seqr: a web-based analysis and collaboration tool for rare disease genomics

Exome and genome sequencing have become the tools of choice for rare disease diagnosis, leading to large amounts of data available for analyses. To identify causal variants in these datasets, powerful filtering and decision support tools that can be efficiently used by clinicians and researchers are required. To address this need, we developed seqr - an open source, web-based tool for family-based monogenic disease analysis that allows researchers to work collaboratively to search and annotate genomic callsets. To date, seqr is being used in several research pipelines and one clinical diagnostic lab. In our own experience through the Broad Institute Center for Mendelian Genomics, seqr has enabled analyses of over 10,000 families, supporting the diagnosis of more than 3,800 individuals with rare disease and discovery of over 300 novel disease genes. Here we describe a framework for genomic analysis in rare disease that leverages seqr's capabilities for variant filtration, annotation, and causal variant identification, as well as support for research collaboration and data sharing. The seqr platform is available as open source software, allowing low-cost participation in rare disease research, and a community effort to support diagnosis and gene discovery in rare disease.

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Web-based tools for collaborative research

Library Hi Tech News ◽

10.1108/lhtn-08-2017-0062 ◽

2017 ◽

Vol 34 (8) ◽

pp. 8-19

Author(s):

Stacy Brody

Keyword(s):

Collaborative Research ◽

Design Methodology ◽

Research Collaboration ◽

Low Cost ◽

Research Process ◽

Web Based ◽

Content Type ◽

Collaborative Tools ◽

Start Ups ◽

Practical Implications

Purpose The purpose of this paper is to profile various types of Web-based tools to facilitate research collaboration within and across institutions. Design/methodology/approach Various Web-based tools were tested by the author. Additionally, tutorial videos and guides were reviewed. Findings There are various free and low-cost tools available to assist in the collaborative research process, and librarians are well-positioned to facilitate their usage. Practical implications Librarians and researchers will learn about various types of tools available at free or at low cost to fulfill needs of the collaborative research process. Social implications As the tools highlighted are either free or of low cost, they are also valuable to start-ups and can be recommended for entrepreneurs. Originality/value As the realm of Web-based collaborative tools continues to evolve, the options must be continually revisited and reviewed for currency.

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Pengembangan Model Aplikasi Administrasi Pelayanan Kesehatan di Puskemas dengan Cloud Computing Berbasiskan Open Source

ComTech Computer Mathematics and Engineering Applications ◽

10.21512/comtech.v4i2.2544 ◽

2013 ◽

Vol 4 (2) ◽

pp. 1026

Author(s):

Honni Honni

Keyword(s):

Health Care ◽

Cloud Computing ◽

Health Services ◽

Open Source ◽

Health Care Administration ◽

Disease Diagnosis ◽

Design System ◽

Literature Study ◽

Web Based ◽

Application Model

Puskemas as community health centers becomes one of the main focuses of development on the agenda of the Government of Indonesia beside education. Therefore, we purpose to develop an affordable online system of health care administration based on open source using cloud computing approach. It can be used for collecting data of patients, diseases, and treatment of patients at Puskesmas. The methods used are literature study related to cloud computing, survey design and data collection infrastructure of information technology thatcan be applied to online health services, analysis of survey data on actual conditions in some centers and other health care centers in Jakarta, then making model of online health services such as physician consulting, prescribing and disease diagnosis. The result is a web-based application system of health care administration of Puskesmas, which utilizes cloud computing technology and development architectures that are both modular and dynamic. The application model combines the benefits of open-source applications with a flexible design system. It also supports mobile devices to improve the quality of patient care. Web-based network structure allows both online and inter-section between institutions which can be accessed anytime, anywhere, through mobile devices.Development application model is also adapted to the function of the business processes and administrative processes that exist in Puskesmas throughout Indonesia. Each model is also expected to be integrated to optimize efficiency and has been adapted to the service system of Dinas Kesehatan and Health Ministery.

