| 1. |
- 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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| 2. |
- Michel, Mirco, et al.
(författare)
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Large-scale structure prediction by improved contact predictions and model quality assessment
- 2017
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811 .- 1460-2059. ; 33:14, s. 123-129
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Tidskriftsartikel (refereegranskat)abstract
- Motivation: Accurate contact predictions can be used for predicting the structure of proteins. Until recently these methods were limited to very big protein families, decreasing their utility. However, recent progress by combining direct coupling analysis with machine learning methods has made it possible to predict accurate contact maps for smaller families. To what extent these predictions can be used to produce accurate models of the families is not known. Results: We present the PconsFold2 pipeline that uses contact predictions from PconsC3, the CONFOLD folding algorithm and model quality estimations to predict the structure of a protein. We show that the model quality estimation significantly increases the number of models that reliably can be identified. Finally, we apply PconsFold2 to 6379 Pfam families of unknown structure and find that PconsFold2 can, with an estimated 90% specificity, predict the structure of up to 558 Pfam families of unknown structure. Out of these 415 have not been reported before. Availability: Datasets as well as models of all the 558 Pfam families are available at http://c3.pcons.net. All programs used here are freely available.
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| 3. |
- Michel, Mirco, et al.
(författare)
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PconsC4: fast, accurate and hassle-free contact predictions
- 2019
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811 .- 1460-2059. ; 35:15, s. 2677-2679
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Tidskriftsartikel (refereegranskat)abstract
- MotivationResidue contact prediction was revolutionized recently by the introduction of direct coupling analysis (DCA). Further improvements, in particular for small families, have been obtained by the combination of DCA and deep learning methods. However, existing deep learning contact prediction methods often rely on a number of external programs and are therefore computationally expensive.ResultsHere, we introduce a novel contact predictor, PconsC4, which performs on par with state of the art methods. PconsC4 is heavily optimized, does not use any external programs and therefore is significantly faster and easier to use than other methods.Availability and implementationPconsC4 is freely available under the GPL license from https://github.com/ElofssonLab/PconsC4. Installation is easy using the pip command and works on any system with Python 3.5 or later and a GCC compiler. It does not require a GPU nor special hardware.Supplementary informationSupplementary data are available at Bioinformatics online.
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| 4. |
- Mirabello, Claudio, et al.
(författare)
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Topology independent structural matching discovers novel templates for protein interfaces
- 2018
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Ingår i: Bioinformatics. - : OXFORD UNIV PRESS. - 1367-4803 .- 1367-4811 .- 1460-2059. ; 34:17, s. 787-794
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Tidskriftsartikel (refereegranskat)abstract
- Motivation: Protein-protein interactions (PPI) are essential for the function of the cellular machinery. The rapid growth of protein-protein complexes with known 3D structures offers a unique opportunity to study PPI to gain crucial insights into protein function and the causes of many diseases. In particular, it would be extremely useful to compare interaction surfaces of monomers, as this would enable the pinpointing of potential interaction surfaces based solely on the monomer structure, without the need to predict the complete complex structure. While there are many structural alignment algorithms for individual proteins, very few have been developed for protein interfaces, and none that can align only the interface residues to other interfaces or surfaces of interacting monomer subunits in a topology independent (non-sequential) manner. Results: We present InterComp, a method for topology and sequence-order independent structural comparisons. The method is general and can be applied to various structural comparison applications. By representing residues as independent points in space rather than as a sequence of residues, InterComp can be applied to a wide range of problems including interface-surface comparisons and interface-interface comparisons. We demonstrate a use-case by applying InterComp to find similar protein interfaces on the surface of proteins. We show that InterComp pinpoints the correct interface for almost half of the targets (283 of 586) when considering the top 10 hits, and for 24% of the top 1, even when no templates can be found with regular sequence-order dependent structural alignment methods.
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| 5. |
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| 6. |
- Afkham, Heydar Maboudi, et al.
