A PiggyBac transposon screen in pancreatic cancer
Ontology highlight
ABSTRACT:
INSTRUMENT(S): Illumina MiSeq
ORGANISM(S): Mus musculus
SUBMITTER:
PROVIDER: E-ERAD-514 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
Similar Datasets
Project description:RNA-Seq from lymphoblastoid cell lines for Gorilla, chimpanzee and bonobo species. This RNA-Seq data has been described in the following article: Scally et al., Nature 2012;483;7388;169-75, DOI: 10.1038/nature10842, and its further analysis can be freely submitted for publication. For information on the proper use of data shared by the Wellcome Trust Sanger Institute (including information on acknowledgement), please see http://www.sanger.ac.uk/datasharing/>
2013-01-09 | E-ERAD-129 | biostudies-arrayexpress
Project description:This data has been described in the following article: Dias et al., American Journal of Human Genetics, 2016, and its further analysis can be freely submitted for publication. For information on the proper use of data shared by the Wellcome Trust Sanger Institute(including information on acknowledgement), please see http://www.sanger.ac.uk/datasharing/
2016-07-12 | E-ERAD-520 | biostudies-arrayexpress
Project description:We used all five current human African trypanosomiasis drugs for genome-scale RNA interference (RNAi) target sequencing (RIT-seq) screens in Trypanosoma brucei, revealing the transporters, organelles, enzymes and metabolic pathways that function to facilitate anti-trypanosomal drug action. RIT-seq profiling links more than fifty additional genes to drug action. This data has been described in the following article [doi:10.1038/nature10771] and its further analysis can be freely submitted for publication. For information on the proper use of data shared by the Wellcome Trust Sanger Institute (including information on acknowledgement), please see http://www.sanger.ac.uk/datasharing/. Protocol - Assembly of the bloodstream-form T. brucei RNAi library and the RNAi target sequencing (RIT-seq) approach were reported previously (Alsford, S. et al. High-throughput phenotyping using parallel sequencing of RNA interference targets in the African trypanosome. Genome Res 21, 915-924, 264 doi:gr.115089.110 [pii] 265 10.1101/gr.115089.110 (2011). Briefly, a tetracycline-inducible RNAi plasmid library, containing randomly sheared genomic fragments (mean ~600 bp) under the control of head-to-head T7 promoters, was targeted to a single genomic locus that had been validated for robust expression. For this study, the library was grown under inducing conditions with drug selection and genomic DNA was isolated from surviving populations. For chemical RIT-seq profiling, adapter-ligated sequencing libraries were prepared from each genomic DNA sample and used to amplify DNA fragments containing RNAi cassette-insert junctions in semi-specific PCR reactions; one primer was specific for the RNAi vector and the other for the Illumina adapter. Size-selected DNA was sequenced with 76 cycle paired-end runs on an Illumina GAII. Illumina sequencing reads containing a nine-base RNAi vector junction sequence were then mapped to the T. brucei reference genome. Where loss of function increases drug tolerance, RNAi-target sequence representation is increased relative to the otherwise susceptible population, revealing hot spots.
2011-11-29 | E-ERAD-30 | biostudies-arrayexpress
Project description:To determine the effect of Mll3 deletion on H3K4me3 Chip signals in intestinal stem cell populations. This data has been described in the following article : Discovery of candidate disease genes in ENU-induced mouse mutants by large-scale sequencing, including a splice-site mutation in nucleoredoxin. Boles MK et al PLoS Genet. 2009 Dec;5(12) and its further analysis can be freely submitted for publication. For information on the proper use of data shared by the Wellcome Trust Sanger Institute (including information on acknowledgement), please see http://www.sanger.ac.uk/datasharing/ Abstract: Briefly we wanted to determine the effect of Mll3 deletion on H3K4me3 binding in cells of the intestine and to compare these to the H3K4me3 pattern found in wildtype animals.
