High-performance web services for querying gene and variant annotation

Xin, Jiwen; Mark, Adam; Afrasiabi, Cyrus; Tsueng, Ginger; Juchler, Moritz; Gopal, Nikhil; Stupp, Gregory S.; Putman, Tim; Ainscough, Benjamin J.; Griffith, Obi L.; Torkamani, Ali; Whetzel, Patricia L.; Mungall, Christopher J.; Mooney, Sean D.; Su, Andrew I.; Wu, Chunlei

doi:10.1186/s13059-016-0953-9

Cited by 178 publications

(154 citation statements)

References 38 publications

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“…To determine the functional significance of novel nonsynonymous variants, the following steps were taken: search COSMIC [24] and TCGA [25] for previous reports (across all tumour types); prioritise variants with a high CADD score [26]; map variants to functional domains of the gene, if possible; check if variant falls in a mutational hotspot using MutationAligner [27] and Cancer HotSpots [28]; and review literature (using GeneRIF queried via MyGene.info [29]). Annotation of CNA was limited to genes found to be recurrently altered in the literature [30,31].…”

Section: Methodsmentioning

confidence: 99%

The Subclonal Architecture of Metastatic Breast Cancer: Results from a Prospective Community-Based Rapid Autopsy Program “CASCADE”

et al. 2016

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BackgroundUnderstanding the cancer genome is seen as a key step in improving outcomes for cancer patients. Genomic assays are emerging as a possible avenue to personalised medicine in breast cancer. However, evolution of the cancer genome during the natural history of breast cancer is largely unknown, as is the profile of disease at death. We sought to study in detail these aspects of advanced breast cancers that have resulted in lethal disease.Methods and FindingsThree patients with oestrogen-receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative breast cancer and one patient with triple negative breast cancer underwent rapid autopsy as part of an institutional prospective community-based rapid autopsy program (CASCADE). Cases represented a range of management problems in breast cancer, including late relapse after early stage disease, de novo metastatic disease, discordant disease response, and disease refractory to treatment. Between 5 and 12 metastatic sites were collected at autopsy together with available primary tumours and longitudinal metastatic biopsies taken during life. Samples underwent paired tumour-normal whole exome sequencing and single nucleotide polymorphism (SNP) arrays. Subclonal architectures were inferred by jointly analysing all samples from each patient. Mutations were validated using high depth amplicon sequencing.Between cases, there were significant differences in mutational burden, driver mutations, mutational processes, and copy number variation. Within each case, we found dramatic heterogeneity in subclonal structure from primary to metastatic disease and between metastatic sites, such that no single lesion captured the breadth of disease. Metastatic cross-seeding was found in each case, and treatment drove subclonal diversification. Subclones displayed parallel evolution of treatment resistance in some cases and apparent augmentation of key oncogenic drivers as an alternative resistance mechanism. We also observed the role of mutational processes in subclonal evolution.Limitations of this study include the potential for bias introduced by joint analysis of formalin-fixed archival specimens with fresh specimens and the difficulties in resolving subclones with whole exome sequencing. Other alterations that could define subclones such as structural variants or epigenetic modifications were not assessed.ConclusionsThis study highlights various mechanisms that shape the genome of metastatic breast cancer and the value of studying advanced disease in detail. Treatment drives significant genomic heterogeneity in breast cancers which has implications for disease monitoring and treatment selection in the personalised medicine paradigm.

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Section: Methodsmentioning

confidence: 99%

The Subclonal Architecture of Metastatic Breast Cancer: Results from a Prospective Community-Based Rapid Autopsy Program “CASCADE”

et al. 2016

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“…Bio2RDF typically retains the source vocabulary of the integrated resources, whereas KaBOB is more similar to Monarch in that it maps OBO ontologies (18). Other data integration approaches include the BioThings API, exemplified by the MyVariant system (32) which aggregates variant data from multiple sources. We are currently working with the BioThings API developers to integrate these different approaches within the Dipper framework.…”

