Detection and analysis of disease-associated single nucleotide polymorphism influencing post-translational modification.

Post-translational modification (PTM) plays a crucial role in biological functions and corresponding disease developments. Discovering disease-associated non-synonymous SNPs (nsSNPs) altering PTM sites can help to estimate the various PTM candidates involved in diseases, therefore, an integrated analysis between SNPs, PTMs and diseases is necessary. However, only a few types of PTMs affected by nsSNPs have been studied without considering disease-association until now. In this study, we developed a new database called PTM-SNP which contains a comprehensive collection of human nsSNPs that affect PTM sites, together with disease information. Total 179,325 PTM-SNPs were collected by aligning missense SNPs and stop-gain SNPs on PTM sites (position 0) or their flanking region (position -7 to 7). Disease-associated SNPs from GWAS catalogs were also matched with detected PTM-SNP to find disease associated PTM-SNPs. Our result shows PTM-SNPs are highly associated with diseases, compared with other nsSNP sites and functional classes including near gene, intron and so on. PTM-SNP can provide an insight about discovering important PTMs involved in the diseases easily through the web site. PTM-SNP is freely available at http://gcode.kaist.ac.kr/ptmsnp.

Finding type 2 diabetes causal single nucleotide polymorphism combinations and functional modules from genome-wide association data.

BACKGROUND: Due to the low statistical power of individual markers from a genome-wide association study (GWAS), detecting causal single nucleotide polymorphisms (SNPs) for complex diseases is a challenge. SNP combinations are suggested to compensate for the low statistical power of individual markers, but SNP combinations from GWAS generate high computational complexity. METHODS: We aim to detect type 2 diabetes (T2D) causal SNP combinations from a GWAS dataset with optimal filtration and to discover the biological meaning of the detected SNP combinations. Optimal filtration can enhance the statistical power of SNP combinations by comparing the error rates of SNP combinations from various Bonferroni thresholds and p-value range-based thresholds combined with linkage disequilibrium (LD) pruning. T2D causal SNP combinations are selected using random forests with variable selection from an optimal SNP dataset. T2D causal SNP combinations and genome-wide SNPs are mapped into functional modules using expanded gene set enrichment analysis (GSEA) considering pathway, transcription factor (TF)-target, miRNA-target, gene ontology, and protein complex functional modules. The prediction error rates are measured for SNP sets from functional module-based filtration that selects SNPs within functional modules from genome-wide SNPs based expanded GSEA. RESULTS: A T2D causal SNP combination containing 101 SNPs from the Wellcome Trust Case Control Consortium (WTCCC) GWAS dataset are selected using optimal filtration criteria, with an error rate of 10.25%. Matching 101 SNPs with known T2D genes and functional modules reveals the relationships between T2D and SNP combinations. The prediction error rates of SNP sets from functional module-based filtration record no significance compared to the prediction error rates of randomly selected SNP sets and T2D causal SNP combinations from optimal filtration. CONCLUSIONS: We propose a detection method for complex disease causal SNP combinations from an optimal SNP dataset by using random forests with variable selection. Mapping the biological meanings of detected SNP combinations can help uncover complex disease mechanisms.

Identification of ubiquitin/ubiquitin-like protein modification from tandem mass spectra with various PTMs.

BACKGROUND: Various solutions have been introduced for the identification of post-translational modification (PTM) from tandem mass spectrometry (MS/MS) in proteomics field but the identification of peptide modifiers, such as Ubiquitin (Ub) and ubiquitin-like proteins (Ubls), is still a challenge. The fragmentation of peptide modifier produce complex shifted ion mass patterns in combination with other PTMs, which makes it difficult to identify and locate the PTMs on a protein sequence. Currently, most PTM identification methods do not consider the complex fragmentation of peptide modifier or deals it separately from the other PTMs. RESULTS: We developed an advanced PTM identification method that inspects possible ion patterns of the most known peptide modifiers as well as other known biological and chemical PTMs to make more comprehensive and accurate conclusion. The proposed method searches all detectable mass differences of measured peaks from their theoretical values and the mass differences within mass tolerance range are grouped as mass shift classes. The most possible locations of multiple PTMs including peptide modifiers can be determined by evaluating all possible scenarios generated by the combination of the qualified mass shift classes.The proposed method showed excellent performance in the test with simulated spectra having various PTMs including peptide modifiers and in the comparison with recently developed methods such as QuickMod and SUMmOn. In the analysis of HUPO Brain Proteome Project (BPP) datasets, the proposed method could find the ubiquitin modification sites that were not identified by other conventional methods. CONCLUSIONS: This work presents a novel method for identifying bothpeptide modifiers that generate complex fragmentation patternsand PTMs that are not fragmented during fragmentation processfrom tandem mass spectra.