2011-12-15 | E-ERAD-42 | biostudies-arrayexpress
Project description:To investigate the role of gene expression in patients with COVID-19. Full article available at DOI: 10.1136/bmjopen-2020-044497.
2023-01-30 | GSE217948 | GEO
Project description:Somatic transposon mutagenesis in mice is an efficient strategy to investigate the genetic mechanisms of tumorigenesis. The identification of tumor driving transposon insertions traditionally requires the generation of large tumor cohorts to obtain information about common insertion sites. Tumor driving insertions are also characterized by their clonal expansion in tumor tissue, a phenomenon that is facilitated by the slow and evolving transformation process of transposon mutagenesis. We describe here an improved approach for the detection of tumor driving insertions that assesses the clonal expansion of insertions by quantifying the relative proportion of sequence reads obtained in individual tumors. To this end, we have developed a protocol for insertion site sequencing that utilizes acoustic shearing of tumor DNA and Illumina sequencing. We analyzed various solid tumors generated by PiggyBac mutagenesis and for each tumor >10^6 reads corresponding to >10^4 insertion sites were obtained. In each tumor, 9 to 25 insertions stood out by their enriched sequence read frequencies when compared to frequencies obtained from tail DNA controls. These enriched insertions are potential clonally expanded tumor driving insertions, and thus identify candidate cancer genes. The candidate cancer genes of our study comprised many established cancer genes, but also novel candidate genes such as Mastermind-like1 (Mamld1) and Diacylglycerolkinase delta (Dgkd). We show that clonal expansion analysis by high-throughput sequencing is a robust approach for the identification of candidate cancer genes in insertional mutagenesis screens on the level of individual tumors. Solid tumors in mice were generated by somatic transposon mutagenesis with a PiggyBac transposon system. Insertion sites of transposons in 11 tumors and 6 non-cancerous tail controls were determined by Illumina high-throughput sequencing. Insertions were determined both on 5' and 3' sides of the transposon (PB5 and PB3, respectively). Quantitative analysis of read numbers revealed enrichment of certain insertions in tumors, but not in controls, and these enriched insertions identify candidate cancer genes.
2013-08-28 | E-GEOD-46210 | biostudies-arrayexpress
Project description:RT-PCR of platelet-poor plasma samples per Freedman JE, Gerstein M, Mick E, Rozowsky J, Levy D, Kitchen R, et al. Diverse human extracellular RNAs are widely detected in human plasma. Nat Commun 2016; 7. doi:10.1038/ncomms11106
2022-08-12 | GSE135758 | GEO
Project description:U2OS cells were co-transfected with PiggyBac (PB) transposon plasmid pPB-SB-CMV-puro-SD3 and the transposase p-hyPBASE. The modified PiggyBac (PB) transposon, which has a constitutively active CMV promoter which can stimulate or disrupt expression of neighboring genes, depending on insertional orientation. For each library, between 107-108 cells were transfected, cultured with the addition of 2 µg/ml puromycin for one week to select cells that had incorporated the transposon, and then used for screens of virus resistance with minimal further expansion. Mutagenized cells were challenged with replication-competent recombinant EboGP-VSV at MOI of 1 or 10 and propagated for up to 3 weeks with regular media changes to select virus-resistant colonies. After virus resistant cells were selected, genomic DNA (gDNA) was isolated from 5-10 X 106 cells and transposon insertion sites were mapped using high throughput sequencing.
2020-09-16 | GSE156598 | GEO
Project description:We quantified the protein abundances of Mycoplasma pneumoniae (Mpn), a genome-reduced organism, by combining selected reaction monitoring of a reduced set of labeled peptides and label-free shotgun mass spectrometry. A set of 77 labeled peptides were synthesized and used as internal standard peptides to precisely and quantitatively measure the absolute concentrations of proteins in a tandem mass spectrometer [1]. The heavy peptides were chosen based on tryptic peptides of endogenous proteins previously observed in mass spectrometry experiments and that covered more than a 1000-fold range of protein concentrations. Three time points of Mpn growth curve (48, 72 and 96 hours) and the heavy peptides were injected per triplicates in order to calculate the endogenous amount of each peptide (single reference point quantitation) [2], resulting in the absolute quantification of 73 reference proteins. A calibration curve between the absolute amount of reference proteins and the proteome-wide protein intensities allowed the estimation of absolute protein abundances for all detected proteins [3]. In order to further improve the quantification, four additional label-free mass spectrometry datasets of Mpn [4] using different protein extraction methods, from a previous project, were combined with the original dataset, which allowed to quantify the abundances of 528 proteins (75% of the proteome of Mpn). 1. Gerber SA, Rush J, Stemman O, Kirschner MW, Gygi SP. Absolute quantification of proteins and phosphoproteins from cell lysates by tandem MS. Proc Natl Acad Sci U S A. 2003;100: 6940–6945. doi:10.1073/pnas.0832254100 2. Campbell J, Rezai T, Prakash A, Krastins B, Dayon L, Ward M, et al. Evaluation of absolute peptide quantitation strategies using selected reaction monitoring. Proteomics. 2011;11: 1148–1152. doi:10.1002/pmic.201000511 3. Schmidt A, Kochanowski K, Vedelaar S, Ahrné E, Volkmer B, Callipo L, et al. The quantitative and condition-dependent Escherichia coli proteome. Nat Biotechnol. 2016;34: 104–110. doi:10.1038/nbt.3418 4. Miravet-Verde S, Ferrar T, Espadas-García G, Mazzolini R, Gharrab A, Sabido E, et al. Unraveling the hidden universe of small proteins in bacterial genomes. Mol Syst Biol. 2019;15: e8290. doi:10.15252/msb.20188290 5. Yus E, Maier T, Michalodimitrakis K, van Noort V, Yamada T, Chen W-H, et al. Impact of genome reduction on bacterial metabolism and its regulation. Science. 2009;326: 1263–1268. doi:10.1126/science.1177263 6. Wiśniewski JR, Zougman A, Nagaraj N, Mann M. Universal sample preparation method for proteome analysis. Nat Methods. 2009;6: 359–362. doi:10.1038/nmeth.1322 7. Picotti P, Bodenmiller B, Mueller LN, Domon B, Aebersold R. Full dynamic range proteome analysis of S. cerevisiae by targeted proteomics. Cell. 2009;138: 795–806. doi:10.1016/j.cell.2009.05.051 8. Martens L, Van Damme P, Van Damme J, Staes A, Timmerman E, Ghesquière B, et al. The human platelet proteome mapped by peptide-centric proteomics: a functional protein profile. Proteomics. 2005;5: 3193–3204. doi:10.1002/pmic.200401142 9. Desiere F, Deutsch EW, King NL, Nesvizhskii AI, Mallick P, Eng J, et al. The peptideatlas project. Nucleic Acids Res. 2006;34: D655–D658. Available: https://academic.oup.com/nar/article-abstract/34/suppl_1/D655/1132687 10. Lange V, Picotti P, Domon B, Aebersold R. Selected reaction monitoring for quantitative proteomics: a tutorial. Mol Syst Biol. 2008;4: 222. doi:10.1038/msb.2008.61 11. MacLean B, Tomazela DM, Shulman N, Chambers M, Finney GL, Frewen B, et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics. 2010;26: 966–968. doi:10.1093/bioinformatics/btq054 12. Perkins DN, Pappin DJ, Creasy DM, Cottrell JS. Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis. 1999;20: 3551–3567. doi:10.1002/(SICI)1522-2683(19991201)20:18<3551::AID-ELPS3551>3.0.CO;2-2 13. Elias JE, Gygi SP. Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nat Methods. 2007;4: 207–214. doi:10.1038/nmeth1019
2023-08-31 | PXD035159 | Pride