Section: Discussionmentioning

confidence: 99%

“…Taking a Monarch page for a single gene, for example ‘fibrinogen gamma chain’, FGG , (https://monarchinitiative.org/gene/NCBIGene:2266). Monarch has integrated data from a variety of human, model organism, and other biomedical sources such as OMIM (1), Orphanet (3), ClinVar (2), HPO (30), KEGG (31), CTD (4), MyGene (32), BioGrid (33) and via orthology in PantherDB (34) we also incorporate Fgg gene data from ZFIN (35) and from MGI (36). No two of these sources represents the identifier for FGG in precisely the same way.…”

Section: Introductionmentioning

confidence: 99%

The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species

et al. 2016

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The correlation of phenotypic outcomes with genetic variation and environmental factors is a core pursuit in biology and biomedicine. Numerous challenges impede our progress: patient phenotypes may not match known diseases, candidate variants may be in genes that have not been characterized, model organisms may not recapitulate human or veterinary diseases, filling evolutionary gaps is difficult, and many resources must be queried to find potentially significant genotype–phenotype associations. Non-human organisms have proven instrumental in revealing biological mechanisms. Advanced informatics tools can identify phenotypically relevant disease models in research and diagnostic contexts. Large-scale integration of model organism and clinical research data can provide a breadth of knowledge not available from individual sources and can provide contextualization of data back to these sources. The Monarch Initiative (monarchinitiative.org) is a collaborative, open science effort that aims to semantically integrate genotype–phenotype data from many species and sources in order to support precision medicine, disease modeling, and mechanistic exploration. Our integrated knowledge graph, analytic tools, and web services enable diverse users to explore relationships between phenotypes and genotypes across species.

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“…1) as well as linking of relevant external supporting information about the variant. The current set of externally linked information includes ClinVar [10], dbSNP [11], dbNSFP [12], COSMIC [13], ExAC [14], and myvariant.info [15]. …”

Section: Methodsmentioning

confidence: 99%

ClinGen Pathogenicity Calculator: a configurable system for assessing pathogenicity of genetic variants

et al. 2017

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BackgroundThe success of the clinical use of sequencing based tests (from single gene to genomes) depends on the accuracy and consistency of variant interpretation. Aiming to improve the interpretation process through practice guidelines, the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) have published standards and guidelines for the interpretation of sequence variants. However, manual application of the guidelines is tedious and prone to human error. Web-based tools and software systems may not only address this problem but also document reasoning and supporting evidence, thus enabling transparency of evidence-based reasoning and resolution of discordant interpretations.ResultsIn this report, we describe the design, implementation, and initial testing of the Clinical Genome Resource (ClinGen) Pathogenicity Calculator, a configurable system and web service for the assessment of pathogenicity of Mendelian germline sequence variants. The system allows users to enter the applicable ACMG/AMP-style evidence tags for a specific allele with links to supporting data for each tag and generate guideline-based pathogenicity assessment for the allele. Through automation and comprehensive documentation of evidence codes, the system facilitates more accurate application of the ACMG/AMP guidelines, improves standardization in variant classification, and facilitates collaborative resolution of discordances. The rules of reasoning are configurable with gene-specific or disease-specific guideline variations (e.g. cardiomyopathy-specific frequency thresholds and functional assays). The software is modular, equipped with robust application program interfaces (APIs), and available under a free open source license and as a cloud-hosted web service, thus facilitating both stand-alone use and integration with existing variant curation and interpretation systems. The Pathogenicity Calculator is accessible at http://calculator.clinicalgenome.org.ConclusionsBy enabling evidence-based reasoning about the pathogenicity of genetic variants and by documenting supporting evidence, the Calculator contributes toward the creation of a knowledge commons and more accurate interpretation of sequence variants in research and clinical care.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-016-0391-z) contains supplementary material, which is available to authorized users.

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High-performance web services for querying gene and variant annotation

Cited by 178 publications

References 38 publications

The Subclonal Architecture of Metastatic Breast Cancer: Results from a Prospective Community-Based Rapid Autopsy Program “CASCADE”

The Subclonal Architecture of Metastatic Breast Cancer: Results from a Prospective Community-Based Rapid Autopsy Program “CASCADE”

The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species

ClinGen Pathogenicity Calculator: a configurable system for assessing pathogenicity of genetic variants

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