Identification of Differentially Expressed Splice Variants by the Proteogenomic Pipeline Splicify
Ontology highlight
ABSTRACT:
OTHER RELATED OMICS DATASETS IN: PXD006486
PROVIDER: PRJNA422579 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR6380170_1.fastq.gz | Fastqsanger.gz | |||
| SRR6380170_2.fastq.gz | Fastqsanger.gz | |||
| SRR6380180_1.fastq.gz | Fastqsanger.gz | |||
| SRR6380180_2.fastq.gz | Fastqsanger.gz | |||
| SRR6380190_1.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 68 |
Similar Datasets
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.
2017-12-16 | GSE108140 | GEO
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.
2017-07-28 | PXD006486 | Pride
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 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.
| S-EPMC5629268 | biostudies-literature
Project description:Aberrant splice variants are involved in the initiation and/or progression of glial brain tumors. We therefore set out to identify splice variants that are differentially expressed between histological subgroups of gliomas. Splice variants were identified using a novel platform that profiles the expression of virtually all known and predicted exons present in the human genome. Exon-level expression profiling was performed on 26 glioblastomas, 22 oligodendrogliomas and 6 control brain samples. Our results demonstrate that Human Exon arrays can identify subgroups of gliomas based on their histological appearance and genetic aberrations. We next used our expression data to identify differentially expressed splice variants. In two independent approaches, we identified 49 and up to 459 exons that are differentially spliced between glioblastomas and oligodendrogliomas a subset of which (47% and 33%) were confirmed by RT-PCR. In addition, exon-level expression profiling also identified >700 novel exons. Expression of ~67% of these candidate novel exons was confirmed by RT-PCR. Our results indicate that exon-level expression profiling can be used to molecularly classify brain tumor subgroups, can identify differentially regulated splice variants and can identify novel exons. The splice variants identified by exon-level expression profiling may help to detect the genetic changes that cause or maintain gliomas and may serve as novel treatment targets. Keywords: cell type comparison 6 adult non diseased brain, 26 glioblastomas, 21 oligodendrogliomas
2008-01-28 | E-GEOD-9385 | biostudies-arrayexpress
Project description:Aberrant splice variants are involved in the initiation and/or progression of glial brain tumors. We therefore set out to identify splice variants that are differentially expressed between histological subgroups of gliomas. Splice variants were identified using a novel platform that profiles the expression of virtually all known and predicted exons present in the human genome. Exon-level expression profiling was performed on 26 glioblastomas, 22 oligodendrogliomas and 6 control brain samples. Our results demonstrate that Human Exon arrays can identify subgroups of gliomas based on their histological appearance and genetic aberrations. We next used our expression data to identify differentially expressed splice variants. In two independent approaches, we identified 49 and up to 459 exons that are differentially spliced between glioblastomas and oligodendrogliomas a subset of which (47% and 33%) were confirmed by RT-PCR. In addition, exon-level expression profiling also identified >700 novel exons. Expression of ~67% of these candidate novel exons was confirmed by RT-PCR. Our results indicate that exon-level expression profiling can be used to molecularly classify brain tumor subgroups, can identify differentially regulated splice variants and can identify novel exons. The splice variants identified by exon-level expression profiling may help to detect the genetic changes that cause or maintain gliomas and may serve as novel treatment targets. Keywords: cell type comparison
2008-01-29 | GSE9385 | GEO
Project description:Purpose: The goal of this study was to use deep sequencing to identify all splice variants of Calcium/calmodulin-dependent kinase II (CaMKII) expressed in the human hippocampus. Methods: Transcripts of CaMKII-encoding genes (CAMK2A, CAMK2B, CAMK2G, and CAMK2D) were sub-amplified by PCR from total RNA extracted from human hippocampal tissue samples from 3 donors. Illumina sequencing libraries were constructed by PCR from these initial pools of amplicons and sequenced on an Illumina MiSeq instrument. Sequencing reads passing quality controls were clustered on the basis of sequence identity or near-identity. Consensus sequences of clusters were mapped with known exons of CaMKII genes to identify the splice variant represented by each cluster. Donor 1 replicate 2, Donor 2, and Donor 3 libraries from genes CAMK2B, CAMK2G, and CAMK2D were first sequenced on a MiSeq Nano flow cell, then re-pooled for read balancing and sequenced again of a full-size MiSeq flow cell. For each library, reads from Nano and full-size flow cells were combined for subsequent analysis. Results: We perfomed the first comprehensive survey of CaMKII transcripts expressed in individual tissue samples (human hippocampus). We detected a total of 79 splice variants of the four human CaMKIIs: CaMKIIα (3), CaMKIIβ (30), CaMKIIγ (24), and CaMKIIδ (22), across tissue samples from 3 donors. This represents the vast majority of possible in-frame CaMKII splice variants (Sloutsky and Stratton, European Journal of Neuroscience, 2020; https://doi.org/10.1111/ejn.14761).
