Identity-Based Matchmaking Encryption with Equality Test.

The identity-based encryption with equality test (IBEET) has become a hot research topic in cloud computing as it provides an equality test for ciphertexts generated under different identities while preserving the confidentiality. Subsequently, for the sake of the confidentiality and authenticity of the data, the identity-based signcryption with equality test (IBSC-ET) has been put forward. Nevertheless, the existing schemes do not consider the anonymity of the sender and the receiver, which leads to the potential leakage of sensitive personal information. How to ensure confidentiality, authenticity, and anonymity in the IBEET setting remains a significant challenge. In this paper, we put forward the concept of the identity-based matchmaking encryption with equality test (IBME-ET) to address this issue. We formalized the system model, the definition, and the security models of the IBME-ET and, then, put forward a concrete scheme. Furthermore, our scheme was confirmed to be secure and practical by proving its security and evaluating its performance.

Genomic Data Sharing for Novel Mendelian Disease Gene Discovery: The Matchmaker Exchange.

In the last decade, exome and/or genome sequencing has become a common test in the diagnosis of individuals with features of a rare Mendelian disorder. Despite its success, this test leaves the majority of tested individuals undiagnosed. This review describes the Matchmaker Exchange (MME), a federated network established to facilitate the solving of undiagnosed rare-disease cases through data sharing. MME supports genomic matchmaking, the act of connecting two or more parties looking for cases with similar phenotypes and variants in the same candidate genes. An application programming interface currently connects six matchmaker nodes-the Database of Chromosomal Imbalance and Phenotype in Humans Using Ensembl Resources (DECIPHER), GeneMatcher, PhenomeCentral, seqr, MyGene2, and the Initiative on Rare and Undiagnosed Diseases (IRUD) Exchange-resulting in a collective data set spanning more than 150,000 cases from more than 11,000 contributors in 88 countries. Here, we describe the successes and challenges of MME, its individual matchmaking nodes, plans for growing the network, and considerations for future directions.

Bridging clinical care and research in Ontario, Canada: Maximizing diagnoses from reanalysis of clinical exome sequencing data.

We examined the utility of clinical and research processes in the reanalysis of publicly-funded clinical exome sequencing data in Ontario, Canada. In partnership with eight sites, we recruited 287 families with suspected rare genetic diseases tested between 2014 and 2020. Data from seven laboratories was reanalyzed with the referring clinicians. Reanalysis of clinically relevant genes identified diagnoses in 4% (13/287); four were missed by clinical testing. Translational research methods, including analysis of novel candidate genes, identified candidates in 21% (61/287). Of these, 24 families have additional evidence through data sharing to support likely diagnoses (8% of cohort). This study indicates few diagnoses are missed by clinical laboratories, the incremental gain from reanalysis of clinically-relevant genes is modest, and the highest yield comes from validation of novel disease-gene associations. Future implementation of translational research methods, including continued reporting of compelling genes of uncertain significance by clinical laboratories, should be considered to maximize diagnoses.

The implementation of an enhanced clinical model to improve the diagnostic yield of exome sequencing for patients with a rare genetic disease: A Canadian experience.

The introduction of clinical exome sequencing (ES) has provided a unique opportunity to decrease the diagnostic odyssey for patients living with a rare genetic disease (RGD). ES has been shown to provide a diagnosis in 29%-57% of patients with a suspected RGD, with as many as 70% remaining undiagnosed. There is a need to advance the clinical model of care by more formally integrating approaches that were previously considered research into an enhanced diagnostic workflow. We developed an Exome Clinic, which set out to evaluate a workflow for improving the diagnostic yield of ES for patients with an undiagnosed RGD. Here, we report the outcomes of 47 families who underwent clinical ES in the first year of the clinic. The diagnostic yield from clinical ES was 40% (19/47). Families who remained undiagnosed after ES had the opportunity for follow-up studies that included phenotyping and candidate variant segregation in relatives, genomic matchmaking, and ES reanalysis. This enhanced diagnostic workflow increased the diagnostic yield to 55% (26/47), predominantly through the resolution of variants and genes of uncertain significance. We advocate that this approach be integrated into mainstream clinical practice and highlight the importance of a coordinated translational approach for patients with RGD.

