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Below are the article matching results from the past week:
Article Abstract:
bioRxiv [Preprint]. 2024 Mar 10:2024.03.05.583643. doi: 10.1101/2024.03.05.583643.
ABSTRACT
The FragPipe computational proteomics platform is gaining widespread popularity among the proteomics research community because of its fast processing speed and user-friendly graphical interface. Although FragPipe produces well-formatted output tables that are ready for analysis, there is still a need for an easy-to-use and user-friendly downstream statistical analysis and visualization tool. FragPipe-Analyst addresses this need by providing an R shiny web server to assist FragPipe users in conducting downstream analyses of the resulting quantitative proteomics data. It supports major quantification workflows including label-free quantification, tandem mass tags, and data-independent acquisition. FragPipe-Analyst offers a range of useful functionalities, such as various missing value imputation options, data quality control, unsupervised clustering, differential expression (DE) analysis using Limma, and gene ontology and pathway enrichment analysis using Enrichr. To support advanced analysis and customized visualizations, we also developed FragPipeAnalystR, an R package encompassing all FragPipe-Analyst functionalities that is extended to support site-specific analysis of post-translational modifications (PTMs). FragPipe-Analyst and FragPipeAnalystR are both open-source and freely available.
Keywords: FragPipe, computational proteomics, downstream analysis, statistical analysis, visualization, R shiny, label-free quantification, tandem mass tags, data-independent acquisition, post-translational modifications.
Section Title: {'statistical analysis': '## Distribution of intensity', 'visualization': '## Pathway Database'}
One-sentence Summary: FragPipe-Analyst and FragPipeAnalystR are open-source tools that assist FragPipe users in conducting downstream statistical analysis and visualization of their quantitative proteomics data, including options for missing value imputation, data quality control, differential expression analysis, and gene ontology and pathway enrichment analysis.
DOI: doi:10.1101/2024.03.05.583643
Article Abstract:
Gigascience. 2024 Jan 2;13:giae005. doi: 10.1093/gigascience/giae005.
ABSTRACT
In classic semiquantitative metabolomics, metabolite intensities are affected by biological factors and other unwanted variations. A systematic evaluation of the data processing methods is crucial to identify adequate processing procedures for a given experimental setup. Current comparative studies are mostly focused on peak area data but not on absolute concentrations. In this study, we evaluated data processing methods to produce outputs that were most similar to the corresponding absolute quantified data. We examined the data distribution characteristics, fold difference patterns between 2 metabolites, and sample variance. We used 2 metabolomic datasets from a retail milk study and a lupus nephritis cohort as test cases. When studying the impact of data normalization, transformation, scaling, and combinations of these methods, we found that the cross-contribution compensating multiple standard normalization (ccmn) method, followed by square root data transformation, was most appropriate for a well-controlled study such as the milk study dataset. Regarding the lupus nephritis cohort study, only ccmn normalization could slightly improve the data quality of the noisy cohort. Since the assessment accounted for the resemblance between processed data and the corresponding absolute quantified data, our results denote a helpful guideline for processing metabolomic datasets within a similar context (food and clinical metabolomics). Finally, we introduce Metabox 2.0, which enables thorough analysis of metabolomic data, including data processing, biomarker analysis, integrative analysis, and data interpretation. It was successfully used to process and analyze the data in this study. An online web version is available at http://metsysbio.com/metabox.
Keywords: Gigascience, metabolomics, data processing, normalization, transformation, scaling, Metabox 2.0, biomarker analysis, integrative analysis, data interpretation.
Section Title: {'metabolomics': '## Batch effects visualization', 'data processing': '## OpenMS & SIRIUS', 'normalization': '## post hoc data normalization', 'scaling': '## Linear Normalizer'}
One-sentence Summary: This text discusses the evaluation of data processing methods for metabolomic datasets and introduces Metabox 2.0 as a tool for thorough analysis, highlighting the importance of proper data processing in food and clinical metabolomics.
DOI: doi:10.1093/gigascience/giae005
Article Abstract:
J Transl Med. 2024 Feb 29;22(1):219. doi: 10.1186/s12967-024-05022-z.
ABSTRACT
BACKGROUND: The rapid emergence and global dissemination of the Omicron variant of SARS-CoV-2 have posed formidable challenges in public health. This scenario underscores the urgent need for an enhanced understanding of Omicron's pathophysiological mechanisms to guide clinical management and shape public health strategies. Our study is aimed at deciphering the intricate molecular mechanisms underlying Omicron infections, particularly focusing on the identification of specific biomarkers.
