| 1. |
- Abdel-Rehim, Abbi, et al.
(författare)
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Protein-ligand binding affinity prediction exploiting sequence constituent homology
- 2023
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Ingår i: Bioinformatics. - 1367-4803 .- 1367-4811. ; 39:8
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Tidskriftsartikel (refereegranskat)abstract
- MOTIVATION: Molecular docking is a commonly used approach for estimating binding conformations and their resultant binding affinities. Machine learning has been successfully deployed to enhance such affinity estimations. Many methods of varying complexity have been developed making use of some or all the spatial and categorical information available in these structures. The evaluation of such methods has mainly been carried out using datasets from PDBbind. Particularly the Comparative Assessment of Scoring Functions (CASF) 2007, 2013, and 2016 datasets with dedicated test sets. This work demonstrates that only a small number of simple descriptors is necessary to efficiently estimate binding affinity for these complexes without the need to know the exact binding conformation of a ligand. RESULTS: The developed approach of using a small number of ligand and protein descriptors in conjunction with gradient boosting trees demonstrates high performance on the CASF datasets. This includes the commonly used benchmark CASF2016 where it appears to perform better than any other approach. This methodology is also useful for datasets where the spatial relationship between the ligand and protein is unknown as demonstrated using a large ChEMBL-derived dataset. AVAILABILITY AND IMPLEMENTATION: Code and data uploaded to https://github.com/abbiAR/PLBAffinity.
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| 2. |
- Bengtsson-Palme, Johan, 1985, et al.
(författare)
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Metaxa2 Database Builder: enabling taxonomic identification from metagenomic or metabarcoding data using any genetic marker
- 2018
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Ingår i: Bioinformatics (Oxford, England). - : Oxford University Press (OUP). - 1367-4811 .- 1367-4803. ; 34:23, s. 4027-4033
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Tidskriftsartikel (refereegranskat)abstract
- Correct taxonomic identification of DNA sequences is central to studies of biodiversity using both shotgun metagenomic and metabarcoding approaches. However, no genetic marker gives sufficient performance across all the biological kingdoms, hampering studies of taxonomic diversity in many groups of organisms. This has led to the adoption of a range of genetic markers for DNA metabarcoding. While many taxonomic classification software tools can be re-trained on these genetic markers, they are often designed with assumptions that impair their utility on genes other than the SSU and LSU rRNA. Here, we present an update to Metaxa2 that enables the use of any genetic marker for taxonomic classification of metagenome and amplicon sequence data.We evaluated the Metaxa2 Database Builder on eleven commonly used barcoding regions and found that while there are wide differences in performance between different genetic markers, our software performs satisfactorily provided that the input taxonomy and sequence data are of high quality.Freely available on the web as part of the Metaxa2 package at http://microbiology.se/software/metaxa2/.Supplementary data are available at Bioinformatics online.
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| 3. |
- Brunius, Carl, 1974, et al.
(författare)
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Prediction and modeling of pre-analytical sampling errors as a strategy to improve plasma NMR metabolomics data
- 2017
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1460-2059 .- 1367-4811. ; 33:22, s. 3567-3574
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Tidskriftsartikel (refereegranskat)abstract
- Biobanks are important infrastructures for life science research. Optimal sample handling regarding e.g. collection and processing of biological samples is highly complex, with many variables that could alter sample integrity and even more complex when considering multiple study centers or using legacy samples with limited documentation on sample management. Novel means to understand and take into account such variability would enable high-quality research on archived samples. This study investigated whether pre-analytical sample variability could be predicted and reduced by modeling alterations in the plasma metabolome, measured by NMR, as a function of pre-centrifugation conditions (1-36 h pre-centrifugation delay time at 4 A degrees C and 22 A degrees C) in 16 individuals. Pre-centrifugation temperature and delay times were predicted using random forest modeling and performance was validated on independent samples. Alterations in the metabolome were modeled at each temperature using a cluster-based approach, revealing reproducible effects of delay time on energy metabolism intermediates at both temperatures, but more pronounced at 22 A degrees C. Moreover, pre-centrifugation delay at 4 A degrees C resulted in large, specific variability at 3 h, predominantly of lipids. Pre-analytical sample handling error correction resulted in significant improvement of data quality, particularly at 22 A degrees C. This approach offers the possibility to predict pre-centrifugation delay temperature and time in biobanked samples before use in costly downstream applications. Moreover, the results suggest potential to decrease the impact of undesired, delay-induced variability. However, these findings need to be validated in multiple, large sample sets and with analytical techniques covering a wider range of the metabolome, such as LC-MS.
