PlantLoc: an accurate web server for predicting plant protein subcellular localization by substantiality motif.
Tang, Shengnan ; Li, Tonghua ; Cong, Peisheng ; Xiong, Wenwei ; Wang, Zhiheng and Sun, Jiangming LU
(2013)
In Nucleic Acids Research
41(W1).
p.441-447
- Abstract
- Knowledge of subcellular localizations (SCLs) of plant proteins relates to their functions and aids in understanding the regulation of biological processes at the cellular level. We present PlantLoc, a highly accurate and fast webserver for predicting the multi-label SCLs of plant proteins. The PlantLoc server has two innovative characters: building localization motif libraries by a recursive method without alignment and Gene Ontology information; and establishing simple architecture for rapidly and accurately identifying plant protein SCLs without a machine learning algorithm. PlantLoc provides predicted SCLs results, confidence estimates and which is the substantiality motif and where it is located on the sequence. PlantLoc achieved the... (More)
- Knowledge of subcellular localizations (SCLs) of plant proteins relates to their functions and aids in understanding the regulation of biological processes at the cellular level. We present PlantLoc, a highly accurate and fast webserver for predicting the multi-label SCLs of plant proteins. The PlantLoc server has two innovative characters: building localization motif libraries by a recursive method without alignment and Gene Ontology information; and establishing simple architecture for rapidly and accurately identifying plant protein SCLs without a machine learning algorithm. PlantLoc provides predicted SCLs results, confidence estimates and which is the substantiality motif and where it is located on the sequence. PlantLoc achieved the highest accuracy (overall accuracy of 80.8%) of identification of plant protein SCLs as benchmarked by using a new test dataset compared other plant SCL prediction webservers. The ability of PlantLoc to predict multiple sites was also significantly higher than for any other webserver. The predicted substantiality motifs of queries also have great potential for analysis of relationships with protein functional regions. The PlantLoc server is available at http://cal.tongji.edu.cn/PlantLoc/. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/3913813
- author
- Tang, Shengnan
; Li, Tonghua
; Cong, Peisheng
; Xiong, Wenwei
; Wang, Zhiheng
and Sun, Jiangming
LU
- organization
- publishing date
- 2013
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Machine Learning, Support Vector Machine
- in
- Nucleic Acids Research
- volume
- 41
- issue
- W1
- pages
- 441 - 447
- publisher
- Oxford University Press
- external identifiers
-
- wos:000323603200070
- pmid:23729470
- scopus:84883581715
- pmid:23729470
- ISSN
- 1362-4962
- DOI
- 10.1093/nar/gkt428
- language
- English
- LU publication?
- yes
- id
- 5c7b21f9-c5da-4b03-8da7-9973db0a3a97 (old id 3913813)
- date added to LUP
- 2016-04-01 09:57:21
- date last changed
- 2023-06-14 12:04:53
@article{5c7b21f9-c5da-4b03-8da7-9973db0a3a97,
abstract = {{Knowledge of subcellular localizations (SCLs) of plant proteins relates to their functions and aids in understanding the regulation of biological processes at the cellular level. We present PlantLoc, a highly accurate and fast webserver for predicting the multi-label SCLs of plant proteins. The PlantLoc server has two innovative characters: building localization motif libraries by a recursive method without alignment and Gene Ontology information; and establishing simple architecture for rapidly and accurately identifying plant protein SCLs without a machine learning algorithm. PlantLoc provides predicted SCLs results, confidence estimates and which is the substantiality motif and where it is located on the sequence. PlantLoc achieved the highest accuracy (overall accuracy of 80.8%) of identification of plant protein SCLs as benchmarked by using a new test dataset compared other plant SCL prediction webservers. The ability of PlantLoc to predict multiple sites was also significantly higher than for any other webserver. The predicted substantiality motifs of queries also have great potential for analysis of relationships with protein functional regions. The PlantLoc server is available at http://cal.tongji.edu.cn/PlantLoc/.}},
author = {{Tang, Shengnan and Li, Tonghua and Cong, Peisheng and Xiong, Wenwei and Wang, Zhiheng and Sun, Jiangming}},
issn = {{1362-4962}},
keywords = {{Machine Learning; Support Vector Machine}},
language = {{eng}},
number = {{W1}},
pages = {{441--447}},
publisher = {{Oxford University Press}},
series = {{Nucleic Acids Research}},
title = {{PlantLoc: an accurate web server for predicting plant protein subcellular localization by substantiality motif.}},
url = {{https://lup.lub.lu.se/search/files/1422921/4191795.pdf}},
doi = {{10.1093/nar/gkt428}},
volume = {{41}},
year = {{2013}},
}