The Carcinogenome Project
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE178201 | GEO | 2021/08/28
REPOSITORIES: GEO
Similar Datasets
Project description:The Library of Integrated Cellular Signatures (LINCS) is an NIH program which funds the generation of perturbational profiles across multiple cell and perturbation types, as well as read-outs, at a massive scale. The LINCS Center for Transcriptomics at the Broad Institute uses the L1000 high-throughput gene-expression assay to build a Connectivity Map which seeks to enable the discovery of functional connections between drugs, genes and diseases through analysis of patterns induced by common gene-expression changes. The platform is GPL20573: Broad Institute Human L1000 epsilon http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL20573 For questions or assistance with this dataset, please email the CMap support team at: clue@broadinstitute.org
2015-07-15 | GSE70138 | GEO
Project description:The Library of Integrated Cellular Signatures (LINCS) is an NIH program which funds the generation of perturbational profiles across multiple cell and perturbation types, as well as read-outs, at a massive scale. The LINCS Center for Transcriptomics at the Broad Institute uses the L1000 high-throughput gene-expression assay to profile a range of cellular models and perturbations of cellular state. These data relate to using RNA-Seq datasets from the GTEx consortium (http://www.gtexportal.org/) to validate the L1000 assay and to improve the statistical model used in imputing the transcriptome. The Platform is GPL20573: Broad Institute Human L1000 epsilon https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL20573 For questions or assistance with this dataset, please email the CMap support team at: clue@broadinstitute.org
2017-03-03 | GSE92743 | GEO
Project description:The Library of Integrated Cellular Signatures (LINCS) is an NIH program which funds the generation of perturbational profiles across multiple cell and perturbation types, as well as read-outs, at a massive scale. The LINCS Center for Transcriptomics at the Broad Institute uses the L1000 high-throughput gene-expression assay to build a Connectivity Map which seeks to enable the discovery of functional connections between drugs, genes and diseases through analysis of patterns induced by common gene-expression changes. These files represent L1000 data generated during the LINCS Pilot Phase (2012-2015), as well as profiles generated for more specific purposes, such as assay development and validation projects or testing custom compounds or non-standard cell lines (not part of the core LINCS cell lines). Note: Related GEO projects include (a) Additional L1000 and RNA-Seq data used to validate the assay and improve the inference model, available at GSE92743 (b) The LINCS “production phase” (also termed Phase II, 2015-2020) which is generating an additional cohort of L1000 data, available at GSE70138. The Platform is GPL20573: Broad Institute Human L1000 epsilon https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL20573 For questions or assistance with this dataset, please email the CMap support team at: clue@broadinstitute.org
2017-03-03 | GSE92742 | GEO
Project description:The Library of Integrated Cellular Signatures (LINCS) is an NIH program which funds the generation of perturbational profiles across multiple cell and perturbation types, as well as read-outs, at a massive scale. The LINCS PCCSE uses two high-throughput liquid chromatography-mass spectrometry (LCMS) assays to study the proteomic changes induced by drug and genetic perturbations. P100 monitors ~100 phosphorylated peptides from cellular proteins that serve as a reduced representation of the phosphoproteome. GCP monitors ~60 modified peptides from histones (e.g., methylated, acetylated, and combinations thereof) encompassing nearly every well-studied post-translational modification on the core nucleosomal histone proteins. Closely complementing these assays is the L1000 high-throughput gene-expression assay. The files available here contain P100, GCP, and L1000 data for 90 small-molecule perturbations in six cell lines (five cancer cell lines and a neurodevelopmental cell model). These data were generated under the auspices of the NIH LINCS Program (www.lincsproject.org). Note: Related GEO projects include a large corpus of additional L1000 data, available at GSE92742. The Platform for the L1000 data is GPL20573: Broad Institute Human L1000 epsilon http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL20573 For questions or assistance with this dataset, please email the Connectivity Map support team at: clue@broadinstitute.org
2017-08-24 | GSE101406 | GEO
