| 1. |
- Abrahamsson, Sanna, et al.
(author)
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Comparison of online learning designs during the COVID-19 pandemic within bioinformatics courses in higher education
- 2021
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In: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1460-2059. ; 37:Suppl 1
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Journal article (peer-reviewed)abstract
- Motivation: Due to the worldwide COVID-19 pandemic, new strategies had to be adopted to move from classroom-based education to online education, in a very short time. The lack of time to set up these strategies, hindered a proper design of online instructions and delivery of knowledge. Bioinformatics-related training and other onsite practical education, tend to rely on extensive practice, where students and instructors have a face-to-face interaction to improve the learning outcome. For these courses to maintain their high quality when adapted as online courses, different designs need to be tested and the students' perceptions need to be heard. Results: This study focuses on short bioinformatics-related courses for graduate students at the University of Gothenburg, Sweden, which were originally developed for onsite training. Once adapted as online courses, several modifications in their design were tested to obtain the best fitting learning strategy for the students. To improve the online learning experience, we propose a combination of: (i) short synchronized sessions, (ii) extended time for own and group practical work, (iii) recorded live lectures and (iv) increased opportunities for feedback in several formats.
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| 2. |
- Abramova, Anna, 1990, et al.
(author)
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CAFE: a software suite for analysis of paired-sample transposon insertion sequencing data
- 2021
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In: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1460-2059. ; 37:1, s. 121-122
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Journal article (peer-reviewed)abstract
- Sequencing of transposon insertion libraries is used to determine the relative fitness of individual mutants at a large scale. However, there is a lack of tools for specifically analyzing data from such experiments with paired sample designs. Here, we introduce CAFE-Coefficient-based Analysis of Fitness by read Enrichment-a software package that can analyze data from paired transposon mutant sequencing experiments, generate fitness coefficients for each gene and condition and perform appropriate statistical testing on these fitness coefficients.
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| 3. |
- Andersson, Alma, et al.
(author)
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sepal : identifying transcript profiles with spatial patterns by diffusion-based modeling
- 2021
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In: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811 .- 1460-2059. ; 37:17, s. 2644-2650
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Journal article (peer-reviewed)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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| 4. |
- Baldassarre, Federico, et al.
(author)
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GraphQA: Protein Model Quality Assessment using Graph Convolutional Networks
- 2020
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In: Bioinformatics. - : Oxford University Press. - 1367-4803 .- 1367-4811 .- 1460-2059. ; 37:3, s. 360-366
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Journal article (peer-reviewed)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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| 5. |
- de Weerd, Hendrik A., et al.
(author)
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MODifieR : an ensemble R package for inference of disease modules from transcriptomics networks
- 2020
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In: Bioinformatics. - : Oxford University Press. - 1367-4803 .- 1367-4811 .- 1460-2059. ; 36:12, s. 3918-3919
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Journal article (peer-reviewed)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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| 6. |
- Ekvall, Markus, et al.
(author)
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Parallelized calculation of permutation tests
- 2020
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In: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811 .- 1460-2059. ; 36:22-23, s. 5392-5397
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Journal article (peer-reviewed)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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| 7. |
- Han, Mengying, et al.
(author)
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ChemHub: a knowledgebase of functional chemicals for synthetic biology studies
- 2021
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In: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811 .- 1460-2059. ; 37:22, s. 4275-4276
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Journal article (peer-reviewed)abstract
- The field of synthetic biology lacks a comprehensive knowledgebase for selecting synthetic target molecules according to their functions, economic applications and known biosynthetic pathways. We implemented ChemHub, a knowledgebase containing >90 000 chemicals and their functions, along with related biosynthesis information for these chemicals that was manually extracted from >600 000 published studies by more than 100 people over the past 10 years.
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| 8. |
- Hiltunen, Markus, et al.
(author)
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ARBitR : an overlap-aware genome assembly scaffolder for linked reads
- 2021
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In: Bioinformatics. - : Oxford University Press. - 1367-4803 .- 1367-4811 .- 1460-2059. ; 37:15, s. 2203-2205
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Journal article (peer-reviewed)abstract
- Summary: Linked genomic sequencing reads contain information that can be used to join sequences together into scaffolds in draft genome assemblies. Existing software for this purpose performs the scaffolding by joining sequences with a gap between them, not considering potential overlaps of contigs. We developed ARBitR to create scaffolds where overlaps are taken into account and show that it can accurately recreate regions where draft assemblies are broken.Availability and implementation: ARBitR is written and implemented in Python3 for Unix-based operative systems. All source code is available at https://github.com/markhilt/ARBitR under the GNU General Public License v3.Contact: markus.hiltunen@ebc.uu.seSupplementary information: Supplementary data are available at Bioinformatics online.
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| 9. |
- Jakobsson, Jon E. T., et al.
(author)
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scConnect : a method for exploratory analysis of cell–cell communication based on single-cell RNA-sequencing data
- 2021
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In: Bioinformatics. - : Oxford University Press. - 1367-4803 .- 1367-4811 .- 1460-2059. ; 37:20, s. 3501-3508
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Journal article (peer-reviewed)abstract
- MotivationCell to cell communication is critical for all multicellular organisms, and single-cell sequencing facilitates the construction of full connectivity graphs between cell types in tissues. Such complex data structures demand novel analysis methods and tools for exploratory analysis.ResultsWe propose a method to predict the putative ligand–receptor interactions between cell types from single-cell RNA-sequencing data. This is achieved by inferring and incorporating interactions in a multi-directional graph, thereby enabling contextual exploratory analysis. We demonstrate that our approach can detect common and specific interactions between cell types in mouse brain and human tumors, and that these interactions fit with expected outcomes. These interactions also include predictions made with molecular ligands integrating information from several types of genes necessary for ligand production and transport. Our implementation is general and can be appended to any transcriptome analysis pipeline to provide unbiased hypothesis generation regarding ligand to receptor interactions between cell populations or for network analysis in silico.Availability and implementationscConnect is open source and available as a Python package at https://github.com/JonETJakobsson/scConnect. scConnect is directly compatible with Scanpy scRNA-sequencing pipelines.
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| 10. |
- Jiang, Richard, et al.
(author)
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Epidemiological modeling in StochSS Live!
- 2021
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In: Bioinformatics. - : Oxford University Press. - 1367-4803 .- 1367-4811 .- 1460-2059. ; 37:17, s. 2787-2788
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Journal article (peer-reviewed)abstract
- We present StochSS Live!, a web-based service for modeling, simulation and analysis of a wide range of mathematical, biological and biochemical systems. Using an epidemiological model of COVID-19, we demonstrate the power of StochSS Live! to enable researchers to quickly develop a deterministic or a discrete stochastic model, infer its parameters and analyze the results.StochSS Live! is freely available at https://live.stochss.org/Supplementary data are available at Bioinformatics online.
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