| 1. |
- 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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| 2. |
- Andersson, Anders, et al.
(författare)
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Dual-genome primer design for construction of DNA microarrays
- 2005
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811. ; 21:3, s. 325-332
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Tidskriftsartikel (refereegranskat)abstract
- Motivation: Microarray experiments using probes covering a whole transcriptome are expensive to initiate, and a major part of the costs derives from synthesizing gene-specific PCR primers or hybridization probes. The high costs may force researchers to limit their studies to a single organism, although comparing gene expression in different species would yield valuable information. Results: We have developed a method, implemented in the software DualPrime, that reduces the number of primers required to amplify the genes of two different genomes. The software identifies regions of high sequence similarity, and from these regions selects PCR primers shared between the genomes, such that either one or, preferentially, both primers in a given PCR can be used for amplification from both genomes. To assure high microarray probe specificity, the software selects primer pairs that generate products of low sequence similarity to other genes within the same genome. We used the software to design PCR primers for 2182 and 1960 genes from the hyperthermophilic archaea Sulfolobus solfataricus and Sulfolobus acidocaldarius, respectively. Primer pairs were shared among 705 pairs of genes, and single primers were shared among 1184 pairs of genes, resulting in a saving of 31% compared to using only unique primers. We also present an alternative primer design method, in which each gene shares primers with two different genes of the other genome, enabling further savings.
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| 3. |
- 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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| 4. |
- 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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| 5. |
- Arvestad, Lars, et al.
(författare)
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Bayesian gene/species tree reconciliation and orthology analysis using MCMC
- 2003
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Ingår i: Bioinformatics. - : Oxford Journals. - 1367-4803 .- 1367-4811. ; 19, s. i7-i15
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Tidskriftsartikel (refereegranskat)abstract
- Motivation: Comparative genomics in general and orthology analysis in particular are becoming increasingly important parts of gene function prediction. Previously, orthology analysis and reconciliation has been performed only with respect to the parsimony model. This discards many plausible solutions and sometimes precludes finding the correct one. In many other areas in bioinformatics probabilistic models have proven to be both more realistic and powerful than parsimony models. For instance, they allow for assessing solution reliability and consideration of alternative solutions in a uniform way. There is also an added benefit in making model assumptions explicit and therefore making model comparisons possible. For orthology analysis, uncertainty has recently been addressed using parsimonious reconciliation combined with bootstrap techniques. However, until now no probabilistic methods have been available. Results: We introduce a probabilistic gene evolution model based on a birth-death process in which a gene tree evolves ‘inside’ a species tree. Based on this model, we develop a tool with the capacity to perform practical orthology analysis, based on Fitch’s original definition, and more generally for reconciling pairs of gene and species trees. Our gene evolution model is biologically sound (Nei et al., 1997) and intuitively attractive. We develop a Bayesian analysis based on MCMC which facilitates approximation of an a posteriori distribution for reconciliations. That is, we can find the most probable reconciliations and estimate the probability of any reconciliation, given the observed gene tree. This also gives a way to estimate the probability that a pair of genes are orthologs. The main algorithmic contribution presented here consists of an algorithm for computing the likelihood of a given reconciliation. To the best of our knowledge, this is the first successful introduction of this type of probabilistic methods, which flourish in phylogeny analysis, into reconciliation and orthology analysis. The MCMC algorithm has been implemented and, although not yet being in its final form, tests show that it performs very well on synthetic as well as biological data. Using standard correspondences, our results carry over to allele trees as well as biogeography.
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| 6. |
- 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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| 7. |
- 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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| 8. |
- Birin, H., et al.
(författare)
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Inferring horizontal transfers in the presence of rearrangements by the minimum evolution criterion
- 2008
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811. ; 24:6, s. 826-832
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Tidskriftsartikel (refereegranskat)abstract
- Motivation: The evolution of viruses is very rapid and in addition to local point mutations (insertion, deletion, substitution) it also includes frequent recombinations, genome rearrangements and horizontal transfer of genetic materials (HGTS). Evolutionary analysis of viral sequences is therefore a complicated matter for two main reasons: First, due to HGTs and recombinations, the right model of evolution is a network and not a tree. Second, due to genome rearrangements, an alignment of the input sequences is not guaranteed. These facts encourage developing methods for inferring phylogenetic networks that do not require aligned sequences as input. Results: In this work, we present the first computational approach which deals with both genome rearrangements and horizontal gene transfers and does not require a multiple alignment as input. We formalize a new set of computational problems which involve analyzing such complex models of evolution. We investigate their computational complexity, and devise algorithms for solving them. Moreover, we demonstrate the viability of our methods on several synthetic datasets as well as four biological datasets.
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| 9. |
- Bonet, Jose, et al.
(författare)
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DeepMP : a deep learning tool to detect DNA base modifications on Nanopore sequencing data
- 2022
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811. ; 38:5, s. 1235-1243
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Tidskriftsartikel (refereegranskat)abstract
- Motivation: DNA methylation plays a key role in a variety of biological processes. Recently, Nanopore long-read sequencing has enabled direct detection of these modifications. As a consequence, a range of computational methods have been developed to exploit Nanopore data for methylation detection. However, current approaches rely on a human-defined threshold to detect the methylation status of a genomic position and are not optimized to detect sites methylated at low frequency. Furthermore, most methods use either the Nanopore signals or the basecalling errors as the model input and do not take advantage of their combination. Results: Here, we present DeepMP, a convolutional neural network-based model that takes information from Nanopore signals and basecalling errors to detect whether a given motif in a read is methylated or not. Besides, DeepMP introduces a threshold-free position modification calling model sensitive to sites methylated at low frequency across cells. We comprehensively benchmarked DeepMP against state-of-the-art methods on Escherichia coli, human and pUC19 datasets. DeepMP outperforms current approaches at read-based and position-based methylation detection across sites methylated at different frequencies in the three datasets. Availability and implementation: DeepMP is implemented and freely available under MIT license at https://github.
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| 10. |
- 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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