Collaborative Platforms and Matchmaking Algorithms for Research and Education, Establishment, and Optimization of Consortia.

Matchmaking has a great position in the rational allocation of resources in several fields, ranging from market operation to people's daily lives. Matchmakers have evolved through artificial intelligence technologies and are being introduced in numerous aspects of industry, research, and academia in solving decision issues, research innovation design, and building robust and efficient networks. The goal of this report is to describe the collaborative platforms and matchmaking algorithms for research and education, as well as the establishment and optimization of consortia.

bioTCIs: Middle-to-Macro Biomolecular Targeted Covalent Inhibitors Possessing Both Semi-Permanent Drug Action and Stringent Target Specificity as Potential Antibody Replacements.

Monoclonal antibody therapies targeting immuno-modulatory targets such as checkpoint proteins, chemokines, and cytokines have made significant impact in several areas, including cancer, inflammatory disease, and infection. However, antibodies are complex biologics with well-known limitations, including high cost for development and production, immunogenicity, a limited shelf-life because of aggregation, denaturation, and fragmentation of the large protein. Drug modalities such as peptides and nucleic acid aptamers showing high-affinity and highly selective interaction with the target protein have been proposed alternatives to therapeutic antibodies. The fundamental limitation of short in vivo half-life has prevented the wide acceptance of these alternatives. Covalent drugs, also known as targeted covalent inhibitors (TCIs), form permanent bonds to target proteins and, in theory, eternally exert the drug action, circumventing the pharmacokinetic limitation of other antibody alternatives. The TCI drug platform, too, has been slow in gaining acceptance because of its potential prolonged side-effect from off-target covalent binding. To avoid the potential risks of irreversible adverse drug effects from off-target conjugation, the TCI modality is broadening from the conventional small molecules to larger biomolecules possessing desirable properties (e.g., hydrolysis resistance, drug-action reversal, unique pharmacokinetics, stringent target specificity, and inhibition of protein-protein interactions). Here, we review the historical development of the TCI made of bio-oligomers/polymers (i.e., peptide-, protein-, or nucleic-acid-type) obtained by rational design and combinatorial screening. The structural optimization of the reactive warheads and incorporation into the targeted biomolecules enabling a highly selective covalent interaction between the TCI and the target protein is discussed. Through this review, we hope to highlight the middle to macro-molecular TCI platform as a realistic replacement for the antibody.

Seven years since the launch of the Matchmaker Exchange: The evolution of genomic matchmaking.

The Matchmaker Exchange (MME) was launched in 2015 to provide a robust mechanism to discover novel disease-gene relationships. It operates as a federated network connecting databases holding relevant data using a common application programming interface, where two or more users are looking for a match for the same gene (two-sided matchmaking). Seven years from its launch, it is clear that the MME is making outstanding contributions to understanding the morbid anatomy of the genome. The number of unique genes present across the MME has steadily increased over time; there are currently >13,520 unique genes (~68% of all protein-coding genes) connected across the MME's eight genomic matchmaking nodes, GeneMatcher, DECIPHER, PhenomeCentral, MyGene2, seqr, Initiative on Rare and Undiagnosed Disease, PatientMatcher, and the RD-Connect Genome-Phenome Analysis Platform. The collective data set accessible across the MME currently includes more than 120,000 cases from over 12,000 contributors in 98 countries. The discovery of potential new disease-gene relationships is happening daily and international collaborative teams are moving these advances forward to publication, now numbering well over 500. Expansion of data sharing into routine clinical practice by clinicians, genetic counselors, and clinical laboratories has ensured access to discovery for even more individuals with undiagnosed rare genetic diseases. Tens of thousands of patients and their family members have been directly or indirectly impacted by the discoveries facilitated by two-sided genomic matchmaking. MME supports further connections to the literature (PubCaseFinder) and to human and model organism resources (Monarch Initiative) and scientists (ModelMatcher). Efforts are now underway to explore additional approaches to matchmaking at the gene or variant level where there is only one querier (one-sided matchmaking). Genomic matchmaking has proven its utility over the past 7 years and will continue to facilitate discoveries in the years to come.

