Analysis of differential expression of genes is often performed to understand how the metabolic activity of an organism is impacted by a perturbation. However, because the system of metabolic regulation is complex and all changes are not directly reflected in the expression levels, interpreting these data can be difficult.
ResultsIn this work, we present a new algorithm and computational tool that uses a genome-scale metabolic reconstruction to infer metabolic changes from differential expression data. Using the framework of constraint-based analysis, our method produces a qualitative hypothesis of a change in metabolic activity. In other words, each reaction of the network is inferred to have increased, decreased, or remained unchanged in flux. In contrast to similar previous approaches, our method does not require a biological objective function and does not assign on/off activity states to genes. An implementation is provided and it is available online. We apply the method to three published datasets to show that it successfully accomplishes its two main goals: confirming or rejecting metabolic changes suggested by differentially expressed genes based on how well they fit in as parts of a coordinated metabolic change, as well as inferring changes in reactions whose genes did not undergo differential expression.
Availability and implementationgithub.com/htpusa/moomin.
Supplementary informationSupplementary data are available at Bioinformatics online.
", "keywords": ["0301 basic medicine", "570", "[SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]", "Metabolic networks; omics data", "Genome", "[INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS]", "0206 medical engineering", "610", "Computational Biology", "[INFO.INFO-DS] Computer Science [cs]/Data Structures and Algorithms [cs.DS]", "02 engineering and technology", "[SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]", "Original Papers", "Models", " Biological", "03 medical and health sciences", "[INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM]", "Algorithms", "Metabolic Networks and Pathways", "[INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM]"]}, "links": [{"href": "https://iris.uniroma1.it/bitstream/11573/1321358/5/Pusa_MOOMIN_2020.pdf"}, {"href": "https://academic.oup.com/bioinformatics/article-pdf/36/2/514/48991611/btz584.pdf"}, {"href": "https://doi.org/10.1093/bioinformatics/btz584"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Bioinformatics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1093/bioinformatics/btz584", "name": "item", "description": "10.1093/bioinformatics/btz584", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1093/bioinformatics/btz584"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-08-22T00:00:00Z"}}, {"id": "10.26434/chemrxiv.13047818.v1", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:20:10Z", "type": "Journal Article", "created": "2020-12-28", "title": "SoluProt: Prediction of Soluble Protein Expression in Escherichia coli", "description": "AbstractMotivationPoor protein solubility hinders the production of many therapeutic and industrially useful proteins. Experimental efforts to increase solubility are plagued by low success rates and often reduce biological activity. Computational prediction of protein expressibility and solubility in Escherichia coli using only sequence information could reduce the cost of experimental studies by enabling prioritization of highly soluble proteins.
ResultsA new tool for sequence-based prediction of soluble protein expression in E.coli, SoluProt, was created using the gradient boosting machine technique with the TargetTrack database as a training set. When evaluated against a balanced independent test set derived from the NESG database, SoluProt\uffe2\uff80\uff99s accuracy of 58.5% and AUC of 0.62 exceeded those of a suite of alternative solubility prediction tools. There is also evidence that it could significantly increase the success rate of experimental protein studies. SoluProt is freely available as a standalone program and a user-friendly webserver at https://loschmidt.chemi.muni.cz/soluprot/.
Availability and implementationhttps://loschmidt.chemi.muni.cz/soluprot/.
Supplementary informationSupplementary data are available at Bioinformatics online.
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Since growers have limited available resources, there is a need for advanced methods to select representative points within a field in order to best satisfy sampling or sensing objectives. The main purpose of this work was to develop a data-driven method for selecting locations across an agricultural field given observations of some covariates at every point in the field. These chosen locations should be representative of the distribution of the covariates in the entire population and represent the spatial variability in the field. They can then be used to sample an unknown target feature whose sampling is expensive and cannot be realistically done at the population scale. An algorithm for determining these optimal sampling locations, namely the multifunctional matching (MFM) criterion, was based on matching of moments (functionals) between sample and population. The selected functionals in this study were standard deviation, mean, and Kendall's tau. An additional algorithm defined the minimal number of observations that could represent the population according to a desired level of accuracy. The MFM was applied to datasets from two agricultural plots: a vineyard and a peach orchard. The data from the plots included measured values of slope, topographic wetness index, normalized difference vegetation index, and apparent soil electrical conductivity. The MFM algorithm selected the number of sampling points according to a representation accuracy of 90% and determined the optimal location of these points. The algorithm was validated against values of vine or tree water status measured as crop water stress index (CWSI). Algorithm performance was then compared to two other sampling methods: the conditioned Latin hypercube sampling (cLHS) model and a uniform random sample with spatial constraints. Comparison among sampling methods was based on measures of similarity between the target variable population distribution and the distribution of the selected sample. MFM represented CWSI distribution better than the cLHS and the uniform random sampling, and the selected locations showed smaller deviations from the mean and standard deviation of the entire population. The MFM functioned better in the vineyard, where spatial variability was larger than in the orchard. In both plots, the spatial pattern of the selected samples captured the spatial variability of CWSI. MFM can be adjusted and applied using other moments/functionals and may be adopted by other disciplines, particularly in cases where small sample sizes are desired.", "keywords": ["2. 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