METHODS: This investigation employed a robust and systematic approach, initially encompassing 15 Omicron-infected patients and an equal number of healthy controls, followed by a validation cohort of 20 individuals per group. The study's methodological framework included a comprehensive multi-omics analysis that integrated proteomics and metabolomics, augmented by extensive bioinformatics. Proteomic exploration was conducted via an advanced Ultra-High-Performance Liquid Chromatography (UHPLC) system linked with mass spectrometry. Concurrently, metabolomic profiling was executed using an Ultra-Performance Liquid Chromatography (UPLC) system. The bioinformatics component, fundamental to this research, entailed an exhaustive analysis of protein-protein interactions, pathway enrichment, and metabolic network dynamics, utilizing state-of-the-art tools such as the STRING database and Cytoscape software, ensuring a holistic interpretation of the data.
RESULTS: Our proteomic inquiry identified eight notably dysregulated proteins (THBS1, ACTN1, ACTC1, POTEF, ACTB, TPM4, VCL, ICAM1) in individuals infected with the Omicron variant. These proteins play critical roles in essential physiological processes, especially within the coagulation cascade and hemostatic mechanisms, suggesting their significant involvement in the pathogenesis of Omicron infection. Complementing these proteomic insights, metabolomic analysis discerned 146 differentially expressed metabolites, intricately associated with pivotal metabolic pathways such as tryptophan metabolism, retinol metabolism, and steroid hormone biosynthesis. This comprehensive metabolic profiling sheds light on the systemic implications of Omicron infection, underscoring profound alterations in metabolic equilibrium.
CONCLUSIONS: This study substantially enriches our comprehension of the physiological ramifications induced by the Omicron variant, with a particular emphasis on the pivotal roles of coagulation and platelet pathways in disease pathogenesis. The discovery of these specific biomarkers illuminates their potential as critical targets for diagnostic and therapeutic strategies, providing invaluable insights for the development of tailored treatments and enhancing patient care in the dynamic context of the ongoing pandemic.
Below are the article matching results from the past week:
Article Abstract:
bioRxiv [Preprint]. 2024 Mar 10:2024.03.05.583643. doi: 10.1101/2024.03.05.583643.
ABSTRACT
The FragPipe computational proteomics platform is gaining widespread popularity among the proteomics research community because of its fast processing speed and user-friendly graphical interface. Although FragPipe produces well-formatted output tables that are ready for analysis, there is still a need for an easy-to-use and user-friendly downstream statistical analysis and visualization tool. FragPipe-Analyst addresses this need by providing an R shiny web server to assist FragPipe users in conducting downstream analyses of the resulting quantitative proteomics data. It supports major quantification workflows including label-free quantification, tandem mass tags, and data-independent acquisition. FragPipe-Analyst offers a range of useful functionalities, such as various missing value imputation options, data quality control, unsupervised clustering, differential expression (DE) analysis using Limma, and gene ontology and pathway enrichment analysis using Enrichr. To support advanced analysis and customized visualizations, we also developed FragPipeAnalystR, an R package encompassing all FragPipe-Analyst functionalities that is extended to support site-specific analysis of post-translational modifications (PTMs). FragPipe-Analyst and FragPipeAnalystR are both open-source and freely available.
PMID:38496650 | PMC:PMC10942459 | DOI:10.1101/2024.03.05.583643
Keywords: FragPipe, computational proteomics, downstream analysis, statistical analysis, visualization, R shiny, label-free quantification, tandem mass tags, data-independent acquisition, post-translational modifications.
Section Title: {'statistical analysis': '## Distribution of intensity', 'visualization': '## Pathway Database'}
One-sentence Summary: FragPipe-Analyst and FragPipeAnalystR are open-source tools that assist FragPipe users in conducting downstream statistical analysis and visualization of their quantitative proteomics data, including options for missing value imputation, data quality control, differential expression analysis, and gene ontology and pathway enrichment analysis.
DOI: doi:10.1101/2024.03.05.583643
Article Abstract:
Gigascience. 2024 Jan 2;13:giae005. doi: 10.1093/gigascience/giae005.
ABSTRACT
In classic semiquantitative metabolomics, metabolite intensities are affected by biological factors and other unwanted variations. A systematic evaluation of the data processing methods is crucial to identify adequate processing procedures for a given experimental setup. Current comparative studies are mostly focused on peak area data but not on absolute concentrations. In this study, we evaluated data processing methods to produce outputs that were most similar to the corresponding absolute quantified data. We examined the data distribution characteristics, fold difference patterns between 2 metabolites, and sample variance. We used 2 metabolomic datasets from a retail milk study and a lupus nephritis cohort as test cases. When studying the impact of data normalization, transformation, scaling, and combinations of these methods, we found that the cross-contribution compensating multiple standard normalization (ccmn) method, followed by square root data transformation, was most appropriate for a well-controlled study such as the milk study dataset. Regarding the lupus nephritis cohort study, only ccmn normalization could slightly improve the data quality of the noisy cohort. Since the assessment accounted for the resemblance between processed data and the corresponding absolute quantified data, our results denote a helpful guideline for processing metabolomic datasets within a similar context (food and clinical metabolomics). Finally, we introduce Metabox 2.0, which enables thorough analysis of metabolomic data, including data processing, biomarker analysis, integrative analysis, and data interpretation. It was successfully used to process and analyze the data in this study. An online web version is available at http://metsysbio.com/metabox.
