The Monarch Initiative in 2024: an analytic platform integrating phenotypes, genes and diseases across species.

Bridging the gap between genetic variations, environmental determinants, and phenotypic outcomes is critical for supporting clinical diagnosis and understanding mechanisms of diseases. It requires integrating open data at a global scale. The Monarch Initiative advances these goals by developing open ontologies, semantic data models, and knowledge graphs for translational research. The Monarch App is an integrated platform combining data about genes, phenotypes, and diseases across species. Monarch's APIs enable access to carefully curated datasets and advanced analysis tools that support the understanding and diagnosis of disease for diverse applications such as variant prioritization, deep phenotyping, and patient profile-matching. We have migrated our system into a scalable, cloud-based infrastructure; simplified Monarch's data ingestion and knowledge graph integration systems; enhanced data mapping and integration standards; and developed a new user interface with novel search and graph navigation features. Furthermore, we advanced Monarch's analytic tools by developing a customized plugin for OpenAI's ChatGPT to increase the reliability of its responses about phenotypic data, allowing us to interrogate the knowledge in the Monarch graph using state-of-the-art Large Language Models. The resources of the Monarch Initiative can be found at monarchinitiative.org and its corresponding code repository at github.com/monarch-initiative/monarch-app.

KG-Hub-building and exchanging biological knowledge graphs.

MOTIVATION: Knowledge graphs (KGs) are a powerful approach for integrating heterogeneous data and making inferences in biology and many other domains, but a coherent solution for constructing, exchanging, and facilitating the downstream use of KGs is lacking. RESULTS: Here we present KG-Hub, a platform that enables standardized construction, exchange, and reuse of KGs. Features include a simple, modular extract-transform-load pattern for producing graphs compliant with Biolink Model (a high-level data model for standardizing biological data), easy integration of any OBO (Open Biological and Biomedical Ontologies) ontology, cached downloads of upstream data sources, versioned and automatically updated builds with stable URLs, web-browsable storage of KG artifacts on cloud infrastructure, and easy reuse of transformed subgraphs across projects. Current KG-Hub projects span use cases including COVID-19 research, drug repurposing, microbial-environmental interactions, and rare disease research. KG-Hub is equipped with tooling to easily analyze and manipulate KGs. KG-Hub is also tightly integrated with graph machine learning (ML) tools which allow automated graph ML, including node embeddings and training of models for link prediction and node classification. AVAILABILITY AND IMPLEMENTATION: https://kghub.org.

Biolink Model: A universal schema for knowledge graphs in clinical, biomedical, and translational science.

Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph-based data models elucidate the interconnectedness among core biomedical concepts, enable data structures to be easily updated, and support intuitive queries, visualizations, and inference algorithms. However, knowledge discovery across these "knowledge graphs" (KGs) has remained difficult. Data set heterogeneity and complexity; the proliferation of ad hoc data formats; poor compliance with guidelines on findability, accessibility, interoperability, and reusability; and, in particular, the lack of a universally accepted, open-access model for standardization across biomedical KGs has left the task of reconciling data sources to downstream consumers. Biolink Model is an open-source data model that can be used to formalize the relationships between data structures in translational science. It incorporates object-oriented classification and graph-oriented features. The core of the model is a set of hierarchical, interconnected classes (or categories) and relationships between them (or predicates) representing biomedical entities such as gene, disease, chemical, anatomic structure, and phenotype. The model provides class and edge attributes and associations that guide how entities should relate to one another. Here, we highlight the need for a standardized data model for KGs, describe Biolink Model, and compare it with other models. We demonstrate the utility of Biolink Model in various initiatives, including the Biomedical Data Translator Consortium and the Monarch Initiative, and show how it has supported easier integration and interoperability of biomedical KGs, bringing together knowledge from multiple sources and helping to realize the goals of translational science.

Knowledge Beacons: Web services for data harvesting of distributed biomedical knowledge.

AVAILABILITY: The API and associated software is open source and currently available for access at https://github.com/NCATS-Tangerine/translator-knowledge-beacon.

The Implicitome: A Resource for Rationalizing Gene-Disease Associations.

