A visual and curatorial approach to clinical variant prioritization and disease gene discovery in genome-wide diagnostics
Background Genome-wide data are increasingly important in the clinical evaluation of human disease. However, the large number of variants observed in individual patients challenges the efficiency and accuracy of diagnostic review. Recent work has shown that systematic integration of clinical phenoty...
Ausführliche Beschreibung
Autor*in: |
James, Regis A. [verfasserIn] |
---|
Format: |
E-Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2016 |
---|
Schlagwörter: |
---|
Anmerkung: |
© James et al. 2016 |
---|
Übergeordnetes Werk: |
Enthalten in: Genome medicine - London : BioMed Central, 2009, 8(2016), 1 vom: 02. Feb. |
---|---|
Übergeordnetes Werk: |
volume:8 ; year:2016 ; number:1 ; day:02 ; month:02 |
Links: |
---|
DOI / URN: |
10.1186/s13073-016-0261-8 |
---|
Katalog-ID: |
SPR030635837 |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | SPR030635837 | ||
003 | DE-627 | ||
005 | 20230519185308.0 | ||
007 | cr uuu---uuuuu | ||
008 | 201007s2016 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.1186/s13073-016-0261-8 |2 doi | |
035 | |a (DE-627)SPR030635837 | ||
035 | |a (SPR)s13073-016-0261-8-e | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
100 | 1 | |a James, Regis A. |e verfasserin |4 aut | |
245 | 1 | 2 | |a A visual and curatorial approach to clinical variant prioritization and disease gene discovery in genome-wide diagnostics |
264 | 1 | |c 2016 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a Computermedien |b c |2 rdamedia | ||
338 | |a Online-Ressource |b cr |2 rdacarrier | ||
500 | |a © James et al. 2016 | ||
520 | |a Background Genome-wide data are increasingly important in the clinical evaluation of human disease. However, the large number of variants observed in individual patients challenges the efficiency and accuracy of diagnostic review. Recent work has shown that systematic integration of clinical phenotype data with genotype information can improve diagnostic workflows and prioritization of filtered rare variants. We have developed visually interactive, analytically transparent analysis software that leverages existing disease catalogs, such as the Online Mendelian Inheritance in Man database (OMIM) and the Human Phenotype Ontology (HPO), to integrate patient phenotype and variant data into ranked diagnostic alternatives. Methods Our tool, “OMIM Explorer” (http://www.omimexplorer.com), extends the biomedical application of semantic similarity methods beyond those reported in previous studies. The tool also provides a simple interface for translating free-text clinical notes into HPO terms, enabling clinical providers and geneticists to contribute phenotypes to the diagnostic process. The visual approach uses semantic similarity with multidimensional scaling to collapse high-dimensional phenotype and genotype data from an individual into a graphical format that contextualizes the patient within a low-dimensional disease map. The map proposes a differential diagnosis and algorithmically suggests potential alternatives for phenotype queries—in essence, generating a computationally assisted differential diagnosis informed by the individual’s personal genome. Visual interactivity allows the user to filter and update variant rankings by interacting with intermediate results. The tool also implements an adaptive approach for disease gene discovery based on patient phenotypes. Results We retrospectively analyzed pilot cohort data from the Baylor Miraca Genetics Laboratory, demonstrating performance of the tool and workflow in the re-analysis of clinical exomes. Our tool assigned to clinically reported variants a median rank of 2, placing causal variants in the top 1 % of filtered candidates across the 47 cohort cases with reported molecular diagnoses of exome variants in OMIM Morbidmap genes. Our tool outperformed Phen-Gen, eXtasy, PhenIX, PHIVE, and hiPHIVE in the prioritization of these clinically reported variants. Conclusions Our integrative paradigm can improve efficiency and, potentially, the quality of genomic medicine by more effectively utilizing available phenotype information, catalog data, and genomic knowledge. | ||
650 | 4 | |a Disease gene discovery |7 (dpeaa)DE-He213 | |
650 | 4 | |a Exome |7 (dpeaa)DE-He213 | |
650 | 4 | |a Semantic similarity |7 (dpeaa)DE-He213 | |
650 | 4 | |a Variant prioritization |7 (dpeaa)DE-He213 | |
700 | 1 | |a Campbell, Ian M. |4 aut | |
700 | 1 | |a Chen, Edward S. |4 aut | |
700 | 1 | |a Boone, Philip M. |4 aut | |
700 | 1 | |a Rao, Mitchell A. |4 aut | |
700 | 1 | |a Bainbridge, Matthew N. |4 aut | |
700 | 1 | |a Lupski, James R. |4 aut | |
700 | 1 | |a Yang, Yaping |4 aut | |
700 | 1 | |a Eng, Christine M. |4 aut | |
700 | 1 | |a Posey, Jennifer E. |4 aut | |
700 | 1 | |a Shaw, Chad A. |4 aut | |
773 | 0 | 8 | |i Enthalten in |t Genome medicine |d London : BioMed Central, 2009 |g 8(2016), 1 vom: 02. Feb. |w (DE-627)594424275 |w (DE-600)2484394-5 |x 1756-994X |7 nnns |
773 | 1 | 8 | |g volume:8 |g year:2016 |g number:1 |g day:02 |g month:02 |
856 | 4 | 0 | |u https://dx.doi.org/10.1186/s13073-016-0261-8 |z kostenfrei |3 Volltext |
912 | |a GBV_USEFLAG_A | ||
912 | |a SYSFLAG_A | ||
912 | |a GBV_SPRINGER | ||
912 | |a SSG-OLC-PHA | ||
912 | |a GBV_ILN_11 | ||
912 | |a GBV_ILN_20 | ||
912 | |a GBV_ILN_22 | ||
912 | |a GBV_ILN_23 | ||
912 | |a GBV_ILN_24 | ||
912 | |a GBV_ILN_31 | ||
912 | |a GBV_ILN_39 | ||
912 | |a GBV_ILN_40 | ||
912 | |a GBV_ILN_60 | ||
912 | |a GBV_ILN_62 | ||
912 | |a GBV_ILN_63 | ||
912 | |a GBV_ILN_65 | ||
912 | |a GBV_ILN_69 | ||
912 | |a GBV_ILN_73 | ||
912 | |a GBV_ILN_74 | ||
912 | |a GBV_ILN_95 | ||
912 | |a GBV_ILN_105 | ||
912 | |a GBV_ILN_110 | ||
912 | |a GBV_ILN_151 | ||
912 | |a GBV_ILN_161 | ||
912 | |a GBV_ILN_170 | ||
912 | |a GBV_ILN_206 | ||
