Following publication of the original article [1], the authors reported an error in affiliation 5.
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http://dx.doi.org/10.1186/s12943-019-1005-3 | DOI Listing |
Proteomics
December 2024
Center for Proteomics and Metabolomics, St. Jude Children's Research Hospital, Memphis, Tennessee, USA.
Advances in high-throughput omics technologies have enabled system-wide characterization of biological samples across multiple molecular levels, such as the genome, transcriptome, and proteome. However, as sample sizes rapidly increase in large-scale multi-omics studies, sample mix-ups have become a prevalent issue, compromising data integrity and leading to erroneous conclusions. The interconnected nature of multi-omics data presents an opportunity to identify and correct these errors.
View Article and Find Full Text PDFMethods Mol Biol
October 2024
Institute of Bioinformatics, Bangalore, India.
Identifying new genes involved in virulence and drug resistance may hold the key to a better understanding of parasitic diseases. The proteogenomic profiling of various Leishmania species, the causative agents of leishmaniasis, has identified several novel genes, N- and C-terminal extensions of proteins, and corrections of existing gene models. Various virulence factors (VFs) responsible for leishmaniasis have been previously annotated through a proteogenomic approach, including the C-terminal extension of heat shock protein 70 (HSP70).
View Article and Find Full Text PDFNat Cancer
November 2024
CAS Key Laboratory of Tissue Microenvironment and Tumor, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China.
Mol Cell Proteomics
July 2024
Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany; Munich Data Science Institute, Technical University of Munich, Garching, Germany. Electronic address:
Rescoring of peptide spectrum matches originating from database search engines enabled by peptide property predictors is exceeding the performance of peptide identification from traditional database search engines. In contrast to the peptide spectrum match scores calculated by traditional database search engines, rescoring peptide spectrum matches generates scores based on comparing observed and predicted peptide properties, such as fragment ion intensities and retention times. These newly generated scores enable a more efficient discrimination between correct and incorrect peptide spectrum matches.
View Article and Find Full Text PDFNPJ Precis Oncol
May 2024
Gynecologic Cancer Center of Excellence, Gynecologic Surgery and Obstetrics, Uniformed Services University of the Health Sciences, Bethesda, MD, USA.
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