ArticleJournal of proteome research2010
Improved LC-MS/MS spectral counting statistics by recovering low-scoring spectra matched to confidently identified peptide sequences.
Article in Journal of proteome research, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
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- The Extracellular Matrix: Its Composition, Function, Remodeling, and Role in Tumorigenesis.Biomimetics (Basel, Switzerland) · 2023Review
- Tailoring the expression of Xyr1 leads to efficient production of lignocellulolytic enzymes in Trichoderma reesei for improved saccharification of corncob residues.Biotechnology for biofuels and bioproducts · 2022Article
- Quantitative Mass Spectrometry-Based Proteomics: An Overview.Methods in molecular biology (Clifton, N.J.) · 2021Review
- A highly efficient protein degradation system in Bacillus sp. CN2: a functional-degradomics study.Applied microbiology and biotechnology · 2021Article
- Novel Synergistic Mechanism for Lignocellulose Degradation by a Thermophilic Filamentous Fungus and a Thermophilic Actinobacterium Based on Functional Proteomics.Frontiers in microbiology · 2020Article
- Insights into the cellulose degradation mechanism of the thermophilic fungusBiotechnology for biofuels · 2020Article
- Quantitative proteomic analysis of trypsin-treated extracellular vesicles to identify the real-vesicular proteins.Journal of extracellular vesicles · 2020Article
- A novel thermostable chitinolytic machinery ofBiotechnology for biofuels · 2019Article
- Quality Assessments of Long-Term Quantitative Proteomic Analysis of Breast Cancer Xenograft Tissues.Journal of proteome research · 2017Article
- The genomic landscape of core-binding factor acute myeloid leukemias.Nature genetics · 2016Article
- Quantitative Analysis of Human Pluripotency and Neural Specification by In-Depth (Phospho)Proteomic Profiling.Stem cell reports · 2016Article
- A multi-model statistical approach for proteomic spectral count quantitation.Journal of proteomics · 2016Article
- SerpinB1 Promotes Pancreatic β Cell Proliferation.Cell metabolism · 2016Article
- Removal of SDS from biological protein digests for proteomic analysis by mass spectrometry.Proteome science · 2016Article
- Compensatory Islet Response to Insulin Resistance Revealed by Quantitative Proteomics.Journal of proteome research · 2015Article
- Highly multiplexed and reproducible ion-current-based strategy for large-scale quantitative proteomics and the application to protein expression dynamics induced by methylprednisolone in 60 rats.Analytical chemistry · 2014Article
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9 authors.
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Abstract
Spectral counting has become a popular method for LC-MS/MS based proteome quantification; however, this methodology is often not reliable when proteins are identified by a small number of spectra. Here, we present a simple strategy to improve spectral counting based quantification for low-abundance proteins by recovering low-quality or low-scoring spectra for confidently identified peptides. In this approach, stringent data filtering criteria were initially applied to achieve confident peptide identifications with low false discovery rate (e.g., < 1% at peptide level) after LC-MS/MS analysis and database search by SEQUEST. Then, all low-scoring MS/MS spectra that matched to this set of confidently identified peptides were recovered, leading to more than 20% increase of total identified spectra. The validity of these recovered spectra was assessed by the parent ion mass measurement error distribution, retention time distribution, and by comparing the individual low score and high score spectra that correspond to the same peptides. The results support that the recovered low-scoring spectra have similar confidence levels in peptide identifications as the spectra passing the initial stringent filter. The application of this strategy of recovering low-scoring spectra significantly improved the spectral count quantification statistics for low-abundance proteins, as illustrated in the identification of mouse brain region specific proteins.
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Registered trials
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