FusionSeq: a Modular Framework for Finding Gene Fusions by Analyzing Paired-End RNA Sequencing Data
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ABSTRACT:
PROVIDER: PRJNA74881 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:We have developed FusionSeq to identify fusion transcripts from paired-end RNA-sequencing. FusionSeq includes filters to remove spurious candidate fusions with artifacts, such as misalignment or random pairing of transcript fragments, and it ranks candidates according to several statistics. It also has a module to identify exact sequences at breakpoint junctions. FusionSeq detected known and novel fusions in a specially sequenced calibration data set, including eight cancers with and without known rearrangements.
| S-EPMC3218660 | biostudies-literature
Project description:FusionSeq: a Modular Framework for Finding Gene Fusions by Analyzing Paired-End RNA Sequencing Data
| PRJNA74883 | ENA
Project description:<p>We have developed FusionSeq to identify fusion transcripts from paired-end RNA-sequencing. FusionSeq includes filters to remove spurious candidate fusions with artifacts such as misalignments or random pairing of transcript fragments and it ranks candidates according to several statistics. It also has a module to identify exact sequences at breakpoint junctions. FusionSeq detected known and novel fusions in a specially sequenced calibration data set, including 8 cancers with and without known rearrangements.</p>
| phs000311.v1.p1 | EGA
Project description:We designed a strategy for microarray analysis that is based on the identification of transcriptional modules formed by genes coordinately expressed in multiple disease data sets. Mapping changes in gene expression at the module level generated disease-specific transcriptional fingerprints that provide a stable framework for the visualization and functional interpretation of microarray data.
2008-08-11 | GSE11908 | GEO
Project description:This SuperSeries is composed of the following subset Series: GSE24770: Heterochronic Evolution Reveals Modular Timing Changes in Budding Yeast Transcriptomes (Mutation Accumulation Lines) GSE24771: Heterochronic Evolution Reveals Modular Timing Changes in Budding Yeast Transcriptomes (Natural Strain Timecourse) Refer to individual Series
2010-10-19 | E-GEOD-24772 | biostudies-arrayexpress
Project description:Basal-like and luminal breast tumors have distinct clinical behavior and molecular profiles, yet the underlying mechanisms are poorly defined. To interrogate processes that determine these distinct phenotypes and their inheritance pattern, we generated somatic cell fusions and performed integrated genetic and epigenetic (DNA methylation and chromatin) profiling. We found that the basal-like trait is generally dominant and it is largely defined by epigenetic repression of luminal transcription factors. Definition of super-enhancers highlighted a core program common in luminal cells but high degree of heterogeneity in basal-like breast cancers that correlates with clinical outcome. We also found that protein extracts of basal-like cells is sufficient to induce luminal-to-basal phenotypic switch implying a trigger of basal-like autoregulatory circuits. We determined that KDM6A might be required for luminal-basal fusions, and identified EN1, TBX18, and TCF4 as candidate transcriptional regulators of luminal-to-basal switch. Our findings highlight the remarkable epigenetic plasticity of breast cancer cells. We investigated the potential contribution of genetic factors to defining cell type-specific gene expression patterns by analyzing allelic inheritance based on Affymetrix SNP6.0 array hybridization of DNA from parental and fusion cells
2015-06-10 | E-GEOD-39412 | biostudies-arrayexpress
Project description:Fusion genes can be oncogenic drivers in a variety of cancer types and represent potential targets for targeted therapy. The BRAF gene is frequently involved in oncogenic fusions, with fusion frequencies of 0.2-3% throughout different cancers. However, BRAF fusions rarely occur in the same gene configuration, potentially challenging personalized therapy design. In particular, the influence that is imposed by the wide variety of fusion partners on the oncogenic role of BRAF during tumor growth and drug response is unknown. Here, we used patient-derived colorectal cancer organoids to functionally characterize and cross-compare previously identified BRAF fusions containing various partner genes (AGAP3, DLG1 and TRIM24) with respect to cellular behaviour, downstream signaling activation and response to targeted therapies. We demonstrate that 5’ partner choice of BRAF fusions affects their subcellular localization and intracellular signaling capacity. In particular the DLG1-BRAF fusion protein showed distinct localization to the plasma membrane and exhibited increased activation of downstream MAPK signaling under unperturbed conditions. Moreover, phosphoproteomics and RNA sequencing identified distinct subsets of affected signaling pathways and altered gene expression of BRAF fusions. The different BRAF fusions exhibited varying sensitivities to simultaneous targeted inhibition of MEK and the EGF receptor family. However, all BRAF fusions conveyed resistance to targeted monotherapy against the EGF receptor family, suggesting that BRAF fusions should be screened alongside other MAPK pathway alterations to identify mCRC patients to exclude from cetuximab treatment
