| 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. |
- Ashwood, C., et al.
(författare)
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Proceedings of the EuBIC-MS 2020 Developers’ Meeting
- 2020
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Ingår i: EuPA Open Proteomics. - : Elsevier B.V.. - 2212-9685. ; 24, s. 1-6
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Tidskriftsartikel (refereegranskat)abstract
- The 2020 European Bioinformatics Community for Mass Spectrometry (EuBIC-MS) Developers’ meeting was held from January 13th to January 17th 2020 in Nyborg, Denmark. Among the participants were scientists as well as developers working in the field of computational mass spectrometry (MS) and proteomics. The 4-day program was split between introductory keynote lectures and parallel hackathon sessions. During the latter, the participants developed bioinformatics tools and resources addressing outstanding needs in the community. The hackathons allowed less experienced participants to learn from more advanced computational MS experts, and to actively contribute to highly relevant research projects. We successfully produced several new tools that will be useful to the proteomics community by improving data analysis as well as facilitating future research. All keynote recordings are available on https://doi.org/10.5281/zenodo.3890181.
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| 3. |
- Chalabi, Morteza H., et al.
(författare)
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CoExpresso : assess the quantitative behavior of protein complexes in human cells
- 2019
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Ingår i: BMC Bioinformatics. - : BMC. - 1471-2105. ; 20
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundTranslational and post-translational control mechanisms in the cell result in widely observable differences between measured gene transcription and protein abundances. Herein, protein complexes are among the most tightly controlled entities by selective degradation of their individual proteins. They furthermore act as control hubs that regulate highly important processes in the cell and exhibit a high functional diversity due to their ability to change their composition and their structure. Better understanding and prediction of these functional states demands methods for the characterization of complex composition, behavior, and abundance across multiple cell states. Mass spectrometry provides an unbiased approach to directly determine protein abundances across different cell populations and thus to profile a comprehensive abundance map of proteins.ResultsWe provide a tool to investigate the behavior of protein subunits in known complexes by comparing their abundance profiles across up to 140 cell types available in ProteomicsDB. Thorough assessment of different randomization methods and statistical scoring algorithms allows determining the significance of concurrent profiles within a complex, therefore providing insights into the conservation of their composition across human cell types as well as the identification of intrinsic structures in complex behavior to determine which proteins orchestrate complex function. This analysis can be extended to investigate common profiles within arbitrary protein groups. CoExpresso can be accessed through http://computproteomics.bmb.sdu.dk/Apps/CoExpresso.ConclusionsWith the CoExpresso web service, we offer a potent scoring scheme to assess proteins for their co-regulation and thereby offer insight into their potential for forming functional groups like protein complexes.
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| 4. |
- Delgado, Luis Fernando
(författare)
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Bioinformatics for microbiome analysis
- 2024
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Marine ecosystems harbour a vast microbial diversity which play a crucial role in ecosystemfunctioning. Advancements in DNA sequencing technologies have transformed our ability to analyse microbial populations comprehensively. Metagenomic sequencing has emerged as a pivotal tool for characterising microbial communities across various environments. Bioinformatics, an interdisciplinary field, facilitates the analysis and interpretation of large biological datasets, including microbiome data.This thesis aims to enhance bioinformatics approaches for analysing marine microbiomes. It comprises four papers covering bioinformatic developments and genomic data analysis across multiple topics, including metagenomics, pangenomics, comparative genomics and population genomics:Paper I evaluated three assembly strategies for constructing gene catalogues from metagenomic samples: individual sample assembly with gene clustering, co-assembly of all samples, and a new hybrid approach, mix assembly. The efficacy of the mix-assembly approach was highlighted for maximising information extraction from metagenomic samples, offering opportunities for further exploration in microbial ecology and environmental genomics.Using the mix-assembly approach, we conducted a comprehensive analysis of 124 metagenomic samples sourced from the Baltic Sea, resulting in the refinement of the Baltic Sea Gene Set (BAGS v1.1), which now encompasses 66.53 million genes annotated for both functionality and taxonomy. In Paper II, we introduced an open-access initiative that provided the mix-assembly pipeline code. We also developed the BAGS-Shiny web application to facilitate user interaction with this extensive gene catalogue.Paper III focused on whole-genome sequencing and assembly of 82 environmental V. vulnificus strains from the Baltic Sea, enabling comprehensive comparative genomic analysis. I developed the PhyloBOTL pipeline, which uses a phylogeny-based approach to identify genes associated with pathogenicity. Comparative genomics of 208 clinical isolates and 199 environmental isolates revealed 58 enriched orthologs in pathogenic strains, including known virulence factors and novel genes. Potential biomarkers for pathogenic V. vulnificus were identified, and primers suitable for PCR-based environmental monitoring were designed (in silico).In Paper IV population genomics analysis was carried out, using the Input_Pogenom pipeline and POGENOM tool, to explore intraspecific biogeographical patterns. Geographical barriers were found to significantly influence aquatic bacteria distribution, with greater genetic differentiation observed between Baltic and Caspian seas than within the Baltic Sea's salinity gradient.
