ago to a 2-Mb region on chromosome 1. Ultimately, only by cloning, capillary sequencing and de novo assembly did we find that each of six families with MCKD1 harbors an equivalent but apparently independently arising mutation in sequence markedly under-represented in massively parallel sequencing data: the insertion of a single cytosine in one copy (but a different copy in each family) of the repeat unit comprising the extremely long (∼1.5-5 kb), GC-rich (>80%) coding variable-number tandem repeat (VNTR) sequence in the MUC1 gene encoding mucin 1. These results provide a cautionary tale about the challenges in identifying the genes responsible for mendelian, let alone more complex, disorders through massively parallel sequencing.

Large biological datasets are being produced at a rapid pace and create substantial storage challenges, particularly in the domain of high-throughput sequencing (HTS). Most approaches currently used to store HTS data are either unable to quickly adapt to the requirements of new sequencing or analysis methods (because they do not support schema evolution), or fail to provide state of the art compression of the datasets. We have devised new approaches to store HTS data that support seamless data schema evolution and compress datasets substantially better than existing approaches. Building on these new approaches, we discuss and demonstrate how a multi-tier data organization can dramatically reduce the storage, computational and network burden of collecting, analyzing, and archiving large sequencing datasets. For instance, we show that spliced RNA-Seq alignments can be stored in less than 4% the size of a BAM file with perfect data fidelity. Compared to the previous compression state of the art, these methods reduce dataset size more than 40% when storing exome, gene expression or DNA methylation datasets. The approaches have been integrated in a comprehensive suite of software tools (http://goby.campagnelab.org) that support common analyses for a range of high-throughput sequencing assays.

Reflects on a career in science education by focusing on significant events that helped shape the practitioner's philosophy of science teaching and learning. (DDR)

Argues for the inseparability of the process and products of science. Included as part of a series of reprints of articles about science teaching and written in 1965. Contains 24 references. (DDR)

Although a few cancer genes are mutated in a high proportion of tumours of a given type (>20%), most are mutated at intermediate frequencies (2-20%). To explore the feasibility of creating a comprehensive catalogue of cancer genes, we analysed somatic point mutations in exome sequences from 4,742 human cancers and their matched normal-tissue samples across 21 cancer types. We found that large-scale genomic analysis can identify nearly all known cancer genes in these tumour types. Our analysis also identified 33 genes that were not previously known to be significantly mutated in cancer, including genes related to proliferation, apoptosis, genome stability, chromatin regulation, immune evasion, RNA processing and protein homeostasis. Down-sampling analysis indicates that larger sample sizes will reveal many more genes mutated at clinically important frequencies. We estimate that near-saturation may be achieved with 600-5,000 samples per tumour type, depending on background mutation frequency. The results may help to guide the next stage of cancer genomics., Comprehensive knowledge of the genes underlying human cancers is a critical foundation for cancer diagnostics, therapeutics, clinical-trial design and selection of rational combination therapies. It is now possible to use [...]