https://3demmethods.i2pc.es/index.php?action=history&feed=atom&title=2024Maurer_PyTME 2026-06-13T12:37:53Z Revision history for this page on the wiki MediaWiki 1.44.2 https://3demmethods.i2pc.es/index.php?title=2024Maurer_PyTME&diff=4697&oldid=prev 2024-08-16T05:56:25Z

<p>Created page with &quot;== Citation == Maurer, Valentin J. / Siggel, Marc / Kosinski, Jan. PyTME (Python Template Matching Engine): A fast, flexible, and multi-purpose template matching library for cryogenic electron microscopy data. 2024. SoftwareX, Vol. 25, p. 101636 == Abstract == Cryogenic electron microscopy (cryo-EM) is a key method in structural and cell biology. Analysis of cryo-EM images requires interpretation of noisy, low-resolution densities, which relies on identifying the most...&quot;</p> <p><b>New page</b></p><div>== Citation ==<br /> <br /> Maurer, Valentin J. / Siggel, Marc / Kosinski, Jan. PyTME (Python Template Matching Engine): A fast, flexible, and multi-purpose template matching library for cryogenic electron microscopy data. 2024. SoftwareX, Vol. 25, p. 101636<br /> <br /> == Abstract ==<br /> <br /> Cryogenic electron microscopy (cryo-EM) is a key method in structural and cell biology. Analysis of cryo-EM<br /> images requires interpretation of noisy, low-resolution densities, which relies on identifying the most probable<br /> orientation of macromolecules in a target using template matching. Many method-specific template-matching<br /> software solutions exist for single-particle cryo-EM, cryo-electron tomography (cryo-ET), or fitting atomic<br /> structures into averaged 3D maps of macromolecules. Here, we report the Python Template Matching Engine<br /> (pyTME), a software engine that consolidates method-specific template matching problems. The underlying<br /> library provides abstract data structures for storing and manipulating input and output data. PyTME runs up to<br /> ten times faster without loss in accuracy compared to existing software with multiple CPUs and GPUs, enabling<br /> template matching of even unbinned cryo-ET data in hours, which was previously nearly impossible due to<br /> technical constraints. Any hardware-specific optimization needed for dealing with large data is automatically<br /> performed to increase ease of use and minimize user intervention. The efficiency and simplicity of pyTME will<br /> enable high throughput mining of a variety of cryo-EM and ET datasets in the future.<br /> <br /> == Keywords ==<br /> <br /> == Links ==<br /> <br /> https://www.sciencedirect.com/science/article/pii/S2352711024000074<br /> <br /> == Related software ==<br /> <br /> == Related methods ==<br /> <br /> == Comments ==</div>

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