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| 5. |
- Deutsch, Eric W., et al.
(författare)
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Expanding the Use of Spectral Libraries in Proteomics
- 2018
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Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 17:12, s. 4051-4060
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Tidskriftsartikel (refereegranskat)abstract
- The 2017 Dagstuhl Seminar on Computational Proteomics provided an opportunity for a broad discussion on ABSTRACT: The 2017 Dagstuhl Seminar on Computational the current state and future directions of the generation and use of peptide tandem mass spectrometry spectral libraries. Their use in proteomics is growing slowly, but there are multiple challenges in the field that must be addressed to further increase the adoption of spectral libraries and related techniques. The primary bottlenecks are the paucity of high quality and comprehensive libraries and the general difficulty of adopting spectral library searching into existing workflows. There are several existing spectral library formats, but none captures a satisfactory level of metadata; therefore, a logical next improvement is to design a more advanced, Proteomics Standards Initiative-approved spectral library format that can encode all of the desired metadata. The group discussed a series of metadata requirements organized into three designations of completeness or quality, tentatively dubbed bronze, silver, and gold. The metadata can be organized at four different levels of granularity: at the collection (library) level, at the individual entry (peptide ion) level, at the peak (fragment ion) level, and at the peak annotation level. Strategies for encoding mass modifications in a consistent manner and the requirement for encoding high-quality and commonly seen but as-yet-unidentified spectra were discussed. The group also discussed related topics, including strategies for comparing two spectra, techniques for generating representative spectra for a library, approaches for selection of optimal signature ions for targeted workflows, and issues surrounding the merging of two or more libraries into one. We present here a review of this field and the challenges that the community must address in order to accelerate the adoption of spectral libraries in routine analysis of proteomics datasets.
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| 6. |
- 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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| 7. |
- Ekvall, Markus, et al.
(författare)
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Prosit Transformer : A transformer for Prediction of MS2 Spectrum Intensities
- 2022
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Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 21:5, s. 1359-1364
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Tidskriftsartikel (refereegranskat)abstract
- Machine learning has been an integral part of interpreting data from mass spectrometry (MS)-based proteomics for a long time. Relatively recently, a machine-learning structure appeared successful in other areas of bioinformatics, Transformers. Furthermore, the implementation of Transformers within bioinformatics has become relatively convenient due to transfer learning, i.e., adapting a network trained for other tasks to new functionality. Transfer learning makes these relatively large networks more accessible as it generally requires less data, and the training time improves substantially. We implemented a Transformer based on the pretrained model TAPE to predict MS2 intensities. TAPE is a general model trained to predict missing residues from protein sequences. Despite being trained for a different task, we could modify its behavior by adding a prediction head at the end of the TAPE model and fine-tune it using the spectrum intensity from the training set to the well-known predictor Prosit. We demonstrate that the predictor, which we call Prosit Transformer, outperforms the recurrent neural-network-based predictor Prosit, increasing the median angular similarity on its holdout set from 0.908 to 0.929. We believe that Transformers will significantly increase prediction accuracy for other types of predictions within MS-based proteomics.