High-throughput experimental methods such as medical sequencing and genome-wide association studies (GWAS) identify increasingly large numbers of potential relations between genetic variants and diseases. Both biological complexity (millions of potential gene-disease associations) and the accelerating rate of data production necessitate computational approaches to prioritize and rationalize potential gene-disease relations. Here, we use concept profile technology to expose from the biomedical literature both explicitly stated gene-disease relations (the explicitome) and a much larger set of implied gene-disease associations (the implicitome). Implicit relations are largely unknown to, or are even unintended by the original authors, but they vastly extend the reach of existing biomedical knowledge for identification and interpretation of gene-disease associations. The implicitome can be used in conjunction with experimental data resources to rationalize both known and novel associations. We demonstrate the usefulness of the implicitome by rationalizing known and novel gene-disease associations, including those from GWAS. To facilitate the re-use of implicit gene-disease associations, we publish our data in compliance with FAIR Data Publishing recommendations [https://www.force11.org/group/fairgroup] using nanopublications. An online tool (http://knowledge.bio) is available to explore established and potential gene-disease associations in the context of other biomedical relations.

Evaluating methods for isolating total RNA and predicting the success of sequencing phylogenetically diverse plant transcriptomes.

Next-generation sequencing plays a central role in the characterization and quantification of transcriptomes. Although numerous metrics are purported to quantify the quality of RNA, there have been no large-scale empirical evaluations of the major determinants of sequencing success. We used a combination of existing and newly developed methods to isolate total RNA from 1115 samples from 695 plant species in 324 families, which represents >900 million years of phylogenetic diversity from green algae through flowering plants, including many plants of economic importance. We then sequenced 629 of these samples on Illumina GAIIx and HiSeq platforms and performed a large comparative analysis to identify predictors of RNA quality and the diversity of putative genes (scaffolds) expressed within samples. Tissue types (e.g., leaf vs. flower) varied in RNA quality, sequencing depth and the number of scaffolds. Tissue age also influenced RNA quality but not the number of scaffolds ≥ 1000 bp. Overall, 36% of the variation in the number of scaffolds was explained by metrics of RNA integrity (RIN score), RNA purity (OD 260/230), sequencing platform (GAIIx vs HiSeq) and the amount of total RNA used for sequencing. However, our results show that the most commonly used measures of RNA quality (e.g., RIN) are weak predictors of the number of scaffolds because Illumina sequencing is robust to variation in RNA quality. These results provide novel insight into the methods that are most important in isolating high quality RNA for sequencing and assembling plant transcriptomes. The methods and recommendations provided here could increase the efficiency and decrease the cost of RNA sequencing for individual labs and genome centers.

Light-regulated and cell-specific methylation of the maize PEPC promoter.

The molecular mechanisms governing PEPC expression in maize remain to be fully defined. Differential methylation of a region in the PEPC promoter has been shown to correlate with transcript accumulation, however, to date, investigations into the role of DNA methylation in maize PEPC expression have relied on the use of methylation-sensitive restriction enzymes. Bisulphite sequencing was used here to provide a single-base resolution methylation map of the maize PEPC promoter. It is shown that four cytosine residues in the PEPC promoter are heavily methylated in maize root tissue. In leaves, de-methylation of these cytosines is dependent on illumination and is coincident with elevated PEPC expression. Furthermore, light-regulated de-methylation of these cytosines occurs only in mesophyll cells. No methylation was discovered in the 0.6 kb promoter required for mesophyll-specific expression indicating that cytosine methylation is not required to direct the cell-specificity of PEPC expression. This raises interesting questions regarding the function of the cell-specific cytosine de-methylation observed in the upstream region of the PEPC promoter.

Characterization of statistical features for plant microRNA prediction.

BACKGROUND: Several tools are available to identify miRNAs from deep-sequencing data, however, only a few of them, like miRDeep, can identify novel miRNAs and are also available as a standalone application. Given the difference between plant and animal miRNAs, particularly in terms of distribution of hairpin length and the nature of complementarity with its duplex partner (or miRNA star), the underlying (statistical) features of miRDeep and other tools, using similar features, are likely to get affected. RESULTS: The potential effects on features, such as minimum free energy, stability of secondary structures, excision length, etc., were examined, and the parameters of those displaying sizable changes were estimated for plant specific miRNAs. We found most of these features acquired a new set of values or distributions for plant specific miRNAs. While the length of conserved positions (nucleus) in mature miRNAs were relatively longer in plants, the difference in distribution of minimum free energy, between real and background hairpins, was marginal. However, the choice of source (species) of background sequences was found to affect both the minimum free energy and miRNA hairpin stability. The new parameters were tested on an Illumina dataset from maize seedlings, and the results were compared with those obtained using default parameters. The newly parameterized model was found to have much improved specificity and sensitivity over its default counterpart. CONCLUSIONS: In summary, the present study reports behavior of few general and tool-specific statistical features for improving the prediction accuracy of plant miRNAs from deep-sequencing data.

