Evidence map›Paper›PMID 20131910›Full record

ArticleJournal of proteome research2010

TagRecon: high-throughput mutation identification through sequence tagging.

Surendra Dasari, Matthew C Chambers, Robbert J Slebos, Lisa J Zimmerman, Amy-Joan L Ham, David L Tabb

Open access · greenAbstract read
In one paragraph

Article in Journal of proteome research, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers.

0numbers the graph read from it
0cells of the map it votes in
58citing papers in PubMed
5.4field-weighted citation impact, top 4% 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

58 citing papers in PubMed, 120 citations in OpenAlex.

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  15. Software Options for the Analysis of MS-Proteomic Data.Methods in molecular biology (Clifton, N.J.) · 2021
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  17. Autophagic Proteome in TwoMicroorganisms · 2020
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  18. Review
  19. Cleavage C-terminal to Asp leads to covalent crosslinking of long-lived human proteins.Biochimica et biophysica acta. Proteins and proteomics · 2019
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  20. Article
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

6 authors at 1 institution in 1 country.

Surendra DasariDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee 37232-8340, USA.
Matthew C Chambers
Robbert J Slebos
Lisa J Zimmerman
Amy-Joan L Ham
David L Tabb
Vanderbilt University Medical Center · US

Funding

Clinical Proteomic Technology Assessment for CancerU24CA126479 · NCI · VANDERBILT UNIVERSITY · PI LIEBLER, DANIEL C · 2006 to 2010
$7.9M
New Proteomic Algorithms to Identify Mutant or Modified ProteinsR01CA126218 · NCI · VANDERBILT UNIVERSITY · PI TABB, DAVID L · 2006 to 2010
$1.8M
NCI NIH HHS R01 CA126218NCI NIH HHS U24 CA126479
6 · The paper itself

Abstract

Shotgun proteomics produces collections of tandem mass spectra that contain all the data needed to identify mutated peptides from clinical samples. Identifying these sequence variations, however, has not been feasible with conventional database search strategies, which require exact matches between observed and expected sequences. Searching for mutations as mass shifts on specified residues through database search can incur significant performance penalties and generate substantial false positive rates. Here we describe TagRecon, an algorithm that leverages inferred sequence tags to identify unanticipated mutations in clinical proteomic data sets. TagRecon identifies unmodified peptides as sensitively as the related MyriMatch database search engine. In both LTQ and Orbitrap data sets, TagRecon outperformed state of the art software in recognizing sequence mismatches from data sets with known variants. We developed guidelines for filtering putative mutations from clinical samples, and we applied them in an analysis of cancer cell lines and an examination of colon tissue. Mutations were found in up to 6% of identified peptides, and only a small fraction corresponded to dbSNP entries. The RKO cell line, which is DNA mismatch repair deficient, yielded more mutant peptides than the mismatch repair proficient SW480 line. Analysis of colon cancer tumor and adjacent tissue revealed hydroxyproline modifications associated with extracellular matrix degradation. These results demonstrate the value of using sequence tagging algorithms to fully interrogate clinical proteomic data sets.

Indexed as

Sequence Tagged SitesAlgorithmsCell Line, TumorChromatography, LiquidColonic NeoplasmsComputational BiologyDatabases, ProteinData MiningDNA Mismatch RepairExtracellular MatrixHumansHydroxyprolineModels, GeneticMutationPeptide FragmentsPeptide MappingHydroxyprolinePeptide Fragments

Identifiers

PMID20131910
PMCPMC2859315
OpenAlexW2020973783

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.