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Quality Assurance

Quality Assurance

Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...

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Updated: Jun 20, 2026

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Published on: July 16, 2017

SMQVP: A Web Application for Spatial Metabolomics Quality Visualization and Processing.

Zhanlong Mei¹, Wan Sun¹, Yun Zhao¹

¹BGI Genomics, Shenzhen 518083, China.

|June 25, 2025

Summary

This summary is machine-generated.

Spatial metabolomics data quality is improved with SMQVP v1.0 software. This tool systematically assesses and preprocesses data, enhancing the reliability of biological insights from spatial metabolomics studies.

Keywords:

data quality control isotopic peak detection mass spectrometry imaging noise ion filtering spatial metabolomics

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Last Updated: Jun 20, 2026

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

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An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome

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Area of Science:

* Spatial metabolomics
* Computational biology
* Data science

Background:

* Spatial metabolomics offers spatially resolved metabolite mapping for biological insights.
* Data quality control and preprocessing are critical bottlenecks impacting reliability.

Purpose of the Study:

* Introduce Spatial Metabolomics data Quality Visualization and Processing (SMQVP v1.0).
* Provide a user-friendly graphical interface for systematic quality assessment and preprocessing of spatial metabolomics data.

Main Methods:

* SMQVP v1.0 incorporates eight modules for quality visualization and evaluation.
* Modules include background consistency, noise ion filtering, and intensity distribution analysis.
* Identification of isotopic and adduct ions is also included.

Main Results:

* SMQVP effectively identified and removed noise signals in AFADESI-based mouse brain data.
* Preprocessing with SMQVP improved clustering accuracy, better reflecting tissue morphology.
* Enhanced data integrity led to more robust downstream analyses.

Conclusions:

* SMQVP is the first systematic approach for spatial metabolomics quality visualization.
* Offers an accessible solution for enhancing data integrity and mitigating technical noise.
* Improves the reliability and robustness of spatial metabolomics findings.