912 | |a GBV_ILN_213 | ||
912 | |a GBV_ILN_230 | ||
912 | |a GBV_ILN_285 | ||
912 | |a GBV_ILN_293 | ||
912 | |a GBV_ILN_602 | ||
912 | |a GBV_ILN_2003 | ||
912 | |a GBV_ILN_2005 | ||
912 | |a GBV_ILN_2009 | ||
912 | |a GBV_ILN_2011 | ||
912 | |a GBV_ILN_2014 | ||
912 | |a GBV_ILN_2055 | ||
912 | |a GBV_ILN_2111 | ||
912 | |a GBV_ILN_4012 | ||
912 | |a GBV_ILN_4037 | ||
912 | |a GBV_ILN_4112 | ||
912 | |a GBV_ILN_4125 | ||
912 | |a GBV_ILN_4126 | ||
912 | |a GBV_ILN_4249 | ||
912 | |a GBV_ILN_4305 | ||
912 | |a GBV_ILN_4306 | ||
912 | |a GBV_ILN_4307 | ||
912 | |a GBV_ILN_4313 | ||
912 | |a GBV_ILN_4322 | ||
912 | |a GBV_ILN_4323 | ||
912 | |a GBV_ILN_4324 | ||
912 | |a GBV_ILN_4325 | ||
912 | |a GBV_ILN_4338 | ||
912 | |a GBV_ILN_4367 | ||
912 | |a GBV_ILN_4700 | ||
951 | |a AR | ||
952 | |d 8 |j 2016 |e 1 |b 02 |c 02 |
author_variant |
r a j ra raj i m c im imc e s c es esc p m b pm pmb m a r ma mar m n b mn mnb j r l jr jrl y y yy c m e cm cme j e p je jep c a s ca cas |
---|---|
matchkey_str |
article:1756994X:2016----::vsaaduaoilpraholnclainpirtztoadiesgndso |
hierarchy_sort_str |
2016 |
publishDate |
2016 |
allfields |
10.1186/s13073-016-0261-8 doi (DE-627)SPR030635837 (SPR)s13073-016-0261-8-e DE-627 ger DE-627 rakwb eng James, Regis A. verfasserin aut A visual and curatorial approach to clinical variant prioritization and disease gene discovery in genome-wide diagnostics 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © James et al. 2016 Background Genome-wide data are increasingly important in the clinical evaluation of human disease. However, the large number of variants observed in individual patients challenges the efficiency and accuracy of diagnostic review. Recent work has shown that systematic integration of clinical phenotype data with genotype information can improve diagnostic workflows and prioritization of filtered rare variants. We have developed visually interactive, analytically transparent analysis software that leverages existing disease catalogs, such as the Online Mendelian Inheritance in Man database (OMIM) and the Human Phenotype Ontology (HPO), to integrate patient phenotype and variant data into ranked diagnostic alternatives. Methods Our tool, “OMIM Explorer” (http://www.omimexplorer.com), extends the biomedical application of semantic similarity methods beyond those reported in previous studies. The tool also provides a simple interface for translating free-text clinical notes into HPO terms, enabling clinical providers and geneticists to contribute phenotypes to the diagnostic process. The visual approach uses semantic similarity with multidimensional scaling to collapse high-dimensional phenotype and genotype data from an individual into a graphical format that contextualizes the patient within a low-dimensional disease map. The map proposes a differential diagnosis and algorithmically suggests potential alternatives for phenotype queries—in essence, generating a computationally assisted differential diagnosis informed by the individual’s personal genome. Visual interactivity allows the user to filter and update variant rankings by interacting with intermediate results. The tool also implements an adaptive approach for disease gene discovery based on patient phenotypes. Results We retrospectively analyzed pilot cohort data from the Baylor Miraca Genetics Laboratory, demonstrating performance of the tool and workflow in the re-analysis of clinical exomes. Our tool assigned to clinically reported variants a median rank of 2, placing causal variants in the top 1 % of filtered candidates across the 47 cohort cases with reported molecular diagnoses of exome variants in OMIM Morbidmap genes. Our tool outperformed Phen-Gen, eXtasy, PhenIX, PHIVE, and hiPHIVE in the prioritization of these clinically reported variants. Conclusions Our integrative paradigm can improve efficiency and, potentially, the quality of genomic medicine by more effectively utilizing available phenotype information, catalog data, and genomic knowledge. Disease gene discovery (dpeaa)DE-He213 Exome (dpeaa)DE-He213 Semantic similarity (dpeaa)DE-He213 Variant prioritization (dpeaa)DE-He213 Campbell, Ian M. aut Chen, Edward S. aut Boone, Philip M. aut Rao, Mitchell A. aut Bainbridge, Matthew N. aut Lupski, James R. aut Yang, Yaping aut Eng, Christine M. aut Posey, Jennifer E. aut Shaw, Chad A. aut Enthalten in Genome medicine London : BioMed Central, 2009 8(2016), 1 vom: 02. Feb. (DE-627)594424275 (DE-600)2484394-5 1756-994X nnns volume:8 year:2016 number:1 day:02 month:02 https://dx.doi.org/10.1186/s13073-016-0261-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2016 1 02 02 |
spelling |
10.1186/s13073-016-0261-8 doi (DE-627)SPR030635837 (SPR)s13073-016-0261-8-e DE-627 ger DE-627 rakwb eng James, Regis A. verfasserin aut A visual and curatorial approach to clinical variant prioritization and disease gene discovery in genome-wide diagnostics 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © James et al. 2016 Background Genome-wide data are increasingly important in the clinical evaluation of human disease. However, the large number of variants observed in individual patients challenges the efficiency and accuracy of diagnostic review. Recent work has shown that systematic integration of clinical phenotype data with genotype information can improve diagnostic workflows and prioritization of filtered rare variants. We have developed visually interactive, analytically transparent analysis software that leverages existing disease catalogs, such as the Online Mendelian Inheritance in Man database (OMIM) and the Human Phenotype Ontology (HPO), to integrate patient phenotype and variant data into ranked diagnostic alternatives. Methods Our tool, “OMIM Explorer” (http://www.omimexplorer.com), extends the biomedical application of semantic similarity methods beyond those reported