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| 4. |
- Brunnsåker, Daniel, 1992, et al.
(författare)
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Interpreting protein abundance in Saccharomyces cerevisiae through relational learning
- 2024
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Ingår i: Bioinformatics. - : Oxford University Press. - 1367-4803 .- 1367-4811. ; 40:2
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Tidskriftsartikel (refereegranskat)abstract
- Motivation: Proteomic profiles reflect the functional readout of the physiological state of an organism. An increased understanding of what controls and defines protein abundances is of high scientific interest. Saccharomyces cerevisiae is a well-studied model organism, and there is a large amount of structured knowledge on yeast systems biology in databases such as the Saccharomyces Genome Database, and highly curated genome-scale metabolic models like Yeast8. These datasets, the result of decades of experiments, are abundant in information, and adhere to semantically meaningful ontologies. Results: By representing this knowledge in an expressive Datalog database we generated data descriptors using relational learning that, when combined with supervised machine learning, enables us to predict protein abundances in an explainable manner. We learnt predictive relationships between protein abundances, function and phenotype; such as a-amino acid accumulations and deviations in chronological lifespan. We further demonstrate the power of this methodology on the proteins His4 and Ilv2, connecting qualitative biological concepts to quantified abundances. Availability and implementation: All data and processing scripts are available at the following Github repository: https://github.com/ DanielBrunnsaker/ProtPredict.
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| 5. |
- Chen, Yu, 1990, et al.
(författare)
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Systematic inference of functional phosphorylation events in yeast metabolism
- 2017
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811. ; 33:13, s. 1995-2001
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Tidskriftsartikel (refereegranskat)abstract
- Motivation: Protein phosphorylation is a post-translational modification that affects proteins by changing their structure and conformation in a rapid and reversible way, and it is an important mechanism for metabolic regulation in cells. Phosphoproteomics enables high-throughput identification of phosphorylation events on metabolic enzymes, but identifying functional phosphorylation events still requires more detailed biochemical characterization. Therefore, development of computational methods for investigating unknown functions of a large number of phosphorylation events identified by phosphoproteomics has received increased attention. Results: We developed a mathematical framework that describes the relationship between phosphorylation level of a metabolic enzyme and the corresponding flux through the enzyme. Using this framework, it is possible to quantitatively estimate contribution of phosphorylation events to flux changes. We showed that phosphorylation regulation analysis, combined with a systematic workflow and correlation analysis, can be used for inference of functional phosphorylation events in steady and dynamic conditions, respectively. Using this analysis, we assigned functionality to phosphorylation events of 17 metabolic enzymes in the yeast Saccharomyces cerevisiae, among which 10 are novel. Phosphorylation regulation analysis cannot only be extended for inference of other functional post-translational modifications but also be a promising scaffold formulti-omics data integration in systems biology.
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| 6. |
- Dalevi, Daniel, 1974, et al.
(författare)
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Bayesian classifiers for detecting HGT using fixed and variable order Markov models of genomic signatures
- 2006
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811. ; 22:5, s. 517-522
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Tidskriftsartikel (refereegranskat)abstract
- Analyses of genomic signatures are gaining attention as they allow studies of species-specific relationships without involving alignments of homologous sequences. A naïve Bayesian classifier was built to discriminate between different bacterial compositions of short oligomers, also known as DNA words. The classifier has proven successful in identifying foreign genes in Neisseria meningitis. In this study we extend the classifier approach using either a fixed higher order Markov model (Mk) or a variable length Markov model (VLMk).
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| 7. |
- Dalevi, Daniel, 1974, et al.
