Evidence map›Paper›PMID 20663773›Full record

ArticleNucleic acids research2010

Characterization and prediction of protein nucleolar localization sequences.

Michelle S Scott, François-Michel Boisvert, Mark D McDowall, Angus I Lamond, Geoffrey J Barton

Open access · goldAbstract readValidation Study
In one paragraph

Article in Nucleic acids research, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 130 papers.

0numbers the graph read from it
0cells of the map it votes in
130citing papers in PubMed
5.7field-weighted citation impact, top 3% 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

130 citing papers in PubMed, 201 citations in OpenAlex.

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  16. Human REXO4 is Required for Cell Cycle Progression.bioRxiv : the preprint server for biology · 2025
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  20. TRMT1L-catalyzed mbioRxiv : the preprint server for biology · 2024
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70 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

5 authors at 2 institutions in 2 countries.

Michelle S ScottDivision of Biological Chemistry and Drug Discovery and Wellcome Trust Centre for Gene Regulation and Expression, College of Life Sciences, University of Dundee, Dow Street, Dundee DD1 5EH, UK. michelle@compbio.dundee.ac.uk
François-Michel Boisvert
Mark D McDowall
Angus I Lamond
Geoffrey J Barton
Wellcome Trust · GBUniversity of Dundee · GB

Funding

Wellcome Trust 073980Wellcome Trust 073980/Z/03/ZWellcome Trust WT083481
6 · The paper itself

Abstract

Although the nucleolar localization of proteins is often believed to be mediated primarily by non-specific retention to core nucleolar components, many examples of short nucleolar targeting sequences have been reported in recent years. In this article, 46 human nucleolar localization sequences (NoLSs) were collated from the literature and subjected to statistical analysis. Of the residues in these NoLSs 48% are basic, whereas 99% of the residues are predicted to be solvent-accessible with 42% in α-helix and 57% in coil. The sequence and predicted protein secondary structure of the 46 NoLSs were used to train an artificial neural network to identify NoLSs. At a true positive rate of 54%, the predictor's overall false positive rate (FPR) is estimated to be 1.52%, which can be broken down to FPRs of 0.26% for randomly chosen cytoplasmic sequences, 0.80% for randomly chosen nucleoplasmic sequences and 12% for nuclear localization signals. The predictor was used to predict NoLSs in the complete human proteome and 10 of the highest scoring previously unknown NoLSs were experimentally confirmed. NoLSs are a prevalent type of targeting motif that is distinct from nuclear localization signals and that can be computationally predicted.

Indexed as

Neural Networks, ComputerProtein Sorting SignalsCell Line, TumorCell NucleolusComputational BiologyHumansNuclear Localization SignalsNuclear ProteinsViral ProteinsNuclear Localization SignalsNuclear ProteinsProtein Sorting SignalsViral Proteins

Identifiers

PMID20663773
PMCPMC2995072
OpenAlexW1966296785

What OpenQuestion holds

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LicenceCC BY-NC
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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.