SpliceVista, a Tool for Splice Variant Identification and Visualization in Shotgun Proteomics Data

摘要

Alternative splicing is a pervasive process in eukaryotic organisms. More than 90% of human genes have alternatively spliced products, and aberrant splicing has been shown to be associated with many diseases. Current methods employed in the detection of splice variants include prediction by clustering of expressed sequence tags, exon microarray, and mRNA sequencing, all methods focusing on RNA-level information. There is a lack of tools for analyzing splice variants at the protein level. Here, we present SpliceVista, a tool for splice variant identification and visualization based on mass spectrometry proteomics data. SpliceVista retrieves gene structure and translated sequences from alternative splicing databases and maps MS-identified peptides to splice variants. The visualization module plots the exon composition of each splice variant and aligns identified peptides with transcript positions. If quantitative mass spectrometry data are used, SpliceVista plots the quantitative patterns for each peptide and provides users with the option to cluster peptides based on their quantitative patterns. SpliceVista can identify splice-variant-specific peptides, providing the possibility for variant-specific analysis. The tool was tested on two experimental datasets (PXD000065 and PXD000134). In A431 cells treated with gefitinib, 2983 splice-variant-specific peptides corresponding to 939 splice variants were identified. Through comparison of splice-variant-centric, protein-centric, and gene-centric quantification, several genes (e.g. EIF4H) were found to have differentially regulated splice variants after gefitinib treatment. The same discrepancy between protein-centric and splice-centric quantification was detected in the other dataset, in which induced pluripotent stem cells were compared with parental fibroblast and human embryotic stem cells. In addition, SpliceVista can be used to visualize novel splice variants inferred from peptide-level evidence. In summary, SpliceVista enables visualization, detection, and differential quantification of protein splice variants that are often missed in current proteomics pipelines. Alternative splicing is a pervasive process in eukaryotic organisms. More than 90% of human genes have alternatively spliced products, and aberrant splicing has been shown to be associated with many diseases. Current methods employed in the detection of splice variants include prediction by clustering of expressed sequence tags, exon microarray, and mRNA sequencing, all methods focusing on RNA-level information. There is a lack of tools for analyzing splice variants at the protein level. Here, we present SpliceVista, a tool for splice variant identification and visualization based on mass spectrometry proteomics data. SpliceVista retrieves gene structure and translated sequences from alternative splicing databases and maps MS-identified peptides to splice variants. The visualization module plots the exon composition of each splice variant and aligns identified peptides with transcript positions. If quantitative mass spectrometry data are used, SpliceVista plots the quantitative patterns for each peptide and provides users with the option to cluster peptides based on their quantitative patterns. SpliceVista can identify splice-variant-specific peptides, providing the possibility for variant-specific analysis. The tool was tested on two experimental datasets (PXD000065 and PXD000134). In A431 cells treated with gefitinib, 2983 splice-variant-specific peptides corresponding to 939 splice variants were identified. Through comparison of splice-variant-centric, protein-centric, and gene-centric quantification, several genes (e.g. EIF4H) were found to have differentially regulated splice variants after gefitinib treatment. The same discrepancy between protein-centric and splice-centric quantification was detected in the other dataset, in which induced pluripotent stem cells were compared with parental fibroblast and human em