gms | German Medical Science

66. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS), 12. Jahreskongress der Technologie- und Methodenplattform für die vernetzte medizinische Forschung e. V. (TMF)

26. - 30.09.2021, online

Introducing Variant Browser – a Meta Search Plattform for Genetic Variants

Meeting Abstract

  • Stefan Sigle - MOLIT Institute gGmbH, Heilbronn, Germany
  • Kevin Kaufmes - MOLIT Institute gGmbH, Heilbronn, Germany
  • Patrick Werner - MOLIT Institute gGmbH, Heilbronn, Germany
  • Sylvia Bochum - SLK Clinics Heilbronn, Heilbronn, Germany
  • Uwe Martens - MOLIT Institute gGmbH, Heilbronn, Germany
  • Christian Fegeler - Heilbronn University of Applied Sciences, Heilbronn, Germany

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. 66. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS), 12. Jahreskongress der Technologie- und Methodenplattform für die vernetzte medizinische Forschung e.V. (TMF). sine loco [digital], 26.-30.09.2021. Düsseldorf: German Medical Science GMS Publishing House; 2021. DocAbstr. 48

doi: 10.3205/21gmds054, urn:nbn:de:0183-21gmds0549

Published: September 24, 2021

© 2021 Sigle et al.
This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. See license information at



Introduction: Revision of annotated data generated by a broad genomic panel (>500 genes) within oncological precision medicine exceeds the resources of a single expert [1] and requires clinical decision support [2]. In order to provide personalized therapy recommendations during a multidisciplinary molecular tumor board (MTB) targeted drug therapies, clinical trials and genetic alterations have to be taken into consideration. As a consequence, the preparation time per case is drastically increased compared to traditional clinical guideline approaches [3], [4].

Methods: Available Knowledge databases (KD) [5], [6], [7], [8], [9], [10], [11], [12] were identified by literature research and evaluated for their application programming interface (API) capabilities. KDs were evaluated on the basis of their content and information retrieval capabilities. The following key factors could be identified: genetic variants, disease processes, drugs and clinical trials. Additionally, terms of service (TOS) of all KDs have been analyzed before inclusion. Variant Browser (VB) was implemented as web-application and the leveraged information sources compared to an existing meta search engine [13].

Results: VB integrates 8 of the 21 identified KDs, which comply to requirements of API accessibility as well as TOS. Only one available KD provided the possibility to define queries for genetic variants while also including additional semantical information like the reference genome sequence information, which is crucial to ensure accurate variant results. Focusing on aspects like transparency and user experience, VB can be customized to fit the user’s information need by a responsive design and dynamically adjustable table views hiding information deemed unnecessary by the user.

Discussion: API search capabilities, information models and nomenclatures of KDs impose serious limitations on knowledge integration due to i) inconsistent naming systems, ii) non-unique data standards and iii) differing taxonomy conventions for genetic variants [14]. Also, multiple queried APIs eventually change their underlying information model, while parameters like update rate and adaptation rate of new evidence into each KD remain unknown. Thus, VB pursues a platform agnostic paradigm, facilitating digital workflow integration within the context of MTB preparation.

Conclusion: Heterogeneous information models and varying semantical depth of different APIs impose limitations on knowledge integration and impact automated decision support. The aspect of enabling the standards-based integration of information found by VB into existing software frameworks and solutions for MTBs is a crucial extension to the current set of features. This prototype tackles the challenge of supporting decentralized Information gathering, but further real-world, user centered, evaluation has to be conducted within a precision medicine setting in MTB preparation to fully leverage the potential of decision support.

The MOLIT Institute is a non-profit organization, funded by donation. The last two authors are the founders of the MOLIT Institute.

The authors declare that an ethics committee vote is not required.


