Genomics4RD: An integrated platform to share Canadian deep-phenotype and multiomic data for international rare disease gene discovery.
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ABSTRACT:
SUBMITTER: Driver HG
PROVIDER: S-EPMC9311832 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundNumerous studies have shown that bacteria form stable associations with host corals and have focused on identifying conserved "core microbiomes" of bacterial associates inferred to be serving key roles in the coral holobiont. Because studies tend to focus on only stony corals (order Scleractinia) or soft corals (order Alcyonacea), it is currently unknown if there are conserved bacteria that are shared by both. A meta-analysis was done of 16S rRNA amplicon data from multiple studies generated via identical methodology to allow direct comparisons of bacterial associates across seven deep-sea corals, including both stony and soft species: Anthothela grandiflora, Anthothela sp., Lateothela grandiflora, Lophelia pertusa, Paramuricea placomus, Primnoa pacifica, and Primnoa resedaeformis.ResultsTwenty-three operational taxonomic units (OTUs) were consistently present in greater than 50% of the coral samples. Seven amplicon sequence variants (ASVs), five of which corresponded to a conserved OTU, were consistently present in greater than 30% of the coral samples including five or greater coral species. A majority of the conserved sequences had close matches with previously identified coral-associated bacteria. While known to dominate tropical and temperate coral microbiomes, Endozoicomonas were extremely rare or absent from these deep-sea corals. An Endozoicomonas OTU associated with Lo. pertusa in this study was most similar to those from shallow-water stony corals, while an OTU associated with Anthothela spp. was most similar to those from shallow-water gorgonians.ConclusionsBacterial sequences have been identified that are conserved at the level of class Anthozoa (i.e., found in both stony and soft corals, shallow and deep). These bacterial associates are therefore hypothesized to play important symbiotic roles and are highlighted for targeted future study. These conserved bacterial associates include taxa with the potential for nitrogen and sulfur cycling, detoxification, and hydrocarbon degradation. There is also some overlap with kit contaminants that need to be resolved. Rarely detected Endozoicomonas sequences are partitioned by whether the host is a stony coral or a soft coral, and the finer clustering pattern reflects the hosts' phylogeny.
| S-EPMC6558771 | biostudies-literature
Project description:There are few better examples of the need for data sharing than in the rare disease community, where patients, physicians, and researchers must search for "the needle in a haystack" to uncover rare, novel causes of disease within the genome. Impeding the pace of discovery has been the existence of many small siloed datasets within individual research or clinical laboratory databases and/or disease-specific organizations, hoping for serendipitous occasions when two distant investigators happen to learn they have a rare phenotype in common and can "match" these cases to build evidence for causality. However, serendipity has never proven to be a reliable or scalable approach in science. As such, the Matchmaker Exchange (MME) was launched to provide a robust and systematic approach to rare disease gene discovery through the creation of a federated network connecting databases of genotypes and rare phenotypes using a common application programming interface (API). The core building blocks of the MME have been defined and assembled. Three MME services have now been connected through the API and are available for community use. Additional databases that support internal matching are anticipated to join the MME network as it continues to grow.
| S-EPMC4610002 | biostudies-literature
Project description:Drug discovery is a rigorous process that requires billion dollars of investments and decades of research to bring a molecule "from bench to a bedside". While virtual docking can significantly accelerate the process of drug discovery, it ultimately lags the current rate of expansion of chemical databases that already exceed billions of molecular records. This recent surge of small molecules availability presents great drug discovery opportunities, but also demands much faster screening protocols. In order to address this challenge, we herein introduce Deep Docking (DD), a novel deep learning platform that is suitable for docking billions of molecular structures in a rapid, yet accurate fashion. The DD approach utilizes quantitative structure-activity relationship (QSAR) deep models trained on docking scores of subsets of a chemical library to approximate the docking outcome for yet unprocessed entries and, therefore, to remove unfavorable molecules in an iterative manner. The use of DD methodology in conjunction with the FRED docking program allowed rapid and accurate calculation of docking scores for 1.36 billion molecules from the ZINC15 library against 12 prominent target proteins and demonstrated up to 100-fold data reduction and 6000-fold enrichment of high scoring molecules (without notable loss of favorably docked entities). The DD protocol can readily be used in conjunction with any docking program and was made publicly available.
