Analysis and Visualization of Quantitative Proteomics Data Using FragPipe-Analyst

Journal of Proteome Research, Год журнала: 2024, Номер unknown

Опубликована: Сен. 10, 2024

The FragPipe computational proteomics platform is gaining widespread popularity among the research community because of its fast processing speed and user-friendly graphical interface. Although produces well-formatted output tables that are ready for analysis, there still a need an easy-to-use downstream statistical analysis visualization tool. FragPipe-Analyst addresses this by providing R shiny web server to assist users in conducting analyses resulting quantitative data. It supports major quantification workflows, including label-free quantification, tandem mass tags, data-independent acquisition. offers range useful functionalities, such as various missing value imputation options, data quality control, unsupervised clustering, differential expression (DE) using Limma, gene ontology pathway enrichment Enrichr. To support advanced customized visualizations, we also developed FragPipeAnalystR, package encompassing all functionalities extended site-specific post-translational modifications (PTMs). FragPipeAnalystR both open-source freely available.

Язык: Английский

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Analysis and visualization of quantitative proteomics data using FragPipe-Analyst

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Март 11, 2024

ABSTRACT The FragPipe computational proteomics platform is gaining widespread popularity among the research community because of its fast processing speed and user-friendly graphical interface. Although produces well-formatted output tables that are ready for analysis, there still a need an easy-to-use downstream statistical analysis visualization tool. FragPipe-Analyst addresses this by providing R shiny web server to assist users in conducting analyses resulting quantitative data. It supports major quantification workflows including label-free quantification, tandem mass tags, data-independent acquisition. offers range useful functionalities, such as various missing value imputation options, data quality control, unsupervised clustering, differential expression (DE) using Limma, gene ontology pathway enrichment Enrichr. To support advanced customized visualizations, we also developed FragPipeAnalystR, package encompassing all functionalities extended site-specific post-translational modifications (PTMs). FragPipeAnalystR both open-source freely available.

Язык: Английский

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Advancements in proteogenomics for preclinical targeted cancer therapy research

Biophysics Reports, Год журнала: 2025, Номер 11(1), С. 56 - 56

Опубликована: Янв. 1, 2025

Advancements in molecular characterization technologies have accelerated targeted cancer therapy research at unprecedented resolution and dimensionality. Integrating comprehensive multi-omic profiling of a tumor, proteogenomics, marks transformative milestone for preclinical research. In this paper, we initially provided an overview proteogenomics research, spanning genomics, transcriptomics, proteomics. Subsequently, the applications were introduced examined from different perspectives, including but not limited to genetic alterations, quantifications, single-cell patterns, post-translational modification levels, subtype signatures, immune landscape. We also paid attention combined multi-omics data analysis pan-cancer analysis. This paper highlights crucial role elucidating mechanisms tumorigenesis, discovering effective therapeutic targets promising biomarkers, developing subtype-specific therapies.

Язык: Английский

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Immune-Based and Novel Therapies in Variant Histology Renal Cell Carcinomas

Cancers, Год журнала: 2025, Номер 17(2), С. 326 - 326

Опубликована: Янв. 20, 2025

Renal cell carcinoma (RCC) is a heterogeneous disease that represents the most common type of kidney cancer. The classification RCC primarily based on distinct morphological and molecular characteristics, with two broad categories: clear (ccRCC) non-clear (nccRCC). Clear predominant subtype, representing about 70–80% all cases, while subtypes collectively make up remaining 20–30%. Non-clear encompasses many histopathological variants, each unique biological clinical characteristics. Additionally, any subtype can undergo sarcomatoid dedifferentiation, which associated poor prognosis rapid progression. Recent advances in profiling have also led to identification molecularly defined further highlighting complexity this disease. While immunotherapy has shown efficacy some variants subpopulations, significant gaps remain treatment rare subtypes. This review explores outcomes across subtypes, including highlights opportunities for improving care through novel therapies, biomarker-driven approaches, inclusive trial designs.

Язык: Английский

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Machine Learning-Enhanced Extraction of Protein Signatures of Renal Cell Carcinoma from Proteomics Data

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2025, Номер unknown

Опубликована: Фев. 17, 2025

Abstract In this study, we generated label-free data-independent acquisition (DIA)-based liquid chromatography (LC)-mass spectrometry (MS) proteomics data from 261 renal cell carcinomas (RCC) and 195 normal adjacent tissues (NAT). The RCC tumors included 48 non-clear (non-ccRCC) 213 ccRCC. A total of 219,740 peptides 11,943 protein groups were identified with 9,787 per sample on average. We adopted a comprehensive approach to select representative samples different mutation sites, considering histopathological, immune, methylation, non-negative matrix factorization (NMF)-based subtypes, along clinical characteristics (gender, grade, stage) capture the complexity diversity ccRCC tumors. used machine learning 55 signatures that distinguish NATs. Furthermore, 39 differentiate tumor subtypes also identified. Our findings offer an extensive perspective proteomic landscape in RCC, illuminating specific proteins serve NATs among various subtypes.

