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pCRM1exportome: database of predicted CRM1-dependent Nuclear Export Signal (NES) motifs in cancer-related genes.

Authors :
Lee Y
Baumhardt JM
Pei J
Chook YM
Grishin NV
Source :
Bioinformatics (Oxford, England) [Bioinformatics] 2020 Feb 01; Vol. 36 (3), pp. 961-963.
Publication Year :
2020

Abstract

Motivation: The consensus pattern of Nuclear Export Signal (NES) is a short sequence motif that is commonly identified in protein sequences, whether the motif acts as an NES (true positive) or not (false positive). Finding more plausible NES functioning regions among the vast array of consensus-matching segments would provide an interesting resource for further experimental validation. Better defined NES should also allow meaningful mapping of cancer-related mutation positions, leading to plausible explanations for the relationship between nuclear export and disease.<br />Results: Possible NES candidate regions are extracted from the cancer-related human reference proteome. Extracted NES are scored for reliability by combining sequence-based and structure-based approaches. The confidently identified NES candidate motifs were checked for overlap with cancer-related mutation positions annotated in the COSMIC database. Among the ∼700 cancer-related sequences in the COSMIC Cancer Gene Census, 178 sequences are predicted to have possible NES motifs containing cancer-related mutations at their key positions. These lists are organized into our database (pCRM1exportome), and other protein sequences in the human reference proteome can also be retrieved by their UniProt IDs.<br />Availability and Implementation: The database is freely available at http://prodata.swmed.edu/pCRM1exportome.<br />Supplementary Information: Supplementary data are available at Bioinformatics online.<br /> (© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Details

Language :
English
ISSN :
1367-4811
Volume :
36
Issue :
3
Database :
MEDLINE
Journal :
Bioinformatics (Oxford, England)
Publication Type :
Academic Journal
Accession number :
31504173
Full Text :
https://doi.org/10.1093/bioinformatics/btz657