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Model organisms are essential to understanding the biological and disease consequences of human genome variation. Bioinformatics resources that support meaningful comparisons of mouse and human genotype-to-phenotype data and knowledge are needed to support the translation from bench to bedside and back again [1]. There is no genome variation resource for mouse comparable to resources available for human genome variation data such as EXAC [2], ClinVar [3], or ClinGen [4]. NCBI resources such as dbSNP and ClinVar no longer accept data from model organisms. While the European Variation Archive (EVA) serves a repository of SNP data for mouse, however, the resource does not accept imputed variation data or curated phenotype annotations associated with variation data that are central to data interpretation and analysis. Although the Mouse Genome Informatics database (MGI) [5] serves as a comprehensive mouse allele registry and curates information about the association of mouse variants with phenotypes and disease, the variation data in MGI are not currently available in format consistent with the Human Genome Variation Society (HGVS) standards [6]. The Mouse Variation Registry (MVAR) will represent the integration of all mouse genome variation data and includes processes to automatically canonicalize variants so that they are uniquely represented in the database with comprehensive annotation and their distribution across strains. The starting dataset used as input into MVAR was downloaded in VCF format [7] (as a 42GB gzipped file) from the Mouse Genomes Project [8] and contains about 81M Single-Nucleotide Variants (SNV), ~9M Deletions and ~8M Insertions. Other data will be obtained from MGI, the Mouse Mutant Repository Database (MMRDB), the Diversity Outbred Database (DODB), and from computationally imputed SNP data. The MVAR data ingest workflow has been developed to normalize, prepare and annotate input variation data. With the help of the GATK framework [9], the first step of the pipeline consists of normalizing i.e., left aligning each variant, and decomposing the multi-allelic variants (where there is more than one variation in a row of data). The next step in the pipeline is made with the use of the Ensembl Variant Effect Predictor (VEP) [10], which annotates the variation data with its corresponding HGVS nomenclature and existing external Id. The final step uses the Jannovar library [11] to enrich the data with Functional Consequence annotations. After the data has been pre-processed through the pipeline, they are inserted into a MySQL database with the help of custom tools developed to create the canonical variants representations. MVAR supports programmatic data access to the registry through an API for interoperability. This API is used by a user-friendly web-application with rich user interfaces to query the database and display results. The API is also available to be a resource for other services or applications over HTTP with JSON data payloads. Wide-used industry frameworks like Angular and Groovy Grails were leveraged to build the MVAR web application. To conclude, the lack of a comprehensive, annotated genome variation resource for mouse is a significant barrier to comparing variation and its biological consequences between mouse and human and limits the impact of many research and resource development programs. The MVAR project seeks to address this resource gap by bringing together investigators that have active projects in the area of genome variation in either mouse or human or both. Many of the investigators on this project have developed independent resources to curate or manage genome variation. This project aims to unify these efforts and build a common data resource. Future work will include the incorporation of structural variants into the MVAR registry.

Visualizing regions of conserved synteny between two genomes is supported by numerous software applications. However, none of the current applications allow researchers to select genome features to display or highlight in blocks of synteny based on the annotated biological properties of the features (e.g., type, function, and/or phenotype association). To address this usability gap, we developed an interactive web-based conserved synteny browser, The Jackson Laboratory (JAX) Synteny Browser. The browser allows researchers to highlight or selectively display genome features in the reference and/or the comparison genome according to the biological attributes of the features. Although the current implementation for the browser is limited to the reference genomes for the laboratory mouse and human, the software platform is intentionally genome agnostic. The JAX Synteny Browser software can be deployed for any two genomes where genome coordinates for syntenic blocks are defined and for which biological attributes of the features in one or both genomes are available in widely used standard bioinformatics file formats. The JAX Synteny Browser is available at: http://syntenybrowser.jax.org/. The code base is available from GitHub: https://github.com/TheJacksonLaboratory/syntenybrowser and is distributed under the Creative Commons Attribution license (CC BY).

The Mouse Phenome Database (MPD; https://phenome.jax.org) is a widely accessed and highly functional data repository housing primary phenotype data for the laboratory mouse accessible via APIs and providing tools to analyze and visualize those data. Data come from investigators around the world and represent a broad scope of phenotyping endpoints and disease-related traits in naïve mice and those exposed to drugs, environmental agents or other treatments. MPD houses rigorously curated per-animal data with detailed protocols. Public ontologies and controlled vocabularies are used for annotation. In addition to phenotype tools, genetic analysis tools enable users to integrate and interpret genome–phenome relations across the database. Strain types and populations include inbred, recombinant inbred, F1 hybrid, transgenic, targeted mutants, chromosome substitution, Collaborative Cross, Diversity Outbred and other mapping populations. Our new analysis tools allow users to apply selected data in an integrated fashion to address problems in trait associations, reproducibility, polygenic syndrome model selection and multi-trait modeling. As we refine these tools and approaches, we will continue to provide users a means to identify consistent, quality studies that have high translational relevance.