Keil A, Sigle S, Bochum S, Fegeler C. Drowning in Information: The lack of integrating Knowledge Databases [Abstract Jahrestagung der Deutschen, Österreichischen und Schweizerischen Gesellschaften für Hämatologie und Medizinische Onkologie, Berlin, 11. - 14. Oktober 2019]. Oncol Res Treat. 2019 Oct;42 Suppl 4:60. DOI: 10.1159/000502425 External link
Aziz A, Kawamoto K, Eilbeck K, Williams MS, Freimuth RR, Hoffman MA, Rasmussen LV, Overby CL, Shirts BH, Hoffman JM, Welch BM. The genomic CDS sandbox: An assessment among domain experts. J Biomed Inform. 2016 Apr;60:84-94. DOI: 10.1016/j.jbi.2015.12.019 External link
Knepper TC, Bell GC, Hicks JK, Padron E, Teer JK, Vo TT, Gillis NK, Mason NT, McLeod HL, Walko CM. Key Lessons Learned from Moffitt's Molecular Tumor Board: The Clinical Genomics Action Committee Experience. Oncologist. 2017;22(2):144-151. DOI: 10.1634/theoncologist.2016-0195 External link
van der Velden DL, van Herpen CML, van Laarhoven HWM, Smit EF, Groen HJM, Willems SM, Nederlof PM, Langenberg MHG, Cuppen E, Sleijfer S, Steeghs N, Voest EE. Molecular Tumor Boards: current practice and future needs. Ann Oncol. 2017 Dec;28(12):3070-3075. DOI: 10.1093/annonc/mdx528 External link
Chakravarty D, Gao J, Phillips SM, Kundra R, Zhang H, Wang J, Rudolph JE, Yaeger R, Soumerai T, Nissan MH, Chang MT, Chandarlapaty S, Traina TA, Paik PK, Ho AL, Hantash FM, Grupe A, Baxi SS, Callahan MK, Snyder A, Chi P, Danila D, Gounder M, Harding JJ, Hellmann MD, Iyer G, Janjigian Y, Kaley T, Levine DA, Lowery M, Omuro A, Postow MA, Rathkopf D, Shoushtari AN, Shukla N, Voss M, Paraiso E, Zehir A, Berger MF, Taylor BS, Saltz LB, Riely GJ, Ladanyi M, Hyman DM, Baselga J, Sabbatini P, Solit DB, Schultz N. OncoKB: A Precision Oncology Knowledge Base. JCO Precis Oncol. 2017 Jul. DOI: 10.1200/PO.17.00011 External link
Griffith M, Spies NC, Krysiak K, McMichael JF, Coffman AC, Danos AM, Ainscough BJ, Ramirez CA, Rieke DT, Kujan L, Barnell EK, Wagner AH, Skidmore ZL, Wollam A, Liu CJ, Jones MR, Bilski RL, Lesurf R, Feng YY, Shah NM, Bonakdar M, Trani L, Matlock M, Ramu A, Campbell KM, Spies GC, Graubert AP, Gangavarapu K, Eldred JM, Larson DE, Walker JR, Good BM, Wu C, Su AI, Dienstmann R, Margolin AA, Tamborero D, Lopez-Bigas N, Jones SJ, Bose R, Spencer DH, Wartman LD, Wilson RK, Mardis ER, Griffith OL. CIViC is a community knowledgebase for expert crowdsourcing the clinical interpretation of variants in cancer. Nat Genet. 2017;49(2):170-174. DOI: 10.1038/ng.3774 External link
Landrum MJ, Lee JM, Benson M, Brown GR, Chao C, Chitipiralla S, Gu B, Hart J, Hoffman D, Jang W, Karapetyan K, Katz K, Liu C, Maddipatla Z, Malheiro A, McDaniel K, Ovetsky M, Riley G, Zhou G, Holmes JB, Kattman BL, Maglott DR. ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Res. 2018;46(D1):D1062-D1067. DOI: 10.1093/nar/gkx1153 External link
Xin J, Afrasiabi C, Mark A, Tsueng G, Su AI, Wu C. Mygene. Info Data Backend Component. Zenodo; 2016. DOI: 10.5281/ZENODO.48145 External link
Slenter DN, Kutmon M, Hanspers K, Riutta A, Windsor J, Nunes N, Mélius J, Cirillo E, Coort SL, Digles D, Ehrhart F, Giesbertz P, Kalafati M, Martens M, Miller R, Nishida K, Rieswijk L, Waagmeester A, Eijssen LMT, Evelo CT, Pico AR, Willighagen EL. WikiPathways: a multifaceted pathway database bridging metabolomics to other omics research. Nucleic Acids Res. 2018 01;46(D1):D661-D667. DOI: 10.1093/nar/gkx1064 External link
Xin J, Mark A, Afrasiabi C, Tsueng G, Juchler M, Gopal N, Stupp GS, Putman TE, Ainscough BJ, Griffith OL, Torkamani A, Whetzel PL, Mungall CJ, Mooney SD, Su AI, Wu C. High-performance web services for querying gene and variant annotation. Genome Biol. 2016 05;17(1):91. DOI: 10.1186/s13059-016-0953-9 External link
Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, Sun Y, Jacobsen A, Sinha R, Larsson E, Cerami E, Sander C, Schultz N. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal. 2013 Apr;6(269):pl1. DOI: 10.1126/scisignal.2004088 External link
Whirl-Carrillo M, McDonagh EM, Hebert JM, Gong L, Sangkuhl K, Thorn CF, Altman RB, Klein TE. Pharmacogenomics knowledge for personalized medicine. Clin Pharmacol Ther. 2012 Oct;92(4):414-7. DOI: 10.1038/clpt.2012.96 External link
Warner JL, Prasad I, Bennett M, Arniella M, Beeghly-Fadiel A, Mandl KD, Alterovitz G. SMART Cancer Navigator: A Framework for Implementing ASCO Workshop Recommendations to Enable Precision Cancer Medicine. JCO Precis Oncol. 2018. DOI: 10.1200/PO.17.00292 External link
Conway JR, Warner JL, Rubinstein WS, Miller RS. Next-Generation Sequencing and the Clinical Oncology Workflow: Data Challenges, Proposed Solutions, and a Call to Action. JCO Precis Oncol. 2019. DOI: 10.1200/PO.19.00232 External link