PhenomeCentral: 7 years of rare disease matchmaking.

A major challenge in validating genetic causes for patients with rare diseases (RDs) is the difficulty in identifying other RD patients with overlapping phenotypes and variants in the same candidate gene. This process, known as matchmaking, requires robust data sharing solutions to be effective. In 2014 we launched PhenomeCentral, a RD data repository capable of collecting computer-readable genotypic and phenotypic data for the purposes of RD matchmaking. Over the past 7 years PhenomeCentral's features have been expanded and its data set has consistently grown. There are currently 1615 users registered on PhenomeCentral, which have contributed over 12,000 patient cases. Most of these cases contain detailed phenotypic terms, with a significant portion also providing genomic sequence data or other forms of clinical information. Matchmaking within PhenomeCentral, and with connections to other data repositories in the Matchmaker Exchange, have collectively resulted in over 60,000 matches, which have facilitated multiple gene discoveries. The collection of deep phenotypic and genotypic data has also positioned PhenomeCentral well to support next generation of matchmaking initiatives that utilize genome sequencing data, ensuring that PhenomeCentral will remain a useful tool in solving undiagnosed RD cases in the years to come.

The RD-Connect Genome-Phenome Analysis Platform: Accelerating diagnosis, research, and gene discovery for rare diseases.

Rare disease patients are more likely to receive a rapid molecular diagnosis nowadays thanks to the wide adoption of next-generation sequencing. However, many cases remain undiagnosed even after exome or genome analysis, because the methods used missed the molecular cause in a known gene, or a novel causative gene could not be identified and/or confirmed. To address these challenges, the RD-Connect Genome-Phenome Analysis Platform (GPAP) facilitates the collation, discovery, sharing, and analysis of standardized genome-phenome data within a collaborative environment. Authorized clinicians and researchers submit pseudonymised phenotypic profiles encoded using the Human Phenotype Ontology, and raw genomic data which is processed through a standardized pipeline. After an optional embargo period, the data are shared with other platform users, with the objective that similar cases in the system and queries from peers may help diagnose the case. Additionally, the platform enables bidirectional discovery of similar cases in other databases from the Matchmaker Exchange network. To facilitate genome-phenome analysis and interpretation by clinical researchers, the RD-Connect GPAP provides a powerful user-friendly interface and leverages tens of information sources. As a result, the resource has already helped diagnose hundreds of rare disease patients and discover new disease causing genes.

PatientMatcher: A customizable Python-based open-source tool for matching undiagnosed rare disease patients via the Matchmaker Exchange network.

The amount of data available from genomic medicine has revolutionized the approach to identify the determinants underlying many rare diseases. The task of confirming a genotype-phenotype causality for a patient affected with a rare genetic disease is often challenging. In this context, the establishment of the Matchmaker Exchange (MME) network has assumed a pivotal role in bridging heterogeneous patient information stored on different medical and research servers. MME has made it possible to solve rare disease cases by "matching" the genotypic and phenotypic characteristics of a patient of interest with patient data available at other clinical facilities participating in the network. Here, we present PatientMatcher (https://github.com/Clinical-Genomics/patientMatcher), an open-source Python and MongoDB-based software solution developed by Clinical Genomics facility at the Science for Life Laboratory in Stockholm. PatientMatcher is designed as a standalone MME server, but can easily communicate via REST API with external applications managing genetic analyses and patient data. The MME node is being implemented in clinical routine in collaboration with the Genomic Medicine Center Karolinska at the Karolinska University Hospital. PatientMatcher is written to implement the MME API and provides several customizable settings, including a custom-fit similarity score algorithm and adjustable matching results notifications.

Genomics4RD: An integrated platform to share Canadian deep-phenotype and multiomic data for international rare disease gene discovery.

