| 1. |
- Aebersold, Ruedi, et al.
(författare)
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How many human proteoforms are there?
- 2018
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Ingår i: Nature Chemical Biology. - : NATURE PUBLISHING GROUP. - 1552-4450 .- 1552-4469. ; 14:3, s. 206-214
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Tidskriftsartikel (refereegranskat)abstract
- Despite decades of accumulated knowledge about proteins and their post-translational modifications (PTMs), numerous questions remain regarding their molecular composition and biological function. One of the most fundamental queries is the extent to which the combinations of DNA-, RNA-and PTM-level variations explode the complexity of the human proteome. Here, we outline what we know from current databases and measurement strategies including mass spectrometry-based proteomics. In doing so, we examine prevailing notions about the number of modifications displayed on human proteins and how they combine to generate the protein diversity underlying health and disease. We frame central issues regarding determination of protein-level variation and PTMs, including some paradoxes present in the field today. We use this framework to assess existing data and to ask the question, "How many distinct primary structures of proteins (proteoforms) are created from the 20,300 human genes?" We also explore prospects for improving measurements to better regularize protein-level biology and efficiently associate PTMs to function and phenotype.
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| 2. |
- 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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| 3. |
- Plubell, Deanna L., et al.
(författare)
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Putting Humpty Dumpty Back Together Again : What Does Protein Quantification Mean in Bottom-Up Proteomics? br
- 2022
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Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 21:4, s. 891-898
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Tidskriftsartikel (refereegranskat)abstract
- Bottom-up proteomics provides peptide measurements and has beeninvaluable for moving proteomics into large-scale analyses. Commonly, a singlequantitative value is reported for each protein-coding gene by aggregating peptidequantities into protein groups following protein inference or parsimony. However, giventhe complexity of both RNA splicing and post-translational protein modification, it isoverly simplistic to assume that all peptides that map to a singular protein-coding genewill demonstrate the same quantitative response. By assuming that all peptides from aprotein-coding sequence are representative of the same protein, we may miss thediscovery of important biological differences. To capture the contributions of existingproteoforms, we need to reconsider the practice of aggregating protein values to a singlequantity per protein-coding gene.
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| 4. |
- Burnum-Johnson, Kristin E., et al.
(författare)
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New Views of Old Proteins : Clarifying the Enigmatic Proteome
- 2022
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Ingår i: Molecular & Cellular Proteomics. - : Elsevier BV. - 1535-9476 .- 1535-9484. ; 21:7
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Tidskriftsartikel (refereegranskat)abstract
- All human diseases involve proteins, yet our current tools to characterize and quantify them are limited. To better elucidate proteins across space, time, and molecular composition, we provide a >10 years of projection for technologies to meet the challenges that protein biology presents. With a broad perspective, we discuss grand opportunities to transition the science of proteomics into a more propulsive enterprise. Extrapolating recent trends, we describe a next generation of approaches to define, quantify, and visualize the multiple dimensions of the proteome, thereby transforming our understanding and interactions with human disease in the coming decade.
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| 5. |
- Käll, Lukas, 1969-, et al.
(författare)
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Assigning significance to peptides identified by tandem mass spectrometry using decoy databases
- 2008
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Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 7:1, s. 29-34
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Tidskriftsartikel (refereegranskat)abstract
- Automated methods for assigning peptides to observed tandem mass spectra typically return a list of peptide-spectrum matches, ranked according to an arbitrary score. In this article, we describe methods for converting these arbitrary scores into more useful statistical significance measures. These methods employ a decoy sequence database as a model of the null hypothesis, and use false discovery rate (FDR) analysis to correct for multiple testing. We first describe a simple FDR inference method and then describe how estimating and taking into account the percentage of incorrectly identified spectra in the entire data set can lead to increased statistical power.
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| 6. |
- Käll, Lukas, 1969-, et al.
(författare)
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Posterior error probabilities and false discovery rates : two sides of the same coin
- 2008
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Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 7:1, s. 40-44
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Tidskriftsartikel (refereegranskat)abstract
- A variety of methods have been described in the literature for assigning statistical significance to peptides identified via tandem mass spectrometry. Here, we explain how two types of scores, the q-value and the posterior error probability, are related and complementary to one another.
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| 7. |
- Käll, Lukas, et al.
