Evidence map›Paper›PMID 34155216›Full record

ArticleNature communications2021

Systematic detection of functional proteoform groups from bottom-up proteomic datasets.

Isabell Bludau, Max Frank, Christian Dörig, Yujia Cai, Moritz Heusel, George Rosenberger, Paola Picotti, Ben C Collins, Hannes Röst, Ruedi Aebersold

Abstract read
In one paragraph

Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
54citing papers in PubMed, 1 pooled it
–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

54 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

10 authors.

Isabell BludauDepartment of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.ORCID http://orcid.org/0000-0002-2601-238X
Max FrankDepartment of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.
Christian DörigDepartment of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.
Yujia CaiDepartment of Molecular Genetics, University of Toronto, Toronto, Canada.ORCID http://orcid.org/0000-0002-3919-7993
Moritz HeuselDepartment of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.
George RosenbergerDepartment of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.ORCID http://orcid.org/0000-0002-1655-6789
Paola PicottiDepartment of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.
Ben C CollinsDepartment of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.ORCID http://orcid.org/0000-0003-0827-3495
Hannes RöstDepartment of Molecular Genetics, University of Toronto, Toronto, Canada. hannes.rost@utoronto.ca.ORCID http://orcid.org/0000-0003-0990-7488
Ruedi AebersoldDepartment of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland. aebersold@imsb.biol.ethz.ch.ORCID http://orcid.org/0000-0002-9576-3267

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To a large extent functional diversity in cells is achieved by the expansion of molecular complexity beyond that of the coding genome. Various processes create multiple distinct but related proteins per coding gene - so-called proteoforms - that expand the functional capacity of a cell. Evaluating proteoforms from classical bottom-up proteomics datasets, where peptides instead of intact proteoforms are measured, has remained difficult. Here we present COPF, a tool for COrrelation-based functional ProteoForm assessment in bottom-up proteomics data. It leverages the concept of peptide correlation analysis to systematically assign peptides to co-varying proteoform groups. We show applications of COPF to protein complex co-fractionation data as well as to more typical protein abundance vs. sample data matrices, demonstrating the systematic detection of assembly- and tissue-specific proteoform groups, respectively, in either dataset. We envision that the presented approach lays the foundation for a systematic assessment of proteoforms and their functional implications directly from bottom-up proteomic datasets.

Indexed as

AlgorithmsAnimalsBenchmarkingHumansMicePeptidesProtein IsoformsProteomicsTandem Mass SpectrometryWorkflowPeptidesProtein Isoforms

Identifiers

PMID34155216
PMCPMC8217233

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.