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Imaging and Molecular Annotation of Xenographs and Tumours (IMAXT): High throughput data and analysis infrastructure

2023; Cambridge University Press; Volume: 3; Linguagem: Inglês

10.1017/s2633903x23000090

2633-903X

E. A. González-Solares, A. Dariush, Carlos González‐Fernández, A. Yoldaş, Alireza Molaeinezhad, Mohammad Al Sa’d, Leigh M. Smith, Tristan Whitmarsh, Neil S. Millar, N. Chornay, Ilaria Falciatori, Atefeh Fatemi, Daniel Goodwin, Laura Kuett, Claire M. Mulvey, Marta Páez Ribes, Fatime Qosaj, Andrew Roth, Ignacio Vázquez-Garćıa, Spencer S. Watson, Jonas Windhager, Samuel Aparício, Bernd Bodenmiller, Ed Boyden, Carlos Caldas, Owen Harris, Sohrab P. Shah, Simon Tavaré, Dario Bressan, Gregory J. Hannon, N. A. Walton,

Scientific Computing and Data Management

With the aim of producing a 3D representation of tumors, imaging and molecular annotation of xenografts and tumors (IMAXT) uses a large variety of modalities in order to acquire tumor samples and produce a map of every cell in the tumor and its host environment. With the large volume and variety of data produced in the project, we developed automatic data workflows and analysis pipelines. We introduce a research methodology where scientists connect to a cloud environment to perform analysis close to where data are located, instead of bringing data to their local computers. Here, we present the data and analysis infrastructure, discuss the unique computational challenges and describe the analysis chains developed and deployed to generate molecularly annotated tumor models. Registration is achieved by use of a novel technique involving spherical fiducial marks that are visible in all imaging modalities used within IMAXT. The automatic pipelines are highly optimized and allow to obtain processed datasets several times quicker than current solutions narrowing the gap between data acquisition and scientific exploitation.