scholarly journals ORT: A workflow linking genome-scale metabolic models with reactive transport codes

2021 ◽  
Author(s):  
Rebecca L. Rubinstein ◽  
Mikayla A. Borton ◽  
Haiyan Zhou ◽  
Michael Shaffer ◽  
David W. Hoyt ◽  
...  

AbstractMotivationAdvanced modeling tools are available for ‘omics-based metabolic modeling and for reactive transport modeling, but there is a disconnect between these methods, which hinders linking models across scales. Microbial processes strongly impact many natural systems, and so better capture of microbial dynamics could greatly improve simulations of these systems.ResultsOur approach, ORT, applied to environmental metagenomic data from a river system predicted nitrogen cycling patterns with site-specific insight into chemical and biological drivers of nitrification and denitrification processes.Availability and ImplementationLive interactive models are available at https://pflotranmodeling.paf.subsurfaceinsights.com/pflotran-simple-model/. Microbiological data is available at NCBI via BioProject ID PRJNA576070. The code for ORT (written in Python 3) is available at https://github.com/subsurfaceinsights. The KBase narrative used for the test case is publicly available at https://narrative.kbase.us/narrative/[email protected] or [email protected] informationSupplementary data are available online.

2018 ◽  
Author(s):  
Federico Baldini ◽  
Almut Heinken ◽  
Laurent Heirendt ◽  
Stefania Magnusdottir ◽  
Ronan M.T. Fleming ◽  
...  

MotivationThe application of constraint-based modeling to functionally analyze metagenomic data has been limited so far, partially due to the absence of suitable toolboxes.ResultsTo address this shortage, we created a comprehensive toolbox to model i) microbe-microbe and host-microbe metabolic interactions, and ii) microbial communities using microbial genome-scale metabolic reconstructions and metagenomic data. The Microbiome Modeling Toolbox extends the functionality of the COBRA Toolbox.AvailabilityThe Microbiome Modeling Toolbox and the tutorials at https://git.io/microbiomeModelingToolbox.


2018 ◽  
Author(s):  
Yasser EL-Manzalawy

AbstractSummary: Recent technological advances in high-throughput metagenomic sequencing have provided unique opportunities for studying the diversity and dynamics of microbial communities under different health or environmental conditions. Graph-based representation of metagenomic data is a promising direction not only for analyzing microbial interactions but also for a broad range of machine learning tasks including feature selection, classification, clustering, anomaly detection, and dimensionality reduction. We present Proxi, an open source Python package for learning different types of proximity graphs from metagenomic data. Currently, three types of proximity graphs are supported: k-nearest neighbor (k-NN) graphs; radius-nearest neighbor (r-NN) graphs; and perturbed k-nearest neighbor (pk-NN) graphs.Availability: Proxi Python source code is freely available at https://bitbucket.org/idsrlab/proxi/.Contact:[email protected] information: Tutorials and online documentation are available at https://proxi.readthedocs.io


2017 ◽  
Author(s):  
Nathan Mih ◽  
Elizabeth Brunk ◽  
Ke Chen ◽  
Edward Catoiu ◽  
Anand Sastry ◽  
...  

AbstractSummaryWorking with protein structures at the genome-scale has been challenging in a variety of ways. Here, we present ssbio, a Python package that provides a framework to easily work with structural information in the context of genome-scale network reconstructions, which can contain thousands of individual proteins. The ssbio package provides an automated pipeline to construct high quality genome-scale models with protein structures (GEM-PROs), wrappers to popular third-party programs to compute associated protein properties, and methods to visualize and annotate structures directly in Jupyter notebooks, thus lowering the barrier of linking 3D structural data with established systems workflows.Availability and Implementationssbio is implemented in Python and available to download under the MIT license at http://github.com/SBRG/ssbio. Documentation and Jupyter notebook tutorials are available at http://ssbio.readthedocs.io/en/latest/. Interactive notebooks can be launched using Binder at https://mybinder.org/v2/gh/SBRG/ssbio/[email protected] InformationSupplementary data are available at Bioinformatics online.


2020 ◽  
Author(s):  
Vivek Sriram ◽  
Manu Shivakumar ◽  
Sang-Hyuk Jung ◽  
Lisa Bang ◽  
Anurag Verma ◽  
...  

AbstractSummaryGiven genetic associations from a PheWAS, a disease-disease network can be constructed where nodes represent phenotypes and edges represent shared genetic associations between phenotypes. To improve the accessibility of the visualization of shared genetic components across phenotypes, we developed the humaN-disEase phenoType MAp GEnerator (NETMAGE), a web-based tool that produces interactive phenotype network visualizations from summarized PheWAS results. Users can search the map by a variety of attributes, and they can select nodes to view information such as related phenotypes, associated SNPs, and other network statistics. As a test case, we constructed a network using UK BioBank PheWAS summary data. By examining the associations between phenotypes in our map, we can potentially identify novel instances of pleiotropy, where loci influence multiple phenotypic traits. Thus, our tool provides researchers with a means to identify prospective genetic targets for drug design, contributing to the exploration of personalized medicine.Availability and implementationOur service runs at https://hdpm.biomedinfolab.com. Source code can be downloaded at https://github.com/dokyoonkimlab/[email protected] informationSupplementary data and user guide are available at Bioinformatics online.


2019 ◽  
Author(s):  
Hannah De los Santos ◽  
Emily J. Collins ◽  
Catherine Mann ◽  
April W. Sagan ◽  
Meaghan S. Jankowski ◽  
...  

