Ontology highlight
ABSTRACT:
PROVIDER: PRJNA935143 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR23461327_1.fastq.gz | Fastqsanger.gz | |||
| SRR23461327_2.fastq.gz | Fastqsanger.gz | |||
| SRR23461328_1.fastq.gz | Fastqsanger.gz | |||
| SRR23461328_2.fastq.gz | Fastqsanger.gz | |||
| SRR23461329_1.fastq.gz | Fastqsanger.gz |
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Project description:Point mutations within the TERT promoter are the most common recurrent somatic non-coding mutation identified across different cancer types, including glioblastoma, melanoma, hepatocellular carcinoma and bladder cancer. They are most abundant at C146T and C124T and more rare at A57C, with the latter originally described as a familial case but subsequently shown also to occur somatically. All three mutations create de novo ETS (E-twenty-six specific) binding sites and result in the reactivation of the TERT gene, allowing cancer cells to achieve replicative immortality. Here, we employed a systematic proteomics screen to identify transcription factors preferentially binding to the C146T, C124T and A57C mutations. While we confirmed binding of multiple ETS factors to the mutant C146T and C124T sequences, we identified E4F1 a an A57C-specific binder and ZNF148 as a TERT WT binder that is excluded from the TERT promoter by the C124T allele. Both proteins are activating transcription factors that bind specifically to the A57C and wildtype (at position 124) TERT promoter sequence in corresponding cell lines and upregulate TERT transcription and telomerase activity.
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Project description:De novo peptide sequencing is a fundamental research area in mass spectrometry (MS) based proteomics. However, those methods have often been evaluated using a couple of simple metrics that do not fully reflect their overall performance. Moreover, there has not been an established method to estimate the false discovery rate (FDR) and the significance of de novo peptide-spectrum matches (PSMs). Here we propose NovoBoard, a comprehensive framework to evaluate the performance of de novo peptide sequencing methods. The framework consists of diverse benchmark datasets (including tryptic, nontryptic, immunopeptidomics, and different species), and a standard set of accuracy metrics to evaluate the fragment ions, amino acids, and peptides of the de novo results. More importantly, a new approach is designed to evaluate de novo peptide sequencing methods on target-decoy spectra and to estimate their FDRs. Our results thoroughly reveal the strengths and weaknesses of different de novo peptide sequencing methods, and how their performances depend on specific applications and the types of data. Our FDR estimation also shows that some tools may perform better than the others in distinguishing between de novo PSMs and random matches, and can be used to assess the significance of de novo PSMs.
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