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The Mouse Phenome Database (MPD; https://phenome.jax.org; RRID:SCR_003212), supported by the US National Institutes of Health, is a Biomedical Data Repository listed in the Trans-NIH Biomedical Informatics Coordinating Committee registry. As an increasingly FAIR-compliant and TRUST-worthy data repository, MPD accepts phenotype and genotype data from mouse experiments and curates, organizes, integrates, archives, and distributes those data using community standards. Data are accompanied by rich metadata, including widely used ontologies and detailed protocols. Data are from all over the world and represent genetic, behavioral, morphological, and physiological disease-related characteristics in mice at baseline or those exposed to drugs or other treatments. MPD houses data from over 6000 strains and populations, representing many reproducible strain types and heterogenous populations such as the Diversity Outbred where each mouse is unique but can be genotyped throughout the genome. A suite of analysis tools is available to aggregate, visualize, and analyze these data within and across studies and populations in an increasingly traceable and reproducible manner. We have refined existing resources and developed new tools to continue to provide users with access to consistent, high-quality data that has translational relevance in a modernized infrastructure that enables interaction with a suite of bioinformatics analytic and data services.

Many bioinformatics cores face a multitude of challenges. We recognized that the primary source of these challenges was the service-centric approach. So, we initiated the transformation of our bioinformatics core, Computational Sciences (CS), at the Jackson Laboratory (Jax) to be a science-centric collaborative research partner for our faculty and project stakeholders. We call our model as collaborative partnership model. With the effective replacement of the service model with the collaborative partnership model, CS now acts as both an effective collaborator and a co-driver of scientific research and innovation at Jax. In this paper, we describe the principles and practices we adopted to realize this transformation and present the resulting growth in the impact of CS in the research enterprise at Jax.

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).

Chronic periodontitis (CP) is a microbial dysbiotic disease linked to increased risk of oral squamous cell carcinomas (OSCCs). To address the underlying mechanisms, mouse and human cell infection models and human biopsy samples were employed. We show that the ‘keystone’ pathogen Porphyromonas gingivalis, disrupts immune surveillance by generating myeloid-derived dendritic suppressor cells (MDDSCs) from monocytes. MDDSCs inhibit CTLs and induce FOXP3 + Tregs through an anti-apoptotic pathway. This pathway, involving pAKT1, pFOXO1, FOXP3, IDO1 and BIM, is activated in humans with CP and in mice orally infected with Mfa1 expressing P. gingivalis strains. Mechanistically, activation of this pathway, demonstrating FOXP3 as a direct FOXO1-target gene, was demonstrated by ChIP-assay in human CP gingiva. Expression of oncogenic but not tumor suppressor markers is consistent with tumor cell proliferation demonstrated in OSCC-P. gingivalis cocultures. Importantly, FimA + P. gingivalis strain MFI invades OSCCs, inducing inflammatory/angiogenic/oncogenic proteins stimulating OSCCs proliferation through CXCR4. Inhibition of CXCR4 abolished Pg-MFI-induced OSCCs proliferation and reduced expression of oncogenic proteins SDF-1/CXCR4, plus pAKT1-pFOXO1. Conclusively, P. gingivalis, through Mfa1 and FimA fimbriae, promotes immunosuppression and oncogenic cell proliferation, respectively, through a two-hit receptor-ligand process involving DC-SIGN+hi/CXCR4+hi, activating a pAKT+hipFOXO1+hiBIM−lowFOXP3+hi and IDO+hi- driven pathway, likely to impact the prognosis of oral cancers in patients with periodontitis.

