Predicting subcellular localization of proteins using machine-learned classifiers

Lu, Zhiyong; Szafron, Duane; Greiner, Russ; Lu, Paul; Wishart, David S.; Poulin, Brett; Anvik, John; Macdonell, Cam; Eisner, Roman

doi:10.1093/bioinformatics/btg447

Cited by 312 publications

(145 citation statements)

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“…For example, TAIR is currently using the TargetP system (Emanuelsson et al, 2000) for annotating the complete subcellular proteome of Arabidopsis (ftp://ftp.arabidopsis.org/home/ tair/Proteins/Properties/TargetP_analysis.tair9). We compared not only TargetP but some other tools, such as LOCtree (Nair and Rost, 2005), PA-SUB (Lu et al, 2004), MultiLoc (Hö glund et al, 2006), WoLF PSORT (Horton et al, 2007), and Plant-PLoc (Chou and Shen, 2007b), all of which originally reported good accuracy. However, a number of previous researchers (Emanuelsson, 2002;Heazlewood et al, 2004Heazlewood et al, , 2005 found only 40% to 50% accuracy of the existing systems in their experimental data sets when testing the available tools for Arabidopsis annotation.…”

Section: Benchmarking On Independent Data Sets and Comparison With Otmentioning

confidence: 99%

“…We compared the performance of AtSubP on two diverse Arabidopsisspecific independent data sets (I and II) with some of the widely used tools, such as TargetP (Emanuelsson et al, 2000), LOCtree (Nair and Rost, 2005), PA-SUB (Lu et al, 2004), MultiLoc (Höglund et al, 2006), WoLF PSORT (Horton et al, 2007), and Plant-PLoc (Chou and Shen, 2007b). Although technically, the comparison with other methods might not be fair, as each of these methods was developed with different sets of training data, our main emphasis was to demonstrate how these general tools performed for individual genome annotation (e.g.…”

Section: Comparison With Other Prediction Programsmentioning

confidence: 99%

“…In plants, some widely used prediction tools are TargetP (Emanuelsson et al, 2000), LOCtree (Nair and Rost, 2005), PA-SUB (Lu et al, 2004), MultiLoc (Hö glund et al, 2006), WoLF PSORT (updated version of PSORT II; Horton et al, 2007), and Plant-PLoc (Chou and Shen, 2007b), all having good accuracy (greater than 70%). A recent computational effort was made in developing a plant species-specific prediction system, RSLpred, for genome-wide subcellular localization annotations of rice (Oryza sativa) proteins (Kaundal and Raghava, 2009).…”

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confidence: 99%

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

2010

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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 (for Arabidopsis subcellular localization predictor) that is based on the combinatorial presence of diverse protein features, such as its amino acid composition, sequence-order effects, terminal information, Position-Specific Scoring Matrix, and similarity search-based Position-Specific Iterated-Basic Local Alignment Search Tool information. When used to predict seven subcellular compartments through a 5-fold cross-validation test, our hybrid-based best classifier achieved an overall sensitivity of 91% with high-confidence precision and Matthews correlation coefficient values of 90.9% and 0.89, respectively. Benchmarking AtSubP on two independent data sets, one from Swiss-Prot and another containing green fluorescent protein-and mass spectrometry-determined proteins, showed a significant improvement in the prediction accuracy of species-specific AtSubP over some widely used "general" tools such as TargetP, LOCtree, PA-SUB, MultiLoc, WoLF PSORT, Plant-PLoc, and our newly created All-Plant method.

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Section: Benchmarking On Independent Data Sets and Comparison With Otmentioning

confidence: 99%

Section: Comparison With Other Prediction Programsmentioning

confidence: 99%

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

2010

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“…Over the years, a number of homology-based predictors have been proposed. For example, Proteome Analyst [9] computes the feature vectors for classification by using the presence or absence of some tokens from certain fields of the homologous sequences in the SwissProt database. Recently, a predictor called PairProSVM was proposed by Mak et al [10], which applies profile alignment to detect weak similarity between protein sequences.…”

Section: Introductionmentioning

confidence: 99%

Protein subcellular localization prediction based on profile alignment and Gene Ontology

Wan

Mak

Kung

2011

2011 IEEE International Workshop on Machine Learning for Signal Processing

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The functions of proteins are closely related to their subcellular locations. Computational methods are required to replace the laborious and time-consuming experimental processes for proteomics research. This paper proposes combining homology-based profile alignment methods and functionaldomain based Gene Ontology (GO) methods to predict the subcellular locations of proteins. The feature vectors constructed by these two methods are recognized by support vector machine (SVM) classifiers, and their scores are fused to enhance classification performance. The paper also investigates different approaches to constructing the GO vectors based on the GO terms returned from InterProScan. The results demonstrate that the GO methods are comparable to profile-alignment methods and overshadow those based on amino-acid compositions. Also, the fusion of these two methods can outperform the individual methods.

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“…A number of systems for predicting protein localization from sequence have been described. 5,8,14,17,18 The limitation of these systems is that they can only assign new proteins to the location categories with which they have been trained. This means that proteins with previously unseen location patterns cannot be properly categorized.…”

Section: Introductionmentioning

confidence: 99%

Large-Scale Automated Analysis of Location Patterns in Randomly Tagged 3T3 Cells

et al. 2007

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Abstract-Location proteomics is concerned with the systematic analysis of the subcellular location of proteins. In order to perform high-resolution, high-throughput analysis of all protein location patterns, automated methods are needed. Here we describe the use of such methods on a large collection of images obtained by automated microscopy to perform high-throughput analysis of endogenous proteins randomly-tagged with a fluorescent protein in NIH 3T3 cells. Cluster analysis was performed to identify the statistically significant location patterns in these images. This allowed us to assign a location pattern to each tagged protein without specifying what patterns are possible. To choose the best feature set for this clustering, we have used a novel method that determines which features do not artificially discriminate between control wells on different plates and uses Stepwise Discriminant Analysis (SDA) to determine which features do discriminate as much as possible among the randomly-tagged wells. Combining this feature set with consensus clustering methods resulted in 35 clusters among the first 188 clones we obtained. This approach represents a powerful automated solution to the problem of identifying subcellular locations on a proteome-wide basis for many different cell types.

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Predicting subcellular localization of proteins using machine-learned classifiers

Cited by 312 publications

References 15 publications

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

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

Protein subcellular localization prediction based on profile alignment and Gene Ontology

Large-Scale Automated Analysis of Location Patterns in Randomly Tagged 3T3 Cells

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