2020-01-16 | PXD013461 | Pride
Project description:We designed a strategy for microarray analysis that is based on the identification of transcriptional modules formed by genes coordinately expressed in multiple disease data sets. Mapping changes in gene expression at the module level generated disease-specific transcriptional fingerprints that provide a stable framework for the visualization and functional interpretation of microarray data. The first step of the module-construction process analyzes expression patterns of transcripts across samples for individual diseases: sets of coordinately expressed transcripts were identified with an unsupervised clustering algorithm; in this case, the GeneSpring Version 7.1 (Agilent) implementation of the K-Means algorithm (k = 30). All transcripts detected in at least one sample were used as input; no screening for differential expression was performed. The second step of the module-construction process analyzed the “clustering behavior” of transcripts across diseases, taking into account the possibility that genes may cocluster in some diseases but not in others. Also, in our example, the transcripts that clustered together across all eight diseases were grouped to form a set of modules (round 1 of selection), and the stringency of the analysis was then decreased gradually to identify transcripts that belong to a similar K-means cluster in only a subset of diseases (round 2: seven out of eight diseases; round 3: six out of eight diseases). It is important to note that the module-selection process is “data-driven” and does not involve manual selection of genes by the investigator. We implemented the module-construction strategy described above, using as input a total of 239 peripheral-blood mononuclear cell (PBMC) samples obtained from individuals with one of the following conditions: systemic juvenile idiopathic arthritis (n = 47), systemic lupus erythematosus (n = 40), type I diabetes (n = 20), metastatic melanoma (n = 39), acute infections (Escherichia coli [n = 22], Staphylococcus aureus [n = 18], Influenza A [n = 16]) or liver-transplant recipients undergoing immunosuppressive therapy (n = 37). Transcriptional profiles were generated with Affymetrix U133A and U133B GeneChips (> 44,000 probe sets). A total of 4742 transcripts, distributed among 28 sets, were selected after running of the module-construction algorithm described above. Each module is assigned a unique identifier indicating the round and order of selection (i.e., M3.1 is the first module identified in the third round of selection). The stringency of this algorithm was tested statistically by implementation of the same module-construction procedure after randomization of the original data set. This process was repeated 200 times, without a single module identified. Therefore, the analysis of gene-cluster membership across multiple diseases provided a stringent means to identify PBMC transcriptional modules.
2008-08-11 | E-GEOD-11908 | biostudies-arrayexpress
Project description:Finding cellular origin of mouse kidney organoids
| PRJEB17924 | ENA
Project description:Kinase fusions are considered oncogenic drivers in numerous types of cancer. In lung adenocarcinoma 5-10% of patients harbor kinase fusions. The most frequently detected kinase fusion involves the Anaplastic Lymphoma Kinase (ALK) and Echinoderm Microtubule-associated protein-Like 4 (EML4). In addition, oncogenic kinase fusions involving the tyrosine kinases RET and ROS1 are found in smaller subsets of patients. In this study, we employed quantitative mass spectrometry-based phosphoproteomics to define the cellular tyrosine phosphorylation patterns induced by different oncogenic kinase fusions identified in patients with lung adenocarcinoma. We show that the cellular expression of the kinase fusions leads to widespread tyrosine phosphorylation. Direct comparison of different kinase fusions demonstrates that the kinase part and not the fusion partner primarily defines the phosphorylation pattern. The tyrosine phosphorylation patterns differed between ALK, ROS1 and RET fusions, suggesting that oncogenic signaling induced by these kinases involves the modulation of different cellular processes.
2021-04-27 | PXD021904 | Pride