scholarly journals SAMMI: a semi-automated tool for the visualization of metabolic networks

2019 ◽  
Vol 36 (8) ◽  
pp. 2616-2617
Author(s):  
Andre Schultz ◽  
Rehan Akbani
Keyword(s):  
Large Scale ◽  
Metabolic Networks ◽  
Source Code ◽  
Supplementary Information ◽  
Network Partitioning ◽  
Web Based ◽  
Constraint Based Modeling ◽  
Context Specific ◽  
Genome Scale ◽  
Automated Tool

Abstract Summary Here we present a browser-based Semi-Automated Metabolic Map Illustrator (SAMMI) for the visualization of metabolic networks. While automated features allow for easy network partitioning, navigation, and node positioning, SAMMI also offers a wide array of manual map editing features. This combination allows for fast, context-specific visualization of metabolic networks as well as the development of standardized, large-scale, visually appealing maps. The implementation of SAMMI with popular constraint-based modeling toolboxes also allows for effortless visualization of simulation results of genome-scale metabolic models. Availability and implementation SAMMI has been implemented as a standalone web-based tool and as plug-ins for the COBRA and COBRApy toolboxes. SAMMI and its COBRA plugins are available under the GPL 3.0 license and are available along with documentation, tutorials, and source code at www.SammiTool.com. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals What does your cell really do? Model-based assessment of mammalian cells metabolic functionalities using omics data

2020 ◽  
Author(s):  
Anne Richelle ◽  
Benjamin P. Kellman ◽  
Alexander T. Wenzel ◽  
Austin W.T. Chiang ◽  
Tyler Reagan ◽  
...  
Keyword(s):  
Mammalian Cells ◽  
Large Scale ◽  
Metabolic Networks ◽  
Omics Data ◽  
Web Based ◽  
Cell Functions ◽  
Metabolic Functions ◽  
Biological Studies ◽  
Biological Entities ◽  
Genome Scale

AbstractLarge-scale omics experiments have become standard in biological studies, leading to a deluge of data. However, researchers still face the challenge of connecting changes in the omics data to changes in cell functions, due to the complex interdependencies between genes, proteins and metabolites. Here we present a novel framework that begins to overcome this problem by allowing users to infer how metabolic functions change, based on omics data. To enable this, we curated and standardized lists of metabolic tasks that mammalian cells can accomplish. We then used genome-scale metabolic networks to define gene modules responsible for each specific metabolic task. We further developed a framework to overlay omics data on these modules to predict pathway usage for each metabolic task. The proposed approach allows one to directly predict how changes in omics experiments change cell or tissue function. We further demonstrated how this new approach can be used to leverage the metabolic functions of biological entities from the single cell to their organization in tissues and organs using multiple transcriptomic datasets (human and mouse). Finally, we created a web-based CellFie module that has been integrated into the list of tools available in GenePattern (www.genepattern.org) to enable adoption of the approach.

scholarly journals OpenBioLink: a benchmarking framework for large-scale biomedical link prediction

2020 ◽  
Vol 36 (13) ◽  
pp. 4097-4098 ◽  
Author(s):  
Anna Breit ◽  
Simon Ott ◽  
Asan Agibetov ◽  
Matthias Samwald
Keyword(s):  
Link Prediction ◽  
Large Scale ◽  
Source Code ◽  
Machine Learning Algorithms ◽  
Knowledge Networks ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Biomedical Knowledge ◽  
High Quality ◽  
Baseline Evaluation

Abstract Summary Recently, novel machine-learning algorithms have shown potential for predicting undiscovered links in biomedical knowledge networks. However, dedicated benchmarks for measuring algorithmic progress have not yet emerged. With OpenBioLink, we introduce a large-scale, high-quality and highly challenging biomedical link prediction benchmark to transparently and reproducibly evaluate such algorithms. Furthermore, we present preliminary baseline evaluation results. Availability and implementation Source code and data are openly available at https://github.com/OpenBioLink/OpenBioLink. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals INDRA-IPM: interactive pathway modeling using natural language with automated assembly

2019 ◽  
Vol 35 (21) ◽  
pp. 4501-4503 ◽  
Author(s):  
Petar V Todorov ◽  
Benjamin M Gyori ◽  
John A Bachman ◽  
Peter K Sorger
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Web Service ◽  
Language Processing ◽  
Source Code ◽  
Supplementary Information ◽  
Expression Data ◽  
Supplementary Data ◽  
Automated Assembly ◽  
Web Based

Abstract Summary INDRA-IPM (Interactive Pathway Map) is a web-based pathway map modeling tool that combines natural language processing with automated model assembly and visualization. INDRA-IPM contextualizes models with expression data and exports them to standard formats. Availability and implementation INDRA-IPM is available at: http://pathwaymap.indra.bio. Source code is available at http://github.com/sorgerlab/indra_pathway_map. The underlying web service API is available at http://api.indra.bio:8000. Supplementary information Supplementary data are available at Bioinformatics online.