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| 8. |
- Freestone, Jack, et al.
(författare)
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Semi-supervised Learning While Controlling the FDR with an Application to Tandem Mass Spectrometry Analysis
- 2024
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Ingår i: Research in Computational Molecular Biology - 28th Annual International Conference, RECOMB 2024, Proceedings. - : Springer Science and Business Media Deutschland GmbH. ; , s. 448-453
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Konferensbidrag (refereegranskat)abstract
- Canonical procedures to control the false discovery rate (FDR) among the list of putative discoveries rely on our ability to compute informative p-values. Competition-based approach offers a fairly novel and increasingly popular alternative when computing such p-values is impractical. The popularity of this approach stems from its wide applicability: instead of computing p-values, which requires knowing the entire null distribution for each null hypothesis, a competition-based approach only requires a single draw from each such null distribution. This drawn example is known as a “decoy” in the mass spectrometry community (which was the first to adopt the competition approach) or as a “knockoff” in the statistics community. The decoy is competed with the original observation so that only the higher scoring of the two is retained. The number of decoy wins is subsequently used to estimate and control the FDR among the target wins. In this paper we offer a novel method to extend the competition-based approach to control the FDR while taking advantage of side information, i.e., additional features that can help us distinguish between correct and incorrect discoveries. Our motivation comes from the problem of peptide detection in tandem mass spectrometry proteomics data. Specifically, we recently showed that a popular mass spectrometry analysis software tool, Percolator, can apparently fail to control the FDR. We address this problem here by developing a general protocol called “RESET” that can take advantage of the additional features, such as the ones Percolator uses, while still theoretically and empirically controlling the FDR.
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| 9. |
- Granholm, Viktor, 1986-, et al.
(författare)
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On Using Samples of Known Protein Content to Assess the Statistical Calibration of Scores Assigned to Peptide-Spectrum Matches in Shotgun Proteomics
- 2011
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Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 10:5, s. 2671-2678
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Tidskriftsartikel (refereegranskat)abstract
- In shotgun proteomics, the quality of a hypothesized match between an observed spectrum and a peptide sequence is quantified by a score function. Because the score function lies at the heart of any peptide identification pipeline, this function greatly affects the final results of a proteomics assay. Consequently, valid statistical methods for assessing the quality of a given score function are extremely important. Previously, several research groups have used samples of known protein composition to assess the quality of a given score function. We demonstrate that this approach is problematic, because the outcome can depend on factors other than the score function itself. We then propose an alternative use of the same type of data to validate a score function. The central idea of our approach is that database matches that are not explained by any protein in the purified sample comprise a robust representation of incorrect matches. We apply our alternative assessment scheme to several commonly used score functions, and we show that our approach generates a reproducible measure of the calibration of a given peptide identification method. Furthermore, we show how our quality test can be useful in the development of novel score functions.
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| 10. |
- Granholm, Viktor, et al.
(författare)
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Quality assessments of peptide-spectrum matches in shotgun proteomics
- 2011
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Ingår i: Proteomics. - : Wiley. - 1615-9853 .- 1615-9861. ; 11:6, s. 1086-1093
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Tidskriftsartikel (refereegranskat)abstract
- The peptide identification process in shotgun proteomics is most frequently solved with search engines. Such search engines assign scores that reflect similarity between the measured fragmentation spectrum and the theoretical spectra of the peptides of a given database. However, the scores from most search engines do not have a direct statistical interpretation. To understand and make use of the significance of peptide identifications, one must thus be familiar with some statistical concepts. Here, we discuss different statistical scores used to show the confidence of an identification and a set of methods to estimate these scores. We also describe the variance of statistical scores and imperfections of scoring functions of peptide-spectrum matches.
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