Evidence map›Paper›PMID 30936560›Full record

ArticleNature biotechnology2019

TagGraph reveals vast protein modification landscapes from large tandem mass spectrometry datasets.

Arun Devabhaktuni, Sarah Lin, Lichao Zhang, Kavya Swaminathan, Carlos G Gonzalez, Niclas Olsson, Samuel M Pearlman, Keith Rawson, Joshua E Elias

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature biotechnology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers.

0numbers the graph read from it
0cells of the map it votes in
64citing papers in PubMed
12.5field-weighted citation impact, top 1% 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

64 citing papers in PubMed, 159 citations in OpenAlex.

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4 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

9 authors at 1 institution in 1 country.

Arun DevabhaktuniDepartment of Chemical and Systems Biology Stanford School of Medicine, Stanford University, Stanford, CA, USA.
Sarah LinDepartment of Chemical and Systems Biology Stanford School of Medicine, Stanford University, Stanford, CA, USA.
Lichao ZhangDepartment of Chemical and Systems Biology Stanford School of Medicine, Stanford University, Stanford, CA, USA.
Kavya SwaminathanDepartment of Chemical and Systems Biology Stanford School of Medicine, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0001-7715-5375
Carlos G GonzalezDepartment of Chemical and Systems Biology Stanford School of Medicine, Stanford University, Stanford, CA, USA.
Niclas OlssonDepartment of Chemical and Systems Biology Stanford School of Medicine, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0002-9746-0542
Samuel M PearlmanDepartment of Chemical and Systems Biology Stanford School of Medicine, Stanford University, Stanford, CA, USA.
Keith RawsonDepartment of Chemical and Systems Biology Stanford School of Medicine, Stanford University, Stanford, CA, USA.
Joshua E EliasDepartment of Chemical and Systems Biology Stanford School of Medicine, Stanford University, Stanford, CA, USA. josh.elias@stanford.edu.ORCID http://orcid.org/0000-0002-8063-3259
Stanford University · US

Funding

Modeling the Molecular Determinants of Induced Anti-Tumor Immune Responses in Mantle Cell LymphomaU01CA194389 · NCI · STANFORD UNIVERSITY · PI ALIZADEH, ASH ARASH, DAVIS, MARK MORRIS · 2015 to 2019
$3.1M
Characterizing head and neck tumor neoantigensand T cells: looking beyond the usual suspectsR01DE027750 · NIDCR · STANFORD UNIVERSITY · PI ELIAS, JOSHUA E, SUNWOO, JOHN B · 2018 to 2022
$2.4M
NCI NIH HHS U01 CA194389NIDCR NIH HHS R01 DE027750
6 · The paper itself

Abstract

Although mass spectrometry is well suited to identifying thousands of potential protein post-translational modifications (PTMs), it has historically been biased towards just a few. To measure the entire set of PTMs across diverse proteomes, software must overcome the dual challenges of covering enormous search spaces and distinguishing correct from incorrect spectrum interpretations. Here, we describe TagGraph, a computational tool that overcomes both challenges with an unrestricted string-based search method that is as much as 350-fold faster than existing approaches, and a probabilistic validation model that we optimized for PTM assignments. We applied TagGraph to a published human proteomic dataset of 25 million mass spectra and tripled confident spectrum identifications compared to its original analysis. We identified thousands of modification types on almost 1 million sites in the proteome. We show alternative contexts for highly abundant yet understudied PTMs such as proline hydroxylation, and its unexpected association with cancer mutations. By enabling broad characterization of PTMs, TagGraph informs as to how their functions and regulation intersect.

Indexed as

Protein Processing, Post-TranslationalSoftwareAlgorithmsAmino Acid SequenceBayes TheoremBiotechnologyCell Line, TumorDatabases, ProteinHumansHydroxylationModels, StatisticalPeptidesProteomeProteomicsSearch EngineSequence AlignmentPeptidesProteome

Identifiers

PMID30936560
PMCPMC6447449
OpenAlexW2930099314

What OpenQuestion holds

Textmetadata
LicenceTDM
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.