Background Switchgrass (Panicum virgatum L.) is an important bioenergy crop widely used for lignocellulosic research. While extensive transcriptomic analyses have been conducted on this species using short read-based sequencing techniques, very little has been reliably derived regarding alternatively spliced (AS) transcripts. Results We present an analysis of transcriptomes of six switchgrass tissue types pooled together, sequenced using Pacific Biosciences (PacBio) single-molecular long-read technology. Our analysis identified 105,419 unique transcripts covering 43,570 known genes and 8795 previously unknown genes. 45,168 are novel transcripts of known genes. A total of 60,096 AS transcripts are identified, 45,628 being novel. We have also predicted 1549 transcripts of genes involved in cell wall construction and remodeling, 639 being novel transcripts of known cell wall genes. Most of the predicted transcripts are validated against Illumina-based short reads. Specifically, 96% of the splice junction sites in all the unique transcripts are validated by at least five Illumina reads. Comparisons between genes derived from our identified transcripts and the current genome annotation revealed that among the gene set predicted by both analyses, 16,640 have different exon–intron structures. Conclusions Overall, substantial amount of new information is derived from the PacBio RNA data regarding both the transcriptome and the genome of switchgrass. Electronic supplementary material The online version of this article (10.1186/s13068-018-1167-z) contains supplementary material, which is available to authorized users.

© 2017 Nature America, Inc., part of Springer Nature. All rights reserved. N6-methyldeoxyadenine (6mA) is a noncanonical DNA base modification present at low levels in plant and animal genomes, but its prevalence and association with genome function in other eukaryotic lineages remains poorly understood. Here we report that abundant 6mA is associated with transcriptionally active genes in early-diverging fungal lineages. Using single-molecule long-read sequencing of 16 diverse fungal genomes, we observed that up to 2.8% of all adenines were methylated in early-diverging fungi, far exceeding levels observed in other eukaryotes and more derived fungi. 6mA occurred symmetrically at ApT dinucleotides and was concentrated in dense methylated adenine clusters surrounding the transcriptional start sites of expressed genes; its distribution was inversely correlated with that of 5-methylcytosine. Our results show a striking contrast in the genomic distributions of 6mA and 5-methylcytosine and reinforce a distinct role for 6mA as a gene-expression-associated epigenomic mark in eukaryotes.

Prenatal and early postnatal environment can persistently alter one's risk of obesity. Environmental effects on hypothalamic developmental epigenetics constitute a likely mechanism underlying such 'developmental programming' of energy balance regulation. To advance our understanding of these processes, it is essential to develop approaches to disentangle the cellular and regional heterogeneity of hypothalamic developmental epigenetics. We therefore performed genome-scale DNA methylation profiling in hypothalamic neurons and non-neuronal cells at postnatal day 0 (P0) and P21 and found, surprisingly, that most of the DNA methylation differences distinguishing these two cell types are established postnatally. In particular, neuron-specific increases in DNA methylation occurred extensively at genes involved in neuronal development. Quantitative bisulfite pyrosequencing verified our methylation profiling results in all 15 regions examined, and expression differences were associated with DNA methylation at several genes. We also identified extensive methylation differences between the arcuate (ARH) and paraventricular nucleus of the hypothalamus (PVH). Integrating these two data sets showed that genomic regions with PVH versus ARH differential methylation strongly overlap with those undergoing neuron-specific increases from P0 to P21, suggesting that these developmental changes occur preferentially in either the ARH or PVH. In particular, neuron-specific methylation increases at the 3' end of Shh localized to the ARH and were positively associated with gene expression. Our data indicate a key role for DNA methylation in establishing the gene expression potential of diverse hypothalamic cell types, and provide the novel insight that early postnatal life is a critical period for cell type-specific epigenetic development in the murine hypothalamus.

Coupling bisulfite conversion with next-generation sequencing (Bisulfite-seq) enables genome-wide measurement of DNA methylation, but poses unique challenges for mapping. However, despite a proliferation of Bisulfite-seq mapping tools, no systematic comparison of their genomic coverage and quantitative accuracy has been reported. We sequenced bisulfite-converted DNA from two tissues from each of two healthy human adults and systematically compared five widely used Bisulfite-seq mapping algorithms: Bismark, BSMAP, Pash, BatMeth and BS Seeker. We evaluated their computational speed and genomic coverage and verified their percentage methylation estimates. With the exception of BatMeth, all mappers covered >70% of CpG sites genome-wide and yielded highly concordant estimates of percentage methylation (r2 ≥ 0.95). Fourfold variation in mapping time was found between BSMAP (fastest) and Pash (slowest). In each library, 8–12% of genomic regions covered by Bismark and Pash were not covered by BSMAP. An experiment using simulated reads confirmed that Pash has an exceptional ability to uniquely map reads in genomic regions of structural variation. Independent verification by bisulfite pyrosequencing generally confirmed the percentage methylation estimates by the mappers. Of these algorithms, Bismark provides an attractive combination of processing speed, genomic coverage and quantitative accuracy, whereas Pash offers considerably higher genomic coverage.