Resources

Proteomics Databases



Metabolomics Databases

• • How AI & Machine Learning Enhance Label-Free Proteomics Data Analysis Efficiency?

  In recent years, proteomics has emerged as a powerful tool for elucidating biological processes and disease mechanisms. Among various approaches, label-free quantitative proteomics (LFQ) has been widely adopted in both fundamental research and clinical applications, owing to its flexible experimental design and minimal sample requirements. However, LFQ faces substantial challenges, including complex data processing workflows, high computational demands, and susceptibility to multiple sources of variability.

• • Glycosylation Site Analysis Workflow

  Glycosylation sites are specific amino acid residues on proteins that are modified by glycosylation. N-glycosylation and O-glycosylation are the most common types of glycosylation modifications. N-glycosylation occurs on amino acids containing nitrogen, usually asparagine (Asn) residues. N-glycosylation generally follows a specific sequence pattern, i.e., "N-X-S/T" (where X can be any amino acid except proline), i.e., asparagine residue followed by any amino acid, then serine (Ser) or threonine (Thr).......

• • DIA vs. DDA in Label-Free Quantitative Proteomics: A Comparative Analysis

  Label-free quantitative proteomics is widely used to investigate dynamic changes in protein expression within biological systems and plays a critical role in areas such as disease mechanism elucidation, drug target discovery, and biomarker identification. Among label-free quantification strategies, Data-Dependent Acquisition (DDA) and Data-Independent Acquisition (DIA) are the two most commonly employed mass spectrometry acquisition approaches, each characterized by distinct technical features and suited...

• • How to Overcoming Reproducibility Issues in Label-Free Proteomics?

  Label-free quantitative proteomics has become widely adopted in studies of disease mechanisms, biomarker discovery, and drug target identification, owing to its high sample throughput, streamlined workflow, and the absence of costly labeling reagents. However, as large-scale studies continue to expand, researchers are increasingly recognizing that poor reproducibility remains one of the major limitations hindering the broader application of label-free quantification. This article systematically examines....

• • Tools for Label-free Peptide Quantification

  Label-free quantitative proteomics (LFQ) has emerged as a crucial method for quantitative analysis in life science research, owing to its simplified sample preparation, broad applicability, and the absence of label-induced interference. Particularly in fields such as systems biology, disease mechanism investigation, biomarker discovery, and drug development, LFQ strategies are playing an increasingly prominent role. This paper systematically reviews the principles and characteristics of mainstream .......

• • FAQs and Solutions for Unlabeled Quantitative Proteomics (LFQ)

  Label-Free Quantification (LFQ) is a widely adopted approach in proteomics for quantifying proteins, favored in both basic research and clinical translation due to its procedural simplicity, elimination of stable isotope labeling, and broad applicability. However, the strong dependence of LFQ experiments on sample preparation, mass spectrometry acquisition, and data analysis presents substantial challenges for researchers in practical applications, including high rates of missing data, poor reproducibility

• • How Mass Spectrometry Enables High-Resolution Label-Free Protein Quantification?

  Label-Free Quantification (LFQ), owing to its simplicity, flexibility, and broad applicability, has become a crucial approach in proteomics research. Achieving high-resolution label-free protein quantification via mass spectrometry requires systematic optimization across all stages—from sample preparation and mass spectrometry acquisition to data processing—to ensure accuracy, sensitivity, and reproducibility of the resulting data. Sample Preparation: Ensuring Consistency and Integrity 1. Sample.......

• • Issues and Resolutions in Label-Free Quantitative Mass Spectrometry

  With ongoing advances in systems biology, label-free quantification (LFQ) proteomics has emerged as a mainstream strategy in life science research. This approach offers distinct advantages, such as eliminating the need for costly isotope labeling, enabling simpler workflows, and supporting high-throughput sample processing. In particular, LFQ based on mass spectrometry (MS) has been extensively adopted in applications such as biomarker discovery, disease mechanism investigation, and drug action analysis....

• • Essential Analysis Tools for Label-Free Proteomics: A Comprehensive Review of MaxQuant and DIA-NN

  Label-Free Proteomics has gained widespread application in basic biological research, disease mechanism elucidation, and biomarker discovery due to its streamlined workflow, relatively low cost, and broad applicability. However, the processing of LFQ data remains challenging, encompassing large-scale mass spectrometry data handling such as feature extraction, quantification, differential analysis, and functional annotation. Therefore, selecting and effectively utilizing appropriate analytical tools is......

• • LC-MS-Based Label-Free Quantification: High-Throughput Protein Identification & Analysis

  In life science research, accurately and comprehensively characterizing changes in protein expression is essential for understanding biological processes and disease mechanisms. Label-free quantitative proteomics (LFQ) is increasingly recognized as a valuable approach for protein quantification, offering advantages such as high flexibility, minimal sample requirements, and cost efficiency. As a core analytical platform, liquid chromatography–mass spectrometry (LC-MS) provides the high-throughput capacity...