AbstractMotivationTime courses utilizing genome scale data are a common approach to identifying the biological pathways that are controlled by the circadian clock, an important regulator of organismal fitness. However, the methods used to detect circadian oscillations in these datasets are not able to accommodate changes in the amplitude of the oscillations over time, leading to an underestimation of the impact of the clock on biological systems.ResultsWe have created a program to efficaciously identify oscillations in large-scale datasets, called the Extended Circadian Harmonic Oscillator application, or ECHO. ECHO utilizes an extended solution of the fixed amplitude mass-spring oscillator that incorporates the amplitude change coefficient. Employing synthetic datasets, we determined that ECHO outperforms existing methods in detecting rhythms with decreasing oscillation amplitudes and recovering phase shift. Rhythms with changing amplitudes identified from published biological datasets revealed distinct functions from those oscillations that were harmonic, suggesting purposeful biologic regulation to create this subtype of circadian rhythms.AvailabilityECHO’s full interface is available athttps://github.com/delosh653/ECHO. An R package for this functionality, echo.find, can be downloaded athttps://CRAN.R-project.org/[email protected] informationSupplementary data are available


Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Kokulapalan Wimalanathan ◽  
Carolyn J. Lawrence-Dill

AbstractAnnotating gene structures and functions to genome assemblies is necessary to make assembly resources useful for biological inference. Gene Ontology (GO) term assignment is the most used functional annotation system, and new methods for GO assignment have improved the quality of GO-based function predictions. The Gene Ontology Meta Annotator for Plants (GOMAP) is an optimized, high-throughput, and reproducible pipeline for genome-scale GO annotation of plants. We containerized GOMAP to increase portability and reproducibility and also optimized its performance for HPC environments. Here we report on the pipeline’s availability and performance for annotating large, repetitive plant genomes and describe how GOMAP was used to annotate multiple maize genomes as a test case. Assessment shows that GOMAP expands and improves the number of genes annotated and annotations assigned per gene as well as the quality (based on $$F_{max}$$ F max ) of GO assignments in maize. GOMAP has been deployed to annotate other species including wheat, rice, barley, cotton, and soy. Instructions and access to the GOMAP Singularity container are freely available online at https://bioinformapping.com/gomap/. A list of annotated genomes and links to data is maintained at https://dill-picl.org/projects/gomap/.


Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3745
Author(s):  
Tristan Revaz ◽  
Fernando Porté-Agel

Large-eddy simulation (LES) with actuator models has become the state-of-the-art numerical tool to study the complex interaction between the atmospheric boundary layer (ABL) and wind turbines. In this paper, a new evaluation of actuator disk models (ADMs) for LES of wind turbine flows is presented. Several details of the implementation of such models are evaluated based on a test case studied experimentally. In contrast to other test cases used in previous similar studies, the present test case consists of a wind turbine immersed in a realistic turbulent boundary-layer flow, for which accurate data for the turbine, the flow, the thrust and the power are available. It is found that the projection of the forces generated by the turbine into the flow solver grid is crucial for rotor predictions, especially for the power, and less important for the wake flow prediction. In this context, the projection of the forces into the flow solver grid should be as accurate as possible, in order to conserve the consistency between the computed axial velocity and the projected axial force. Also, the projection of the force is found to be much more important in the rotor plane directions than in the streamwise direction. It is found that for the case of a wind turbine immersed in a realistic turbulent boundary-layer flow, the potential spurious numerical oscillations originating from sharp force projections are not harmful to the results. By comparing an advanced model which computes the non-uniform distribution of the turbine forces over the rotor with a simple model which assumes uniform effects of the turbine forces, it is found that both can lead to accurate results for the far wake flow and the thrust and power predictions. However, the comparison shows that the advanced model leads to better results for the near wake flow. In addition, it is found that the simple model overestimates the rotor velocity prediction in comparison to the advanced model. These elements are explained by the lack of local feedback between the axial velocity and the axial force in the simple model. By comparing simulations with and without including the effects of the nacelle and tower, it is found that the consideration of the nacelle and tower is relatively important both for the near wake and the power prediction, due to the shadow effects. The grid resolution is not found to be critical once a reasonable resolution is used, i.e. in the order of 10 grid points along each direction across the rotor. The comparison with the experimental data shows that an accurate prediction of the flow, thrust, and power is possible with a very reasonable computational cost. Overall, the results give important guidelines for the implementation of ADMs for LES.


2018 ◽  
Vol 35 (13) ◽  
pp. 2332-2334 ◽  
Author(s):  
Federico Baldini ◽  
Almut Heinken ◽  
Laurent Heirendt ◽  
Stefania Magnusdottir ◽  
Ronan M T Fleming ◽  
...  

Abstract Motivation The application of constraint-based modeling to functionally analyze metagenomic data has been limited so far, partially due to the absence of suitable toolboxes. Results To address this gap, we created a comprehensive toolbox to model (i) microbe–microbe and host–microbe metabolic interactions, and (ii) microbial communities using microbial genome-scale metabolic reconstructions and metagenomic data. The Microbiome Modeling Toolbox extends the functionality of the constraint-based reconstruction and analysis toolbox. Availability and implementation The Microbiome Modeling Toolbox and the tutorials at https://git.io/microbiomeModelingToolbox.


2016 ◽  
Author(s):  
Stephen G. Gaffney ◽  
Jeffrey P. Townsend

ABSTRACTSummaryPathScore quantifies the level of enrichment of somatic mutations within curated pathways, applying a novel approach that identifies pathways enriched across patients. The application provides several user-friendly, interactive graphic interfaces for data exploration, including tools for comparing pathway effect sizes, significance, gene-set overlap and enrichment differences between projects.Availability and ImplementationWeb application available at pathscore.publichealth.yale.edu. Site implemented in Python and MySQL, with all major browsers supported. Source code available at github.com/sggaffney/pathscore with a GPLv3 [email protected] InformationAdditional documentation can be found at http://pathscore.publichealth.yale.edu/faq.


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