| S-EPMC7318080 | biostudies-literature
Project description:Increasingly affordable high-throughput molecular profiling technologies have made feasible the measurement of omics-wide interindividual variations for the purposes of predicting cancer prognosis. While multiple types of genetic, epigenetic and expression changes have been implicated in ovarian cancer, existing prognostic biomarker strategies are constrained to analyzing a single class of molecular variations. The extra predictive power afforded by the integration of multiple omics types remains largely unexplored. In this study, we performed integrative analysis on tumor-based exome-, transcriptome- and methylome-wide molecular profiles from The Cancer Genome Atlas (TCGA) for variations in cancer-relevant genes to construct robust, cross-validated multiomic predictors for ovarian cancer survival. These integrated polygenic survival scores (PSSs) were able to predict 5-year overall (OS) and progression-free survival in the Caucasian subsample with high accuracy (AUROC = 0.87 and 0.81, respectively). These findings suggest that the PSSs are able to predict long-term OS in TCGA patients with accuracy beyond that of previously proposed protein-based biomarker strategies. Our findings reveal the promise of an integrated omics-based approach in enhancing existing prognostic strategies. Future investigations should be aimed toward prospective external validation, strategies for standardizing application and the integration of germline variants.
| S-EPMC6031025 | biostudies-literature
Project description:According to the World Health Organization, more than 1 billion people are at risk of or are affected by neglected tropical diseases. Examples of such diseases include trypanosomiasis, which causes sleeping sickness; leishmaniasis; and Chagas disease, all of which are prevalent in Africa, South America, and India. Our aim within the New Medicines for Trypanosomatidic Infections project was to use (1) synthetic and natural product libraries, (2) screening, and (3) a preclinical absorption, distribution, metabolism, and excretion-toxicity (ADME-Tox) profiling platform to identify compounds that can enter the trypanosomatidic drug discovery value chain. The synthetic compound libraries originated from multiple scaffolds with known antiparasitic activity and natural products from the Hypha Discovery MycoDiverse natural products library. Our focus was first to employ target-based screening to identify inhibitors of the protozoan Trypanosoma brucei pteridine reductase 1 ( TbPTR1) and second to use a Trypanosoma brucei phenotypic assay that made use of the T. brucei brucei parasite to identify compounds that inhibited cell growth and caused death. Some of the compounds underwent structure-activity relationship expansion and, when appropriate, were evaluated in a preclinical ADME-Tox assay panel. This preclinical platform has led to the identification of lead-like compounds as well as validated hits in the trypanosomatidic drug discovery value chain.
| S-EPMC6484532 | biostudies-literature
Project description:Mitochondria are epicentres of eukaryotic metabolism and bioenergetics. Pioneering efforts in recent decades have established the core protein componentry of these organelles1 and have linked their dysfunction to more than 150 distinct disorders2,3. Still, hundreds of mitochondrial proteins lack clear functions4, and the underlying genetic basis for approximately 40% of mitochondrial disorders remains unresolved5. Here, to establish a more complete functional compendium of human mitochondrial proteins, we profiled more than 200 CRISPR-mediated HAP1 cell knockout lines using mass spectrometry-based multiomics analyses. This effort generated approximately 8.3 million distinct biomolecule measurements, providing a deep survey of the cellular responses to mitochondrial perturbations and laying a foundation for mechanistic investigations into protein function. Guided by these data, we discovered that PIGY upstream open reading frame (PYURF) is an S-adenosylmethionine-dependent methyltransferase chaperone that supports both complex I assembly and coenzyme Q biosynthesis and is disrupted in a previously unresolved multisystemic mitochondrial disorder. We further linked the putative zinc transporter SLC30A9 to mitochondrial ribosomes and OxPhos integrity and established RAB5IF as the second gene harbouring pathogenic variants that cause cerebrofaciothoracic dysplasia. Our data, which can be explored through the interactive online MITOMICS.app resource, suggest biological roles for many other orphan mitochondrial proteins that still lack robust functional characterization and define a rich cell signature of mitochondrial dysfunction that can support the genetic diagnosis of mitochondrial diseases.