Despite recent progress in the understanding of the genetic etiologies of rare diseases (RDs), a significant number remain intractable to diagnostic and discovery efforts. Broad data collection and sharing of information among RD researchers is therefore critical. In 2018, the Care4Rare Canada Consortium launched the project C4R-SOLVE, a subaim of which was to collect, harmonize, and share both retrospective and prospective Canadian clinical and multiomic data. Here, we introduce Genomics4RD, an integrated web-accessible platform to share Canadian phenotypic and multiomic data between researchers, both within Canada and internationally, for the purpose of discovering the mechanisms that cause RDs. Genomics4RD has been designed to standardize data collection and processing, and to help users systematically collect, prioritize, and visualize participant information. Data storage, authorization, and access procedures have been developed in collaboration with policy experts and stakeholders to ensure the trusted and secure access of data by external researchers. The breadth and standardization of data offered by Genomics4RD allows researchers to compare candidate disease genes and variants between participants (i.e., matchmaking) for discovery purposes, while facilitating the development of computational approaches for multiomic data analyses and enabling clinical translation efforts for new genetic technologies in the future.

ModelMatcher: A scientist-centric online platform to facilitate collaborations between stakeholders of rare and undiagnosed disease research.

Next-generation sequencing is a prevalent diagnostic tool for undiagnosed diseases and has played a significant role in rare disease gene discovery. Although this technology resolves some cases, others are given a list of possibly damaging genetic variants necessitating functional studies. Productive collaborations between scientists, clinicians, and patients (affected individuals) can help resolve such medical mysteries and provide insights into in vivo function of human genes. Furthermore, facilitating interactions between scientists and research funders, including nonprofit organizations or commercial entities, can dramatically reduce the time to translate discoveries from bench to bedside. Several systems designed to connect clinicians and researchers with a shared gene of interest have been successful. However, these platforms exclude some stakeholders based on their role or geography. Here we describe ModelMatcher, a global online matchmaking tool designed to facilitate cross-disciplinary collaborations, especially between scientists and other stakeholders of rare and undiagnosed disease research. ModelMatcher is integrated into the Rare Diseases Models and Mechanisms Network and Matchmaker Exchange, allowing users to identify potential collaborators in other registries. This living database decreases the time from when a scientist or clinician is making discoveries regarding their genes of interest, to when they identify collaborators and sponsors to facilitate translational and therapeutic research.

Mutations in ATP1A1 Cause Dominant Charcot-Marie-Tooth Type 2.

Although mutations in more than 90 genes are known to cause CMT, the underlying genetic cause of CMT remains unknown in more than 50% of affected individuals. The discovery of additional genes that harbor CMT2-causing mutations increasingly depends on sharing sequence data on a global level. In this way-by combining data from seven countries on four continents-we were able to define mutations in ATP1A1, which encodes the alpha1 subunit of the Na+,K+-ATPase, as a cause of autosomal-dominant CMT2. Seven missense changes were identified that segregated within individual pedigrees: c.143T>G (p.Leu48Arg), c.1775T>C (p.Ile592Thr), c.1789G>A (p.Ala597Thr), c.1801_1802delinsTT (p.Asp601Phe), c.1798C>G (p.Pro600Ala), c.1798C>A (p.Pro600Thr), and c.2432A>C (p.Asp811Ala). Immunostaining peripheral nerve axons localized ATP1A1 to the axolemma of myelinated sensory and motor axons and to Schmidt-Lanterman incisures of myelin sheaths. Two-electrode voltage clamp measurements on Xenopus oocytes demonstrated significant reduction in Na+ current activity in some, but not all, ouabain-insensitive ATP1A1 mutants, suggesting a loss-of-function defect of the Na+,K+ pump. Five mutants fall into a remarkably narrow motif within the helical linker region that couples the nucleotide-binding and phosphorylation domains. These findings identify a CMT pathway and a potential target for therapy development in degenerative diseases of peripheral nerve axons.

RNA matchmaking in chromatin regulation.