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Essentiality-specific pathogenicity prioritization gene score to improve filtering of disease sequence data

Briefings in Bioinformatics ◽

10.1093/bib/bbaa029 ◽

2020 ◽

Author(s):

Dareen Alyousfi ◽

Diana Baralle ◽

Andrew Collins

Keyword(s):

Developmental Disorders ◽

Sequence Data ◽

Single Gene ◽

Causal Variant ◽

Monogenic Disease ◽

Disease Genes ◽

Gene Score ◽

Gene Essentiality ◽

Individual Gene ◽

Pathogenic Variation

Abstract The causal genetic variants underlying more than 50% of single gene (monogenic) disorders are yet to be discovered. Many patients with conditions likely to have a monogenic basis do not receive a confirmed molecular diagnosis which has potential impacts on clinical management. We have developed a gene-specific score, essentiality-specific pathogenicity prioritization (ESPP), to guide the recognition of genes likely to underlie monogenic disease variation to assist in filtering of genome sequence data. When a patient genome is sequenced, there are frequently several plausibly pathogenic variants identified in different genes. Recognition of the single gene most likely to include pathogenic variation can guide the identification of a causal variant. The ESPP score integrates gene-level scores which are broadly related to gene essentiality. Previous work towards the recognition of monogenic disease genes proposed a model with increasing gene essentiality from ‘non-essential’ to ‘essential’ genes (for which pathogenic variation may be incompatible with survival) with genes liable to contain disease variation positioned between these two extremes. We demonstrate that the ESPP score is useful for recognizing genes with high potential for pathogenic disease-related variation. Genes classed as essential have particularly high scores, as do genes recently recognized as strong candidates for developmental disorders. Through the integration of individual gene-specific scores, which have different properties and assumptions, we demonstrate the utility of an essentiality-based gene score to improve sequence genome filtering.

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Low Cost VoIP Architecture Using Open Source Software Component in Tertiary Institutions

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY ◽

10.24297/ijct.v3i1a.2721 ◽

2012 ◽

Vol 3 (1) ◽

pp. 11-14

Author(s):

Ebtesam Najim AlShemmary ◽

Bahaa Qasim Al-Musawi

Keyword(s):

Open Source ◽

Open Source Software ◽

Large Scale ◽

Low Cost ◽

Enterprise Network ◽

Web Based ◽

Tertiary Institutions ◽

Data Services ◽

Media Systems ◽

The University

Governments and their agencies are often challenged by high cost and flexible telephonic, Web based data services. Emerging technologies, such as those of Voice over Internet Protocol (VoIP) that allow convergent systems where voice and Web technologies can utilize the same network to provide both services, can be used to improve such services. This paper describe VoIP system for the enterprise network (e.g. company, university) that have been developed based on Asterisk which is a kind of open source software to implement IP-PBX system. Through the development and evaluation, we have confirmed that VoIP system based on Asterisk is very powerful as a whole and most PBX functions to be required for the enterprise network can be realized. Interesting findings include that the University of Kufa has a potential to implement the project. By connecting multiple Asterisk servers located in different sites based on IAX2, large scale enterprise network can be developed. Since the software recommended for installation is open source, the project could be used as a source of valuable information by students who specialize in real-time multi-media systems.

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Open technologies for monitoring systems aimed at disaster risk reduction

10.7287/peerj.preprints.2132v1 ◽

2016 ◽

Author(s):

Massimiliano Cannata ◽

Yann Chemin ◽

Milan Petar Antonovic ◽

Lahiru Wijesinghe ◽

Vivien Deparday

Keyword(s):

Open Source ◽

Risk Reduction ◽

Disaster Risk Reduction ◽

Low Cost ◽

Disaster Risk ◽

Web Based ◽

Notification System ◽

Practical Challenge ◽

Do It Yourself ◽

Open Source Hardware

This research is spearheading the integration of Free and Open Source Software (FOSS) and Open Source Hardware (OSHW) in the field of agri-meteorology applications to disaster risk reduction, flood and droughts. A Do-It-Yourself weather station based on OSHW standards has been developed from local sources in Sri Lanka, reporting by SMS to tank/reservoir managers when rainfall is higher than 10mm/h. These weather stations are soon going to be reprogrammed to report to istSOS, a FOSS web-based Sensor-Observation-Service compliant system, which will collate live reporting of rainfall every hour and before if intensities are dimmed worrying for flood risks. This is both a scientific, technological, and practical challenge toward a very low cost real time disaster risk notification system in places where climate, economy and maintenance supports are themselves other challenges.

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How to design a registry for undiagnosed patients in the framework of rare disease diagnosis – suggestions on software, data set and coding system

10.21203/rs.3.rs-122656/v2 ◽

2021 ◽

Author(s):

Alexandra Berger ◽

Anne-Kathrin Rustemeier ◽

Jens Göbel ◽

Dennis Kadioglu ◽

Vanessa Britz ◽

...

