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Bundle Trac
Summary: BundleTrac is an effective method for tracing hundreds of filaments in an Actin bundle. It is a semi-automatic modeling approach in which a seed point is provided for each filament. BundleTrac works in two steps. The first step improves the filamentous pattern present in the image using cross-correlation-based density averaging. In the subsequent step, individual filaments are traced employing 7-peaks Gaussian kernels and 2D-convolution optimization. When applied to an experimental density image of Stereocilium, BundleTrac yields high agreement with manual annotation.
Spaghetti Tracer
Summary: Spaghetti Tracer is an automated framework based on dynamic programming, designed to model the structure of semi-regular filaments in complex 3D reconstructions of subcellular components. The framework assumes that tomograms can be rotated to align filaments along a mean direction. It first identifies potential filament segments from local seed points, then iteratively expands them using dynamic programming. To assess its performance, we validate the framework using simulated tomograms that replicate real experimental noise and appearance. By comparing to known ground truth, we conduct a statistical analysis using precision, recall, and F1 scores, demonstrating the effectiveness of Spaghetti Tracer.
Struuweel Tracer
Summary:StruuwelTracer is an innovative computational framework designed to trace randomly-oriented actin filaments in cryo-electron tomography (cryo-ET) maps. The method starts by accumulating densities along paths from automatically determined seed points, generating short linear filament segments. These segments are then analyzed and classified using a pruning map. Through an iterative process, the segments are fused into longer, curved filaments based on several criteria. Testing on simulated tomograms of Dictyostelium discoideum filopodia and experimental tomograms of mouse fibroblast lamellipodia demonstrates high efficacy, with F1-scores ranging from 0.85 to 0.90.
Relevant Publications:
[J2] Sazzed, S., Song, J., Kovacs, J.A., Wriggers, W., Auer, M., and He, J., Tracing actin filament bundles in three-dimensional electron tomography density maps of hair cell stereocilia, In Molecules, 2018.
Impact Factor: 4.6
[J1] Song, J.; Patterson, R.; Metlagel, Z.; Krey, J.F.; Hao, S.; Wang, L.; Ng, B.; Sazzed, S.; Kovacs, J.; Wriggers, W., He, J., Barr-Gillespie P. G., and Auer, M. , A cryo-tomography-based volumetric model of the actin core of mouse vestibular hair cell stereocilia lacking plastin 1, In Journal of Structural Biology, 2020.
Impact Factor: 3.0
[C1] Haslam, D.; Sazzed, S.; Wriggers, W.; Kovcas, J.; Song, J.; Auer, M.; & He, J, A Pattern Recognition Tool for Medium-Resolution Cryo-EM Density Maps and Low-Resolution Cryo-ET Density Maps, In International Symposium on Bioinformatics Research and Applications (ISBRA), 2018.
Relevant Publications:
[J1] Sazzed, S., Scheible, P., He, J., & Wriggers, W.;, Spaghetti Tracer: A Framework for Tracing Semiregular Filamentous Densities in 3D Tomograms, In Biomolecules, 2022.
Impact Factor: 5.5
[C2] Sazzed, S., Scheible, P., He, J., & Wriggers, W., Tracing filaments in simulated 3D cryo-electron tomography maps using a fast dynamic programming algorithm , In IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2021.
[C1] Scheible, P.; Sazzed, S;, He, J.; & Wriggers, W., Tomosim: Simulation of filamentous cryo- electron tomograms, In IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2021.
Relevant Publications:
[J1] Sazzed, S., Scheible, P., He, J., & Wriggers, W., Untangling Irregular Actin Cytoskeleton Architectures in Tomograms of the Cell With Struwwel Tracer , International Journal of Molecular Sciences (IJMS), 2023 .
Impact Factor: 5.6
[C1] Sazzed, S., Scheible, P., He, J., & Wriggers, W., Tracing Randomly Oriented Filaments in a Simulated Actin Network Tomogram , In IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2022.
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