Dataset Information

Identification of Differentially Expressed Splice Variants by the Proteogenomic Pipeline Splicify.

ABSTRACT: Proteogenomics, i.e. comprehensive integration of genomics and proteomics data, is a powerful approach identifying novel protein biomarkers. This is especially the case for proteins that differ structurally between disease and control conditions. As tumor development is associated with aberrant splicing, we focus on this rich source of cancer specific biomarkers. To this end, we developed a proteogenomic pipeline, Splicify, which can detect differentially expressed protein isoforms. Splicify is based on integrating RNA massive parallel sequencing data and tandem mass spectrometry proteomics data to identify protein isoforms resulting from differential splicing between two conditions. Proof of concept was obtained by applying Splicify to RNA sequencing and mass spectrometry data obtained from colorectal cancer cell line SW480, before and after siRNA-mediated downmodulation of the splicing factors SF3B1 and SRSF1. These analyses revealed 2172 and 149 differentially expressed isoforms, respectively, with peptide confirmation upon knock-down of SF3B1 and SRSF1 compared with their controls. Splice variants identified included RAC1, OSBPL3, MKI67, and SYK. One additional sample was analyzed by PacBio Iso-Seq full-length transcript sequencing after SF3B1 downmodulation. This analysis verified the alternative splicing identified by Splicify and in addition identified novel splicing events that were not represented in the human reference genome annotation. Therefore, Splicify offers a validated proteogenomic data analysis pipeline for identification of disease specific protein biomarkers resulting from mRNA alternative splicing. Splicify is publicly available on GitHub (https://github.com/NKI-TGO/SPLICIFY) and suitable to address basic research questions using pre-clinical model systems as well as translational research questions using patient-derived samples, e.g. allowing to identify clinically relevant biomarkers.

SUBMITTER: Komor MA

PROVIDER: S-EPMC5629268 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

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Publications

Identification of Differentially Expressed Splice Variants by the Proteogenomic Pipeline Splicify.

Komor Malgorzata A MA Pham Thang V TV Hiemstra Annemieke C AC Piersma Sander R SR Bolijn Anne S AS Schelfhorst Tim T Delis-van Diemen Pien M PM Tijssen Marianne M Sebra Robert P RP Ashby Meredith M Meijer Gerrit A GA Jimenez Connie R CR Fijneman Remond J A RJA

Molecular & cellular proteomics : MCP 20170726 10

Proteogenomics, <i>i.e.</i> comprehensive integration of genomics and proteomics data, is a powerful approach identifying novel protein biomarkers. This is especially the case for proteins that differ structurally between disease and control conditions. As tumor development is associated with aberrant splicing, we focus on this rich source of cancer specific biomarkers. To this end, we developed a proteogenomic pipeline, Splicify, which can detect differentially expressed protein isoforms. Splic ...[more]

PMID: 28747380

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Project description:Proteogenomics, i.e. comprehensive integration of genomics and proteomics data, is a powerful approach identifying novel protein biomarkers. This is especially the case for proteins that differ structurally between disease and control conditions. As tumor development is associated with aberrant splicing, we focus on this rich source of cancer specific biomarkers. To this end, we developed a proteogenomic pipeline, Splicify, which is able to detect differentially expressed protein isoforms. Splicify is based on integrating RNA massive parallel sequencing data and tandem mass spectrometry proteomics data to identify protein isoforms resulting from differential splicing between two conditions. Proof of concept was obtained by applying Splicify to RNA sequencing and mass spectrometry data obtained from colorectal cancer cell line SW480, before and after siRNA-mediated down-modulation of the splicing factors SF3B1 and SRSF1. These analyses revealed 2172 and 149 differentially expressed isoforms, respectively, with peptide confirmation upon knock-down of SF3B1 and SRSF1 compared to their controls. Splice variants identified included RAC1, OSBPL3, MKI67 and SYK. One additional sample was analyzed by PacBio Iso-Seq full-length transcript sequencing after SF3B1 down-modulation. This analysis verified the alternative splicing identified by Splicify and in addition identified novel splicing events that were not represented in the human reference genome annotation. Therefore, Splicify offers a validated proteogenomic data analysis pipeline for identification of disease specific protein biomarkers resulting from mRNA alternative splicing. Splicify is publicly available on GitHub (https://github.com/NKI-TGO/SPLICIFY) and suitable to address basic research questions using pre-clinical model systems as well as translational research questions using patient-derived samples, e.g. allowing to identify clinically relevant biomarkers. This dataset corresponds to a publication in Molecular & Cellular Proteomics 16: 10.1074/mcp.TIR117.000056, 1850–1863, 2017, PMID: 28747380. Mass spectrometry data corresponding to this entry is available at ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD006486.

Dataset Information

Identification of Differentially Expressed Splice Variants by the Proteogenomic Pipeline Splicify.

Publications

Identification of Differentially Expressed Splice Variants by the Proteogenomic Pipeline Splicify.

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