PMID:38488666 | PMC:PMC10941642 | DOI:10.1093/gigascience/giae005
Keywords: Gigascience, metabolomics, data processing, normalization, transformation, scaling, Metabox 2.0, biomarker analysis, integrative analysis, data interpretation.
Section Title: {'metabolomics': '## Batch effects visualization', 'data processing': '## OpenMS & SIRIUS', 'normalization': '## post hoc data normalization', 'scaling': '## Linear Normalizer'}
One-sentence Summary: This text discusses the evaluation of data processing methods for metabolomic datasets and introduces Metabox 2.0 as a tool for thorough analysis, highlighting the importance of proper data processing in food and clinical metabolomics.
DOI: doi:10.1093/gigascience/giae005
Article Abstract:
J Transl Med. 2024 Feb 29;22(1):219. doi: 10.1186/s12967-024-05022-z.
ABSTRACT
BACKGROUND: The rapid emergence and global dissemination of the Omicron variant of SARS-CoV-2 have posed formidable challenges in public health. This scenario underscores the urgent need for an enhanced understanding of Omicron's pathophysiological mechanisms to guide clinical management and shape public health strategies. Our study is aimed at deciphering the intricate molecular mechanisms underlying Omicron infections, particularly focusing on the identification of specific biomarkers.
METHODS: This investigation employed a robust and systematic approach, initially encompassing 15 Omicron-infected patients and an equal number of healthy controls, followed by a validation cohort of 20 individuals per group. The study's methodological framework included a comprehensive multi-omics analysis that integrated proteomics and metabolomics, augmented by extensive bioinformatics. Proteomic exploration was conducted via an advanced Ultra-High-Performance Liquid Chromatography (UHPLC) system linked with mass spectrometry. Concurrently, metabolomic profiling was executed using an Ultra-Performance Liquid Chromatography (UPLC) system. The bioinformatics component, fundamental to this research, entailed an exhaustive analysis of protein-protein interactions, pathway enrichment, and metabolic network dynamics, utilizing state-of-the-art tools such as the STRING database and Cytoscape software, ensuring a holistic interpretation of the data.
RESULTS: Our proteomic inquiry identified eight notably dysregulated proteins (THBS1, ACTN1, ACTC1, POTEF, ACTB, TPM4, VCL, ICAM1) in individuals infected with the Omicron variant. These proteins play critical roles in essential physiological processes, especially within the coagulation cascade and hemostatic mechanisms, suggesting their significant involvement in the pathogenesis of Omicron infection. Complementing these proteomic insights, metabolomic analysis discerned 146 differentially expressed metabolites, intricately associated with pivotal metabolic pathways such as tryptophan metabolism, retinol metabolism, and steroid hormone biosynthesis. This comprehensive metabolic profiling sheds light on the systemic implications of Omicron infection, underscoring profound alterations in metabolic equilibrium.
CONCLUSIONS: This study substantially enriches our comprehension of the physiological ramifications induced by the Omicron variant, with a particular emphasis on the pivotal roles of coagulation and platelet pathways in disease pathogenesis. The discovery of these specific biomarkers illuminates their potential as critical targets for diagnostic and therapeutic strategies, providing invaluable insights for the development of tailored treatments and enhancing patient care in the dynamic context of the ongoing pandemic.
PMID:38424541 | PMC:PMC10905948 | DOI:10.1186/s12967-024-05022-z
Keywords: J Transl Med, Omicron variant, SARS-CoV-2, biomarkers, proteomics, metabolomics, bioinformatics, coagulation, platelet pathways, pandemic.
Section Title: {'biomarkers': '## Unsupervised methods', 'proteomics': '## Omics integration', 'metabolomics': '## Batch effects visualization', 'bioinformatics': '## Workflow'}
One-sentence Summary: This study investigates the molecular mechanisms and biomarkers associated with Omicron variant infections, providing insights for potential diagnostic and therapeutic strategies in the ongoing pandemic.
DOI: doi:10.1186/s12967-024-05022-z
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