(författare)
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Semi-supervised learning for peptide identification from shotgun proteomics datasets
- 2007
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Ingår i: Nature Methods. - : Springer Science and Business Media LLC. - 1548-7091 .- 1548-7105. ; 4:11, s. 923-925
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Tidskriftsartikel (refereegranskat)abstract
- Shotgun proteomics uses liquid chromatography-tandem mass spectrometry to identify proteins in complex biological samples. We describe an algorithm, called Percolator, for improving the rate of confident peptide identifications from a collection of tandem mass spectra. Percolator uses semi-supervised machine learning to discriminate between correct and decoy spectrum identifications, correctly assigning peptides to 17% more spectra from a tryptic Saccharomyces cerevisiae dataset, and up to 77% more spectra from non-tryptic digests, relative to a fully supervised approach.
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| 8. |
- Merrihew, Gennifer E., et al.
(författare)
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Use of shotgun proteomics for the identification, confirmation, and correction of C. elegans gene annotations
- 2008
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Ingår i: Genome Research. - : Cold Spring Harbor Laboratory. - 1088-9051 .- 1549-5469. ; 18:10, s. 1660-1669
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Tidskriftsartikel (refereegranskat)abstract
- We describe a general mass spectrometry-based approach for gene annotation of any organism and demonstrate its effectiveness using the nematode Caenorhabditis elegans. We detected 6779 C. elegans proteins (67,047 peptides), including 384 that, although annotated in WormBase WS150, lacked cDNA or other prior experimental support. We also identified 429 new coding sequences that were unannotated in WS150. Nearly half (192/429) of the new coding sequences were confirmed with RT-PCR data. Thirty-three (approximately 8%) of the new coding sequences had been predicted to be pseudogenes, 151 (approximately 35%) reveal apparent errors in gene models, and 245 (57%) appear to be novel genes. In addition, we verified 6010 exon-exon splice junctions within existing WormBase gene models. Our work confirms that mass spectrometry is a powerful experimental tool for annotating sequenced genomes. In addition, the collection of identified peptides should facilitate future proteomics experiments targeted at specific proteins of interest.
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| 9. |
- Park, Christopher Y., et al.
(författare)
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Rapid and accurate peptide identification from tandem mass spectra
- 2008
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Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 7:7, s. 3022-3027
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Tidskriftsartikel (refereegranskat)abstract
- Mass spectrometry, the core technology in the field of proteomics, promises to enable scientists to identify and quantify the entire complement of proteins in a complex biological sample. Currently, the primary bottleneck in this type of experiment is computational. Existing algorithms for interpreting mass spectra are slow and fail to identify a large proportion of the given spectra. We describe a database search program called Crux that reimplements and extends the widely used database search program Sequest. For speed, Crux uses a peptide indexing scheme to rapidly retrieve candidate peptides for a given spectrum. For each peptide in the target database, Crux generates shuffled decoy peptides on the fly, providing a good null model and, hence, accurate false discovery rate estimates. Crux also implements two recently described postprocessing methods: a p value calculation based upon fitting a Weibull distribution to the observed scores, and a semisupervised method that learns to discriminate between target and decoy matches. Both methods significantly improve the overall rate of peptide identification. Crux is implemented in C and is distributed with source code freely to noncommercial users.
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| 10. |
- The, Matthew, et al.
(författare)
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Fast and accurate protein false discovery rates on large-scale proteomics data sets with Percolator 3.0
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Percolator is a widely used software tool that increases yield in shotgun proteomics experiments and assigns reliable statistical confidence measures, such as q values and posterior error probabilities, to peptides and peptide-spectrum matches (PSMs) from such experiments. Percolator's processing speed has been sufficient for typical data sets consisting of hundreds of thousands of PSMs. With our new scalable approach, we can now also analyze millions of PSMs in a matter of minutes on a commodity computer. Furthermore,with the increasing awareness for the need for reliable statistics on the protein level, we compared several easy-to-understand protein inference methods and implemented the best-performing method - grouping proteins by their corresponding sets of theoretical peptides and then considering only the best-scoring peptide for each protein - in the Percolator package. We used Percolator 3.0 to analyze the data from a recent study of the draft human proteome containing 25 million spectra (PM:24870542).The source code and Ubuntu, Windows, MacOS and Fedora binary packages are available from http://percolator.ms/ under an Apache 2.0 license.
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