<i>merlin</i> v4.0: an updated platform for the reconstruction of high-quality genome-scale metabolic models

Capela, João; Lagoa, Davide Rafael Santos; Rodrigues, Rúben; Cunha, Emanuel; Cruz, Fernando; Barbosa, Ana; Bastos, José Roberto Henrique; Lima, Diogo; Ferreira, E. C.; Rocha, Miguel; Dias, Óscar

doi:10.1101/2021.02.24.432752

Cited by 11 publications

(13 citation statements)

References 105 publications

(178 reference statements)

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“…ChiMera produces models in different formats, SBML3, JSON and YAML. These models can be used as input for Merlin (15) or PSAMM (8), enabling advanced users to perform manual curation of their models. The gene essentiality predictive metrics were higher in manually curated models.…”

Section: Resultsmentioning

confidence: 99%

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ChiMera: An easy to use pipeline for Bacterial Genome Based Metabolic Network Reconstruction, Evaluation and Visualization

Tamasco

Silva

Silva‐Rocha³

2021

Preprint

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Several genome scale metabolic reconstruction tools have been developed in the last decades. They have helped to construct many metabolic models, which have contributed to a variety of fields, e.g., genetic engineering, drug discovery, prediction of phenotypes, and other model-driven discoveries. However, the use of these programs requires a higher level of bioinformatic skills, and most of them are not scalable for multiple genomes. Moreover, the functionalities required to build models are generally scattered through multiple tools, requiring knowledge of their utilization.Here, we present ChiMera, which combines the most efficient tools in model reconstruction, prediction, and visualization. ChiMera uses CarveMe top-down approach based on genomic evidence to prune a global model with a high level of curation, generating a draft genome able to produce growth predictions using flux balance analysis for gram-positive and gram-negative bacteria. ChiMera also contains two modules of visualization implemented, predefined and universal. The first generates maps for the most important pathways, e.g., core-metabolism, fatty acid oxidation and biosynthesis, nucleotides and amino acids biosynthesis, glycolysis, and others. The second module produces a genome-wide metabolic map, which can be used to harvest KEGG pathway information for each compound in the model. A module of gene essentiality and knockout is also present. Overall, ChiMera combines model creation, gap-filling, FBA and metabolic visualization to create a simulation ready genome-scale model, helping genetic engineering projects, prediction of phenotypes, and other model-driven discoveries in a friendly manner.

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Section: Resultsmentioning

confidence: 99%

“…Merlin offers a vast workspace for its users. Its graphical interface allows users to re-annotate genomes using BLAST or HMMER, and also integrate data from NCBI and KEGG to its draft model (15). This tool is preferable for those focusing on manual curation of single organisms with expertise in metabolic engineering and model creation (4).…”

Section: Discussionmentioning

confidence: 99%

ChiMera: An easy to use pipeline for Bacterial Genome Based Metabolic Network Reconstruction, Evaluation and Visualization

Tamasco

Silva

Silva‐Rocha³

2021

Preprint

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“…Besides the webservice application, BioISO is also available as a plugin for merlin [22]. This plugin allows using BioISO to supply an equally user-friendly view of the errors associated with a given model being reconstructed within merlin .…”

Section: Methodsmentioning

confidence: 99%

BioISO: an objective-oriented application for assisting the curation of genome-scale metabolic models

Cruz

Capela

Ferreira

et al. 2021

Preprint

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As the reconstruction of Genome-Scale Metabolic Models becomes standard practice in systems biology, the number of organisms having at least one metabolic model at the genome-scale is peaking at an unprecedented scale. The automation of several laborious tasks, such as gap-finding and gap-filling, allowed to develop GSMMs for poorly described organisms. However, such models' quality can be compromised by the automation of several steps, which may lead to erroneous phenotype simulations. The Biological networks constraint-based In Silico Optimization (BioISO) is a computational tool aimed at accelerating the reconstruction of Genome-Scale Metabolic Models. This tool facilitates the manual curation steps by reducing the large search spaces often met when debugging in silico biological models. BioISO uses a recursive relation-like algorithm and Flux Balance Analysis to evaluate and guide debugging of in silico phenotype simulations. The potential of BioISO to guide the debugging of model reconstructions was showcased using GSMMs available in literature and compared with the results of two other state-of-the-art gap-filling tools (Meneco and fastGapFill). Furthermore, BioISO was used as Meneco's gap-finding algorithm to reduce the number of proposed solutions (reaction sets) for filling the gaps. BioISO was implemented as a webserver available at https://bioiso.bio.di.uminho.pt; and integrated into merlin as a plugin. BioISO's implementation as a Python package can also be retrieved from https://github.com/BioSystemsUM/BioISO.

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“…Many tools rely simply on homology or orthology to predict transporters. These tools include the metabolic network tools merlin [3–5], Pantograph [6], and TransATH [7] that process the complete proteome and predict each transport reaction, which means identifying the transport protein and the specific substrate, as well as cellular compartment.…”

Section: Introductionmentioning

confidence: 99%

TooT-SC: Predicting Eleven Substrate Classes of Transmembrane Transport Proteins

Alballa

Butler

2022

Preprint

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Background: Transporters form a significant proportion of the proteome and play an important role in mediating the movement of compounds across membranes. Transport proteins are difficult to characterize experimentally, so there is a need for computational tools that predict the substrates transported in order to annotate the large number of genomes being sequenced. Recently we developed a dataset of eleven substrate classes from Swiss-Prot using the ChEBI ontology as the basis for the definition of the classes. Results: We extend our earlier work TranCEP, which predicted seven substrate classes, to the new dataset with eleven substrate classes. Like TranCEP, TooT-SC combines pairwise amino acid composition (PAAC) of the protein, with evolutionary information captured in a multiple sequence alignment (MSA) using TM-Coffee, and restriction to important positions of the alignment using TCS. Our experimental results show that TooT-SC significantly outperforms the state-of-the-art predictors, including our earlier work, with an overall MCC of 0.82 and the MCC for the eleven classes ranging from 0.66 to 1.00. Conclusion: TooT-SC is a useful tool with high performance covering a broad range of substrate classes. The results quantify the contribution made by each type of information used during the prediction process. We believe the methodology is applicable more generally for protein sequence analysis.

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merlin v4.0: an updated platform for the reconstruction of high-quality genome-scale metabolic models

Cited by 11 publications

References 105 publications

ChiMera: An easy to use pipeline for Bacterial Genome Based Metabolic Network Reconstruction, Evaluation and Visualization

ChiMera: An easy to use pipeline for Bacterial Genome Based Metabolic Network Reconstruction, Evaluation and Visualization

BioISO: an objective-oriented application for assisting the curation of genome-scale metabolic models

TooT-SC: Predicting Eleven Substrate Classes of Transmembrane Transport Proteins

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