No abstract

Since its 2001 debut, the University of California, Santa Cruz (UCSC) Genome Browser (http://genome.ucsc.edu/) team has provided continuous support to the international genomics and biomedical communities through a web-based, open source platform designed for the fast, scalable display of sequence alignments and annotations landscaped against a vast collection of quality reference genome assemblies. The browser's publicly accessible databases are the backbone of a rich, integrated bioinformatics tool suite that includes a graphical interface for data queries and downloads, alignment programs, command-line utilities and more. This year's highlights include newly designed home and gateway pages; a new ‘multi-region’ track display configuration for exon-only, gene-only and custom regions visualization; new genome browsers for three species (brown kiwi, crab-eating macaque and Malayan flying lemur); eight updated genome assemblies; extended support for new data types such as CRAM, RNA-seq expression data and long-range chromatin interaction pairs; and the unveiling of a new supported mirror site in Japan.

Cytosine, 5-methylcytosine, and 5-hydroxymethylcytosine were identified during translocation of single DNA template strands through a modified Mycobacterium smegmatis porin A (M2MspA) nanopore under control of phi29 DNA polymerase. This identification was based on three consecutive ionic current states that correspond to passage of modified or unmodified CG dinucleotides and their immediate neighbors through the nanopore limiting aperture. To establish quality scores for these calls, we examined ∼3,300 translocation events for 48 distinct DNA constructs. Each experiment analyzed a mixture of cytosine-, 5-methylcytosine-, and 5-hydroxymethylcytosine-bearing DNA strands that contained a marker that independently established the correct cytosine methylation status at the target CG of each molecule tested. To calculate error rates for these calls, we established decision boundaries using a variety of machine-learning methods. These error rates depended upon the identity of the bases immediately 5′ and 3′ of the targeted CG dinucleotide, and ranged from 1.7% to 12.2% for a single-pass read. We estimate that Q40 values (0.01% error rates) for methylation status calls could be achieved by reading single molecules 5-19 times depending upon sequence context.MspA | epigenetics E pigenetic modifications of DNA help regulate gene transcription in biological cells. In mammals, 5-methylcytosine (mC) modification of CG dinucleotides is known to influence development (1, 2) and contribute to human diseases including cancer (3). Other modifications have been detected at carbon 5 of cytosine including 5-hydroxymethylcytosine (hmC) (4), and more recently 5-formylcytosine, and 5-carboxycytosine (5). Physiological roles for hmC in carcinogenesis and embryonic stem cell differentiation have been proposed (6).High-throughput techniques for mC detection are based on bisulfite treatment of genomic DNA (7). In the conventional assay, cytosine (but not mC nor hmC) is converted to uracil (8). Thus, positions not converted to uracil identify cytosines that were modified in the original genomic sequence. In a landmark paper, Lister et al. (9) used this technique to map genome-wide cytosine methylation in human embryonic stem cells and fetal lung fibroblasts at single-nucleotide precision. Recently, bisulfite strategies for discriminating between mC and hmC using the Tet1 enzyme (10) or by chemical modification of hmC (11) have been described.Single-molecule techniques have emerged as possible alternatives to bisulfite treatment for detecting epigenetic modifications of DNA (12). These single-molecule approaches share several useful features including few processing steps before sequence analysis, long reads that routinely exceed several thousand nucleotides, and the ability to read native DNA strands in heterogeneous mixtures. The most advanced of these single-molecule techniques, from Pacific Biosciences, uses fluorescence to detect labeled nucleotide triphosphates during daughter-strand elongation. This elongation is catalyzed by a DNA pol...

Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single tissue or small sets of tissues. Here, we built networks that additionally capture the regulation of relative isoform abundance and splicing, along with tissue-specific connections unique to each of a diverse set of tissues. We used the Genotype-Tissue Expression (GTEx) project v6 RNA sequencing data across 50 tissues and 449 individuals. First, we developed a framework called Transcriptome-Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the interplay between the regulation of splicing and transcription. We built TWNs for 16 tissues and found that hubs in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome. Next, we used a Bayesian biclustering model that identifies network edges unique to a single tissue to reconstruct Tissue-Specific Networks (TSNs) for 26 distinct tissues and 10 groups of related tissues. Finally, we found genetic variants associated with pairs of adjacent nodes in our networks, supporting the estimated network structures and identifying 20 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships of the human transcriptome across tissues.

This cohort study evaluates the feasibility and utility of incorporating comparative gene expression information into the precision medicine framework for difficult-to-treat pediatric and young adult patients with cancer.