Evidence map›Paper›PMID 42878755›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2027

Preprocessing Large-Scale Proteomics Data.

Xindong Sun, Markus Ralser, Christoph B Messner

Abstract read
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Article in Methods in molecular biology (Clifton, N.J.), 2027. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Xindong SunPrecision Proteomics Center, Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, 7265, Davos, Switzerland.
Markus RalserDepartment of Biochemistry, Charité Universitätsmedizin Berlin, Berlin, 10117, Germany.
Christoph B MessnerPrecision Proteomics Center, Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, 7265, Davos, Switzerland. christoph.messner@siaf.uzh.ch.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent advances in mass spectrometry instrumentation and computational analysis have established label-free data-independent acquisition (DIA) as a powerful tool in quantitative proteomics, offering depth, throughput, and reproducibility. These developments now enable large-scale projects with hundreds or even thousands of samples, opening new applications in systems biology and biomedicine. However, large-scale datasets require special consideration during data preprocessing. Here, we present a practical guide for preprocessing large-scale DIA datasets, illustrated using a public dataset. We cover key steps, including database searching, normalization, batch correction, missing value imputation, and evaluation of preprocessing strategies, and provide R code together with practical considerations.

Indexed as

Computational BiologyProteomicsDatabases, ProteinHumansMass SpectrometrySoftwareBatch effect correctionData-independent acquisition (DIA)Data preprocessingLabel-free quantificationLarge-scale proteomicsMissing value imputationNormalization

Identifiers

PMID42878755

What OpenQuestion holds

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Registered trials

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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.