Graph methods for the investigation of metabolic networks in parasitology

Parasitology ◽  
2010 ◽  
Vol 137 (9) ◽  
pp. 1393-1407 ◽  
Author(s):  
LUDOVIC COTTRET ◽  
FABIEN JOURDAN
Keyword(s):  
Metabolic Network ◽  
Large Scale ◽  
Metabolic Networks ◽  
Graph Model ◽  
New Drugs ◽  
Mathematical Methods ◽  
Graph Models ◽  
Metabolic Network Reconstruction ◽  
Network Analyses ◽  
Genome Scale

SUMMARYRecently, a way was opened with the development of many mathematical methods to model and analyze genome-scale metabolic networks. Among them, methods based on graph models enable to us quickly perform large-scale analyses on large metabolic networks. However, it could be difficult for parasitologists to select the graph model and methods adapted to their biological questions. In this review, after briefly addressing the problem of the metabolic network reconstruction, we propose an overview of the graph-based approaches used in whole metabolic network analyses. Applications highlight the usefulness of this kind of approach in the field of parasitology, especially by suggesting metabolic targets for new drugs. Their development still represents a major challenge to fight against the numerous diseases caused by parasites.

scholarly journals gplas: a comprehensive tool for plasmid analysis using short-read graphs

2020 ◽  
Vol 36 (12) ◽  
pp. 3874-3876 ◽  
Author(s):  
Sergio Arredondo-Alonso ◽  
Martin Bootsma ◽  
Yaïr Hein ◽  
Malbert R C Rogers ◽  
Jukka Corander ◽  
...  
Keyword(s):  
Large Scale ◽  
Sequence Data ◽  
Bacterial Genome ◽  
Workflow Management ◽  
Supplementary Information ◽  
Whole Genome Sequencing Data ◽  
Network Partitioning ◽  
Sequencing Data ◽  
Genetic Traits ◽  
Short Read

Abstract Summary Plasmids can horizontally transmit genetic traits, enabling rapid bacterial adaptation to new environments and hosts. Short-read whole-genome sequencing data are often applied to large-scale bacterial comparative genomics projects but the reconstruction of plasmids from these data is facing severe limitations, such as the inability to distinguish plasmids from each other in a bacterial genome. We developed gplas, a new approach to reliably separate plasmid contigs into discrete components using sequence composition, coverage, assembly graph information and network partitioning based on a pruned network of plasmid unitigs. Gplas facilitates the analysis of large numbers of bacterial isolates and allows a detailed analysis of plasmid epidemiology based solely on short-read sequence data. Availability and implementation Gplas is written in R, Bash and uses a Snakemake pipeline as a workflow management system. Gplas is available under the GNU General Public License v3.0 at https://gitlab.com/sirarredondo/gplas.git. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals epiTAD: a web application for visualizing chromosome conformation capture data in the context of genetic epidemiology

2019 ◽  
Vol 35 (21) ◽  
pp. 4462-4464
Author(s):  
Jordan H Creed ◽  
Garrick Aden-Buie ◽  
Alvaro N Monteiro ◽  
Travis A Gerke
Keyword(s):  
Genetic Epidemiology ◽  
In Silico ◽  
Web Application ◽  
Large Scale ◽  
Source Code ◽  
Chromosome Conformation Capture ◽  
Chromosome Conformation ◽  
Web Based ◽  
Genome Wide ◽  
Public Data

Abstract Summary Complementary advances in genomic technology and public data resources have created opportunities for researchers to conduct multifaceted examination of the genome on a large scale. To meet the need for integrative genome wide exploration, we present epiTAD. This web-based tool enables researchers to compare genomic 3D organization and annotations across multiple databases in an interactive manner to facilitate in silico discovery. Availability and implementation epiTAD can be accessed at https://apps.gerkelab.com/epiTAD/ where we have additionally made publicly available the source code and a Docker containerized version of the application.

scholarly journals Metabolic network-guided binning of metagenomic sequence fragments

2015 ◽  
Vol 32 (6) ◽  
pp. 867-874 ◽  
Author(s):  
Matthew B. Biggs ◽  
Jason A. Papin
Keyword(s):  
Metabolic Network ◽  
Dna Sequences ◽  
Metabolic Networks ◽  
Human Microbiome ◽  
Environmental Dna ◽  
Human Microbiome Project ◽  
Supplementary Information ◽  
Metagenomic Sequence ◽  
Connectivity Score ◽  
Genome Scale