(författare)
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Uncertainty estimation of predictions of peptides' chromatographic retention times in shotgun proteomics
- 2017
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Ingår i: Bioinformatics. - : OXFORD UNIV PRESS. - 1367-4803 .- 1367-4811. ; 33:4, s. 508-513
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Tidskriftsartikel (refereegranskat)abstract
- Motivation: Liquid chromatography is frequently used as a means to reduce the complexity of peptide-mixtures in shotgun proteomics. For such systems, the time when a peptide is released from a chromatography column and registered in the mass spectrometer is referred to as the peptide's retention time. Using heuristics or machine learning techniques, previous studies have demonstrated that it is possible to predict the retention time of a peptide from its amino acid sequence. In this paper, we are applying Gaussian Process Regression to the feature representation of a previously described predictor ELUDE. Using this framework, we demonstrate that it is possible to estimate the uncertainty of the prediction made by the model. Here we show how this uncertainty relates to the actual error of the prediction. Results: In our experiments, we observe a strong correlation between the estimated uncertainty provided by Gaussian Process Regression and the actual prediction error. This relation provides us with new means for assessment of the predictions. We demonstrate how a subset of the peptides can be selected with lower prediction error compared to the whole set. We also demonstrate how such predicted standard deviations can be used for designing adaptive windowing strategies.
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| 7. |
- Anil, Anandashankar, et al.
(författare)
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HiCapTools : a software suite for probe design and proximity detection for targeted chromosome conformation capture applications
- 2018
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Ingår i: Bioinformatics. - : OXFORD UNIV PRESS. - 1367-4803 .- 1367-4811. ; 34:4, s. 675-677
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Tidskriftsartikel (refereegranskat)abstract
- Folding of eukaryotic genomes within nuclear space enables physical and functional contacts between regions that are otherwise kilobases away in sequence space. Targeted chromosome conformation capture methods (T2C, chi-C and HiCap) are capable of informing genomic contacts for a subset of regions targeted by probes. We here present HiCapTools, a software package that can design sequence capture probes for targeted chromosome capture applications and analyse sequencing output to detect proximities involving targeted fragments. Two probes are designed for each feature while avoiding repeat elements and non-unique regions. The data analysis suite processes alignment files to report genomic proximities for each feature at restriction fragment level and is isoform-aware for gene features. Statistical significance of contact frequencies is evaluated using an empirically derived background distribution. Targeted chromosome conformation capture applications are invaluable for locating target genes of disease-associated variants found by genome-wide association studies. Hence, we believe our software suite will prove to be useful for a wider user base within clinical and functional applications.
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| 8. |
- Basu, Sankar Chandra, et al.
(författare)
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Finding correct protein-protein docking models using ProQDock
- 2016
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Ingår i: Bioinformatics. - : OXFORD UNIV PRESS. - 1367-4803 .- 1367-4811. ; 32:12, s. 262-270
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Tidskriftsartikel (refereegranskat)abstract
- Motivation: Protein-protein interactions are a key in virtually all biological processes. For a detailed understanding of the biological processes, the structure of the protein complex is essential. Given the current experimental techniques for structure determination, the vast majority of all protein complexes will never be solved by experimental techniques. In lack of experimental data, computational docking methods can be used to predict the structure of the protein complex. A common strategy is to generate many alternative docking solutions (atomic models) and then use a scoring function to select the best. The success of the computational docking technique is, to a large degree, dependent on the ability of the scoring function to accurately rank and score the many alternative docking models. Results: Here, we present ProQDock, a scoring function that predicts the absolute quality of docking model measured by a novel protein docking quality score (DockQ). ProQDock uses support vector machines trained to predict the quality of protein docking models using features that can be calculated from the docking model itself. By combining different types of features describing both the protein-protein interface and the overall physical chemistry, it was possible to improve the correlation with DockQ from 0.25 for the best individual feature (electrostatic complementarity) to 0.49 for the final version of ProQDock. ProQDock performed better than the state-of-the-art methods ZRANK and ZRANK2 in terms of correlations, ranking and finding correct models on an independent test set. Finally, we also demonstrate that it is possible to combine ProQDock with ZRANK and ZRANK2 to improve performance even further.
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| 9. |
- 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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| 10. |
- Bernhem, Kristoffer, et al.
(författare)
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SMLocalizer, a GPU accelerated ImageJ plugin for single molecule localization microscopy
- 2018
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Ingår i: Bioinformatics. - : Oxford University Press. - 1367-4803 .- 1367-4811. ; 34:1, s. 137-
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Tidskriftsartikel (refereegranskat)abstract
- SMLocalizer combines the availability of ImageJ with the power of GPU processing for fast and accurate analysis of single molecule localization microscopy data. Analysis of 2D and 3D data in multiple channels is supported.
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