Physiological and proteomic responses of rice peduncles to drought stress.

Panicle exsertion, an essential physiological process for obtaining high grain yield in rice is mainly driven by peduncle (uppermost internode) elongation. Drought at heading/panicle emergence prevented peduncle elongation from reaching its maximum length even after re-watering. This inhibitory effect of drought resulted in delayed heading and trapping spikelets lower down the panicle inside the flag-leaf sheath, thus increasing sterility in the lower un-exserted spikelets and also among the upper superior spikelets whose exsertion was delayed. Intermittent drought stress caused a significant reduction in relative water content (RWC) and an increase in the abscisic acid (ABA) level of the peduncles, while both returned to normal levels upon re-watering. Semi-quantitative reverse transcription polymerase chain reaction (RT-PCR) analysis revealed the down-regulation of GA biosynthetic genes during drought. 2D-PAGE analysis of proteins from peduncles collected under well-watered, drought-stressed, and re-watered plants revealed at least twofold differential changes in expression of 31 proteins in response to drought and most of these changes were largely reversed by re-watering. The results indicate that ABA-GA antagonism is a key focal point for understanding the failure of panicle exsertion under drought stress and the consequent increase in spikelet sterility.

The DBCLS BioHackathon: standardization and interoperability for bioinformatics web services and workflows. The DBCLS BioHackathon Consortium*.

Web services have become a key technology for bioinformatics, since life science databases are globally decentralized and the exponential increase in the amount of available data demands for efficient systems without the need to transfer entire databases for every step of an analysis. However, various incompatibilities among database resources and analysis services make it difficult to connect and integrate these into interoperable workflows. To resolve this situation, we invited domain specialists from web service providers, client software developers, Open Bio* projects, the BioMoby project and researchers of emerging areas where a standard exchange data format is not well established, for an intensive collaboration entitled the BioHackathon 2008. The meeting was hosted by the Database Center for Life Science (DBCLS) and Computational Biology Research Center (CBRC) and was held in Tokyo from February 11th to 15th, 2008. In this report we highlight the work accomplished and the common issues arisen from this event, including the standardization of data exchange formats and services in the emerging fields of glycoinformatics, biological interaction networks, text mining, and phyloinformatics. In addition, common shared object development based on BioSQL, as well as technical challenges in large data management, asynchronous services, and security are discussed. Consequently, we improved interoperability of web services in several fields, however, further cooperation among major database centers and continued collaborative efforts between service providers and software developers are still necessary for an effective advance in bioinformatics web service technologies.

Transcriptional regulatory network triggered by oxidative signals configures the early response mechanisms of japonica rice to chilling stress.

BACKGROUND: The transcriptional regulatory network involved in low temperature response leading to acclimation has been established in Arabidopsis. In japonica rice, which can only withstand transient exposure to milder cold stress (10 degrees C), an oxidative-mediated network has been proposed to play a key role in configuring early responses and short-term defenses. The components, hierarchical organization and physiological consequences of this network were further dissected by a systems-level approach. RESULTS: Regulatory clusters responding directly to oxidative signals were prominent during the initial 6 to 12 hours at 10 degrees C. Early events mirrored a typical oxidative response based on striking similarities of the transcriptome to disease, elicitor and wounding induced processes. Targets of oxidative-mediated mechanisms are likely regulated by several classes of bZIP factors acting on as1/ocs/TGA-like element enriched clusters, ERF factors acting on GCC-box/JAre-like element enriched clusters and R2R3-MYB factors acting on MYB2-like element enriched clusters.Temporal induction of several H2O2-induced bZIP, ERF and MYB genes coincided with the transient H2O2 spikes within the initial 6 to 12 hours. Oxidative-independent responses involve DREB/CBF, RAP2 and RAV1 factors acting on DRE/CRT/rav1-like enriched clusters and bZIP factors acting on ABRE-like enriched clusters. Oxidative-mediated clusters were activated earlier than ABA-mediated clusters. CONCLUSION: Genome-wide, physiological and whole-plant level analyses established a holistic view of chilling stress response mechanism of japonica rice. Early response regulatory network triggered by oxidative signals is critical for prolonged survival under sub-optimal temperature. Integration of stress and developmental responses leads to modulated growth and vigor maintenance contributing to a delay of plastic injuries.