Язык: Английский

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PepCentric Enables Fast Repository-Scale Proteogenomics Searches

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2025, Номер unknown

Опубликована: Фев. 28, 2025

Identifying novel peptides arising from alternative splicing, mutations, or non-canonical translations is a crucial yet challenging aspect of proteogenomics. We introduce PepCentric, scalable computational platform and web-based portal utilizing advanced 2-D fragment indexing for rapid peptide-centric searches across extensive mass spectrometry datasets. With robust false discovery rate control optimized search performance, PepCentric offers an efficient tool validating exploring proteomic variations. In matter seconds, users can their proteins against 2.3 billion spectra collected 66700 runs, making it practical to rapidly validate proteogenomic hypotheses.

Язык: Английский

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Biomarkers in advanced renal cell carcinoma: current practice and future directions

Current Opinion in Oncology, Год журнала: 2025, Номер unknown

Опубликована: Март 27, 2025

Purpose of review This focuses on contemporary research into potential prognostic and therapeutic biomarkers for advanced renal cell carcinoma (RCC) published over the past 18 months. Beyond serum lab values, there is no consensus use specific this purpose. Potential being investigated consist genetic, protein, immunologic, radiologic candidates. Recent findings New insights in genomic include a better understanding VHL mutational heterogeneity, tumor burden, importance genes like PBRM1 SETD2 . Protein such as C-reactive protein (CRP) PDZK1 have demonstrated utility predicting disease progression, response, survival, while immunologic PSMD2, cytokines, Tregs continue to shed light microenvironment immune evasion. Emerging imaging biomarkers, from CAIX-targeted radiotracers PSMA-based PET-CT, offer noninvasive diagnostic tools that may revolutionize RCC management. Summary There are several promising currently under investigation RCC.

Язык: Английский

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Multi-omic profiling in breast cancer: utility for advancing diagnostics and clinical care

Expert Review of Molecular Diagnostics, Год журнала: 2025, Номер unknown

Опубликована: Апрель 7, 2025

Breast cancer remains a major global health challenge. While advances in precision oncology have contributed to improvements patient outcomes and provided deeper understanding of the biological mechanisms that drive disease, historically, research patients' allocation treatment heavily relied on single-omic approaches, analyzing individual molecular dimensions such as genomics, transcriptomics, or proteomics. these deep insights into breast biology, they often fail offer complete disease's complex landscape. In this review, authors explore recent advancements multi-omic realm using clinical data show how integration can more holistic alterations their functional consequences underlying cancer. The overall developments AI are expected complement diagnostics through potentially refining prognostic models, selection. Overcoming challenges cost, complexity, lack standardization is crucial for unlocking full potential multi-omics care enable advancement personalized treatments improve outcomes.

Язык: Английский

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MSFragger-DDA+ enhances peptide identification sensitivity with full isolation window search

Nature Communications, Год журнала: 2025, Номер 16(1)

Опубликована: Апрель 8, 2025

Abstract Liquid chromatography-mass spectrometry based proteomics, particularly in the bottom-up approach, relies on digestion of proteins into peptides for subsequent separation and analysis. The most prevalent method identifying from data-dependent acquisition mass data is database search. Traditional tools typically focus a single peptide per tandem spectrum, often neglecting frequent occurrence co-fragmentations leading to chimeric spectra. Here, we introduce MSFragger-DDA+, search algorithm that enhances identification by detecting co-fragmented with high sensitivity speed. Utilizing MSFragger’s fragment ion indexing algorithm, MSFragger-DDA+ performs comprehensive within full isolation window each followed robust feature detection, filtering, rescoring procedures refine results. Evaluation against established across diverse datasets demonstrated that, integrated FragPipe computational platform, significantly increases while maintaining stringent false discovery rate control. It also uniquely suited wide-window data. provides an efficient accurate solution identification, enhancing detection low-abundance peptides. Coupled enables more analysis proteomics

Язык: Английский

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Targeting KIT with Antibody-Drug Conjugates in Chromophobe Renal Cell Carcinoma

Clinical Genitourinary Cancer, Год журнала: 2025, Номер unknown, С. 102359 - 102359

Опубликована: Апрель 1, 2025

Язык: Английский

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