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@ARTICLE{Kotowicz:266775,
      author       = {Kotowicz, Malwina and Fengler, Sven and Kurkowsky, Birgit
                      and Meyer-Berhorn, Anja and Moretti, Elisa and Blersch,
                      Josephine and Shumanska, Magdalena and Schmidt, Gisela and
                      Kreye, Jakob and Hoof, Scott and Sánchez-Sendín, Elisa and
                      Reincke, Momsen and Krüger, Lars and Prüss, Harald and
                      Denner, Philip and Fava, Eugenio and Stappert, Dominik},
      title        = {{G}ain efficiency with streamlined and automated data
                      processing: {E}xamples from high-throughput monoclonal
                      antibody production},
      journal      = {bioRxiv beta},
      address      = {Cold Spring Harbor},
      publisher    = {Cold Spring Harbor Laboratory, NY},
      reportid     = {DZNE-2024-00038},
      year         = {2023},
      abstract     = {Data management and sample tracking in complex biological
                      workflows are essential steps to ensure necessary
                      documentation and guarantee the reusability of data and
                      metadata. Currently, these steps pose challenges related to
                      correct annotation and labeling, error detection, and
                      safeguarding the quality of documentation. With growing
                      acquisition of biological data and the expanding
                      automatization of laboratory workflows, manual processing of
                      samples is no longer favorable, as it is time- and
                      resource-consuming, is prone to biases and errors, and lacks
                      scalability and standardization. Thus, managing
                      heterogeneous biological data calls for efficient and
                      tailored systems, especially in laboratories run by
                      biologists with limited computational expertise. Here, we
                      showcase how to meet these challenges with a modular
                      pipeline for data processing, facilitating the complex
                      production of monoclonal antibodies from single B-cells. We
                      present best practices for development of data processing
                      pipelines concerned with extensive acquisition of biological
                      data that undergoes continuous manipulation and analysis.
                      Moreover, we assess the versatility of proposed design
                      principles through a proof-of-concept data processing
                      pipeline for automated induced pluripotent stem cell culture
                      and differentiation. We show that our approach streamlines
                      data management operations, speeds up experimental cycles
                      and leads to enhanced reproducibility. Finally, adhering to
                      the presented guidelines will promote compliance with FAIR
                      principles upon publishing.},
      cin          = {LAT / LIS / Tech Transfer / AG Prüß / AG Fava},
      ddc          = {570},
      cid          = {I:(DE-2719)1040190 / I:(DE-2719)1040260 /
                      I:(DE-2719)1030028 / I:(DE-2719)1810003 /
                      I:(DE-2719)1040000},
      pnm          = {899 - ohne Topic (POF4-899) / 353 - Clinical and Health
                      Care Research (POF4-353)},
      pid          = {G:(DE-HGF)POF4-899 / G:(DE-HGF)POF4-353},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.1101/2023.12.14.571214},
      url          = {https://pub.dzne.de/record/266775},
}