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1. Nextstrain: real-time tracking of pathogen evolution

   https://doi.org/10.1093/bioinformatics/bty407  

   Hadfield, James ; Megill, Colin ; Bell, Sidney M. ; Huddleston, John ; Potter, Barney ; Callender, Charlton ; Sagulenko, Pavel ; Bedford, Trevor ; Neher, Richard A. ; Kelso, ed., Janet ( May 2018 , Bioinformatics)

   Understanding the spread and evolution of pathogens is important for effective public health measures and surveillance. Nextstrain consists of a database of viral genomes, a bioinformatics pipeline for phylodynamics analysis, and an interactive visualization platform. Together these present a real-time view into the evolution and spread of a range of viral pathogens of high public health importance. The visualization integrates sequence data with other data types such as geographic information, serology, or host species. Nextstrain compiles our current understanding into a single accessible location, open to health professionals, epidemiologists, virologists and the public alike.

   All code (predominantly JavaScript and Python) is freely available from github.com/nextstrain and the web-application is available at nextstrain.org.

    

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2. HiCube: interactive visualization of multiscale and multimodal Hi-C and 3D genome data

   https://doi.org/10.1093/bioinformatics/btad154  

   Ye, Tiantian ; Hu, Yangyang ; Pun, Sydney ; Ma, Wenxiu ; Alkan, ed., Can ( March 2023 , Bioinformatics)

   HiCube is a lightweight web application for interactive visualization and exploration of diverse types of genomics data at multiscale resolutions. Especially, HiCube displays synchronized views of Hi-C contact maps and 3D genome structures with user-friendly annotation and configuration tools, thereby facilitating the study of 3D genome organization and function.

   HiCube is implemented in Javascript and can be installed via NPM. The source code is freely available at GitHub (https://github.com/wmalab/HiCube).

    

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3. 3DClusterViSu: 3D clustering analysis of super-resolution microscopy data by 3D Voronoi tessellations

   https://doi.org/10.1093/bioinformatics/bty200  

   Andronov, Leonid ; Michalon, Jonathan ; Ouararhni, Khalid ; Orlov, Igor ; Hamiche, Ali ; Vonesch, Jean-Luc ; Klaholz, Bruno P. ; Murphy, ed., Robert ( April 2018 , Bioinformatics)

   Single-molecule localization microscopy (SMLM) can play an important role in integrated structural biology approaches to identify, localize and determine the 3D structure of cellular structures. While many tools exist for the 3D analysis and visualization of crystal or cryo-EM structures little exists for 3D SMLM data, which can provide unique insights but are particularly challenging to analyze in three dimensions especially in a dense cellular context.

   We developed 3DClusterViSu, a method based on 3D Voronoi tessellations that allows local density estimation, segmentation and quantification of 3D SMLM data and visualization of protein clusters within a 3D tool. We show its robust performance on microtubules and histone proteins H2B and CENP-A with distinct spatial distributions. 3DClusterViSu will favor multi-scale and multi-resolution synergies to allow integrating molecular and cellular levels in the analysis of macromolecular complexes.

   3DClusterViSu is available under http://cbi-dev.igbmc.fr/cbi/voronoi3D.

   Supplementary figures are available at Bioinformatics online.

    

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4. Deep learning-based subdivision approach for large scale macromolecules structure recovery from electron cryo tomograms

   https://doi.org/10.1093/bioinformatics/btx230  

   Xu, Min ; Chai, Xiaoqi ; Muthakana, Hariank ; Liang, Xiaodan ; Yang, Ge ; Zeev-Ben-Mordehai, Tzviya ; Xing, Eric P. ( July 2017 , Bioinformatics)

   Cellular Electron CryoTomography (CECT) enables 3D visualization of cellular organization at near-native state and in sub-molecular resolution, making it a powerful tool for analyzing structures of macromolecular complexes and their spatial organizations inside single cells. However, high degree of structural complexity together with practical imaging limitations makes the systematic de novo discovery of structures within cells challenging. It would likely require averaging and classifying millions of subtomograms potentially containing hundreds of highly heterogeneous structural classes. Although it is no longer difficult to acquire CECT data containing such amount of subtomograms due to advances in data acquisition automation, existing computational approaches have very limited scalability or discrimination ability, making them incapable of processing such amount of data.

   To complement existing approaches, in this article we propose a new approach for subdividing subtomograms into smaller but relatively homogeneous subsets. The structures in these subsets can then be separately recovered using existing computation intensive methods. Our approach is based on supervised structural feature extraction using deep learning, in combination with unsupervised clustering and reference-free classification. Our experiments show that, compared with existing unsupervised rotation invariant feature and pose-normalization based approaches, our new approach achieves significant improvements in both discrimination ability and scalability. More importantly, our new approach is able to discover new structural classes and recover structures that do not exist in training data.

   Source code freely available at http://www.cs.cmu.edu/∼mxu1/software.

   Supplementary data are available at Bioinformatics online.

    

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5. BioModME for building and simulating dynamic computational models of complex biological systems

   https://doi.org/10.1093/bioadv/vbae023  

   Womack, Justin A. ; Shah, Viren ; Audi, Said H. ; Terhune, Scott S. ; Dash, Ranjan K. ; Lengauer, ed., Thomas ( February 2024 , Bioinformatics Advances)

   Molecular mechanisms of biological functions and disease processes are exceptionally complex, and our ability to interrogate and understand relationships is becoming increasingly dependent on the use of computational modeling. We have developed “BioModME,” a standalone R-based web application package, providing an intuitive and comprehensive graphical user interface to help investigators build, solve, visualize, and analyze computational models of complex biological systems. Some important features of the application package include multi-region system modeling, custom reaction rate laws and equations, unit conversion, model parameter estimation utilizing experimental data, and import and export of model information in the Systems Biology Matkup Language format. The users can also export models to MATLAB, R, and Python languages and the equations to LaTeX and Mathematical Markup Language formats. Other important features include an online model development platform, multi-modality visualization tool, and efficient numerical solvers for differential-algebraic equations and optimization.

   All relevant software information including documentation and tutorials can be found at https://mcw.marquette.edu/biomedical-engineering/computational-systems-biology-lab/biomodme.php. Deployed software can be accessed at https://biomodme.ctsi.mcw.edu/. Source code is freely available for download at https://github.com/MCWComputationalBiologyLab/BioModME.

    

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