Automated Genome Annotation and Metabolic Model Reconstruction in the SEED and Model SEED

2013 ◽  
pp. 17-45 ◽  
Author(s):  
Scott Devoid ◽  
Ross Overbeek ◽  
Matthew DeJongh ◽  
Veronika Vonstein ◽  
Aaron A. Best ◽  
...  
Keyword(s):  
Genome Annotation ◽  
Metabolic Model ◽  
Model Reconstruction

Tapping the Wealth of Microbial Data in High-Throughput Metabolic Model Reconstruction

2014 ◽  
pp. 19-45
Author(s):  
Ric Colasanti ◽  
Janaka N. Edirisinghe ◽  
Tahmineh Khazaei ◽  
José P. Faria ◽  
Sam Seaver ◽  
...  
Keyword(s):  
High Throughput ◽  
Metabolic Model ◽  
Model Reconstruction

scholarly journals Methods for automated genome-scale metabolic model reconstruction

2018 ◽  
Vol 46 (4) ◽  
pp. 931-936 ◽  
Author(s):  
José P. Faria ◽  
Miguel Rocha ◽  
Isabel Rocha ◽  
Christopher S. Henry
Keyword(s):  
Next Generation Sequencing ◽  
Sequence Data ◽  
Metabolic Model ◽  
Model Reconstruction ◽  
Next Generation ◽  
Genome Sequences ◽  
Metabolic Models ◽  
Genome Scale ◽  
Generation Sequencing

In the era of next-generation sequencing and ubiquitous assembly and binning of metagenomes, new putative genome sequences are being produced from isolate and microbiome samples at ever-increasing rates. Genome-scale metabolic models have enormous utility for supporting the analysis and predictive characterization of these genomes based on sequence data. As a result, tools for rapid automated reconstruction of metabolic models are becoming critically important for supporting the analysis of new genome sequences. Many tools and algorithms have now emerged to support rapid model reconstruction and analysis. Here, we are comparing and contrasting the capabilities and output of a variety of these tools, including ModelSEED, Raven Toolbox, PathwayTools, SuBliMinal Toolbox and merlin.

scholarly journals Addressing uncertainty in genome-scale metabolic model reconstruction and analysis

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
David B. Bernstein ◽  
Snorre Sulheim ◽  
Eivind Almaas ◽  
Daniel Segrè
Keyword(s):  
Metabolic Model ◽  
Ensemble Modeling ◽  
Model Reconstruction ◽  
Predictive Capacity ◽  
Sources Of Uncertainty ◽  
Basic Understanding ◽  
Biological Insight ◽  
Integration Algorithms ◽  
Genome Scale

AbstractThe reconstruction and analysis of genome-scale metabolic models constitutes a powerful systems biology approach, with applications ranging from basic understanding of genotype-phenotype mapping to solving biomedical and environmental problems. However, the biological insight obtained from these models is limited by multiple heterogeneous sources of uncertainty, which are often difficult to quantify. Here we review the major sources of uncertainty and survey existing approaches developed for representing and addressing them. A unified formal characterization of these uncertainties through probabilistic approaches and ensemble modeling will facilitate convergence towards consistent reconstruction pipelines, improved data integration algorithms, and more accurate assessment of predictive capacity.

scholarly journals On the inconsistent treatment of gene-protein-reaction rules in context-specific metabolic models

2019 ◽  
Author(s):  
Miguel Ponce-de-León ◽  
Iñigo Apaolaza ◽  
Alfonso Valencia ◽  
Francisco J. Planes
Keyword(s):  
Gene Expression ◽  
Metabolic Model ◽  
Specific Model ◽  
Omics Data ◽  
Model Reconstruction ◽  
Reconstruction Algorithms ◽  
Reconstruction Methods ◽  
Metabolic Models ◽  
Context Specific ◽  
Genome Scale

