Evidence map›Paper›PMID 40739704›Full record

ArticleProteomics2025

Proteoform Identification Using Multiplexed Top-Down Mass Spectra.

Zhige Wang, Xingzhao Xiong, Xiaowen Liu

Abstract read
In one paragraph

Article in Proteomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Zhige WangDepartment of Computer Science, Tulane University, New Orleans, Louisiana, USA.ORCID 0009-0002-1168-5597
Xingzhao XiongDeming Department of Medicine, Tulane University, New Orleans, Louisiana, USA.
Xiaowen LiuDeming Department of Medicine, Tulane University, New Orleans, Louisiana, USA.ORCID 0000-0003-4139-1127

Funding

Computational tools for top down mass spectrometry based proteoform identification and proteogenomicsR01GM118470 · NIGMS · TULANE UNIVERSITY OF LOUISIANA · PI Xiaowen Liu · 2016 to 2026
$2.7M
Quantitative top-down proteomics of human colorectal cancer cells and tumorsR01CA247863 · NCI · MICHIGAN STATE UNIVERSITY · PI HUMMON, AMANDA B., LIU, XIAOWEN · 2021 to 2025
$1.9M
NCI NIH HHS R01 CA247863NIGMS NIH HHS R01 GM118470NIH HHS R01CA247863NIH HHS R01GM118470
6 · The paper itself

Abstract

Top-down mass spectrometry (TDMS) is the method of choice for analyzing intact proteoforms, as well as their posttranslational modifications and sequence variations. In top-down tandem mass spectrometry (TD-MS/MS) experiments, multiple proteoforms are often co-fragmented, resulting in multiplexed TD-MS/MS spectra. Due to their increased complexity, compared to spectra from single proteoforms, multiplexed TD-MS/MS spectra present significant challenges for proteoform identification and quantification. Here we present TopMPI, a new computational tool specifically designed for the identification of multiplexed TD-MS/MS spectra. Experimental results demonstrate that TopMPI substantially increases the sensitivity and accuracy of proteoform identification in multiplexed TD-MS/MS spectral analysis compared to existing tools. SUMMARY: Top-down mass spectrometry (TDMS) is a powerful technique for analyzing intact proteoforms; however, identifying multiple co-fragmented proteoforms from multiplexed tandem mass spectrometry (MS/MS) spectra remains a significant challenge. In this paper, we introduce TopMPI, a new computational tool specifically designed to identify multiplexed TD-MS/MS spectra using a two-round database search strategy. Compared to existing tools, TopMPI significantly improves the sensitivity and accuracy of proteoform identification from multiplexed MS/MS spectra. The development of TopMPI enhances the identification of low abundance proteoforms in complex biological samples and increases the potential of TDMS for discovering proteoform biomarkers in disease studies.

Indexed as

ProteomicsSoftwareTandem Mass SpectrometryHumansdatabase searchingmultiplexingproteoform identificationtop‐down proteomics

Identifiers

PMID40739704
PMCPMC12716115

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.