in previous studies. The tool also provides a simple interface for translating free-text clinical notes into HPO terms, enabling clinical providers and geneticists to contribute phenotypes to the diagnostic process. The visual approach uses semantic similarity with multidimensional scaling to collapse high-dimensional phenotype and genotype data from an individual into a graphical format that contextualizes the patient within a low-dimensional disease map. The map proposes a differential diagnosis and algorithmically suggests potential alternatives for phenotype queries—in essence, generating a computationally assisted differential diagnosis informed by the individual’s personal genome. Visual interactivity allows the user to filter and update variant rankings by interacting with intermediate results. The tool also implements an adaptive approach for disease gene discovery based on patient phenotypes. Results We retrospectively analyzed pilot cohort data from the Baylor Miraca Genetics Laboratory, demonstrating performance of the tool and workflow in the re-analysis of clinical exomes. Our tool assigned to clinically reported variants a median rank of 2, placing causal variants in the top 1 % of filtered candidates across the 47 cohort cases with reported molecular diagnoses of exome variants in OMIM Morbidmap genes. Our tool outperformed Phen-Gen, eXtasy, PhenIX, PHIVE, and hiPHIVE in the prioritization of these clinically reported variants. Conclusions Our integrative paradigm can improve efficiency and, potentially, the quality of genomic medicine by more effectively utilizing available phenotype information, catalog data, and genomic knowledge. Disease gene discovery (dpeaa)DE-He213 Exome (dpeaa)DE-He213 Semantic similarity (dpeaa)DE-He213 Variant prioritization (dpeaa)DE-He213 Campbell, Ian M. aut Chen, Edward S. aut Boone, Philip M. aut Rao, Mitchell A. aut Bainbridge, Matthew N. aut Lupski, James R. aut Yang, Yaping aut Eng, Christine M. aut Posey, Jennifer E. aut Shaw, Chad A. aut Enthalten in Genome medicine London : BioMed Central, 2009 8(2016), 1 vom: 02. Feb. (DE-627)594424275 (DE-600)2484394-5 1756-994X nnns volume:8 year:2016 number:1 day:02 month:02 https://dx.doi.org/10.1186/s13073-016-0261-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2016 1 02 02 |
allfields_unstemmed |
10.1186/s13073-016-0261-8 doi (DE-627)SPR030635837 (SPR)s13073-016-0261-8-e DE-627 ger DE-627 rakwb eng James, Regis A. verfasserin aut A visual and curatorial approach to clinical variant prioritization and disease gene discovery in genome-wide diagnostics 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © James et al. 2016 Background Genome-wide data are increasingly important in the clinical evaluation of human disease. However, the large number of variants observed in individual patients challenges the efficiency and accuracy of diagnostic review. Recent work has shown that systematic integration of clinical phenotype data with genotype information can improve diagnostic workflows and prioritization of filtered rare variants. We have developed visually interactive, analytically transparent analysis software that leverages existing disease catalogs, such as the Online Mendelian Inheritance in Man database (OMIM) and the Human Phenotype Ontology (HPO), to integrate patient phenotype and variant data into ranked diagnostic alternatives. Methods Our tool, “OMIM Explorer” (http://www.omimexplorer.com), extends the biomedical application of semantic similarity methods beyond those reported in previous studies. The tool also provides a simple interface for translating free-text clinical notes into HPO terms, enabling clinical providers and geneticists to contribute phenotypes to the diagnostic process. The visual approach uses semantic similarity with multidimensional scaling to collapse high-dimensional phenotype and genotype data from an individual into a graphical format that contextualizes the patient within a low-dimensional disease map. The map proposes a differential diagnosis and algorithmically suggests potential alternatives for phenotype queries—in essence, generating a computationally assisted differential diagnosis informed by the individual’s personal genome. Visual interactivity allows the user to filter and update variant rankings by interacting with intermediate results. The tool also implements an adaptive approach for disease gene discovery based on patient phenotypes. Results We retrospectively analyzed pilot cohort data from the Baylor Miraca Genetics Laboratory, demonstrating performance of the tool and workflow in the re-analysis of clinical exomes. Our tool assigned to clinically reported variants a median rank of 2, placing causal variants in the top 1 % of filtered candidates across the 47 cohort cases with reported molecular diagnoses of exome variants in OMIM Morbidmap genes. Our tool outperformed Phen-Gen, eXtasy, PhenIX, PHIVE, and hiPHIVE in the prioritization of these clinically reported variants. Conclusions Our integrative paradigm can improve efficiency and, potentially, the quality of genomic medicine by more effectively utilizing available phenotype information, catalog data, and genomic knowledge. Disease gene discovery (dpeaa)DE-He213 Exome (dpeaa)DE-He213 Semantic similarity (dpeaa)DE-He213 Variant prioritization (dpeaa)DE-He213 Campbell, Ian M. aut Chen, Edward S. aut Boone, Philip M. aut Rao, Mitchell A. aut Bainbridge, Matthew N. aut Lupski, James R. aut Yang, Yaping aut Eng, Christine M. aut Posey, Jennifer E. aut Shaw, Chad A. aut Enthalten in Genome medicine London : BioMed Central, 2009 8(2016), 1 vom: 02. Feb. (DE-627)594424275 (DE-600)2484394-5 1756-994X nnns volume:8 year:2016 number:1 day:02 month:02 https://dx.doi.org/10.1186/s13073-016-0261-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2016 1 02 02 |
allfieldsGer |