| S-EPMC9310563 | biostudies-literature
Project description:MotivationModern biological screens yield enormous numbers of measurements, and identifying and interpreting statistically significant associations among features are essential. In experiments featuring multiple high-dimensional datasets collected from the same set of samples, it is useful to identify groups of associated features between the datasets in a way that provides high statistical power and false discovery rate (FDR) control.ResultsHere, we present a novel hierarchical framework, HAllA (Hierarchical All-against-All association testing), for structured association discovery between paired high-dimensional datasets. HAllA efficiently integrates hierarchical hypothesis testing with FDR correction to reveal significant linear and non-linear block-wise relationships among continuous and/or categorical data. We optimized and evaluated HAllA using heterogeneous synthetic datasets of known association structure, where HAllA outperformed all-against-all and other block-testing approaches across a range of common similarity measures. We then applied HAllA to a series of real-world multiomics datasets, revealing new associations between gene expression and host immune activity, the microbiome and host transcriptome, metabolomic profiling and human health phenotypes.Availability and implementationAn open-source implementation of HAllA is freely available at http://huttenhower.sph.harvard.edu/halla along with documentation, demo datasets and a user group.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC9235493 | biostudies-literature
Project description:The Tohoku Medical Megabank Organization reports the whole-genome sequences of 1,070 healthy Japanese individuals and construction of a Japanese population reference panel (1KJPN). Here we identify through this high-coverage sequencing (32.4 × on average), 21.2 million, including 12 million novel, single-nucleotide variants (SNVs) at an estimated false discovery rate of <1.0%. This detailed analysis detected signatures for purifying selection on regulatory elements as well as coding regions. We also catalogue structural variants, including 3.4 million insertions and deletions, and 25,923 genic copy-number variants. The 1KJPN was effective for imputing genotypes of the Japanese population genome wide. These data demonstrate the value of high-coverage sequencing for constructing population-specific variant panels, which covers 99.0% SNVs of minor allele frequency ≥0.1%, and its value for identifying causal rare variants of complex human disease phenotypes in genetic association studies.
| S-EPMC4560751 | biostudies-literature
Project description:Rare cell populations provide a patient-centric tool to monitor disease treatment, response, and resistance. However, understanding rare cells is a complex problem, which requires cell isolation/purification and downstream molecular interrogation - processes challenged by non-target populations, which vary patient-to-patient and change with disease. As such, cell isolation platforms must be amenable to a range of sample types while maintaining high efficiency and purity. The multiplexed technology for automated extraction (mTAE) is a versatile magnetic bead-based isolation platform that facilitates positive, negative, and combinatorial selection with integrated protein staining and nucleic acid isolation. mTAE is validated by isolating circulating tumor cells (CTCs) - a model rare cell population - from breast and prostate cancer patient samples. Negative selection yielded high efficiency capture of CTCs while positive selection yielded higher purity with an average of only 95 contaminant cells captured per milliliter of processed whole blood. With combinatorial selection, an overall increase in capture efficiency was observed, highlighting the potential significance of integrating multiple capture approaches on a single platform. Following capture (and staining), on platform nucleic acid extraction enabled the detection of androgen receptor-related transcripts from CTCs isolated from prostate cancer patients. The flexibility (e.g. negative, positive, combinatorial selection) and capabilities (e.g. isolation, protein staining, and nucleic acid extraction) of mTAE enable users to freely interrogate specific cell populations, a capability required to understand the potential of emerging rare cell populations and readily adapt to the heterogeneity presented across clinical samples.
| S-EPMC6402328 | biostudies-literature
Project description:Proteomics, metabolomics, and transcriptomics generate comprehensive data sets, and current biocomputational capabilities allow their efficient integration for systems biology analysis. Published multiomics studies cover methodological advances as well as applications to biological questions. However, few studies have focused on the development of a high-throughput, unified sample preparation approach to complement high-throughput omic analytics. This report details the automation, benchmarking, and application of a strategy for transcriptomic, proteomic, and metabolomic analyses from a common sample. The approach, sample preparation for multi-omics technologies (SPOT), provides equivalent performance to typical individual omic preparation methods but greatly enhances throughput and minimizes the resources required for multiomic experiments. SPOT was applied to a multiomics time course experiment for zinc-treated HL-60 cells. The data reveal Zn effects on NRF2 antioxidant and NFkappaB signaling. High-throughput approaches such as these are critical for the acquisition of temporally resolved, multicondition, large multiomic data sets such as those necessary to assess complex clinical and biological concerns. Ultimately, this type of approach will provide an expanded understanding of challenging scientific questions across many fields.
| S-EPMC6469931 | biostudies-literature