| 1. |
- McIlwain, Sean, et al.
(författare)
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Crux : Rapid Open Source Protein Tandem Mass Spectrometry Analysis
- 2014
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Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 13:10, s. 4488-4491
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Tidskriftsartikel (refereegranskat)abstract
- Efficiently and accurately analyzing big protein tandem mass spectrometry data sets requires robust software that incorporates state-of-the-art computational, machine learning, and statistical methods. The Crux mass spectrometry analysis software toolkit (http://cruxtoolkit.sourceforge.net) is an open source project that aims to provide users with a cross-platform suite of analysis tools for interpreting protein mass spectrometry data.
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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. |
- Moruz, Luminita, 1982-, et al.
(författare)
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Mass Fingerprinting of Complex Mixtures : Protein Inference from High-Resolution Peptide Masses and Predicted Retention Times
- 2013
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Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 12:12, s. 5730-5741
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Tidskriftsartikel (refereegranskat)abstract
- In typical shotgun experiments, the mass spectrometer records the masses of a large set of ionized analytes but fragments only a fraction of them. In the subsequent analyses, normally only the fragmented ions are used to compile a set of peptide identifications, while the unfragmented ones are disregarded. In this work, we show how the unfragmented ions, here denoted MS1-features, can be used to increase the confidence of the proteins identified in shotgun experiments. Specifically, we propose the usage of in silico mass tags, where the observed MS1-features are matched against de novo predicted masses and retention times for all peptides derived from a sequence database. We present a statistical model to assign protein-level probabilities based on the MS1-features and combine this data with the fragmentation spectra. Our approach was evaluated for two triplicate data sets from yeast and human, respectively, leading to up to 7% more protein identifications at a fixed protein-level false discovery rate of 1%. The additional protein identifications were validated both in the context of the mass spectrometry data and by examining their estimated transcript levels generated using RNA-Seq. The proposed method is reproducible, straightforward to apply, and can even be used to reanalyze and increase the yield of existing data sets.
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| 4. |
- Serang, Oliver, et al.
(författare)
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Recognizing Uncertainty Increases Robustness and Reproducibility of Mass Spectrometry-based Protein Inferences
- 2012
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Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 11:12, s. 5586-5591
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Tidskriftsartikel (refereegranskat)abstract
- Parsimony and protein grouping are widely employed to enforce economy in the number of identified proteins, with the goal of increasing the quality and reliability of protein identifications; however, in a counterintuitive manner, parsimony and protein grouping may actually decrease the reproducibility and interpretability of protein identifications. We present a simple illustration demonstrating ways in which parsimony and protein grouping may lower the reproducibility or interpretability of results. We then provide an example of a data set where a probabilistic method increases the reproducibility and interpretability of identifications made on replicate analyses of Human Du145 prostate cancer cell lines.
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| 5. |
- The, Matthew, et al.
(författare)
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A Protein Standard That Emulates Homology for the Characterization of Protein Inference Algorithms
- 2018
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Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 17:5, s. 1879-1886
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Tidskriftsartikel (refereegranskat)abstract
- A natural way to benchmark the performance of an analytical experimental setup is to use samples of known measured analytes are peptides and not the actual proteins one of the inherent problems of interpreting data is that the composition and see to what degree one can correctly infer the content of such a sample from the data. For shotgun proteomics, themselves. As some proteins share proteolytic peptides, there might be more than one possible causative set of proteins resulting in a given set of peptides and there is a need for mechanisms that infer proteins from lists of detected peptides. A weakness of commercially available samples of known content is that they consist of proteins that are deliberately selected for producing tryptic peptides that are unique to a single protein. Unfortunately, such samples do not expose any complications in protein inference. Hence, for a realistic benchmark of protein inference procedures, there is a need for samples of known content where the present proteins share peptides with known absent proteins. Here, we present such a standard, that is based on E. coli expressed human protein fragments. To illustrate the application of this standard, we benchmark a set of different protein inference procedures on the data. We observe that inference procedures excluding shared peptides provide more accurate estimates of errors compared to methods that include information from shared peptides, while still giving a reasonable performance in terms of the number of identified proteins. We also demonstrate that using a sample of known protein content without proteins with shared tryptic peptides can give a false sense of accuracy for many protein inference methods.
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