10.1186/s13073-016-0261-8 doi (DE-627)SPR030635837 (SPR)s13073-016-0261-8-e DE-627 ger DE-627 rakwb eng James, Regis A. verfasserin aut A visual and curatorial approach to clinical variant prioritization and disease gene discovery in genome-wide diagnostics 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © James et al. 2016 Background Genome-wide data are increasingly important in the clinical evaluation of human disease. However, the large number of variants observed in individual patients challenges the efficiency and accuracy of diagnostic review. Recent work has shown that systematic integration of clinical phenotype data with genotype information can improve diagnostic workflows and prioritization of filtered rare variants. We have developed visually interactive, analytically transparent analysis software that leverages existing disease catalogs, such as the Online Mendelian Inheritance in Man database (OMIM) and the Human Phenotype Ontology (HPO), to integrate patient phenotype and variant data into ranked diagnostic alternatives. Methods Our tool, “OMIM Explorer” (http://www.omimexplorer.com), extends the biomedical application of semantic similarity methods beyond those reported in previous studies. The tool also provides a simple interface for translating free-text clinical notes into HPO terms, enabling clinical providers and geneticists to contribute phenotypes to the diagnostic process. The visual approach uses semantic similarity with multidimensional scaling to collapse high-dimensional phenotype and genotype data from an individual into a graphical format that contextualizes the patient within a low-dimensional disease map. The map proposes a differential diagnosis and algorithmically suggests potential alternatives for phenotype queries—in essence, generating a computationally assisted differential diagnosis informed by the individual’s personal genome. Visual interactivity allows the user to filter and update variant rankings by interacting with intermediate results. The tool also implements an adaptive approach for disease gene discovery based on patient phenotypes. Results We retrospectively analyzed pilot cohort data from the Baylor Miraca Genetics Laboratory, demonstrating performance of the tool and workflow in the re-analysis of clinical exomes. Our tool assigned to clinically reported variants a median rank of 2, placing causal variants in the top 1 % of filtered candidates across the 47 cohort cases with reported molecular diagnoses of exome variants in OMIM Morbidmap genes. Our tool outperformed Phen-Gen, eXtasy, PhenIX, PHIVE, and hiPHIVE in the prioritization of these clinically reported variants. Conclusions Our integrative paradigm can improve efficiency and, potentially, the quality of genomic medicine by more effectively utilizing available phenotype information, catalog data, and genomic knowledge. Disease gene discovery (dpeaa)DE-He213 Exome (dpeaa)DE-He213 Semantic similarity (dpeaa)DE-He213 Variant prioritization (dpeaa)DE-He213 Campbell, Ian M. aut Chen, Edward S. aut Boone, Philip M. aut Rao, Mitchell A. aut Bainbridge, Matthew N. aut Lupski, James R. aut Yang, Yaping aut Eng, Christine M. aut Posey, Jennifer E. aut Shaw, Chad A. aut Enthalten in Genome medicine London : BioMed Central, 2009 8(2016), 1 vom: 02. Feb. (DE-627)594424275 (DE-600)2484394-5 1756-994X nnns volume:8 year:2016 number:1 day:02 month:02 https://dx.doi.org/10.1186/s13073-016-0261-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2016 1 02 02 |
allfieldsSound |
10.1186/s13073-016-0261-8 doi (DE-627)SPR030635837 (SPR)s13073-016-0261-8-e DE-627 ger DE-627 rakwb eng James, Regis A. verfasserin aut A visual and curatorial approach to clinical variant prioritization and disease gene discovery in genome-wide diagnostics 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © James et al. 2016 Background Genome-wide data are increasingly important in the clinical evaluation of human disease. However, the large number of variants observed in individual patients challenges the efficiency and accuracy of diagnostic review. Recent work has shown that systematic integration of clinical phenotype data with genotype information can improve diagnostic workflows and prioritization of filtered rare variants. We have developed visually interactive, analytically transparent analysis software that leverages existing disease catalogs, such as the Online Mendelian Inheritance in Man database (OMIM) and the Human Phenotype Ontology (HPO), to integrate patient phenotype and variant data into ranked diagnostic alternatives. Methods Our tool, “OMIM Explorer” (http://www.omimexplorer.com), extends the biomedical application of semantic similarity methods beyond those reported in previous studies. The tool also provides a simple interface for translating free-text clinical notes into HPO terms, enabling clinical providers and geneticists to contribute phenotypes to the diagnostic process. The visual approach uses semantic similarity with multidimensional scaling to collapse high-dimensional phenotype and genotype data from an individual into a graphical format that contextualizes the patient within a low-dimensional disease map. The map proposes a differential diagnosis and algorithmically suggests potential alternatives for phenotype queries—in essence, generating a computationally assisted differential diagnosis informed by the individual’s personal genome. Visual interactivity allows the user to filter and update variant rankings by interacting with intermediate results. The tool also implements an adaptive approach for disease gene discovery based on patient phenotypes. Results We retrospectively analyzed pilot cohort data from the Baylor Miraca Genetics Laboratory, demonstrating performance of the tool and workflow in the re-analysis of clinical exomes. Our tool assigned to clinically reported