Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Background: As a gold-standard quantitative technique based on mass spectrometry, multiple reaction monitoring (MRM) has been widely used in proteomics and metabolomics. In the analysis of MRM data, as no peak picking algorithm can achieve perfect accuracy, manual inspection is necessary to correct the errors. In large cohort analysis scenarios, the time required for manual inspection is often considerable. Apart from the commercial software that comes with mass spectrometers, the open-source and free software Skyline is the most popular software for quantitative omics. However, this software is not optimized for manual inspection of hundreds of samples, the interactive experience also needs to be improved.
Results: Here we introduce MRMPro, a web-based MRM data analysis platform for efficient manual inspection. MRMPro supports data analysis of MRM and schedule MRM data acquired by mass spectrometers of mainstream vendors. With the goal of improving the speed of manual inspection, we implemented a collaborative review system based on cloud architecture, allowing multiple users to review through browsers. To reduce bandwidth usage and improve data retrieval speed, we proposed a MRM data compression algorithm, which reduced data volume by more than 60% and 80% respectively compared to vendor and mzML format. To improve the efficiency of manual inspection, we proposed a retention time drift estimation algorithm based on similarity of chromatograms. The estimated retention time drifts were then used for peak alignment and automatic EIC grouping. Compared with Skyline, MRMPro has higher quantification accuracy and better manual inspection support.
Conclusions: In this study, we proposed MRMPro to improve the usability of manual calibration for MRM data analysis. MRMPro is free for non-commercial use. Researchers can access MRMPro through http://mrmpro.csibio.com/ . All major mass spectrometry formats (wiff, raw, mzML, etc.) can be analyzed on the platform. The final identification results can be exported to a common.xlsx format for subsequent analysis.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10848457 | PMC |
| http://dx.doi.org/10.1186/s12859-024-05685-x | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Automated Coffee Roast Level Classification Using Machine Learning and Deep Learning Models.
J Food Sci
September 2025
Faculty of Computing, Federal University of Uberlandia, Uberlândia, Brazil.
The coffee roasting process is a critical factor in determining the final quality of the beverage, influencing its flavour, aroma, and acidity. Traditionally, roast-level classification has relied on manual inspection, which is time-consuming, subjective, and prone to inconsistencies. However, advancements in machine learning (ML) and computer vision, particularly convolutional neural networks (CNNs), have shown great promise in automating and improving the accuracy of this process.
View Article and Find Full Text PDFApplication of computer vision for automated detection of different lesions in pig lungs: An exploratory study.
Prev Vet Med
September 2025
Istituto Zooprofilattico Sperimentale della Lombardia e dell'Emilia Romagna 'Bruno Ubertini' (IZSLER), Via Bianchi 7/9, Brescia 25124, Italy. Electronic address:
Accurate classification of lung lesions at necropsy is crucial for guiding the diagnostic process and ensuring effective management of porcine respiratory diseases. Post-mortem inspection of the lungs during slaughter also provides valuable insights into disease occurrence, offering useful feedback on the efficacy of on-farm prevention and control strategies. However, manual assessment protocols may be impaired by high slaughtering speeds and low inter-rater agreement, which limits continuous data collection and hinders comparability.
View Article and Find Full Text PDFDifferential Chromatin Accessibility, Gene Expression, and mRNA Splicing Between Developing Cochlear Inner and Outer Hair Cells.
J Assoc Res Otolaryngol
September 2025
Department of Anesthesiology, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.
Purpose: The mammalian cochlea has two types of low abundance and highly specialized inner (IHC) and outer (OHC) mechanosensory hair cells. Their malfunction or death is a common cause of congenital and acquired deafness. IHCs and OHCs exhibit different transcriptomes during development.
View Article and Find Full Text PDFEnhancing Entomological Surveillance: Real-Time Monitoring of Mosquito Activity with the VECTRACK System in Rural and Urban Areas.
Biology (Basel)
August 2025
National Institute of Health Doutor Ricardo Jorge (INSA), Centre for Vectors and Infectious Diseases Research (CEVDI), Avenida da Liberdade n.-5, 2965-575 Águas de Moura, Portugal.
Background: Mosquitoes from the (.) genus are vectors of dengue, Zika, chikungunya, and other arboviruses, posing a significant public health threat. In 2005, was detected for the first time in Madeira Island, Portugal, in the city of Funchal, and has since become established in the region.
View Article and Find Full Text PDFSSD-YOLO: a lightweight network for rice leaf disease detection.
Front Plant Sci
August 2025
School of Information Engineering, Henan Institute of Science and Technology, Xinxiang, China.
Rice leaf diseases significantly impact yield and quality. Traditional diagnostic methods rely on manual inspection and empirical knowledge, making them subjective and prone to errors. This study proposes an improved YOLOv8-based rice disease detection method (SSD-YOLO) to enhance diagnostic accuracy and efficiency.
View Article and Find Full Text PDF