Beyond being the product of gene expression, RNA can also influence the regulation of chromatin. The majority of the human genome has the capacity to be transcribed and the majority of the non-protein-coding transcripts made by RNA Polymerase II are enriched in the nucleus. Many chromatin regulators can bind to these ncRNAs in the nucleus; in some cases, there are clear examples of direct RNA-mediated chromatin regulation mechanisms stemming from these interactions, while others have yet to be determined. Recent studies have highlighted examples of chromatin regulation via RNA matchmaking, a term we use broadly here to describe intermolecular base-pairing interactions between one RNA molecule and an RNA or DNA match. This review provides examples of RNA matchmaking that regulates chromatin processes and summarizes the technical approaches used to capture these events.

A Hybrid Semantic Knowledge Integration and Sharing Approach for Distributed Smart Environments.

Distributed systems provide smart functionality to everyday objects with the help of wireless sensors using the internet. Since the last decade, the industry is struggling to develop efficient and intelligent protocols to integrate a huge number of smart objects in distributed computing environments. However, the main challenge for smart and distributed system designers lies in the integration of a large number of heterogeneous components for faster, cheaper, and more efficient functionalities. To deal with this issue, practitioners are using edge computing along with server and desktop technology for the development of smart applications by using Service-Oriented Architecture (SOA) where every smart object offers its functionality as a service, enabling other objects to interact with them dynamically. In order to make such a system, researchers have considered context-awareness and Quality of Service (QoS) attributes of device services. However, context modeling is a complicated task since it could include everything around the applications. Moreover, it is also important to consider non-functional interactions that may have an impact on the behavior of the complete system. In this regard, various research dimensions are explored. However, rich context-aware modeling, QoS, user priorities, grouping, and value type direction along with uncertainty are not considered properly while modeling of incomplete or partial domain knowledge during ontology engineering, resulting in low accuracy of results. In this paper, we present a semantic and logic-based formal framework (hybrid) to find the best service among many candidate services by considering the limitations of existing frameworks. Experimental results of the proposed framework show the improvement of the discovered results.

ClinGen's GenomeConnect registry enables patient-centered data sharing.

GenomeConnect, the NIH-funded Clinical Genome Resource (ClinGen) patient registry, engages patients in data sharing to support the goal of creating a genomic knowledge base to inform clinical care and research. Participant self-reported health information and genomic variants from genetic testing reports are curated and shared with public databases, such as ClinVar. There are four primary benefits of GenomeConnect: (1) sharing novel genomic data-47.9% of variants were new to ClinVar, highlighting patients as a genomic data source; (2) contributing additional phenotypic information-of the 52.1% of variants already in ClinVar, GenomeConnect provided enhanced case-level data; (3) providing a way for patients to receive variant classification updates if the reporting laboratory submits to ClinVar-97.3% of responding participants opted to receive such information and 13 updates have been identified; and (4) supporting connections with others, including other participants, clinicians, and researchers to enable the exchange of information and support-60.4% of participants have opted to partake in participant matching. Moving forward, ClinGen plans to increase patient-centric data sharing by partnering with other existing patient groups. By engaging patients, more information is contributed to the public knowledge base, benefiting both patients and the genomics community.

Identification of a Recognizable Progressive Skeletal Dysplasia Caused by RSPRY1 Mutations.

Skeletal dysplasias are highly variable Mendelian phenotypes. Molecular diagnosis of skeletal dysplasias is complicated by their extreme clinical and genetic heterogeneity. We describe a clinically recognizable autosomal-recessive disorder in four affected siblings from a consanguineous Saudi family, comprising progressive spondyloepimetaphyseal dysplasia, short stature, facial dysmorphism, short fourth metatarsals, and intellectual disability. Combined autozygome/exome analysis identified a homozygous frameshift mutation in RSPRY1 with resulting nonsense-mediated decay. Using a gene-centric "matchmaking" system, we were able to identify a Peruvian simplex case subject whose phenotype is strikingly similar to the original Saudi family and whose exome sequencing had revealed a likely pathogenic homozygous missense variant in the same gene. RSPRY1 encodes a hypothetical RING and SPRY domain-containing protein of unknown physiological function. However, we detect strong RSPRY1 protein localization in murine embryonic osteoblasts and periosteal cells during primary endochondral ossification, consistent with a role in bone development. This study highlights the role of gene-centric matchmaking tools to establish causal links to genes, especially for rare or previously undescribed clinical entities.