Multifunctional crop trait ontology for breeders' data: field book, annotation, data discovery and semantic enrichment of the literature.

BACKGROUND AND AIMS: Agricultural crop databases maintained in gene banks of the Consultative Group on International Agricultural Research (CGIAR) are valuable sources of information for breeders. These databases provide comparative phenotypic and genotypic information that can help elucidate functional aspects of plant and agricultural biology. To facilitate data sharing within and between these databases and the retrieval of information, the crop ontology (CO) database was designed to provide controlled vocabulary sets for several economically important plant species. METHODOLOGY: Existing public ontologies and equivalent catalogues of concepts covering the range of crop science information and descriptors for crops and crop-related traits were collected from breeders, physiologists, agronomists, and researchers in the CGIAR consortium. For each crop, relationships between terms were identified and crop-specific trait ontologies were constructed following the Open Biomedical Ontologies (OBO) format standard using the OBO-Edit tool. All terms within an ontology were assigned a globally unique CO term identifier. PRINCIPAL RESULTS: The CO currently comprises crop-specific traits for chickpea (Cicer arietinum), maize (Zea mays), potato (Solanum tuberosum), rice (Oryza sativa), sorghum (Sorghum spp.) and wheat (Triticum spp.). Several plant-structure and anatomy-related terms for banana (Musa spp.), wheat and maize are also included. In addition, multi-crop passport terms are included as controlled vocabularies for sharing information on germplasm. Two web-based online resources were built to make these COs available to the scientific community: the 'CO Lookup Service' for browsing the CO; and the 'Crops Terminizer', an ontology text mark-up tool. CONCLUSIONS: The controlled vocabularies of the CO are being used to curate several CGIAR centres' agronomic databases. The use of ontology terms to describe agronomic phenotypes and the accurate mapping of these descriptions into databases will be important steps in comparative phenotypic and genotypic studies across species and gene-discovery experiments.

Genomewide SNP variation reveals relationships among landraces and modern varieties of rice.

Rice, the primary source of dietary calories for half of humanity, is the first crop plant for which a high-quality reference genome sequence from a single variety was produced. We used resequencing microarrays to interrogate 100 Mb of the unique fraction of the reference genome for 20 diverse varieties and landraces that capture the impressive genotypic and phenotypic diversity of domesticated rice. Here, we report the distribution of 160,000 nonredundant SNPs. Introgression patterns of shared SNPs revealed the breeding history and relationships among the 20 varieties; some introgressed regions are associated with agronomic traits that mark major milestones in rice improvement. These comprehensive SNP data provide a foundation for deep exploration of rice diversity and gene-trait relationships and their use for future rice improvement.

RiceGeneThresher: a web-based application for mining genes underlying QTL in rice genome.

RiceGeneThresher is a public online resource for mining genes underlying genome regions of interest or quantitative trait loci (QTL) in rice genome. It is a compendium of rice genomic resources consisting of genetic markers, genome annotation, expressed sequence tags (ESTs), protein domains, gene ontology, plant stress-responsive genes, metabolic pathways and prediction of protein-protein interactions. RiceGeneThresher system integrates these diverse data sources and provides powerful web-based applications, and flexible tools for delivering customized set of biological data on rice. Its system supports whole-genome gene mining for QTL by querying using DNA marker intervals or genomic loci. RiceGeneThresher provides biologically supported evidences that are essential for targeting groups or networks of genes involved in controlling traits underlying QTL. Users can use it to discover and to assign the most promising candidate genes in preparation for the further gene function validation analysis. The web-based application is freely available at http://rice.kps.ku.ac.th.

The generation challenge programme platform: semantic standards and workbench for crop science.

The Generation Challenge programme (GCP) is a global crop research consortium directed toward crop improvement through the application of comparative biology and genetic resources characterization to plant breeding. A key consortium research activity is the development of a GCP crop bioinformatics platform to support GCP research. This platform includes the following: (i) shared, public platform-independent domain models, ontology, and data formats to enable interoperability of data and analysis flows within the platform; (ii) web service and registry technologies to identify, share, and integrate information across diverse, globally dispersed data sources, as well as to access high-performance computational (HPC) facilities for computationally intensive, high-throughput analyses of project data; (iii) platform-specific middleware reference implementations of the domain model integrating a suite of public (largely open-access/-source) databases and software tools into a workbench to facilitate biodiversity analysis, comparative analysis of crop genomic data, and plant breeding decision making.

Interoperability with Moby 1.0--it's better than sharing your toothbrush!

