NDLI: iLoc-Hum: using the accumulation-label scale to predict subcellular locations of human proteins with both single and multiple sites

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Molecular BioSystems Vol -7

Molecular BioSystems Vol -6

Molecular BioSystems Vol -5

Molecular BioSystems Vol -4

Molecular BioSystems Vol -3

Molecular BioSystems Vol -2

iLoc-Hum: using the accumulation-label scale to predict subcellular locations of human proteins with both single and multiple sites

Content Provider	Royal Society of Chemistry (RSC)
Author	Chou, Kuo-Chen Wu, Zhi-Cheng Xiao, Xuan
Copyright Year	2012
Abstract	Although numerous efforts have been made for predicting the subcellular locations of proteins based on their sequence information, it still remains as a challenging problem, particularly when query proteins may have the multiplex character, i.e., they simultaneously exist, or move between, two or more different subcellular location sites. Most of the existing methods were established on the assumption: a protein has one, and only one, subcellular location. Actually, recent evidence has indicated an increasing number of human proteins having multiple subcellular locations. This kind of multiplex proteins should not be ignored because they may bear some special biological functions worthy of our attention. Based on the accumulation-label scale, a new predictor, called iLoc-Hum, was developed for identifying the subcellular localization of human proteins with both single and multiple location sites. As a demonstration, the jackknife cross-validation was performed with iLoc-Hum on a benchmark dataset of human proteins that covers the following 14 location sites: centrosome, cytoplasm, cytoskeleton, endoplasmic reticulum, endosome, extracellular, Golgi apparatus, lysosome, microsome, mitochondrion, nucleus, peroxisome, plasma membrane, and synapse, where some proteins belong to two, three or four locations but none has 25% or higher pairwise sequence identity to any other in the same subset. For such a complicated and stringent system, the overall success rate achieved by iLoc-Hum was 76%, which is remarkably higher than that by any of the existing predictors that also have the capacity to deal with this kind of system. Further comparisons were also made via two independent datasets; all indicated that the success rates by iLoc-Hum were even more significantly higher than its counterparts. As a user-friendly web-server, iLoc-Hum is freely accessible to the public at http://icpr.jci.edu.cn/bioinfo/iLoc-Hum or http://www.jci-bioinfo.cn/iLoc-Hum. For the convenience of most experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results by choosing either a straightforward submission or a batch submission, without the need to follow the complicated mathematical equations involved.
Starting Page	629
Ending Page	641
Page Count	13
File Format	HTM / HTML PDF
ISSN	1742206X
Volume Number	8
Issue Number	2
Journal	Molecular BioSystems
DOI	10.1039/c1mb05420a
Language	English
Publisher	Royal Society of Chemistry
Access Restriction	Open
Subject Keyword	Cross-validation (statistics) Cell membrane Lysosome Microsome Protein Golgi apparatus Mitochondrion Synapse Peroxisome Cytoskeleton Golgi Jackknife resampling Endoplasmic reticulum Endosome Centrosome Cytoplasm
Content Type	Text
Resource Type	Article
Subject	Molecular Biology Biotechnology

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iLoc-Animal: a multi-label learning classifier for predicting subcellular localization of animal proteins

iLoc-Plant: a multi-label classifier for predicting the subcellular localization of plant proteins with both single and multiple sites

Cytotoxic Effect of Amyloid-β1-42 Oligomers on Endoplasmic Reticulum and Golgi Apparatus Arrangement in SH-SY5Y Neuroblastoma Cells.

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iLoc-Hum: using the accumulation-label scale to predict subcellular locations of human proteins with both single and multiple sites