14 documents found, page 1 of 2

DeepTransyt, annotation of transporter proteins using deep learning

Genome-Scale Metabolic (GSM) models are essential tools for studying and engineering cellular metabolism, with transporter annotation representing a critical step in their reconstruction. However, traditional annotation methods, such as homology-based approaches like BLAST, are very dependent on the existence of available data, struggling when it comes to novel examples. To address these challenges, DeepTransyt...

TranSyT, an innovative framework for identifying transport systems

The importance and rate of development of genome-scale metabolic models have been growing for the last few years, increasing the demand for software solutions that automate several steps of this process. However, since TRIAGEâs release, software development for the automatic integration of transport reactions into models has stalled.Here, we present the Transport Systems Tracker (TranSyT). Unlike other transpor...

TranSyT, an innovative framework for identifying transport systems

The importance and rate of development of genome-scale metabolic models have been growing for the last years, increasing the demand for software solutions that automate several steps of this process. However, since TRIAGEs release, software development for automatic integration of transport reactions into models has stalled. Here we present the Transport Systems Tracker (TranSyT), the next iteration of TRIAGE. ...

MEMOTE for standardized genome-scale metabolic model testing

Reconstructing metabolic reaction networks enables the development of testable hypotheses of an organisms metabolism under different conditions1. State-of-the-art genome-scale metabolic models (GEMs) can include thousands of metabolites and reactions that are assigned to subcellular locations. Geneproteinreaction (GPR) rules and annotations using database information can add meta-information to GEMs. GEMs with ...

Publisher Correction: MEMOTE for standardized genome-scale metabolic model testing

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Automated reconstruction and comparison of metabolic models for diverse fungal ...

Improving automated model reconstruction across phylogenetically diverse genome...

Methods for automated genome-scale metabolic model reconstruction

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 ...

Automated pathway curation and improving metabolic model reconstruction based o...

Metabolic models generated by automated reconstruction pipelines are widely used for high-throughput prediction of microbial phenotypes. However, the generation of accurate in-silico phenotype predictions based solely on genomic data continues to be a challenge as metabolic models often require extensive gapfilling in order to produce biomass. As a result, the true physiological profile of an organism can be al...

Computing and applying atomic regulons to understand gene expression and regula...

Understanding gene function and regulation is essential for the interpretation prediction and ultimate design of cell responses to changes in the environment. An important step toward meeting the challenge of understanding gene function and regulation is the identification of sets of genes that are always co-expressed. These gene sets Atomic Regulons ARs represent fundamental units of function within a cell and...