Marine algae perform approximately half of global carbon fixation, but their growth is often limited by the availability of phosphate or other nutrients. As oceans warm, the area of phosphate-limited surface waters is predicted to increase, resulting in ocean desertification. Understanding the responses of key eukaryotic phytoplankton to nutrient limitation is therefore critical. We used advanced photo-bioreactors to investigate how the widespread marine green alga Micromonas commoda grows under transitions from replete nutrients to chronic phosphate limitation and subsequent relief, analysing photosystem changes and broad cellular responses using proteomics, transcriptomics and biophysical measurements. We find that physiological and protein expression responses previously attributed to stress are critical to supporting stable exponential growth when phosphate is limiting. Unexpectedly, the abundance of most proteins involved in light harvesting does not change, but an ancient light-harvesting-related protein, LHCSR, is induced and dissipates damaging excess absorbed light as heat throughout phosphate limitation. Concurrently, a suite of uncharacterized proteins with narrow phylogenetic distributions increase multifold. Notably, of the proteins that exhibit significant changes, 70% are not differentially expressed at the mRNA transcript level, highlighting the importance of post-transcriptional processes in microbial eukaryotes. Nevertheless, transcript–protein pairs with concordant changes were identified that will enable more robust interpretation of eukaryotic phytoplankton responses in the field from metatranscriptomic studies. Our results show that P-limited Micromonas responds quickly to a fresh pulse of phosphate by rapidly increasing replication, and that the protein network associated with this ability is composed of both conserved and phylogenetically recent proteome systems that promote dynamic phosphate homeostasis. That an ancient mechanism for mitigating light stress is central to sustaining growth during extended phosphate limitation highlights the possibility of interactive effects arising from combined stressors under ocean change, which could reduce the efficacy of algal strategies for optimizing marine photosynthesis.

The Mouse Phenome Database (MPD; https://phenome.jax.org) is a widely used resource that provides access to primary experimental trait data, genotypic variation, protocols and analysis tools for mouse genetic studies. Data are contributed by investigators worldwide 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 individual animal data with detailed, searchable protocols, and makes these data available to other resources via API. MPD provides rigorous curation of experimental data and supporting documentation using relevant ontologies and controlled vocabularies. Most data in MPD are from inbreds and other reproducible strains such that the data are cumulative over time and across laboratories. The resource has been expanded to include the QTL Archive and other primary phenotype data from mapping crosses as well as advanced high-diversity mouse populations including the Collaborative Cross and Diversity Outbred mice. Furthermore, MPD provides a means of assessing replicability and reproducibility across experimental conditions and protocols, benchmarking assays in users’ own laboratories, identifying sensitized backgrounds for making new mouse models with genome editing technologies, analyzing trait co-inheritance, finding the common genetic basis for multiple traits and assessing sex differences and sex-by-genotype interactions.

Summary Plant secondary cell walls constitute the majority of plant biomass. They are predominantly found in xylem cells, which are derived from vascular initials during vascularization. Little is known about these processes in grass species despite their emerging importance as biomass feedstocks. The targeted biofuel crop Sorghum bicolor has a sequenced and well‐annotated genome, making it an ideal monocot model for addressing vascularization and biomass deposition.Here we generated tissue‐specific transcriptome and DNA methylome data from sorghum shoots, roots and developing root vascular and nonvascular tissues.Many genes associated with vascular development in other species show enriched expression in developing vasculature. However, several transcription factor families varied in vascular expression in sorghum compared with Arabidopsis and maize. Furthermore, differential expression of genes associated with DNA methylation were identified between vascular and nonvascular tissues, implying that changes in DNA methylation are a feature of sorghum root vascularization, which we confirmed using tissue‐specific DNA methylome data. Roots treated with a DNA methylation inhibitor also showed a significant decrease in root length.Tissues and organs can be discriminated based on their genomic methylation patterns and methylation context. Consequently, tissue‐specific changes in DNA methylation are part of the normal developmental process.

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.