variants a median rank of 2, placing causal variants in the top 1 % of filtered candidates across the 47 cohort cases with reported molecular diagnoses of exome variants in OMIM Morbidmap genes. Our tool outperformed Phen-Gen, eXtasy, PhenIX, PHIVE, and hiPHIVE in the prioritization of these clinically reported variants. Conclusions Our integrative paradigm can improve efficiency and, potentially, the quality of genomic medicine by more effectively utilizing available phenotype information, catalog data, and genomic knowledge. Disease gene discovery (dpeaa)DE-He213 Exome (dpeaa)DE-He213 Semantic similarity (dpeaa)DE-He213 Variant prioritization (dpeaa)DE-He213 Campbell, Ian M. aut Chen, Edward S. aut Boone, Philip M. aut Rao, Mitchell A. aut Bainbridge, Matthew N. aut Lupski, James R. aut Yang, Yaping aut Eng, Christine M. aut Posey, Jennifer E. aut Shaw, Chad A. aut Enthalten in Genome medicine London : BioMed Central, 2009 8(2016), 1 vom: 02. Feb. (DE-627)594424275 (DE-600)2484394-5 1756-994X nnns volume:8 year:2016 number:1 day:02 month:02 https://dx.doi.org/10.1186/s13073-016-0261-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2016 1 02 02 |
language |
English |
source |
Enthalten in Genome medicine 8(2016), 1 vom: 02. Feb. volume:8 year:2016 number:1 day:02 month:02 |
sourceStr |
Enthalten in Genome medicine 8(2016), 1 vom: 02. Feb. volume:8 year:2016 number:1 day:02 month:02 |
format_phy_str_mv |
Article |
institution |
findex.gbv.de |
topic_facet |
Disease gene discovery Exome Semantic similarity Variant prioritization |
isfreeaccess_bool |
true |
container_title |
Genome medicine |
authorswithroles_txt_mv |
James, Regis A. @@aut@@ Campbell, Ian M. @@aut@@ Chen, Edward S. @@aut@@ Boone, Philip M. @@aut@@ Rao, Mitchell A. @@aut@@ Bainbridge, Matthew N. @@aut@@ Lupski, James R. @@aut@@ Yang, Yaping @@aut@@ Eng, Christine M. @@aut@@ Posey, Jennifer E. @@aut@@ Shaw, Chad A. @@aut@@ |
publishDateDaySort_date |
2016-02-02T00:00:00Z |
hierarchy_top_id |
594424275 |
id |
SPR030635837 |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR030635837</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230519185308.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2016 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1186/s13073-016-0261-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR030635837</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s13073-016-0261-8-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">James, Regis A.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A visual and curatorial approach to clinical variant prioritization and disease gene discovery in genome-wide diagnostics</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© James et al. 2016</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Background Genome-wide data are increasingly important in the clinical evaluation of human disease. However, the large number of variants observed in individual patients challenges the efficiency and accuracy of diagnostic review. Recent work has shown that systematic integration of clinical phenotype data with genotype information can improve diagnostic workflows and prioritization of filtered rare variants. We have developed visually interactive, analytically transparent analysis software that leverages existing disease catalogs, such as the Online Mendelian Inheritance in Man database (OMIM) and the Human Phenotype Ontology (HPO), to integrate patient phenotype and variant data into ranked diagnostic alternatives. Methods Our tool, “OMIM Explorer” (http://www.omimexplorer.com), extends the biomedical application of semantic similarity methods beyond those reported in previous studies. The tool also provides a simple interface for translating free-text clinical notes into HPO terms, enabling clinical providers and geneticists to contribute phenotypes to the diagnostic process. The visual approach uses semantic similarity with multidimensional scaling to collapse high-dimensional phenotype and genotype data from an individual into a graphical format that contextualizes the patient within a low-dimensional disease map. The map proposes a differential diagnosis and algorithmically suggests potential alternatives for phenotype queries—in essence, generating a computationally assisted differential diagnosis informed by the individual’s personal genome. Visual interactivity allows the user to filter and update variant rankings by interacting with intermediate results. The tool also implements an adaptive approach for disease gene discovery based on patient phenotypes. Results We retrospectively analyzed pilot cohort data from the Baylor Miraca Genetics Laboratory, demonstrating performance of the tool and workflow in the re-analysis of clinical exomes. Our tool assigned to clinically reported variants a median rank of 2, placing causal variants in the top 1 % of filtered candidates across the 47 cohort cases with reported molecular diagnoses of exome variants in OMIM Morbidmap genes. Our tool outperformed Phen-Gen, eXtasy, PhenIX, PHIVE, and hiPHIVE in the prioritization of these clinically reported variants. Conclusions Our integrative paradigm can improve efficiency and, potentially, the quality of genomic medicine by more effectively utilizing available phenotype information, catalog data, and genomic knowledge.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Disease gene discovery</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Exome</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Semantic similarity</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Variant prioritization</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Campbell, Ian M.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chen, Edward S.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Boone, Philip M.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Rao, Mitchell A.