Gain efficiency with streamlined and automated data processing: Examples from high-throughput monoclonal antibody production.

ABSTRACT: Data management and sample tracking in complex biological workflows are essential steps to ensure necessary documentation and guarantee 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 sample data is no longer favorable, as it is time- and resource-consuming, 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 challeng

SUBMITTER: Kotowicz M 

PROVIDER: S-EPMC12212921 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature