{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Wang L"],"funding":["H2020 Marie Sklodowska-Curie Actions","Vetenskapsraådet","Knut och Alice Wallenbergs Stiftelse","Cancerfonden"],"pagination":["105-115"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12809502"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["13(Pt 1)"],"pubmed_abstract":["Three-dimensional electron diffraction (3D ED), also known as microcrystal electron diffraction (microED), is an emerging method for determining structures from submicron-sized crystals. With the development of rapid and convenient data collection protocols, acquiring dozens of datasets in a single 3D ED/microED session has become routine. A fast and automated workflow for processing, scaling and merging a large number of 3D ED/microED datasets can significantly accelerate the structure determination process. Herein, we present an XDS-based pipeline with a graphical user interface for automated real-time and offline batch 3D ED/microED data processing (AutoLEI). We demonstrate the functionality and applications of the pipeline through four examples, using both offline and real-time data processing capabilities. The samples include small organic molecules, metal-organic frameworks (MOFs) and proteins, showcasing the versatility and efficiency of AutoLEI in various applications."],"journal":["IUCrJ"],"pubmed_title":["AutoLEI: An XDS-based pipeline with graphical user interface for automated real-time and offline batch 3D ED/microED data processing."],"pmcid":["PMC12809502"],"funding_grant_id":["2019.0124","24-3848-Pj","2022-03596","2022-03681","2019-00815","956099"],"pubmed_authors":["Zou X","Hofer G","Stenmark P","Hutchinson ES","Chen Y","Xu H","Wang L"],"additional_accession":[]},"is_claimable":false,"name":"AutoLEI: An XDS-based pipeline with graphical user interface for automated real-time and offline batch 3D ED/microED data processing.","description":"Three-dimensional electron diffraction (3D ED), also known as microcrystal electron diffraction (microED), is an emerging method for determining structures from submicron-sized crystals. With the development of rapid and convenient data collection protocols, acquiring dozens of datasets in a single 3D ED/microED session has become routine. A fast and automated workflow for processing, scaling and merging a large number of 3D ED/microED datasets can significantly accelerate the structure determination process. Herein, we present an XDS-based pipeline with a graphical user interface for automated real-time and offline batch 3D ED/microED data processing (AutoLEI). We demonstrate the functionality and applications of the pipeline through four examples, using both offline and real-time data processing capabilities. The samples include small organic molecules, metal-organic frameworks (MOFs) and proteins, showcasing the versatility and efficiency of AutoLEI in various applications.","dates":{"release":"2026-01-01T00:00:00Z","publication":"2026 Jan","modification":"2026-06-06T16:05:51.486Z","creation":"2026-06-02T03:12:17.203Z"},"accession":"S-EPMC12809502","cross_references":{"pubmed":["41431443"],"doi":["10.1107/S2052252525010784"]}}