Keyword(s):

Open Source ◽

Rare Disease ◽

Rare Diseases ◽

Action Plan ◽

Correct Diagnosis ◽

Disease Patient ◽

Disease Diagnosis ◽

Clinical Findings ◽

Snomed Ct ◽

Data Set

Abstract Background: About 30 million people in the EU and USA, respectively, suffer from a rare disease. Driven by European legislative requirements, national strategies for the improvement of care in rare diseases are being developed. To improve timely and correct diagnosis for patients with rare diseases, the development of a registry for undiagnosed patients was recommended by the German National Action Plan. In this paper we focus on the question on how such a registry for undiagnosed patients can be built and which information it should contain.Results: To develop a registry for undiagnosed patients, a software for data acquisition and storage, an appropriate data set and an applicable terminology/classification system for the data collected are needed. We have used the open-source software OSSE (Open-Source Registry System for Rare Diseases) to build the registry for undiagnosed patients. Our data set is based on the minimal data set for rare disease patient registries recommended by the European Rare Disease Registries Platform. We extended this Common Data Set to also include symptoms, clinical findings and other diagnoses. In order to ensure findability, comparability and statistical analysis, symptoms, clinical findings and diagnoses have to be encoded. We evaluated three medical ontologies (SNOMED CT, HPO and LOINC) for their usefulness. With exact matches of 98% of tested medical terms, a mean number of five deposited synonyms, SNOMED CT seemed to fit our needs best. HPO and LOINC provided 73% and 31% of exacts matches of clinical terms respectively. Allowing more generic codes for a defined symptom, with SNOMED CT 99%, with HPO 89% and with LOINC 39% of terms could be encoded.Conclusions: With the use of the OSSE software and a data set, which, in addition to the Common Data Set, focuses on symptoms and clinical findings, a functioning and meaningful registry for undiagnosed patients can be implemented. The next step is the implementation of the registry in centres for rare diseases. With the help of medical informatics and big data analysis, case similarity analyses could be realized and aid as a decision-support tool enabling diagnosis of some patients.

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Identification of rare-disease genes in diverse undiagnosed cases using whole blood transcriptome sequencing and large control cohorts

10.1101/408492 ◽

2018 ◽

Cited By ~ 5

Author(s):

Laure Frésard ◽

Craig Smail ◽

Kevin S. Smith ◽

Nicole M. Ferraro ◽

Nicole A. Teran ◽

...

Keyword(s):

Rare Disease ◽

Developmental Delay ◽

Whole Blood ◽

Disease Diagnosis ◽

Global Developmental Delay ◽

Disease Genes ◽

Compound Heterozygous ◽

Mendelian Disease ◽

Rna Seq ◽

Pathogenic Variants

AbstractRNA sequencing (RNA-seq) is a complementary approach for Mendelian disease diagnosis for patients in whom exome-sequencing is not informative. For both rare neuromuscular and mitochondrial disorders, its application has improved diagnostic rates. However, the generalizability of this approach to diverse Mendelian diseases has yet to be evaluated. We sequenced whole blood RNA from 56 cases with undiagnosed rare diseases spanning 11 diverse disease categories to evaluate the general application of RNA-seq to Mendelian disease diagnosis. We developed a robust approach to compare rare disease cases to existing large sets of RNA-seq controls (N=1,594 external and N=31 family-based controls) and demonstrated the substantial impacts of gene and variant filtering strategies on disease gene identification when combined with RNA-seq. Across our cohort, we observed that RNA-seq yields a 8.5% diagnostic rate. These diagnoses included diseases where blood would not intuitively reflect evidence of disease. We identified RARS2 as an under-expression outlier containing compound heterozygous pathogenic variants for an individual exhibiting profound global developmental delay, seizures, microcephaly, hypotonia, and progressive scoliosis. We also identified a new splicing junction in KCTD7 for an individual with global developmental delay, loss of milestones, tremors and seizures. Our study provides a broad evaluation of blood RNA-seq for the diagnosis of rare disease.

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How to design a registry for undiagnosed patients in the framework of rare disease diagnosis: suggestions on software, data set and coding system

Orphanet Journal of Rare Diseases ◽

10.1186/s13023-021-01831-3 ◽

2021 ◽

Vol 16 (1) ◽

Author(s):

Alexandra Berger ◽

Anne-Kathrin Rustemeier ◽

Jens Göbel ◽

Dennis Kadioglu ◽

Vanessa Britz ◽

...

Keyword(s):

Open Source ◽

Rare Disease ◽

Rare Diseases ◽

Action Plan ◽

Correct Diagnosis ◽

Disease Patient ◽

Disease Diagnosis ◽

Clinical Findings ◽

Snomed Ct ◽

Data Set

Abstract Background About 30 million people in the EU and USA, respectively, suffer from a rare disease. Driven by European legislative requirements, national strategies for the improvement of care in rare diseases are being developed. To improve timely and correct diagnosis for patients with rare diseases, the development of a registry for undiagnosed patients was recommended by the German National Action Plan. In this paper we focus on the question on how such a registry for undiagnosed patients can be built and which information it should contain. Results To develop a registry for undiagnosed patients, a software for data acquisition and storage, an appropriate data set and an applicable terminology/classification system for the data collected are needed. We have used the open-source software Open-Source Registry System for Rare Diseases (OSSE) to build the registry for undiagnosed patients. Our data set is based on the minimal data set for rare disease patient registries recommended by the European Rare Disease Registries Platform. We extended this Common Data Set to also include symptoms, clinical findings and other diagnoses. In order to ensure findability, comparability and statistical analysis, symptoms, clinical findings and diagnoses have to be encoded. We evaluated three medical ontologies (SNOMED CT, HPO and LOINC) for their usefulness. With exact matches of 98% of tested medical terms, a mean number of five deposited synonyms, SNOMED CT seemed to fit our needs best. HPO and LOINC provided 73% and 31% of exacts matches of clinical terms respectively. Allowing more generic codes for a defined symptom, with SNOMED CT 99%, with HPO 89% and with LOINC 39% of terms could be encoded. Conclusions With the use of the OSSE software and a data set, which, in addition to the Common Data Set, focuses on symptoms and clinical findings, a functioning and meaningful registry for undiagnosed patients can be implemented. The next step is the implementation of the registry in centres for rare diseases. With the help of medical informatics and big data analysis, case similarity analyses could be realized and aid as a decision-support tool enabling diagnosis of some undiagnosed patients.

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How to Design a Registry for Undiagnosed Patients in the Framework of Rare Disease Diagnosis – Suggestions on Software, Data Set and Coding System

10.21203/rs.3.rs-122656/v1 ◽

2020 ◽

Author(s):

Alexandra Berger ◽

Anne-Kathrin Rustemeier ◽

Jens Göbel ◽

Dennis Kadioglu ◽

Vanessa Britz ◽

...

Keyword(s):

Open Source ◽

Rare Disease ◽

Rare Diseases ◽

Action Plan ◽

Correct Diagnosis ◽

Disease Patient ◽

Disease Diagnosis ◽

Clinical Findings ◽

Snomed Ct ◽

Data Set

Abstract Background: About 30 million people in the EU and USA, respectively, suffer from a rare disease. Driven by European legislative requirements, national strategies for the improvement of care in rare diseases are being developed. To improve timely and correct diagnosis for patients with rare diseases, the development of a registry for undiagnosed patients was recommended by the German National Action Plan. In this paper we focus on the question on how such a registry for undiagnosed patients can be built and which information it should contain.Results: To develop a registry for undiagnosed patients, a software for data acquisition and storage, an appropriate data set and an applicable terminology/classification system for the data collected is needed. We used the open source software OSSE (Open Source Registry System for Rare Diseases) to build the registry for undiagnosed patients. Our data set is based on the minimal data set for rare disease patient registries recommended for European Rare Disease Registries Platform. We extended this Common Data Set to also include symptoms, clinical findings and other diagnoses. In order to ensure findability, comparability and statistical analysis, symptoms, clinical findings and diagnoses have to be encoded. We evaluated three medical ontologies (SNOMED – CT, HPO and LOINC) for their usefulness. With exact matches of 98% of tested medical terms, a mean number of five deposited synonyms, SNOMED CT seemed to fit our needs best. HPO and LOINC provided 73% and 31% of exacts matches of clinical terms respectively. Allowing more generic codes for a defined symptom, with SNOMED-CT 99%, with HPO 89% and with LOINC 39% of terms could be encoded.Conclusions: With the use of the OSSE software and a data set, which, in addition to the Common Data Set, focuses on symptoms and clinical findings, a functioning and meaningful registry for undiagnosed patients can be implemented. The next step is the implementation of the registry in centres for rare diseases. With the help of medical informatics and big data analysis, case similarity analyses could be realized and aid as a decision-support tool enabling diagnosis of some patients.

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