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1. Evaluation of methods for generative modeling of cell and nuclear shape

   https://doi.org/10.1093/bioinformatics/bty983  

   Ruan, Xiongtao ; Murphy, Robert F. ; Wren, ed., Jonathan ( December 2018 , Bioinformatics)

   Cell shape provides both geometry for, and a reflection of, cell function. Numerous methods for describing and modeling cell shape have been described, but previous evaluation of these methods in terms of the accuracy of generative models has been limited.

   Here we compare traditional methods and deep autoencoders to build generative models for cell shapes in terms of the accuracy with which shapes can be reconstructed from models. We evaluated the methods on different collections of 2D and 3D cell images, and found that none of the methods gave accurate reconstructions using low dimensional encodings. As expected, much higher accuracies were observed using high dimensional encodings, with outline-based methods significantly outperforming image-based autoencoders. The latter tended to encode all cells as having smooth shapes, even for high dimensions. For complex 3D cell shapes, we developed a significant improvement of a method based on the spherical harmonic transform that performs significantly better than other methods. We obtained similar results for the joint modeling of cell and nuclear shape. Finally, we evaluated the modeling of shape dynamics by interpolation in the shape space. We found that our modified method provided lower deformation energies along linear interpolation paths than other methods. This allows practical shape evolution in high dimensional shape spaces. We conclude that our improved spherical harmonic based methods are preferable for cell and nuclear shape modeling, providing better representations, higher computational efficiency and requiring fewer training images than deep learning methods.

   All software and data is available at http://murphylab.cbd.cmu.edu/software.

   Supplementary data are available at Bioinformatics online.

    

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2. Aether: leveraging linear programming for optimal cloud computing in genomics

   https://doi.org/10.1093/bioinformatics/btx787  

   Luber, Jacob M. ; Tierney, Braden T. ; Cofer, Evan M. ; Patel, Chirag J. ; Kostic, Aleksandar D. ; Berger, ed., Bonnie ( December 2017 , Bioinformatics)

   Across biology, we are seeing rapid developments in scale of data production without a corresponding increase in data analysis capabilities.

   Here, we present Aether (http://aether.kosticlab.org), an intuitive, easy-to-use, cost-effective and scalable framework that uses linear programming to optimally bid on and deploy combinations of underutilized cloud computing resources. Our approach simultaneously minimizes the cost of data analysis and provides an easy transition from users’ existing HPC pipelines.

   Data utilized are available at https://pubs.broadinstitute.org/diabimmune and with EBI SRA accession ERP005989. Source code is available at (https://github.com/kosticlab/aether). Examples, documentation and a tutorial are available at http://aether.kosticlab.org.

   Supplementary data are available at Bioinformatics online.

    

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3. 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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4. Network inference performance complexity: a consequence of topological, experimental and algorithmic determinants

   https://doi.org/10.1093/bioinformatics/btz105  

   Muldoon, Joseph J. ; Yu, Jessica S. ; Fassia, Mohammad-Kasim ; Bagheri, Neda ; Valencia, ed., Alfonso ( February 2019 , Bioinformatics)

   Network inference algorithms aim to uncover key regulatory interactions governing cellular decision-making, disease progression and therapeutic interventions. Having an accurate blueprint of this regulation is essential for understanding and controlling cell behavior. However, the utility and impact of these approaches are limited because the ways in which various factors shape inference outcomes remain largely unknown.

   We identify and systematically evaluate determinants of performance—including network properties, experimental design choices and data processing—by developing new metrics that quantify confidence across algorithms in comparable terms. We conducted a multifactorial analysis that demonstrates how stimulus target, regulatory kinetics, induction and resolution dynamics, and noise differentially impact widely used algorithms in significant and previously unrecognized ways. The results show how even if high-quality data are paired with high-performing algorithms, inferred models are sometimes susceptible to giving misleading conclusions. Lastly, we validate these findings and the utility of the confidence metrics using realistic in silico gene regulatory networks. This new characterization approach provides a way to more rigorously interpret how algorithms infer regulation from biological datasets.

   Code is available at http://github.com/bagherilab/networkinference/.

   Supplementary data are available at Bioinformatics online.

    

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5. NanoPack: visualizing and processing long-read sequencing data

   https://doi.org/10.1093/bioinformatics/bty149  

   De Coster, Wouter ; D’Hert, Svenn ; Schultz, Darrin T. ; Cruts, Marc ; Van Broeckhoven, Christine ; Berger, ed., Bonnier ( March 2018 , Bioinformatics)

   Here we describe NanoPack, a set of tools developed for visualization and processing of long-read sequencing data from Oxford Nanopore Technologies and Pacific Biosciences.

   The NanoPack tools are written in Python3 and released under the GNU GPL3.0 License. The source code can be found at https://github.com/wdecoster/nanopack, together with links to separate scripts and their documentation. The scripts are compatible with Linux, Mac OS and the MS Windows 10 subsystem for Linux and are available as a graphical user interface, a web service at http://nanoplot.bioinf.be and command line tools.

   Supplementary data are available at Bioinformatics online.

    

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