Abstract Motivation: Most microbes on Earth have never been grown in a laboratory, and can only be studied through DNA sequences. Environmental DNA sequence samples are complex mixtures of fragments from many different species, often unknown. There is a pressing need for methods that can reliably reconstruct genomes from complex metagenomic samples in order to address questions in ecology, bioremediation, and human health. Results: We present the SOrting by NEtwork Completion (SONEC) approach for assigning reactions to incomplete metabolic networks based on a metabolite connectivity score. We successfully demonstrate proof of concept in a set of 100 genome-scale metabolic network reconstructions, and delineate the variables that impact reaction assignment accuracy. We further demonstrate the integration of SONEC with existing approaches (such as cross-sample scaffold abundance profile clustering) on a set of 94 metagenomic samples from the Human Microbiome Project. We show that not only does SONEC aid in reconstructing species-level genomes, but it also improves functional predictions made with the resulting metabolic networks. Availability and implementation: The datasets and code presented in this work are available at: https://bitbucket.org/mattbiggs/sorting_by_network_completion/. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

scholarly journals FALCONET: an R package to accelerate automatic visualisation of genome scale metabolic models

2019 ◽  
Author(s):  
Hongzhong Lu ◽  
Zhengming Zhu ◽  
Eduard J Kerkhoven ◽  
Jens Nielsen
Keyword(s):  
Metabolic Networks ◽  
R Package ◽  
Network Size ◽  
Research Community ◽  
Supplementary Information ◽  
Large Network ◽  
Strain Design ◽  
Scale Models ◽  
Genome Scale ◽  
Integrative Omics

AbstractSummaryFALCONET (FAst visuaLisation of COmputational NETworks) enables the automatic for-mation and visualisation of metabolic maps from genome-scale models with R and CellDesigner, readily facilitating the visualisation of multi-layers omics datasets in the context of metabolic networks.MotivationUntil now, numerous GEMs have been reconstructed and used as scaffolds to conduct integrative omics analysis and in silico strain design. Due to the large network size of GEMs, it is challenging to produce and visualize these networks as metabolic maps for further in-depth analyses.ResultsHere, we presented the R package - FALCONET, which facilitates drawing and visualizing metabolic maps in an automatic manner. This package will benefit the research community by allowing a wider use of GEMs in systems biology.Availability and implementationFALCONET is available on https://github.com/SysBioChalmers/FALCONET and released under the MIT [email protected] informationSupplementary data are available online.

scholarly journals An extended reconstruction of human gut microbiota metabolism for personalized nutrition

2020 ◽  
Author(s):  
Telmo Blasco ◽  
Sergio Pérez-Burillo ◽  
Francesco Balzerani ◽  
Alberto Lerma-Aguilera ◽  
Daniel Hinojosa-Nogueira ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Metabolic Networks ◽  
Degradation Pathways ◽  
Personalized Nutrition ◽  
Human Gut ◽  
Human Gut Microbiota ◽  
Lack Of Information ◽  
Constraint Based Modeling ◽  
Genome Scale ◽  
Complex Question

ABSTRACTUnderstanding how diet and gut microbiota interact in the context of human health is a key question in personalized nutrition. Genome-scale metabolic networks and constraint-based modeling approaches are promising to systematically address this complex question. However, when applied to nutritional questions, a major issue in existing reconstructions is the lack of information about degradation pathways of relevant nutrients in the diet that are metabolized by the gut microbiota. Here, we present AGREDA, an extended reconstruction of the human gut microbiota metabolism for personalized nutrition. AGREDA includes the degradation pathways of 231 nutrients present in the human diet and allows us to more comprehensively simulate the interplay between food and gut microbiota. We show that AGREDA is more accurate than existing reconstructions in predicting output metabolites of the gut microbiota. Finally, using AGREDA, we established relevant metabolic differences among clinical subgroups of Spanish children: lean, obese, allergic to foods and celiac.

scholarly journals Tn-Core: context-specific reconstruction of core metabolic models using Tn-seq data

2017 ◽  
Author(s):  
George C diCenzo ◽  
Alessio Mengoni ◽  
Marco Fondi
Keyword(s):  
Sinorhizobium Meliloti ◽  
Metabolic Networks ◽  
De Novo ◽  
Supplementary Information ◽  
Data Sets ◽  
Microbial Cell Factories ◽  
Cell Factories ◽  
Functional Models ◽  
Metabolic Models ◽  
Context Specific

ABSTRACTMotivationTn-seq (transposon mutagenesis and sequencing) and constraint-based metabolic modelling represent highly complementary approaches. They can be used to probe the core genetic and metabolic networks underlying a biological process, revealing invaluable information for synthetic biology engineering of microbial cell factories. However, while algorithms exist for integration of –omics data sets with metabolic models, no method has been explicitly developed for integration of Tn-seq data with metabolic reconstructions.ResultsWe report the development of Tn-Core, a Matlab toolbox designed to generate gene-centric, context-specific core reconstructions consistent with experimental Tn-seq data. Extensions of this algorithm allow: i) the generation of context-specific functional models through integration of both Tn-seq and RNA-seq data; ii) to visualize redundancy in core metabolic processes; and iii) to assist in curation ofde novodraft metabolic models. The utility of Tn-Core is demonstrated primarily using aSinorhizobium melilotimodel as a case study.Availability and implementationThe software can be downloaded fromhttps://github.com/diCenzo-GC/Tn-Core. All results presented in this work have been obtained with Tn-Core v. [email protected],[email protected] informationSupplementary data are available at Bioinformatics online.

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