Herr Dr. Tobias Brix
ODM Data Analysis—A tool for the automatic validation, monitoring and generation of generic descriptive statistics of patient data
BY: Brix T.; Bruland P.; et al.
PlOS ONE Published online: 2018 June 22
Herr Dr. Philipp Bruland
Connecting healthcare and clinical research: Workflow optimizations through seamless integration of EHR, pseudonymization services and EDC systems
BY: Bruland P.; Doods J.; Brix T.; Dugas M.; Storck M.
Elsevier B.V. Published online: 2018 September 10
Herr Dr. Julian Varghese
Effects of computerized decision support system implementations on patient outcomes in inpatient care: a systematic review
BY: Varghese J.; Kleine M.; Geßner S. I.; Sandmann S.; Dugas M.
Journal of the American Medical Informatics Association Published online: 2018 May 01
CDEGenerator: an online platform to learn from existing data models to build model registries
BY: Varghese J.; Fujarski M.; Hegselmann S.; Neuhaus P.; Dugas M.
Dovepress Published online: 2018 August 10
Web-Based Information Infrastructure Increases the Interrater Reliability of Medical Coders: Quasi-Experimental Study
BY: Varghese J.; SandmannS.; Dugas M.
JMIR Published online: 2018 October 15
appreci8: a pipeline for precise variant calling integrating 8 tools.
Bioinformatics. 2018 12 15;34(24):4205-4212
Authors: Sandmann S, Karimi M, de Graaf AO, Rohde C, Göllner S, Varghese J, Ernsting J, Walldin G, van der Reijden BA, Müller-Tidow C, Malcovati L, Hellström-Lindberg E, Jansen JH, Dugas M
Motivation: The application of next-generation sequencing in research and particularly in clinical routine requires valid variant calling results. However, evaluation of several commonly used tools has pointed out that not a single tool meets this requirement. False positive as well as false negative calls necessitate additional experiments and extensive manual work. Intelligent combination and output filtration of different tools could significantly improve the current situation.
Results: We developed appreci8, an automatic variant calling pipeline for calling single nucleotide variants and short indels by combining and filtering the output of eight open-source variant calling tools, based on a novel artifact- and polymorphism score. Appreci8 was trained on two data sets from patients with myelodysplastic syndrome, covering 165 Illumina samples. Subsequently, appreci8's performance was tested on five independent data sets, covering 513 samples. Variation in sequencing platform, target region and disease entity was considered. All calls were validated by re-sequencing on the same platform, a different platform or expert-based review. Sensitivity of appreci8 ranged between 0.93 and 1.00, while positive predictive value ranged between 0.65 and 1.00. In all cases, appreci8 showed superior performance compared to any evaluated alternative approach.
Availability and implementation: Appreci8 is freely available at https://hub.docker.com/r/wwuimi/appreci8/. Sequencing data (BAM files) of the 678 patients analyzed with appreci8 have been deposited into the NCBI Sequence Read Archive (BioProjectID: 388411; https://www.ncbi.nlm.nih.gov/bioproject/PRJNA388411).
Supplementary information: Supplementary data are available at Bioinformatics online.
PMID: 29945233 [PubMed - indexed for MEDLINE]