Aether: leveraging linear programming for optimal cloud computing in genomics

Abstract Motivation Across biology, we are seeing rapid developments in scale of data production without a corresponding increase in data analysis capabilities. Results: 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. Availability and implementation 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. Contact [email protected] or [email protected] Supplementary information Supplementary data are available at Bioinformatics online

A systematic machine learning and data type comparison yields metagenomic predictors of infant age, sex, breastfeeding, antibiotic usage, country of origin, and delivery type.

The microbiome is a new frontier for building predictors of human phenotypes. However, machine learning in the microbiome is fraught with issues of reproducibility, driven in large part by the wide range of analytic models and metagenomic data types available. We aimed to build robust metagenomic predictors of host phenotype by comparing prediction performances and biological interpretation across 8 machine learning methods and 4 different types of metagenomic data. Using 1,570 samples from 300 infants, we fit 7,865 models for 6 host phenotypes. We demonstrate the dependence of accuracy on algorithm choice and feature definition in microbiome data and propose a framework for building microbiome-derived indicators of host phenotype. We additionally identify biological features predictive of age, sex, breastfeeding status, historical antibiotic usage, country of origin, and delivery type. Our complete results can be viewed at http://apps.chiragjpgroup.org/ubiome_predictions/

Postmortem diffusion MRI of the entire human spinal cord at microscopic resolution

The human spinal cord is a central nervous system structure that plays an important role in normal motor and sensory function, and can be affected by many debilitating neurologic diseases. Due to its clinical importance, the spinal cord is frequently the subject of imaging research. Common methods for visualizing spinal cord anatomy and pathology include histology and magnetic resonance imaging (MRI), both of which have unique benefits and drawbacks. Postmortem microscopic resolution MRI of fixed specimens, sometimes referred to as magnetic resonance microscopy (MRM), combines many of the benefits inherent to both techniques. However, the elongated shape of the human spinal cord, along with hardware and scan time limitations, have restricted previous microscopic resolution MRI studies (both in vivo and ex vivo) to small sections of the cord. Here we present the first MRM dataset of the entire postmortem human spinal cord. These data include 50 μm isotropic resolution anatomic image data and 100 μm isotropic resolution diffusion data, made possible by a 280 h long multi-segment acquisition and automated image segment composition. We demonstrate the use of these data for spinal cord lesion detection, automated volumetric gray matter segmentation, and quantitative spinal cord morphometry including estimates of cross sectional dimensions and gray matter fraction throughout the length of the cord. Keywords: Spinal cord, Magnetic resonance microscopy, Tractography, Human, Gray matte

A high-resolution interactive atlas of the human brainstem using magnetic resonance imaging

Conventional atlases of the human brainstem are limited by the inflexible, sparsely-sampled, two-dimensional nature of histology, or the low spatial resolution of conventional magnetic resonance imaging (MRI). Postmortem high-resolution MRI circumvents the challenges associated with both modalities. A single human brainstem specimen extending from the rostral diencephalon through the caudal medulla was prepared for imaging after the brain was removed from a 65-year-old male within 24 h of death. The specimen was formalin-fixed for two weeks, then rehydrated and placed in a custom-made MRI compatible tube and immersed in liquid fluorocarbon. MRI was performed in a 7-Tesla scanner with 120 unique diffusion directions. Acquisition time for anatomic and diffusion images were 14 h and 208 h, respectively. Segmentation was performed manually. Deterministic fiber tractography was done using strategically chosen regions of interest and avoidance, with manual editing using expert knowledge of human neuroanatomy. Anatomic and diffusion images were rendered with isotropic resolutions of 50 μm and 200 μm, respectively. Ninety different structures were segmented and labeled, and 11 different fiber bundles were rendered with tractography. The complete atlas is available online for interactive use at https://www.civmvoxport.vm.duke.edu/voxbase/login.php?return_url=%2Fvoxbase%2F. This atlas presents multiple contrasting datasets and selected tract reconstruction with unprecedented resolution for MR imaging of the human brainstem. There are immediate applications in neuroanatomical education, with the potential to serve future applications for neuroanatomical research and enhanced neurosurgical planning through “safe” zones of entry into the human brainstem

