Discovery of the p53 targetome in MCF7 cells from RNA-seq data
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ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Janky Rekin's
PROVIDER: S-ECPF-GEOD-47042 | biostudies-other |
REPOSITORIES: biostudies-other
Similar Datasets
Project description:RNA-seq and ChIP-seq on MCF-7 breast cancer cell line upon activation of p53 by the non-genotoxic small molecule Nutlin-3a RNA-seq on MCF7 without (NS) or with Nutlin-3a stimulation (S), in duplicate, using illumina HiSeq 2000
2014-05-27 | E-GEOD-47042 | biostudies-arrayexpress
Project description:RNA-seq and ChIP-seq on MCF-7 breast cancer cell line upon activation of p53 by the non-genotoxic small molecule Nutlin-3a
2014-05-27 | GSE47042 | GEO
Project description:This SuperSeries is composed of the SubSeries listed below. iRegulon: from a gene list to a gene regulatory network using large motif and track collections. RekinM-bM-^@M-^Ys Janky, Annelien Verfaillie, Hana ImrichovM-CM-!, Bram Van de Sande, Laura Standaert, Valerie Christiaens, Gert Hulselmans, Koen Herten, Marina Naval Sanchez, Delphine Potier, Dmitry Svetlichnyy, Zeynep Kalender Atak, Mark Fiers, Jean-Christophe Marine, and Stein Aerts. PLOS Computational Biology, 2014. Refer to individual Series
2014-05-27 | E-GEOD-47043 | biostudies-arrayexpress
Project description:This SuperSeries is composed of the SubSeries listed below.
2014-05-27 | GSE47043 | GEO
Project description:RNA-seq and ChIP-seq on MCF-7 breast cancer cell line upon activation of p53 by the non-genotoxic small molecule Nutlin-3a ChIP-seq on p53 in MCF7 with Nutlin-3a stimulation (S) in triplicate, and the control (input), in stimulated and non stimulated, using illumina HiSeq 2000
2014-05-27 | E-GEOD-47041 | biostudies-arrayexpress
Project description:RNA-seq and ChIP-seq on MCF-7 breast cancer cell line upon activation of p53 by the non-genotoxic small molecule Nutlin-3a
2014-05-27 | GSE47041 | GEO
Project description:BACKGROUND:Single nucleotide polymorphisms (SNP) have been applied as important molecular markers in genetics and breeding studies. The rapid advance of next generation sequencing (NGS) provides a high-throughput means of SNP discovery. However, SNP development is limited by the availability of reliable SNP discovery methods. Especially, the optimum assembler and SNP caller for accurate SNP prediction from next generation sequencing data are not known. RESULTS:Herein we performed SNP prediction based on RNA-seq data of peach and mandarin peel tissue under a comprehensive comparison of two paired-end read lengths (125?bp and 150?bp), five assemblers (Trinity, IDBA, oases, SOAPdenovo, Trans-abyss) and two SNP callers (GATK and GBS). The predicted SNPs were compared with the authentic SNPs identified via PCR amplification followed by gene cloning and sequencing procedures. A total of 40 and 240 authentic SNPs were presented in five anthocyanin biosynthesis related genes in peach and in nine carotenogenic genes in mandarin. Putative SNPs predicted from the same RNA-seq data with different strategies led to quite divergent results. The rate of false positive SNPs was significantly lower when the paired-end read length was 150?bp compared with 125?bp. Trinity was superior to the other four assemblers and GATK was substantially superior to GBS due to a low rate of missing authentic SNPs. The combination of assembler Trinity, SNP caller GATK, and the paired-end read length 150?bp had the best performance in SNP discovery with 100% accuracy both in peach and in mandarin cases. This strategy was applied to the characterization of SNPs in peach and mandarin transcriptomes. CONCLUSIONS:Through comparison of authentic SNPs obtained by PCR cloning strategy and putative SNPs predicted from different combinations of five assemblers, two SNP callers, and two paired-end read lengths, we provided a reliable and efficient strategy, Trinity-GATK with 150?bp paired-end read length, for SNP discovery from RNA-seq data. This strategy discovered SNP at 100% accuracy in peach and mandarin cases and might be applicable to a wide range of plants and other organisms.
| S-EPMC6391812 | biostudies-literature
Project description:High-throughput RNA sequencing (RNA-seq) promises to revolutionize our understanding of genes and their role in human disease by characterizing the RNA content of tissues and cells. The realization of this promise, however, is conditional on the development of effective computational methods for the identification and quantification of transcripts from incomplete and noisy data. In this article, we introduce iReckon, a method for simultaneous determination of the isoforms and estimation of their abundances. Our probabilistic approach incorporates multiple biological and technical phenomena, including novel isoforms, intron retention, unspliced pre-mRNA, PCR amplification biases, and multimapped reads. iReckon utilizes regularized expectation-maximization to accurately estimate the abundances of known and novel isoforms. Our results on simulated and real data demonstrate a superior ability to discover novel isoforms with a significantly reduced number of false-positive predictions, and our abundance accuracy prediction outmatches that of other state-of-the-art tools. Furthermore, we have applied iReckon to two cancer transcriptome data sets, a triple-negative breast cancer patient sample and the MCF7 breast cancer cell line, and show that iReckon is able to reconstruct the complex splicing changes that were not previously identified. QT-PCR validations of the isoforms detected in the MCF7 cell line confirmed all of iReckon's predictions and also showed strong agreement (r(2) = 0.94) with the predicted abundances.
| S-EPMC3589540 | biostudies-literature
Project description:RNA-Seq is a recent and efficient technique that uses the capabilities of next-generation sequencing technology for characterizing and quantifying transcriptomes. One important task using gene-expression data is to identify a small subset of genes that can be used to build diagnostic classifiers particularly for cancer diseases. Microarray based classifiers are not directly applicable to RNA-Seq data due to its discrete nature. Overdispersion is another problem that requires careful modeling of mean and variance relationship of the RNA-Seq data. In this study, we present voomDDA classifiers: variance modeling at the observational level (voom) extensions of the nearest shrunken centroids (NSC) and the diagonal discriminant classifiers. VoomNSC is one of these classifiers and brings voom and NSC approaches together for the purpose of gene-expression based classification. For this purpose, we propose weighted statistics and put these weighted statistics into the NSC algorithm. The VoomNSC is a sparse classifier that models the mean-variance relationship using the voom method and incorporates voom's precision weights into the NSC classifier via weighted statistics. A comprehensive simulation study was designed and four real datasets are used for performance assessment. The overall results indicate that voomNSC performs as the sparsest classifier. It also provides the most accurate results together with power-transformed Poisson linear discriminant analysis, rlog transformed support vector machines and random forests algorithms. In addition to prediction purposes, the voomNSC classifier can be used to identify the potential diagnostic biomarkers for a condition of interest. Through this work, statistical learning methods proposed for microarrays can be reused for RNA-Seq data. An interactive web application is freely available at http://www.biosoft.hacettepe.edu.tr/voomDDA/.
| S-EPMC5633036 | biostudies-literature
Project description:To improve our understanding of the effect of differential methylation on gene expression between normal and tumor breast cells, we carried out RNA sequencing evaluate mRNA expression changes in normal breast tissue and MCF7 breast cancer cell line. We identified 6632 protein-coding genes to be significantly differentially expressed (FDR threshold of 0.05) in the MCF7 cells. IPA Bio-Function enrichment analysis showed the 'cell death and survival' and 'cellular growth and proliferation' are the top 2 terms associated with differentially expressed genes. Cancer versus normal analysis of The Cancer Genome Atlas (TCGA) Breast data revealed genes that were differentially up and down-regulated in MCF7 are mostly respectively over- and under-expressed in clinical specimens.
| S-ECPF-MTAB-1961 | biostudies-other