Abstract
Identifying membrane proteins and their multi-functional types is an indispensable yet challenging topic in proteomics and bioinformatics. In this article, we provide data that are used for training and testing Mem-ADSVM (Wan et al., 2016. “Mem-ADSVM: a two-layer multi-label predictor for identifying multi-functional types of membrane proteins” [1]), a two-layer multi-label predictor for predicting multi-functional types of membrane proteins.
Specifications Table
| Subject area | Biology |
| More specific subject area | Bioinformatics/Computational Biology |
| Type of data | Text |
| How data was acquired | Process datasets that were obtained by searching against the UniProtKB/Swiss-Prot database with a series of stringent criteria |
| Data format | Analyzed |
| Experimental factors | Proteins were manually annotated and were extracted from UniProtKB. |
| Experimental features | For each protein sequence, its associated gene ontology (GO) information was retrieved by searching a compact GO-term database[2], [3], [4]with its homologous accession number. |
| Data source location | Hong Kong SAR, China |
| Data accessibility | The dataset is available with this article andhttp://bioinfo.eie.polyu.edu.hk/MemADSVMServer/datasets.html |
Value of the data
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Knowing the functional types of membrane proteins can be helpful to elucidate the biological functions of membrane proteins.
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This article presents the first comprehensive dataset that contains non-membrane proteins, single-functional-type membrane proteins and multi-functional-type membrane proteins.
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The dataset presented here can be used as an important benchmark dataset to evaluate the performance of membrane-protein predictors.
1. Data
Using benchmark datasets for evaluating the performance of predictors are of great significance in various domains of bioinformatics [5], [6], [7], [8], [9], [10], such as membrane protein type prediction [11]. However, existing benchmark datasets for predicting membrane proteins are either incomplete or non-stringent. This data article describes a stringent and comprehensive benchmark dataset that comprises non-membrane proteins, single-functional-type membrane proteins and multi-functional-type membrane proteins. All of the benchmark datasets (Dataset II(C) together with Dataset I, Dataset II(A) and Dataset II(B)) are accessible from the link in http://bioinfo.eie.polyu.edu.hk/MemADSVMServer/datasets.html.
2. Experimental design, materials and methods
The dataset (we named as ‘Dataset II(C)’) here is a benchmark dataset to evaluate Mem-ADSVM [1], a webserver to identify membrane proteins and their multi-functional types. Dataset II(C) was created based on two previous datasets [5], [8], which we named as Dataset I [5] and Dataset II(A) [8]. First, we retrieved all of the 7965 non-membrane proteins in Dataset I. The procedures to create Dataset I are as follows: (1) select proteins in the UniProtKB/Swiss-Prot database; (2) exclude those protein sequences annotated with “fragment” (3) exclude those protein sequences with less than 50 amino acid residues; (4) remove those protein sequences annotated with ambiguous words, such as “by similarity”, “potential”, “probable”, etc.; (5) remove those sequences which are annotated with “membrane protein” (6) use BLASTCLUST [12] to reduce the sequence similarity to no more than 80%. The procedures for obtaining Dataset II(A) are similar to those for Dataset I except that the former collected membrane proteins instead of excluding them, and the former reduced the sequence identity to 25% instead of 80%. Because the sequence identity of Dataset I (80%) was much higher than that of Dataset II(A) (25%), we used BLASTCLUST to reduce the sequence similarity to 25%, leading to 2009 non-membrane proteins. Then, we combined these 2009 non-membrane proteins with Dataset II(A) (5307 membrane proteins) to constitute Dataset II(C) with a total of 7316 proteins, of which 7126 belong to one type, 185 to two types and 5 to three types. Specifically, the distribution of Dataset II(C) is as follows: (1) 626 single-pass type I, (2) 299 single-pass type II, (3) 42 single-pass type III, (4) 73 single-pass type IV, (5) 2437 multi-pass, (6) 403 Lipid-anchor, (7) 172 GPI-anchor, (8) 1450 peripheral and (9) 2009 non-membrane.
Acknowledgments
This work was in part supported by the RGC of Hong Kong SAR Grant nos. PolyU152117/14E and PolyU152068/15E.
Footnotes
Transparency associated with this article can be found in the online version at doi:10.1016/j.dib.2016.05.024.
Supplementary data associated with this article can be found in the online version at doi:10.1016/j.dib.2016.05.024.
Contributor Information
Shibiao Wan, Email: shibiao.wan@connect.polyu.hk.
Man-Wai Mak, Email: enmwmak@polyu.edu.hk.
Sun-Yuan Kung, Email: kung@princeton.edu.
Appendix A. Transparency document
Supplementary material
Appendix B. Supplementary material
Supplementary material
References
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Supplementary Materials
Supplementary material
Supplementary material