Evidence map›Paper›PMID 33500498›Full record

ArticleScientific reports2021

Multi-Q 2 software facilitates isobaric labeling quantitation analysis with improved accuracy and coverage.

Ching-Tai Chen, Jen-Hung Wang, Cheng-Wei Cheng, Wei-Che Hsu, Chu-Ling Ko, Wai-Kok Choong, Ting-Yi Sung

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.5field-weighted citation impact, top 44% of its field
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

3 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 4 institutions in 2 countries.

Ching-Tai ChenInstitute of Information Science, Academia Sinica, 128 Academia Road, Section 2, Nankang, Taipei, 115, Taiwan. ctchen@iis.sinica.edu.tw.
Jen-Hung WangInstitute of Information Science, Academia Sinica, 128 Academia Road, Section 2, Nankang, Taipei, 115, Taiwan.
Cheng-Wei ChengGenomics Research Center, Academia Sinica, Taipei, 115, Taiwan.
Wei-Che HsuInstitute of Information Science, Academia Sinica, 128 Academia Road, Section 2, Nankang, Taipei, 115, Taiwan.
Chu-Ling KoDepartment of Computer Science and Engineering, University of California San Diego, La Jolla, CA, 92093, USA.
Wai-Kok ChoongInstitute of Information Science, Academia Sinica, 128 Academia Road, Section 2, Nankang, Taipei, 115, Taiwan.
Ting-Yi SungInstitute of Information Science, Academia Sinica, 128 Academia Road, Section 2, Nankang, Taipei, 115, Taiwan. tsung@iis.sinica.edu.tw.
Institute of Information Science, Academia Sinica · TWGenomics Research Center, Academia Sinica · TWNational Yang Ming Chiao Tung University · TWUniversity of California, San Diego · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mass spectrometry-based proteomics using isobaric labeling for multiplex quantitation has become a popular approach for proteomic studies. We present Multi-Q 2, an isobaric-labeling quantitation tool which can yield the largest quantitation coverage and improved quantitation accuracy compared to three state-of-the-art methods. Multi-Q 2 supports identification results from several popular proteomic data analysis platforms for quantitation, offering up to 12% improvement in quantitation coverage for accepting identification results from multiple search engines when compared with MaxQuant and PatternLab. It is equipped with various quantitation algorithms, including a ratio compression correction algorithm, and results in up to 336 algorithmic combinations. Systematic evaluation shows different algorithmic combinations have different strengths and are suitable for different situations. We also demonstrate that the flexibility of Multi-Q 2 in customizing algorithmic combination can lead to improved quantitation accuracy over existing tools. Moreover, the use of complementary algorithmic combinations can be an effective strategy to enhance sensitivity when searching for biomarkers from differentially expressed proteins in proteomic experiments. Multi-Q 2 provides interactive graphical interfaces to process quantitation and to display ratios at protein, peptide, and spectrum levels. It also supports a heatmap module, enabling users to cluster proteins based on their abundance ratios and to visualize the clustering results. Multi-Q 2 executable files, sample data sets, and user manual are freely available at http://ms.iis.sinica.edu.tw/COmics/Software_Multi-Q2.html .

Identifiers

PMID33500498
PMCPMC7838301
OpenAlexW3123326894

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.