Dataset Information

LUMC, Molecular Epidemiology

ABSTRACT: Organisation EGAO00000000282

PROVIDER: EGAO00000000282 | EGA |

REPOSITORIES: EGA

ACCESS DATA

Publications

Mobster: accurate detection of mobile element insertions in next generation sequencing data.

Thung Djie Tjwan DT de Ligt Joep J Vissers Lisenka E M LE Steehouwer Marloes M Kroon Mark M de Vries Petra P Slagboom Eline P EP Ye Kai K Veltman Joris A JA Hehir-Kwa Jayne Y JY

Genome biology 20140101 10

Mobile elements are major drivers in changing genomic architecture and can cause disease. The detection of mobile elements is hindered due to the low mappability of their highly repetitive sequences. We have developed an algorithm, called Mobster, to detect non-reference mobile element insertions in next generation sequencing data from both whole genome and whole exome studies. Mobster uses discordant read pairs and clipped reads in combination with consensus sequences of known active mobile ele ...[more]

PMID: 25348035

Similar Datasets

Project description:Gliomas are the most common type of central nervous system tumors. Almost all patients diagnosed with these tumors have a poor prognostic outcome. We aimed to identify novel glioma prognosis-associated candidate genes. We used WebArrayDB software to integrate and analyze 771 glioma microarray datasets (WHO II-IV). We focused on a subset of the significantly up-regulated genes, the minichromosome maintenance (MCM) family. We used 59 glioma frozen samples (F/M 18/21, age 38.9 ± 14.3y, 14 II grade, 21 III grade and 24 IV grade). The samples consisted of AO (n = 27), AA (n = 9), and GBM (n = 23). We also used 6 control samples: trauma (n = 4) and epilepsy (n = 2). The frozen samples predict the relationship between the expression of MCMs and patients outcome by qPCR. We found that MCM expression was significantly up-regulated in glioma samples. High MCM2 mRNA expression appeared to be strongly associated with poor overall survival in high-grade glioma. Furthermore, we found that MCM7 is strongly correlated with patient outcome in WHO grade II-IV. MCM3 expression was to be up-regulated in glioma and correlate with overall survival in WHO grade III. MCM2, MCM3, and MCM7 expression level were of greater prognostic relevance than histological diagnosis according to the current WHO classification system. The results suggest that MCM2, MCM3, and MCM7 aberrations in glioma tumors portend a particularly aggressive clinical behavior. The Cancer Genome Atlas Research Network (TCGA) has recently completed mRNA, mRNA, promotor methylation, and DNA copy number profiling of more than 500 glioblastomas (GBMs) and exome sequencing on 213 of these samples. Here, we report on the results from analysis of this large genomic data set. Mutation analysis identified 83 significantly mutated genes, of which seven genes were mutated in more than 10% of the samples: HYDIN (10%), NF1 (10.3%), PIK3CA (10.3%), RYR2 (13.6%), PTEN (31.9%), EGFR (31.9%), and TP53 (38.5%). DNA copy number analysis of 502 GBMs refined previously identified focal DNA copy number events to single genes, such as PDGFRA/SOX2/CCNE1 amplification and PTEN/RB1/SMYD3 deletion. Gene expression profiles from 542 GBMs, 198 of which were included in previous publications, were classified according to the four gene expression subtypes. Associations between gene expression subtype and genomic alterations were recapitulated, such as NF1 deletion/mutation and Mesenchymal subtype, and novel associations were uncovered, such as CDK4 amplification and Proneural expression phenotype. Methylation data were used to characterize G-CIMP status of 381 GBMs. Analysis of the mutation clonality revealed an increased degree of subclonal mutations in G-CIMP GBMs compared to non-G-CIMP GBM. Pathway analysis confirmed previously identified RB1/TP53 and receptor tyrosine kinase pathways but additionally suggests that epigenetic and chromatin modification pathways are frequently involved in gliomagenesis. Comparing co-occurrence patterns showed that specific genomic alterations are more likely to occur in the same sample, suggested re-enforcing roles of these alterations. Examples are amplification of MET and EGFR in 22% of GBMs, and IDH1 and TP53 mutations in 5% of GBM. In summary, our cohort size provides a basis for integrated types of analysis for which large numbers of patient samples are required. We provide a complete and rich data set that provides important new insights into the genomic basis of GBM.

Dataset Information

LUMC, Molecular Epidemiology

Publications

Mobster: accurate detection of mobile element insertions in next generation sequencing data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets