2010
DOI: 10.1104/pp.110.156851
|View full text |Cite
|
Sign up to set email alerts
|

Combining Machine Learning and Homology-Based Approaches to Accurately Predict Subcellular Localization in Arabidopsis

Abstract: A complete map of the Arabidopsis (Arabidopsis thaliana) proteome is clearly a major goal for the plant research community in terms of determining the function and regulation of each encoded protein. Developing genome-wide prediction tools such as for localizing gene products at the subcellular level will substantially advance Arabidopsis gene annotation. To this end, we performed a comprehensive study in Arabidopsis and created an integrative support vector machine-based localization predictor called AtSubP (… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

3
74
1
1

Year Published

2011
2011
2017
2017

Publication Types

Select...
6
2
1

Relationship

0
9

Authors

Journals

citations
Cited by 82 publications
(79 citation statements)
references
References 63 publications
3
74
1
1
Order By: Relevance
“…Because XRCC4 is a DSB repair protein, it was predicted to localize to the nucleus (AtSubP) (Kaundal et al, 2010). However, our results surprisingly showed localization in both cytoplasmic and nuclear compartments (see Supplemental Figure 5 online).…”
contrasting
confidence: 53%
See 1 more Smart Citation
“…Because XRCC4 is a DSB repair protein, it was predicted to localize to the nucleus (AtSubP) (Kaundal et al, 2010). However, our results surprisingly showed localization in both cytoplasmic and nuclear compartments (see Supplemental Figure 5 online).…”
contrasting
confidence: 53%
“…For localization predictions, XRCC4 sequence was processed with AtSubP (Kaundal et al, 2010), a species-specific predictor for protein localization. For localization assay, At-XRCC4 ORF was fused to GFP at the N terminus, and the construct was subsequently agroinfiltrated into N. benthamiana leaves, followed by observation at 48 HAI under a fluorescence microscope.…”
Section: Y2h Subcellular Localization and Bifc Assaysmentioning
confidence: 99%
“…A complete map of a plant proteome is clearly a major goal for plant research community in terms of determining the function and regulation of each encoded protein. An integrative support vector machine-based localization predictor called AtSubP was developed (Kaundal et al 2010) which was based on the combinatorial presence of diverse protein features, viz., amino acid composition, sequence-order effects, terminal information, position-specific scoring matrix, and similarity search-based, positionspecific, iterated Basic Local Alignment Search Tool information. The model predicted seven subcellular compartments through fivefold crossvalidation test and achieved an overall sensitivity of 91 % with high-confidence precision and Matthew's correlation coefficient values of 90.9 % and 0.89, respectively.…”
Section: Applications Of Support Vector Machine In Plant Biologymentioning
confidence: 99%
“…To date, the machine learning approaches have ranged from use of primary amino acid sequences, localization-specific functional domains, and sorting signals of proteins, to mining of functional annotation data (Mott et al, 2002;Guda and Subramaniam, 2005;Gardy and Brinkman, 2006;Garg et al, 2009). Some of these approaches have been used in combination (Kaundal et al, 2010). In the current studies, machine learning algorithms profiling protein physicochemical properties were used to explore the subcellular location of the native and GFP-tagged SULT1C1 proteins.…”
Section: Introductionmentioning
confidence: 99%