Switchgrass ( is a perennial native North American grass present in two ecotypes: upland, found primarily in the northern range of switchgrass habitats, and lowland, found largely in the southern reaches of switchgrass habitats. Previous studies focused on a diversity panel of primarily northern switchgrass, so to expand our knowledge of genetic diversity in a broader set of North American switchgrass, exome capture sequence data were generated for 632 additional, primarily lowland individuals. In total, over 37 million single nucleotide polymorphisms (SNPs) were identified and a set of 1.9 million high-confidence SNPs were obtained from 1169 individuals from 140 populations (67 upland, 65 lowland, 8 admixed) were used in downstream analyses of genetic diversity and population structure. Seven separate population groups were identified with moderate genetic differentiation [mean fixation index (Fst) estimate of 0.06] between the lowland and the upland populations. Ecotype-specific and population-specific SNPs were identified for use in germplasm evaluations. Relative to rice ( L.), maize ( L.), soybean [ (L.) Merr.], and Gaertn., analyses of nucleotide diversity revealed a high degree of genetic diversity (0.0135) across all individuals, consistent with the outcrossing mode of reproduction and the polyploidy of switchgrass. This study supports the hypothesis that repeated glaciation events, ploidy barriers, and restricted gene flow caused by flowering time differences have resulted in distinct gene pools across ecotypes and geographic regions. These data provide a resource to associate alleles with traits of interest for forage, restoration, and biofuel feedstock efforts in switchgrass.

Marine algae perform approximately half of global carbon fixation, but their growth is often limited by the availability of phosphate or other nutrients

© 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.

During development, a small but significant number of CpG islands (CGIs) become methylated. The timing of developmentally programmed CGI methylation and associated mechanisms of transcriptional regulation during cellular differentiation, however, remain poorly characterized. Here, we used genome-wide DNA methylation microarrays to identify epigenetic changes during human embryonic stem cell (hESC) differentiation. We discovered a group of CGIs associated with developmental genes that gain methylation after hESCs differentiate. Conversely, erasure of methylation was observed at the identified CGIs during subsequent reprogramming to induced pluripotent stem cells (iPSCs), further supporting a functional role for the CGI methylation. Both global gene expression profiling and quantitative reverse transcription-PCR (RT-PCR) validation indicated opposing effects of CGI methylation in transcriptional regulation during differentiation, with promoter CGI methylation repressing and 3' CGI methylation activating transcription. By studying diverse human tissues and mouse models, we further confirmed that developmentally programmed 3' CGI methylation confers tissue- and cell-type-specific gene activation in vivo. Importantly, luciferase reporter assays provided evidence that 3' CGI methylation regulates transcriptional activation via a CTCF-dependent enhancer-blocking mechanism. These findings expand the classic view of mammalian CGI methylation as a mechanism for transcriptional silencing and indicate a functional role for 3' CGI methylation in developmental gene regulation.

Panicum virgatum L. (switchgrass) is a polyploid, perennial grass species that is native to North America, and is being developed as a future biofuel feedstock crop. Switchgrass is present primarily in two ecotypes: a northern upland ecotype, composed of tetraploid and octoploid accessions, and a southern lowland ecotype, composed of primarily tetraploid accessions. We employed high-coverage exome capture sequencing (~2.4 Tb) to genotype 537 individuals from 45 upland and 21 lowland populations. From these data, we identified ~27 million single-nucleotide polymorphisms (SNPs), of which 1 590 653 high-confidence SNPs were used in downstream analyses of diversity within and between the populations. From the 66 populations, we identified five primary population groups within the upland and lowland ecotypes, a result that was further supported through genetic distance analysis. We identified conserved, ecotype-restricted, non-synonymous SNPs that are predicted to affect the protein function of CONSTANS (CO) and EARLY HEADING DATE 1 (EHD1), key genes involved in flowering, which may contribute to the phenotypic differences between the two ecotypes. We also identified, relative to the near-reference Kanlow population, 17 228 genes present in more copies than in the reference genome (up-CNVs), 112 630 genes present in fewer copies than in the reference genome (down-CNVs) and 14 430 presence/absence variants (PAVs), affecting a total of 9979 genes, including two upland-specific CNV clusters. In total, 45 719 genes were affected by an SNP, CNV, or PAV across the panel, providing a firm foundation to identify functional variation associated with phenotypic traits of interest for biofuel feedstock production.

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.