An RNA matchmaker protein regulates the activity of the long noncoding RNA HOTAIR.

The human long noncoding RNA (lncRNA) HOTAIR acts in trans to recruit the Polycomb repressive complex 2 (PRC2) to the HOXD gene cluster and to promote gene silencing during development. In breast cancers, overexpression of HOTAIR increases metastatic potential via the repression of many additional genes. It has remained unclear what factors determine HOTAIR-dependent PRC2 activity at specific genomic loci, particularly when high levels of HOTAIR result in aberrant gene silencing. To identify additional proteins that contribute to the specific action of HOTAIR, we performed a quantitative proteomic analysis of the HOTAIR interactome. We found that the most specific interaction was between HOTAIR and the heterogeneous nuclear ribonucleoprotein (hnRNP) A2/B1, a member of a family of proteins involved in nascent mRNA processing and RNA matchmaking. Our data suggest that A2/B1 are key contributors to HOTAIR-mediated chromatin regulation in breast cancer cells: A2/B1 knockdown reduces HOTAIR-dependent breast cancer cell invasion and decreases PRC2 activity at the majority of HOTAIR-dependent loci. We found that the B1 isoform, which differs from A2 by 12 additional amino acids, binds with highest specificity to HOTAIR. B1 also binds chromatin and associates preferentially with RNA transcripts of HOTAIR gene targets. We furthermore demonstrate a direct RNA-RNA interaction between HOTAIR and a target transcript that is enhanced by B1 binding. Together, these results suggest a model in which B1 matches HOTAIR with transcripts of target genes on chromatin, leading to repression by PRC2.

PhenomeCentral: a portal for phenotypic and genotypic matchmaking of patients with rare genetic diseases.

The discovery of disease-causing mutations typically requires confirmation of the variant or gene in multiple unrelated individuals, and a large number of rare genetic diseases remain unsolved due to difficulty identifying second families. To enable the secure sharing of case records by clinicians and rare disease scientists, we have developed the PhenomeCentral portal (https://phenomecentral.org). Each record includes a phenotypic description and relevant genetic information (exome or candidate genes). PhenomeCentral identifies similar patients in the database based on semantic similarity between clinical features, automatically prioritized genes from whole-exome data, and candidate genes entered by the users, enabling both hypothesis-free and hypothesis-driven matchmaking. Users can then contact other submitters to follow up on promising matches. PhenomeCentral incorporates data for over 1,000 patients with rare genetic diseases, contributed by the FORGE and Care4Rare Canada projects, the US NIH Undiagnosed Diseases Program, the EU Neuromics and ANDDIrare projects, as well as numerous independent clinicians and scientists. Though the majority of these records have associated exome data, most lack a molecular diagnosis. PhenomeCentral has already been used to identify causative mutations for several patients, and its ability to find matching patients and diagnose these diseases will grow with each additional patient that is entered.

The genomic birthday paradox: how much is enough?

Genomic matchmaking databases (GMDs) allow participants to submit genomic and phenotypic data with the goal of identifying previously uncharacterized disease-associated genes by "matching" to other comparable cases. Current estimates suggest that there are at least 3,000 Mendelian disease-associated genes that have not yet been characterized as such, but the true number may be substantially higher. Therefore, GMDs are addressing a pressing medical need, and it is important to ask how they should be designed and how much data they should strive to contain in order to identify a certain number of these genes. In this work, we argue that genomic matchmaking has similarities to the so-called "birthday paradox," which refers to the observation that within a group of just 23 persons, two people will have the same birthday with probability greater than 50%. We develop a series of simulations to provide a rough estimate of the number of cases required and to explore the influence of parameters such as genetic heterogeneity, mode of inheritance, background variation, precision of phenotypic descriptions, disease prevalence, and the accuracy of bioinformatics pathogenicity prediction programs on the performance of genomic matchmaking.