The BioMoby project was initiated in 2001 from within the model organism database community. It aimed to standardize methodologies to facilitate information exchange and access to analytical resources, using a consensus driven approach. Six years later, the BioMoby development community is pleased to announce the release of the 1.0 version of the interoperability framework, registry Application Programming Interface and supporting Perl and Java code-bases. Together, these provide interoperable access to over 1400 bioinformatics resources worldwide through the BioMoby platform, and this number continues to grow. Here we highlight and discuss the features of BioMoby that make it distinct from other Semantic Web Service and interoperability initiatives, and that have been instrumental to its deployment and use by a wide community of bioinformatics service providers. The standard, client software, and supporting code libraries are all freely available at http://www.biomoby.org/.

Development of a novel data mining tool to find cis-elements in rice gene promoter regions.

BACKGROUND: Information on more than 35 000 full-length Oryza sativa cDNAs, together with associated microarray gene expression data collected under various treatment conditions, has made it feasible to identify motifs that are conserved in gene promoters and may act as cis-regulatory elements with key roles under the various conditions. RESULTS: We have developed a novel tool that searches for cis-element candidates in the upstream, downstream, or coding regions of differentially regulated genes. The tool first lists cis-element candidates by motif searching based on the supposition that if there are cis-elements playing important roles in the regulation of a given set of genes, they will be statistically overrepresented and will be conserved. Then it evaluates the likelihood scores of the listed candidate motifs by association rule analysis. This strategy depends on the idea that motifs overrepresented in the promoter region could play specific roles in the regulation of expression of these genes. The tool is designed so that any biological researchers can use it easily at the publicly accessible Internet site http://hpc.irri.cgiar.org/tool/nias/ces. We evaluated the accuracy and utility of the tool by using a dataset of auxin-inducible genes that have well-studied cis-elements. The test showed the effectiveness of the tool in identifying significant relationships between cis-element candidates and related sets of genes. CONCLUSION: The tool lists possible cis-element motifs corresponding to genes of interest, and it will contribute to the deeper understanding of gene regulatory mechanisms in plants.

The Generation Challenge Programme comparative plant stress-responsive gene catalogue.

The Generation Challenge Programme (GCP; www.generationcp.org) has developed an online resource documenting stress-responsive genes comparatively across plant species. This public resource is a compendium of protein families, phylogenetic trees, multiple sequence alignments (MSA) and associated experimental evidence. The central objective of this resource is to elucidate orthologous and paralogous relationships between plant genes that may be involved in response to environmental stress, mainly abiotic stresses such as water deficit ('drought'). The web-based graphical user interface (GUI) of the resource includes query and visualization tools that allow diverse searches and browsing of the underlying project database. The web interface can be accessed at http://dayhoff.generationcp.org.

Curated genome annotation of Oryza sativa ssp. japonica and comparative genome analysis with Arabidopsis thaliana.

We present here the annotation of the complete genome of rice Oryza sativa L. ssp. japonica cultivar Nipponbare. All functional annotations for proteins and non-protein-coding RNA (npRNA) candidates were manually curated. Functions were identified or inferred in 19,969 (70%) of the proteins, and 131 possible npRNAs (including 58 antisense transcripts) were found. Almost 5000 annotated protein-coding genes were found to be disrupted in insertional mutant lines, which will accelerate future experimental validation of the annotations. The rice loci were determined by using cDNA sequences obtained from rice and other representative cereals. Our conservative estimate based on these loci and an extrapolation suggested that the gene number of rice is approximately 32,000, which is smaller than previous estimates. We conducted comparative analyses between rice and Arabidopsis thaliana and found that both genomes possessed several lineage-specific genes, which might account for the observed differences between these species, while they had similar sets of predicted functional domains among the protein sequences. A system to control translational efficiency seems to be conserved across large evolutionary distances. Moreover, the evolutionary process of protein-coding genes was examined. Our results suggest that natural selection may have played a role for duplicated genes in both species, so that duplication was suppressed or favored in a manner that depended on the function of a gene.

International crop information system for germplasm data management.

Passport and phenotypic data on germplasm and breeding lines are available from worldwide sources in various electronic formats. These data can be collated into a single database format to enable strategic interrogation to make the best use of data for effective germplasm use and enhancement. The International Crop Information System (http://www.icis.cgiar.org) is an open-source project under development by a global community of crop researchers and includes applications designed to achieve the storage and interrogation of pedigree and phenotypic data.