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A comparative analysis of computational approaches and algorithms for protein subcomplex identification.

ABSTRACT: High-throughput AP-MS methods have allowed the identification of many protein complexes. However, most post-processing methods of this type of data have been focused on detection of protein complexes and not its subcomplexes. Here, we review the results of some existing methods that may allow subcomplex detection and propose alternative methods in order to detect subcomplexes from AP-MS data. We assessed and drew comparisons between the use of overlapping clustering methods, methods based in the core-attachment model and our own prediction strategy (TRIBAL). The hypothesis behind TRIBAL is that subcomplex-building information may be concealed in the multiple edges generated by an interaction repeated in different contexts in raw data. The CACHET method offered the best results when the evaluation of the predicted subcomplexes was carried out using both the hypergeometric and geometric scores. TRIBAL offered the best performance when using a strict meet-min score.

SUBMITTER: Zaki N

PROVIDER: S-EPMC3939454 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature

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A comparative analysis of computational approaches and algorithms for protein subcomplex identification.

Zaki Nazar N Mora Antonio A

Scientific reports 20140303

High-throughput AP-MS methods have allowed the identification of many protein complexes. However, most post-processing methods of this type of data have been focused on detection of protein complexes and not its subcomplexes. Here, we review the results of some existing methods that may allow subcomplex detection and propose alternative methods in order to detect subcomplexes from AP-MS data. We assessed and drew comparisons between the use of overlapping clustering methods, methods based in the ...[more]

PMID: 24584908

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Project description:BACKGROUND: Finding the genetic causes of quantitative traits is a complex and difficult task. Classical methods for mapping quantitative trail loci (QTL) in miceuse an F2 cross between two strains with substantially different phenotype and an interval mapping method to compute confidence intervals at each position in the genome. This process requires significant resources for breeding and genotyping, and the data generated are usually only applicable to one phenotype of interest. Recently, we reported the application of a haplotype association mapping method which utilizes dense genotyping data across a diverse panel of inbred mouse strains and a marker association algorithm that is independent of any specific phenotype. As the availability of genotyping data grows in size and density, analysis of these haplotype association mapping methods should be of increasing value to the statistical genetics community. RESULTS: We describe a detailed comparative analysis of variations on our marker association method. In particular, we describe the use of inferred haplotypes from adjacent SNPs, parametric and nonparametric statistics, and control of multiple testing error. These results show that nonparametric methods are slightly better in the test cases we study, although the choice of test statistic may often be dependent on the specific phenotype and haplotype structure being studied. The use of multi-SNP windows to infer local haplotype structure is critical to the use of a diverse panel of inbred strains for QTL mapping. Finally, because the marginal effect of any single gene in a complex disease is often relatively small, these methods require the use of sensitive methods for controlling family-wise error. We also report our initial application of this method to phenotypes cataloged in the Mouse Phenome Database. CONCLUSION: The use of inbred strains of mice for QTL mapping has many advantages over traditional methods. However, there are also limitations in comparison to the traditional linkage analysis from F2 and RI lines. Application of these methods requires careful consideration of algorithmic choices based on both theoretical and practical factors. Our findings suggest general guidelines, though a complete evaluation of these methods can only be performed as more genetic data in complex diseases becomes available.

Dataset Information

A comparative analysis of computational approaches and algorithms for protein subcomplex identification.

Publications

A comparative analysis of computational approaches and algorithms for protein subcomplex identification.

Similar Datasets

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