| 1. |
- Adhikari, Subash, et al.
(författare)
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A high-stringency blueprint of the human proteome
- 2020
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 11:1
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Forskningsöversikt (refereegranskat)abstract
- The Human Proteome Organization (HUPO) launched the Human Proteome Project (HPP) in 2010, creating an international framework for global collaboration, data sharing, quality assurance and enhancing accurate annotation of the genome-encoded proteome. During the subsequent decade, the HPP established collaborations, developed guidelines and metrics, and undertook reanalysis of previously deposited community data, continuously increasing the coverage of the human proteome. On the occasion of the HPP’s tenth anniversary, we here report a 90.4% complete high-stringency human proteome blueprint. This knowledge is essential for discerning molecular processes in health and disease, as we demonstrate by highlighting potential roles the human proteome plays in our understanding, diagnosis and treatment of cancers, cardiovascular and infectious diseases.
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| 2. |
- Jagtap, Pratik D., et al.
(författare)
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The Association of Biomolecular Resource Facilities Proteome Informatics Research Group Study on Metaproteomics (iPRG-2020)
- 2023
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Ingår i: Journal of biomolecular techniques : JBT. - : Association of Biomolecular Resource Facilities. - 1943-4731. ; 34:3
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Tidskriftsartikel (refereegranskat)abstract
- Metaproteomics research using mass spectrometry data has emerged as a powerful strategy to understand the mechanisms underlying microbiome dynamics and the interaction of microbiomes with their immediate environment. Recent advances in sample preparation, data acquisition, and bioinformatics workflows have greatly contributed to progress in this field. In 2020, the Association of Biomolecular Research Facilities Proteome Informatics Research Group launched a collaborative study to assess the bioinformatics options available for metaproteomics research. The study was conducted in 2 phases. In the first phase, participants were provided with mass spectrometry data files and were asked to identify the taxonomic composition and relative taxa abundances in the samples without supplying any protein sequence databases. The most challenging question asked of the participants was to postulate the nature of any biological phenomena that may have taken place in the samples, such as interactions among taxonomic species. In the second phase, participants were provided a protein sequence database composed of the species present in the sample and were asked to answer the same set of questions as for phase 1. In this report, we summarize the data processing methods and tools used by participants, including database searching and software tools used for taxonomic and functional analysis. This study provides insights into the status of metaproteomics bioinformatics in participating laboratories and core facilities.
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| 3. |
- Neely, Benjamin A., et al.
(författare)
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Toward an Integrated Machine Learning Model of a Proteomics Experiment
- 2023
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Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 22:3, s. 681-696
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Forskningsöversikt (refereegranskat)abstract
- In recent years machine learning has made extensive progress in modeling many aspects of mass spectrometry data. We brought together proteomics data generators, repository managers, and machine learning experts in a workshop with the goals to evaluate and explore machine learning applications for realistic modeling of data from multidimensional mass spectrometry-based proteomics analysis of any sample or organism. Following this sample-to-data roadmap helped identify knowledge gaps and define needs. Being able to generate bespoke and realistic synthetic data has legitimate and important uses in system suitability, method development, and algorithm benchmarking, while also posing critical ethical questions. The interdisciplinary nature of the workshop informed discussions of what is currently possible and future opportunities and challenges. In the following perspective we summarize these discussions in the hope of conveying our excitement about the potential of machine learning in proteomics and to inspire future research.
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| 4. |
- Palmblad, Magnus, et al.
(författare)
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Capillary electrophoresis - A bibliometric analysis
- 2023
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Ingår i: TrAC. Trends in analytical chemistry. - : Elsevier. - 0165-9936 .- 1879-3142. ; 159
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Forskningsöversikt (refereegranskat)abstract
- We have explored the history of the field of capillary electrophoresis using bibliometric methods. The analysis shows that 416 prolific researchers are connected in a single, large, co-authorship network based on publications on capillary electrophoresis between 1980 and 2021, with a few pioneers having remained active throughout much of this time period. Looking at research topics revealed electro-chemistry, sensors, nanotechnology and metabolomics as 'hot' topics, with fundamental method development being more 'mature', and reveal that capillary electrophoresis technology have matured over a 30-year time period, with research efforts moving from separations to quantitative measurements to biomedical applications. The citation patterns showed the strongest coupling between journals of similar scope. Interactive versions of the bibliometric network visualizations are available on-line at https://tinyurl.com/2z7q7wcx (researcher co-authorship network), https://tinyurl.com/2jmhsgxx (research topic network) and https://tinyurl.com/2lnfzzgn (journal bibliographic coupling citation network).(c) 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
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| 5. |
- Palmblad, Magnus, et al.
(författare)
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Interpretation of the DOME Recommendations for Machine Learning in Proteomics and Metabolomics
- 2022
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Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 21:4, s. 1204-1207
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Tidskriftsartikel (refereegranskat)abstract
- Machine learning is increasingly applied in proteomics and metabolomics to predict molecular structure, function, and physicochemical properties, including behavior in chromatography, ion mobility, and tandem mass spectrometry. These must be described in sufficient detail to apply or evaluate the performance of trained models. Here we look at and interpret the recently published and general DOME (Data, Optimization, Model, Evaluation) recommendations for conducting and reporting on machine learning in the specific context of proteomics and metabolomics.
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| 6. |
- Palmblad, Magnus, et al.
(författare)
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Semantic Annotation of Experimental Methods in Analytical Chemistry
- 2022
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Ingår i: Analytical Chemistry. - : American Chemical Society (ACS). - 0003-2700 .- 1520-6882. ; 94:44, s. 15464-15471
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Tidskriftsartikel (refereegranskat)abstract
- A major obstacle for reusing and integrating existing data is finding the data that is most relevant in a given context. The primary metadata resource is the scientific literature describing the experiments that produced the data. To stimulate the development of natural language processing methods for extracting this information from articles, we have manually annotated 100 recent open access publications in Analytical Chemistry as semantic graphs. We focused on articles mentioning mass spectrometry in their experimental sections, as we are particularly interested in the topic, which is also within the domain of several ontologies and controlled vocabularies. The resulting gold standard dataset is publicly available and directly applicable to validating automated methods for retrieving this metadata from the literature. In the process, we also made a number of observations on the structure and description of experiments and open access publication in this journal.
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| 7. |
- Palmblad, Magnus, et al.
(författare)
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Text mining and computational chemistry reveal trends in applications and applicability of capillary electrophoresis
- 2023
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Ingår i: TrAC. Trends in analytical chemistry. - : Elsevier. - 0165-9936 .- 1879-3142. ; 159
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Forskningsöversikt (refereegranskat)abstract
- Capillary electrophoresis has matured into a highly sensitive and widely applied analytical method over the last forty years. Here we combine text mining and computational chemistry to paint, with very broad strokes, the applicability and trends in the scientific literature on capillary electrophoresis, simulta-neously demonstrating that this is not only possible, but reveal both expected and unexpected details of this history. All software and data are freely available on GitHub (https://github.com/ReinV/SCOPE) and OSF (https://osf.io/e56zt/).
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