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Bainbridge, Matthew N.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lupski, James R.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yang, Yaping</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Eng, Christine M.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Posey, Jennifer E.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Shaw, Chad A.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Genome medicine</subfield><subfield code="d">London : BioMed Central, 2009</subfield><subfield code="g">8(2016), 1 vom: 02. Feb.</subfield><subfield code="w">(DE-627)594424275</subfield><subfield code="w">(DE-600)2484394-5</subfield><subfield code="x">1756-994X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:8</subfield><subfield code="g">year:2016</subfield><subfield code="g">number:1</subfield><subfield code="g">day:02</subfield><subfield code="g">month:02</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1186/s13073-016-0261-8</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">8</subfield><subfield code="j">2016</subfield><subfield code="e">1</subfield><subfield code="b">02</subfield><subfield code="c">02</subfield></datafield></record></collection>
|
author |
James, Regis A. |
spellingShingle |
James, Regis A. misc Disease gene discovery misc Exome misc Semantic similarity misc Variant prioritization A visual and curatorial approach to clinical variant prioritization and disease gene discovery in genome-wide diagnostics |
authorStr |
James, Regis A. |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)594424275 |
format |
electronic Article |
delete_txt_mv |
keep |
author_role |
aut aut aut aut aut aut aut aut aut aut aut |
collection |
springer |
remote_str |
true |
illustrated |
Not Illustrated |
issn |
1756-994X |
topic_title |
A visual and curatorial approach to clinical variant prioritization and disease gene discovery in genome-wide diagnostics Disease gene discovery (dpeaa)DE-He213 Exome (dpeaa)DE-He213 Semantic similarity (dpeaa)DE-He213 Variant prioritization (dpeaa)DE-He213 |
topic |
misc Disease gene discovery misc Exome misc Semantic similarity misc Variant prioritization |
topic_unstemmed |
misc Disease gene discovery misc Exome misc Semantic similarity misc Variant prioritization |
topic_browse |
misc Disease gene discovery misc Exome misc Semantic similarity misc Variant prioritization |
format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
cr |
hierarchy_parent_title |
Genome medicine |
hierarchy_parent_id |
594424275 |
hierarchy_top_title |
Genome medicine |
isfreeaccess_txt |
true |
familylinks_str_mv |
(DE-627)594424275 (DE-600)2484394-5 |
title |
A visual and curatorial approach to clinical variant prioritization and disease gene discovery in genome-wide diagnostics |
ctrlnum |
(DE-627)SPR030635837 (SPR)s13073-016-0261-8-e |
title_full |
A visual and curatorial approach to clinical variant prioritization and disease gene discovery in genome-wide diagnostics |
author_sort |
James, Regis A. |
journal |
Genome medicine |
journalStr |
Genome medicine |
lang_code |
eng |
isOA_bool |
true |
recordtype |
marc |
publishDateSort |
2016 |
contenttype_str_mv |
txt |
author_browse |
James, Regis A. Campbell, Ian M. Chen, Edward S. Boone, Philip M. Rao, Mitchell A. Bainbridge, Matthew N. Lupski, James R. Yang, Yaping Eng, Christine M. Posey, Jennifer E. Shaw, Chad A. |
container_volume |
8 |
format_se |
Elektronische Aufsätze |
author-letter |
James, Regis A. |
doi_str_mv |
10.1186/s13073-016-0261-8 |
title_sort |
visual and curatorial approach to clinical variant prioritization and disease gene discovery in genome-wide diagnostics |
title_auth |
A visual and curatorial approach to clinical variant prioritization and disease gene discovery in genome-wide diagnostics |
abstract |
Background Genome-wide data are increasingly important in the clinical evaluation of human disease. However, the large number of variants observed in individual patients challenges the efficiency and accuracy of diagnostic review. Recent work has shown that systematic integration of clinical phenotype data with genotype information can improve diagnostic workflows and prioritization of filtered rare variants. We have developed visually interactive, analytically transparent analysis software that leverages existing disease catalogs, such as the Online Mendelian Inheritance in Man database (OMIM) and the Human Phenotype Ontology (HPO), to integrate patient phenotype and variant data into ranked diagnostic alternatives. Methods Our tool, “OMIM Explorer” (http://www.omimexplorer.com), extends the biomedical application of semantic similarity methods beyond those reported in previous studies. The tool also provides a simple interface for translating free-text clinical notes into HPO terms, enabling clinical providers and geneticists to contribute phenotypes to the diagnostic process. The visual approach uses semantic similarity with multidimensional scaling to collapse high-dimensional phenotype and genotype data from an individual into a graphical format that contextualizes the patient within a low-dimensional disease map. The map proposes a differential diagnosis and algorithmically suggests potential alternatives for phenotype queries—in essence, generating a computationally assisted differential diagnosis informed by the individual’s personal genome. Visual interactivity allows the user to filter and update variant rankings by interacting with intermediate results. The tool also implements an adaptive approach for disease gene discovery based on patient phenotypes. Results We retrospectively analyzed pilot cohort data from the Baylor Miraca Genetics Laboratory, demonstrating performance of the tool and workflow in the re-analysis of clinical exomes. Our tool assigned to clinically reported variants a median rank of 2, placing causal variants in the top 1 % of filtered candidates across the 47 cohort cases with reported molecular diagnoses of exome variants in OMIM Morbidmap genes. Our tool outperformed Phen-Gen, eXtasy, PhenIX, PHIVE, and hiPHIVE in the prioritization of these clinically reported variants. Conclusions Our integrative paradigm can improve efficiency and, potentially, the quality of genomic medicine by more effectively utilizing available phenotype information, catalog data, and genomic knowledge. © James et al. 2016 |