ABSTRACTWith the publication of high-quality genome-scale metabolic models for several organisms, the Systems Biology community has developed a number of algorithms for their analysis making use of ever growing –omics data. In particular, the reconstruction of the first genome-scale human metabolic model, Recon1, promoted the development of Context-Specific Model (CS-Model) reconstruction methods. This family of algorithms aims to identify the set of metabolic reactions that are active in a cell in a given condition using omics data, such as gene expression levels. Different CS-Model reconstruction algorithms have their own strengths and weaknesses depending on the problem under study and omics data available. However, after careful inspection, we found that all of these algorithms share common issues in the way GPR rules and gene expression data are treated. The first issue is related with how gapfilling reactions are managed after the reconstruction is conducted. The second issue concerns the molecular context, which is used to build the CS-model but neglected for posterior analyses. To evaluate the effect of these issues, we reconstructed ∼400 CS-Models of cancer cell lines and conducted gene essentiality analysis, using CRISPR–Cas9 essentiality data for validation purposes. Altogether, our results illustrate the importance of correcting the errors introduced during the GPR translation in many of the published metabolic reconstructions.

Pantograph: A template-based method for genome-scale metabolic model reconstruction

2015 ◽  
Vol 13 (02) ◽  
pp. 1550006 ◽  
Author(s):  
Nicolas Loira ◽  
Anna Zhukova ◽  
David James Sherman
Keyword(s):  
Metabolic Model ◽  
Scale Model ◽  
Model Reconstruction ◽  
Specific Knowledge ◽  
Manual Curation ◽  
A Genome ◽  
Iterative Improvement ◽  
Genome Scale ◽  
Genome Scale Model ◽  
Template Reaction

Genome-scale metabolic models are a powerful tool to study the inner workings of biological systems and to guide applications. The advent of cheap sequencing has brought the opportunity to create metabolic maps of biotechnologically interesting organisms. While this drives the development of new methods and automatic tools, network reconstruction remains a time-consuming process where extensive manual curation is required. This curation introduces specific knowledge about the modeled organism, either explicitly in the form of molecular processes, or indirectly in the form of annotations of the model elements. Paradoxically, this knowledge is usually lost when reconstruction of a different organism is started. We introduce the Pantograph method for metabolic model reconstruction. This method combines a template reaction knowledge base, orthology mappings between two organisms, and experimental phenotypic evidence, to build a genome-scale metabolic model for a target organism. Our method infers implicit knowledge from annotations in the template, and rewrites these inferences to include them in the resulting model of the target organism. The generated model is well suited for manual curation. Scripts for evaluating the model with respect to experimental data are automatically generated, to aid curators in iterative improvement. We present an implementation of the Pantograph method, as a toolbox for genome-scale model reconstruction, curation and validation. This open source package can be obtained from: http://pathtastic.gforge.inria.fr .

scholarly journals An approach to pathway reconstruction using whole genome metabolic models and sensitive sequence searching

2009 ◽  
Vol 6 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Mansoor Saqi ◽  
Richard Jb. Dobson ◽  
Preben Kraben ◽  
David A. Hodgson ◽  
David L. Wild
Keyword(s):  
Genome Annotation ◽  
Functional Annotation ◽  
Streptomyces Coelicolor ◽  
Metabolic Model ◽  
Open Reading Frames ◽  
Biochemical Pathway ◽  
Genome Data ◽  
Pathway Reconstruction ◽  
Metabolic Models ◽  
Reading Frames

SummaryMetabolic models have the potential to impact on genome annotation and on the interpretation of gene expression and other high throughput genome data. The genome of Streptomyces coelicolor genome has been sequenced and some 30% of the open reading frames (ORFs) lack any functional annotation. A recently constructed metabolic network model for S. coelicolor highlights biochemical functions which should exist to make the metabolic model complete and consistent. These include 205 reactions for which no ORF is associated. Here we combine protein functional predictions for the unannotated open reading frames in the genome with ‘missing but expected’ functions inferred from the metabolic model. The approach allows function predictions to be evaluated in the context of the biochemical pathway reconstruction, and feed back iteratively into the metabolic model. We describe the approach and discuss a few illustrative examples.

Sign in / Sign up

Export Citation Format

Share Document