(författare)
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Expected Gene Order Distances and Model Selection in Bacteria
- 2008
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Ingår i: Bioinformatics. - Oxford, United Kingdom : Oxford University Press. - 1367-4803 .- 1367-4811. ; 24:11, s. 1332-1338
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Tidskriftsartikel (refereegranskat)abstract
- Motivation: The evolutionary distance inferred from gene-order comparisons of related bacteria is dependent on the model. Therefore, it is highly important to establish reliable assumptions before inferring its magnitude. Results: We investigate the patterns of dotplots between species of bacteria with the purpose of model selection in gene-order problems. We find several categories of data which can be explained by carefully weighing the contributions of reversals, transpositions, symmetrical reversals, single gene transpositions and single gene reversals. We also derive method of moments distance estimates for some previously uncomputed cases, such as symmetrical reversals, single gene reversals and their combinations, as well as the single gene transpositions edit distance.
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| 8. |
- Ebmeyer, Stefan, 1990, et al.
(författare)
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GEnView: a gene-centric, phylogeny-based comparative genomics pipeline for bacterial genomes and plasmids
- 2022
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811. ; 38:6, s. 1727-1728
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Tidskriftsartikel (refereegranskat)abstract
- Comparing genomic loci of a given bacterial gene across strains and species can provide insights into their evolution, including information on e.g. acquired mobility, the degree of conservation between different taxa or indications of horizontal gene transfer events. While thousands of bacterial genomes are available to date, there is no software that facilitates comparisons of individual gene loci for a large number of genomes. GEnView (Genetic Environment View) is a Python-based pipeline for the comparative analysis of gene-loci in a large number of bacterial genomes, providing users with automated, taxon-selective access to the >800.000 genomes and plasmids currently available in the NCBI Assembly and RefSeq databases, and is able to process local genomes that are not deposited at NCBI, enabling searches for genomic sequences and to analyze their genetic environments through the interactive visualization and extensive metadata files created by GEnView.
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| 9. |
- Gennemark, Peter, 1974, et al.
(författare)
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Benchmarks for identification of ordinary differential equations from time series data
- 2009
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1460-2059 .- 1367-4803 .- 1367-4811. ; 25:6, s. 780-786
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Tidskriftsartikel (refereegranskat)abstract
- Motivation: In recent years, the biological literature has seen a significant increase of reported methods for identifying both structure and parameters of ordinary differential equations (ODEs) from time series data. A natural way to evaluate the performance of such methods is to try them on a sufficient number of realistic test cases. However, weak practices in specifying identification problems and lack of commonly accepted benchmark problems makes it difficult to evaluate and compare different methods. Results: To enable better evaluation and comparisons between different methods, we propose how to specify identification problems as optimization problems with a model space of allowed reactions (e.g. reaction kinetics like Michaelis - Menten or S-systems), ranges for the parameters, time series data and an error function. We also define a file format for such problems. We then present a collection of more than 40 benchmark problems for ODE model identification of cellular systems. The collection includes realistic problems of different levels of difficulty w.r.t. size and quality of data. We consider both problems with simulated data from known systems, and problems with real data. Finally, we present results based on our identification algorithm for all benchmark problems. In comparison with publications on which we have based some of the benchmark problems, our approach allows all problems to be solved without the use of supercomputing.
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| 10. |
- Guillot, Gilles, 1972, et al.
(författare)
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Correcting for ascertainment bias in the inference of population structure
- 2009
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811. ; 25:4, s. 552-554
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Tidskriftsartikel (refereegranskat)abstract
- Background: The ascertainment process of molecular markers amounts to disregard loci carrying alleles with low frequencies. This can result in strong biases in inferences under population genetics models if not properly taken into account by the inference algorithm. Attempting to model this censoring process in view of making inference of population structure (i.e. identifying clusters of individuals) brings up challenging numerical difficulties. Method: These difficulties are related to the presence of intractable normalizing constants in Metropolis-Hastings acceptance ratios. This can be solved via an Markov chain Monte Carlo (MCMC) algorithm known as single variable exchange algorithm (SVEA). Result: We show how this general solution can be implemented for a class of clustering models of broad interest in population genetics that includes the models underlying the computer programs STRUCTURE, GENELAND and GESTE. We also implement the method proposed for a simple example and show that it allows us to reduce the bias substantially.
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