2020-07-21 | GSE154030 | GEO
Project description:The identifcation of alternatively spliced transcript variants specific to particular biological processes in tumours should increase our understanding of cancer. Hypoxia is an important factor in cancer biology and associated splice variants may present new markers to help with planning treatment. A method was developed to analyse alternative splicing in exon array data, using probeset multiplicity to identify genes with changes in expression across their loci, and a combination of the splicing index and a new metric based on the variation of reliability weighted fold changes to detect changes in the splicing patterns. The approach was validated on a cancer/normal sample dataset in which alternative splicing events had been confirmed using RT-PCR. We then analysed ten head and neck squamous cell carcinomas using exon arrays and identified differentially expressed splice variants in five samples with high versus five with low levels of hypoxia-associated genes (Winter et al, 2007; Cancer Res 67:3441-9). The analysis identified a splice variant of LAMA3 (Laminin 3), LAMA3-A, known to be involved in tumour cell invasion and progression. The full-length transcript of the gene (LAMA3-B) did not appear to be hypoxia-associated. The results were confirmed using qualitative real time PCR. In a series of 59 prospectively-collected head and neck tumours (Winter et al, 2007; Cancer Res 67:3441-9), expression of LAMA3-A had prognostic significance whereas LAMA3-B did not. This work illustrates the potential for alternatively spliced transcripts to act as biomarkers of disease prognosis with improved specificity for particular tissues or conditions over assays which do not discriminate between splice variants.
2009-10-07 | GSE18300 | GEO
Project description:Identification of CaMKII splice variants expressed in the human hippocampus
| PRJNA644901 | ENA
Project description:Proteins with a PDZ (for PSD-95, DLG, ZO-1) and one to three LIM (for Lin11, Isl-1, Mec-3) domains are scaffolding sarcomeric and cytoskeletal elements that form structured muscle fibres and provide for the link to intracellular signalling by selectively associating protein kinases, ion channels, and transcription factors with the mechanical stress-strain sensors. Enigma homolog (ENH) is a PDZ-LIM protein with four splice variants: ENH1 with an N-terminal PDZ domain and three C-terminal LIM domains and ENH2, ENH3, and ENH4 without LIM domains. We addressed the functional role of ENH alternative splicing.We studied the expression of the four ENH isoforms in the heart during development and in a mouse model of heart hypertrophy. All four isoforms are expressed in the heart but the pattern of expression is clearly different between embryonic, neonatal, and adult stages. ENH1 appears as the embryonic isoform, whereas ENH2, ENH3, and ENH4 are predominant in adult heart. Moreover, alternative splicing of ENH was changed following induction of heart hypertrophy, producing an ENH isoform pattern similar to that of neonatal heart. Next, we tested a possible causal role of ENH1 and ENH4 in the development of cardiac hypertrophy. When overexpressed in rat neonatal cardiomyocytes, ENH1 promoted the expression of hypertrophy markers and increased cell volume, whereas, on the contrary, ENH4 overexpression prevented these changes.Antagonistic splice variants of ENH may play a central role in the adaptive changes of the link between mechanical stress-sensing and signalling occurring during embryonic development and/or heart hypertrophy.
| S-EPMC2868178 | biostudies-other
Project description:Analysis of human macrophage splice variants
| PRJNA448000 | ENA