Evidence map›Paper›PMID 27809316›Full record

ArticleNature protocols2016

The MaxQuant computational platform for mass spectrometry-based shotgun proteomics.

Stefka Tyanova, Tikira Temu, Juergen Cox

Abstract read
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In one paragraph

Article in Nature protocols, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2,790 papers.

0numbers the graph read from it
0cells of the map it votes in
2,790citing 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

2,790 citing papers in PubMed.

  1. Article
  2. A Uniquome-Based Method for Proteins Identification by Mass Spectrometry.Methods in molecular biology (Clifton, N.J.) · 2027
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2,730 more citing papers are in PubMed but not listed here.

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.

Stefka TyanovaComputational Systems Biochemistry, Max-Planck Institute for Biochemistry, Martinsried, Germany.
Tikira TemuComputational Systems Biochemistry, Max-Planck Institute for Biochemistry, Martinsried, Germany.
Juergen CoxComputational Systems Biochemistry, Max-Planck Institute for Biochemistry, Martinsried, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

MaxQuant is one of the most frequently used platforms for mass-spectrometry (MS)-based proteomics data analysis. Since its first release in 2008, it has grown substantially in functionality and can be used in conjunction with more MS platforms. Here we present an updated protocol covering the most important basic computational workflows, including those designed for quantitative label-free proteomics, MS1-level labeling and isobaric labeling techniques. This protocol presents a complete description of the parameters used in MaxQuant, as well as of the configuration options of its integrated search engine, Andromeda. This protocol update describes an adaptation of an existing protocol that substantially modifies the technique. Important concepts of shotgun proteomics and their implementation in MaxQuant are briefly reviewed, including different quantification strategies and the control of false-discovery rates (FDRs), as well as the analysis of post-translational modifications (PTMs). The MaxQuant output tables, which contain information about quantification of proteins and PTMs, are explained in detail. Furthermore, we provide a short version of the workflow that is applicable to data sets with simple and standard experimental designs. The MaxQuant algorithms are efficiently parallelized on multiple processors and scale well from desktop computers to servers with many cores. The software is written in C# and is freely available at http://www.maxquant.org.

Indexed as

Mass SpectrometryProtein Processing, Post-TranslationalProteomicsSoftware

Identifiers

PMID27809316

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.