abstractGer |
Background Genome-wide data are increasingly important in the clinical evaluation of human disease. However, the large number of variants observed in individual patients challenges the efficiency and accuracy of diagnostic review. Recent work has shown that systematic integration of clinical phenotype data with genotype information can improve diagnostic workflows and prioritization of filtered rare variants. We have developed visually interactive, analytically transparent analysis software that leverages existing disease catalogs, such as the Online Mendelian Inheritance in Man database (OMIM) and the Human Phenotype Ontology (HPO), to integrate patient phenotype and variant data into ranked diagnostic alternatives. Methods Our tool, “OMIM Explorer” (http://www.omimexplorer.com), extends the biomedical application of semantic similarity methods beyond those reported in previous studies. The tool also provides a simple interface for translating free-text clinical notes into HPO terms, enabling clinical providers and geneticists to contribute phenotypes to the diagnostic process. The visual approach uses semantic similarity with multidimensional scaling to collapse high-dimensional phenotype and genotype data from an individual into a graphical format that contextualizes the patient within a low-dimensional disease map. The map proposes a differential diagnosis and algorithmically suggests potential alternatives for phenotype queries—in essence, generating a computationally assisted differential diagnosis informed by the individual’s personal genome. Visual interactivity allows the user to filter and update variant rankings by interacting with intermediate results. The tool also implements an adaptive approach for disease gene discovery based on patient phenotypes. Results We retrospectively analyzed pilot cohort data from the Baylor Miraca Genetics Laboratory, demonstrating performance of the tool and workflow in the re-analysis of clinical exomes. Our tool assigned to clinically reported variants a median rank of 2, placing causal variants in the top 1 % of filtered candidates across the 47 cohort cases with reported molecular diagnoses of exome variants in OMIM Morbidmap genes. Our tool outperformed Phen-Gen, eXtasy, PhenIX, PHIVE, and hiPHIVE in the prioritization of these clinically reported variants. Conclusions Our integrative paradigm can improve efficiency and, potentially, the quality of genomic medicine by more effectively utilizing available phenotype information, catalog data, and genomic knowledge. © James et al. 2016 |
abstract_unstemmed |
Background Genome-wide data are increasingly important in the clinical evaluation of human disease. However, the large number of variants observed in individual patients challenges the efficiency and accuracy of diagnostic review. Recent work has shown that systematic integration of clinical phenotype data with genotype information can improve diagnostic workflows and prioritization of filtered rare variants. We have developed visually interactive, analytically transparent analysis software that leverages existing disease catalogs, such as the Online Mendelian Inheritance in Man database (OMIM) and the Human Phenotype Ontology (HPO), to integrate patient phenotype and variant data into ranked diagnostic alternatives. Methods Our tool, “OMIM Explorer” (http://www.omimexplorer.com), extends the biomedical application of semantic similarity methods beyond those reported in previous studies. The tool also provides a simple interface for translating free-text clinical notes into HPO terms, enabling clinical providers and geneticists to contribute phenotypes to the diagnostic process. The visual approach uses semantic similarity with multidimensional scaling to collapse high-dimensional phenotype and genotype data from an individual into a graphical format that contextualizes the patient within a low-dimensional disease map. The map proposes a differential diagnosis and algorithmically suggests potential alternatives for phenotype queries—in essence, generating a computationally assisted differential diagnosis informed by the individual’s personal genome. Visual interactivity allows the user to filter and update variant rankings by interacting with intermediate results. The tool also implements an adaptive approach for disease gene discovery based on patient phenotypes. Results We retrospectively analyzed pilot cohort data from the Baylor Miraca Genetics Laboratory, demonstrating performance of the tool and workflow in the re-analysis of clinical exomes. Our tool assigned to clinically reported variants a median rank of 2, placing causal variants in the top 1 % of filtered candidates across the 47 cohort cases with reported molecular diagnoses of exome variants in OMIM Morbidmap genes. Our tool outperformed Phen-Gen, eXtasy, PhenIX, PHIVE, and hiPHIVE in the prioritization of these clinically reported variants. Conclusions Our integrative paradigm can improve efficiency and, potentially, the quality of genomic medicine by more effectively utilizing available phenotype information, catalog data, and genomic knowledge. © James et al. 2016 |
collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 |
container_issue |
1 |
title_short |