Ten Quick Tips for Deep Learning in Biology

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others.Comment: 23 pages, 2 figure

Outcome measures for pediatric laryngotracheal reconstruction: International consensus statement

© 2018 The American Laryngological, Rhinological and Otological Society, Inc. Objectives: Develop multidisciplinary and international consensus on patient, disease, procedural, and perioperative factors, as well as key outcome measures and complications, to be reported for pediatric airway reconstruction studies. Methods: Standard Delphi methods were applied. Participants proposed items in three categories: 1) patient/disease characteristics, 2) procedural/intraoperative/perioperative factors, and 3) outcome measures and complications. Both general and anatomic site-specific measures were elicited. Participants also suggested specific operations to be encompassed by this project. We then used iterative ranking and review to develop consensus lists via a priori Delphi consensus criteria. Results: Thirty-three pediatric airway experts from eight countries in North and South America, Europe, and Australia participated, representing otolaryngology (including International Pediatric Otolaryngology Group members), pulmonology, general surgery, and cardiothoracic surgery. Consensus led to inclusion of 19 operations comprising open expansion, resection, and slide procedures of the larynx, trachea, and bronchi as well as three endoscopic procedures. Consensus was achieved on multiple patient/comorbidity (10), disease/stenosis (7), perioperative-/intraoperative-/procedure-related (16) factors. Consensus was reached on multiple outcome and complication measures, both general and site-specific (8 general, 13 supraglottic, 15 glottic, 17 subglottic, 8 cervical tracheal, 12 thoracic tracheal). The group was able to clarify how each outcome should be measured, with specific instruments defined where applicable. Conclusion: This consensus statement provides a framework to communicate results consistently and reproducibly, facilitating meta-analyses, quality improvement, transfer of information, and surgeon self-assessment. It also clarifies expert opinion on which patient, disease, procedural, and outcome measures may be important to consider in any pediatric airway reconstruction patient. Level of Evidence: 5 Laryngoscope, 129:244–255, 2019

Outcome measures for pediatric laryngotracheal reconstruction: International consensus statement.

© 2018 The American Laryngological, Rhinological and Otological Society, Inc. Objectives: Develop multidisciplinary and international consensus on patient, disease, procedural, and perioperative factors, as well as key outcome measures and complications, to be reported for pediatric airway reconstruction studies. Methods: Standard Delphi methods were applied. Participants proposed items in three categories: 1) patient/disease characteristics, 2) procedural/intraoperative/perioperative factors, and 3) outcome measures and complications. Both general and anatomic site-specific measures were elicited. Participants also suggested specific operations to be encompassed by this project. We then used iterative ranking and review to develop consensus lists via a priori Delphi consensus criteria. Results: Thirty-three pediatric airway experts from eight countries in North and South America, Europe, and Australia participated, representing otolaryngology (including International Pediatric Otolaryngology Group members), pulmonology, general surgery, and cardiothoracic surgery. Consensus led to inclusion of 19 operations comprising open expansion, resection, and slide procedures of the larynx, trachea, and bronchi as well as three endoscopic procedures. Consensus was achieved on multiple patient/comorbidity (10), disease/stenosis (7), perioperative-/intraoperative-/procedure-related (16) factors. Consensus was reached on multiple outcome and complication measures, both general and site-specific (8 general, 13 supraglottic, 15 glottic, 17 subglottic, 8 cervical tracheal, 12 thoracic tracheal). The group was able to clarify how each outcome should be measured, with specific instruments defined where applicable. Conclusion: This consensus statement provides a framework to communicate results consistently and reproducibly, facilitating meta-analyses, quality improvement, transfer of information, and surgeon self-assessment. It also clarifies expert opinion on which patient, disease, procedural, and outcome measures may be important to consider in any pediatric airway reconstruction patient. Level of Evidence: 5 Laryngoscope, 129:244–255, 2019

Opportunities and obstacles for deep learning in biology and medicine

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems—patient classification, fundamental biological processes and treatment of patients—and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine