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| 2. |
- Andersson, Alma, et al.
(författare)
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sepal : identifying transcript profiles with spatial patterns by diffusion-based modeling
- 2021
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811 .- 1460-2059. ; 37:17, s. 2644-2650
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Tidskriftsartikel (refereegranskat)abstract
- Motivation: Collection of spatial signals in large numbers has become a routine task in multiple omics-fields, but parsing of these rich datasets still pose certain challenges. In whole or near-full transcriptome spatial techniques, spurious expression profiles are intermixed with those exhibiting an organized structure. To distinguish profiles with spatial patterns from the background noise, a metric that enables quantification of spatial structure is desirable. Current methods designed for similar purposes tend to be built around a framework of statistical hypothesis testing, hence we were compelled to explore a fundamentally different strategy. Results: We propose an unexplored approach to analyze spatial transcriptomics data, simulating diffusion of individual transcripts to extract genes with spatial patterns. The method performed as expected when presented with synthetic data. When applied to real data, it identified genes with distinct spatial profiles, involved in key biological processes or characteristic for certain cell types. Compared to existing methods, ours seemed to be less informed by the genes' expression levels and showed better time performance when run with multiple cores.
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| 3. |
- Baldassarre, Federico, et al.
(författare)
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GraphQA: Protein Model Quality Assessment using Graph Convolutional Networks
- 2020
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Ingår i: Bioinformatics. - : Oxford University Press. - 1367-4803 .- 1367-4811 .- 1460-2059. ; 37:3, s. 360-366
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Tidskriftsartikel (refereegranskat)abstract
- MotivationProteins are ubiquitous molecules whose function in biological processes is determined by their 3D structure. Experimental identification of a protein’s structure can be time-consuming, prohibitively expensive, and not always possible. Alternatively, protein folding can be modeled using computational methods, which however are not guaranteed to always produce optimal results.GraphQA is a graph-based method to estimate the quality of protein models, that possesses favorable properties such as representation learning, explicit modeling of both sequential and 3D structure, geometric invariance, and computational efficiency.ResultsGraphQA performs similarly to state-of-the-art methods despite using a relatively low number of input features. In addition, the graph network structure provides an improvement over the architecture used in ProQ4 operating on the same input features. Finally, the individual contributions of GraphQA components are carefully evaluated.Availability and implementationPyTorch implementation, datasets, experiments, and link to an evaluation server are available through this GitHub repository: github.com/baldassarreFe/graphqaSupplementary informationSupplementary material is available at Bioinformatics online.
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| 4. |
- Brameier, Markus, et al.
(författare)
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NucPred - Predicting nuclear localization of proteins
- 2007
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811 .- 1460-2059. ; 23:9, s. 1159-1160
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Tidskriftsartikel (refereegranskat)abstract
- NucPred analyzes patterns in eukaryotic protein sequences and predicts if a protein spends at least some time in the nucleus or no time at all. Subcellular location of proteins represents functional information, which is important for understanding protein interactions, for the diagnosis of human diseases and for drug discovery. NucPred is a novel web tool based on regular expression matching and multiple program classifiers induced by genetic programming. A likelihood score is derived from the programs for each input sequence and each residue position. Different forms of visualization are provided to assist the detection of nuclear localization signals (NLSs). The NucPred server also provides access to additional sources of biological information (real and predicted) for a better validation and interpretation of results.
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| 5. |
- 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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| 6. |
- Chalk, Alistair M, et al.
(författare)
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siRNA specificity searching incorporating mismatch tolerance data.
- 2008
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1460-2059. ; 24:10, s. 1316-1317
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Tidskriftsartikel (refereegranskat)abstract
- Artificially synthesized short interfering RNAs (siRNAs) are widely used in functional genomics to knock down specific target genes. One ongoing challenge is to guarantee that the siRNA does not elicit off-target effects. Initial reports suggested that siRNAs were highly sequence-specific; however, subsequent data indicates that this is not necessarily the case. It is still uncertain what level of similarity and other rules are required for an off-target effect to be observed, and scoring schemes have not been developed to look beyond simple measures such as the number of mismatches or the number of consecutive matching bases present. We created design rules for predicting the likelihood of a non-specific effect and present a web server that allows the user to check the specificity of a given siRNA in a flexible manner using a combination of methods. The server finds potential off-target matches in the corresponding RefSeq database and ranks them according to a scoring system based on experimental studies of specificity. AVAILABILITY: The server is available at http://informatics-eskitis.griffith.edu.au/SpecificityServer.
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| 7. |
- Chatterjee, Saikat, et al.
(författare)
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SEK: Sparsity exploiting k-mer-based estimation of bacterial community composition
- 2014
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Ingår i: Bioinformatics. - : Oxford University Press. - 1460-2059 .- 1367-4803 .- 1367-4811. ; 30:17, s. 2423-2431
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Tidskriftsartikel (refereegranskat)abstract
- Motivation: Estimation of bacterial community composition from a high-throughput sequenced sample is an important task in metagenomics applications. As the sample sequence data typically harbors reads of variable lengths and different levels of biological and technical noise, accurate statistical analysis of such data is challenging. Currently popular estimation methods are typically time-consuming in a desktop computing environment.Results: Using sparsity enforcing methods from the general sparse signal processing field (such as compressed sensing), we derive a solution to the community composition estimation problem by a simultaneous assignment of all sample reads to a pre-processed reference database. A general statistical model based on kernel density estimation techniques is introduced for the assignment task, and the model solution is obtained using convex optimization tools. Further, we design a greedy algorithm solution for a fast solution. Our approach offers a reasonably fast community composition estimation method, which is shown to be more robust to input data variation than a recently introduced related method.Availability and implementation: A platform-independent Matlab implementation of the method is freely available at http://www.ee.kth.se/ctsoftware; source code that does not require access to Matlab is currently being tested and will be made available later through the above Web site.
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| 8. |
- Davila Lopez, Marcela, et al.
(författare)
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eGOB: eukaryotic Gene Order Browser.
- 2011
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Ingår i: Bioinformatics (Oxford, England). - : Oxford University Press (OUP). - 1367-4811 .- 1460-2059 .- 1367-4803. ; 27:8, s. 1150-1
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Tidskriftsartikel (refereegranskat)abstract
- A large number of genomes have been sequenced, allowing a range of comparative studies. Here, we present the eukaryotic Gene Order Browser with information on the order of protein and non-coding RNA (ncRNA) genes of 74 different eukaryotic species. The browser is able to display a gene of interest together with its genomic context in all species where that gene is present. Thereby, questions related to the evolution of gene organization and non-random gene order may be examined. The browser also provides access to data collected on pairs of adjacent genes that are evolutionarily conserved. AVAILABILITY: eGOB as well as underlying data are freely available at http://egob.biomedicine.gu.se SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. CONTACT: tore.samuelsson@medkem.gu.se.
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| 9. |
- de Weerd, Hendrik A., et al.
(författare)
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MODifieR : an ensemble R package for inference of disease modules from transcriptomics networks
- 2020
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Ingår i: Bioinformatics. - : Oxford University Press. - 1367-4803 .- 1367-4811 .- 1460-2059. ; 36:12, s. 3918-3919
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Tidskriftsartikel (refereegranskat)abstract
- MOTIVATION: Complex diseases are due to the dense interactions of many disease-associated factors that dysregulate genes that in turn form so-called disease modules, which have shown to be a powerful concept for understanding pathological mechanisms. There exist many disease module inference methods that rely on somewhat different assumptions, but there is still no gold standard or best performing method. Hence, there is a need for combining these methods to generate robust disease modules.RESULTS: We developed MODule IdentiFIER (MODifieR), an ensemble R package of nine disease module inference methods from transcriptomics networks. MODifieR uses standardized input and output allowing the possibility to combine individual modules generated from these methods into more robust disease-specific modules, contributing to a better understanding of complex diseases.AVAILABILITY: MODifieR is available under the GNU GPL license and can be freely downloaded from https://gitlab.com/Gustafsson-lab/MODifieR and as a Docker image from https://hub.docker.com/r/ddeweerd/modifier.SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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| 10. |
- Ekvall, Markus, et al.
(författare)
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Parallelized calculation of permutation tests
- 2020
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811 .- 1460-2059. ; 36:22-23, s. 5392-5397
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Tidskriftsartikel (refereegranskat)abstract
- Motivation: Permutation tests offer a straightforward framework to assess the significance of differences in sample statistics. A significant advantage of permutation tests are the relatively few assumptions about the distribution of the test statistic are needed, as they rely on the assumption of exchangeability of the group labels. They have great value, as they allow a sensitivity analysis to determine the extent to which the assumed broad sample distribution of the test statistic applies. However, in this situation, permutation tests are rarely applied because the running time of naive implementations is too slow and grows exponentially with the sample size. Nevertheless, continued development in the 1980s introduced dynamic programming algorithms that compute exact permutation tests in polynomial time. Albeit this significant running time reduction, the exact test has not yet become one of the predominant statistical tests for medium sample size. Here, we propose a computational parallelization of one such dynamic programming-based permutation test, the Green algorithm, which makes the permutation test more attractive. Results: Parallelization of the Green algorithm was found possible by non-trivial rearrangement of the structure of the algorithm. A speed-up-by orders of magnitude-is achievable by executing the parallelized algorithm on a GPU. We demonstrate that the execution time essentially becomes a non-issue for sample sizes, even as high as hundreds of samples. This improvement makes our method an attractive alternative to, e.g. the widely used asymptotic Mann-Whitney U-test.
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