A visual and curatorial approach to clinical variant prioritization and disease gene discovery in genome-wide diagnostics |
url |
https://dx.doi.org/10.1186/s13073-016-0261-8 |
remote_bool |
true |
author2 |
Campbell, Ian M. Chen, Edward S. Boone, Philip M. Rao, Mitchell A. Bainbridge, Matthew N. Lupski, James R. Yang, Yaping Eng, Christine M. Posey, Jennifer E. Shaw, Chad A. |
author2Str |
Campbell, Ian M. Chen, Edward S. Boone, Philip M. Rao, Mitchell A. Bainbridge, Matthew N. Lupski, James R. Yang, Yaping Eng, Christine M. Posey, Jennifer E. Shaw, Chad A. |
ppnlink |
594424275 |
mediatype_str_mv |
c |
isOA_txt |
true |
hochschulschrift_bool |
false |
doi_str |
10.1186/s13073-016-0261-8 |
up_date |
2024-07-03T19:15:05.218Z |
_version_ |
1803586481916215296 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR030635837</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230519185308.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2016 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1186/s13073-016-0261-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR030635837</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s13073-016-0261-8-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">James, Regis A.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A visual and curatorial approach to clinical variant prioritization and disease gene discovery in genome-wide diagnostics</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© James et al. 2016</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Background Genome-wide data are increasingly important in the clinical evaluation of human disease. However, the large number of variants observed in individual patients challenges the efficiency and accuracy of diagnostic review. Recent work has shown that systematic integration of clinical phenotype data with genotype information can improve diagnostic workflows and prioritization of filtered rare variants. We have developed visually interactive, analytically transparent analysis software that leverages existing disease catalogs, such as the Online Mendelian Inheritance in Man database (OMIM) and the Human Phenotype Ontology (HPO), to integrate patient phenotype and variant data into ranked diagnostic alternatives. Methods Our tool, “OMIM Explorer” (http://www.omimexplorer.com), extends the biomedical application of semantic similarity methods beyond those reported in previous studies. The tool also provides a simple interface for translating free-text clinical notes into HPO terms, enabling clinical providers and geneticists to contribute phenotypes to the diagnostic process. The visual approach uses semantic similarity with multidimensional scaling to collapse high-dimensional phenotype and genotype data from an individual into a graphical format that contextualizes the patient within a low-dimensional disease map. The map proposes a differential diagnosis and algorithmically suggests potential alternatives for phenotype queries—in essence, generating a computationally assisted differential diagnosis informed by the individual’s personal genome. Visual interactivity allows the user to filter and update variant rankings by interacting with intermediate results. The tool also implements an adaptive approach for disease gene discovery based on patient phenotypes. Results We retrospectively analyzed pilot cohort data from the Baylor Miraca Genetics Laboratory, demonstrating performance of the tool and workflow in the re-analysis of clinical exomes. Our tool assigned to clinically reported variants a median rank of 2, placing causal variants in the top 1 % of filtered candidates across the 47 cohort cases with reported molecular diagnoses of exome variants in OMIM Morbidmap genes. Our tool outperformed Phen-Gen, eXtasy, PhenIX, PHIVE, and hiPHIVE in the prioritization of these clinically reported variants. Conclusions Our integrative paradigm can improve efficiency and, potentially, the quality of genomic medicine by more effectively utilizing available phenotype information, catalog data, and genomic knowledge.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Disease gene discovery</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Exome</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Semantic similarity</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Variant prioritization</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Campbell, Ian M.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chen, Edward S.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Boone, Philip M.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Rao, Mitchell A.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Bainbridge, Matthew N.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lupski, James R.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yang, Yaping</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Eng, Christine M.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Posey, Jennifer E.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Shaw, Chad A.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Genome medicine</subfield><subfield code="d">London : BioMed Central, 2009</subfield><subfield code="g">8(2016), 1 vom: 02. Feb.</subfield><subfield code="w">(DE-627)594424275</subfield><subfield code="w">(DE-600)2484394-5</subfield><subfield code="x">1756-994X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:8</subfield><subfield code="g">year:2016</subfield><subfield code="g">number:1</subfield><subfield code="g">day:02</subfield><subfield code="g">month:02</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1186/s13073-016-0261-8</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">8</subfield><subfield code="j">2016</subfield><subfield code="e">1</subfield><subfield code="b">02</subfield><subfield code="c">02</subfield></datafield></record></collection>
|
score |
7.4008036 |