Project description:This GEO Series contains datasets used in the paper "Evaluation Of RNAi And CRISPR Technologies By Large Scale Gene Expression Profiling In The Connectivity Map". The application of RNA interference (RNAi) to mammalian cells has provided the means to perform phenotypic screens to determine the functions of genes. Although RNAi has revolutionized loss of function genetic experiments, it has been difficult to systematically assess the prevalence and consequences of off-target effects. The Connectivity Map (CMAP) represents an unprecedented resource to study the gene expression consequences of expressing short hairpin RNAs (shRNAs). Analysis of signatures for over 13,000 shRNAs applied in 9 cell lines revealed that miRNA-like off-target effects of RNAi are far stronger and more pervasive than generally appreciated. We show that mitigating off-target effects is feasible in these datasets via computational methodologies to produce a Consensus Gene Signature (CGS). In addition, we compared RNAi technology to clustered regularly interspaced short palindromic repeat (CRISPR)-based knockout by analysis of 373 sgRNAs in 6 cells lines, and show that the on-target efficacies are comparable, but CRISPR technology is far less susceptible to systematic off-target effects. These results will help guide the proper use and analysis of loss-of-function reagents for the determination of gene function. Note: Related GEO projects include a large corpus of additional L1000 data, available at GSE92742. The Platform is GPL20573: Broad Institute Human L1000 epsilon https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL20573 For questions or assistance with this dataset, please email the CMap support team at: clue@broadinstitute.org
2017-10-30 | GSE106127 | GEO
Project description:We tested the hypothesis that BHT is an endocrine disruptor by using a Next Generation Risk Assessment (NGRA) method. Four different cell lines: A549, HCC1428, HepG2 and MCF7 were treated with BHT and a series of BHT analogs at 5 different concentrations, RNA was isolated from cell extracts and run on the L1000 gene array platform. A toxicogenomics-based assessment was performed by comparing BHT’s unique genomic signature to a large external database containing signatures of other compounds (including many known endocrine disruptors) to identify if any endocrine disruption-related modes of action (MoAs) are prevalent among BHT and other compounds with similar genomic signatures. In addition, we performed a toxicogenomics-based structure activity relationship (SAR) assessment of BHT and a series of structurally similar analogues to understand if endocrine disruption is a relevant MoA for chemicals that are considered suitable analogs to BHT using the P&G read across framework (Wu et al., 2010). The platform is GPL20573: Broad Institute Human L1000 epsilon http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL20573
2022-10-17 | GSE211553 | GEO
Project description:This SuperSeries is composed of the SubSeries listed below. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE199202 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE199203
2022-03-23 | GSE199211 | GEO
Project description:We used high density oligonucleotide arrays to measure the relative expression levels of >39,000 genes and ESTs in the NOD mouse (a murine model of T1D and other autoimmune conditions), four NOD-derived diabetes resistant congenic strains and two nondiabetic control strains. Owing to the importance of T cells in the development of T1D we decided to target two organs, the spleen and thymus. We profiled gene expression in thymi from four week old female mice and spleens from three month old female mice. For each of the 14 strain-tissue combinations (7 strains x 2 tissues) we performed two independent replicate experiments, making the total number of hybridizations performed 28. In each case, an RNA population from a pool of two or three organs was generated to minimize the chances of within strain variation masking genuine variation between the strains. To minimize any variation introduced during target preparation, all samples within a given replicate group were processed in parallel. Three samples from group two (spleen: B10.H2g7_S2 http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gsm594 (GSM594); thymus: B10.H2g7_T2 http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gsm608 (GSM608), B10.H2g7 Idd3_T2 http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gsm609 (GSM609)) and one from group one (spleen B10.H2g7_S1 http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gsm587 (GSM587) failed a preliminary quality control check. Consequently, the steps involved in preparing labelled cRNA from the initial starting material had to be repeated for these four samples at a later date. A subsequent comparison of the variation seen between samples within the same replicate group (samples processed in parallel) versus samples in distinct replicate groups (samples processed at different times) revealed that the variation between the two groups (i.e. owing to independent sample preparation) was higher than the small amount of variation between the closely matched strains. Ignoring the variability introduced during sample preparation was therefore likely to result in spurious changes being detected and genuine strain-specific differences in gene expression being masked. Consequently, we decided to exclude the four samples that had had to be prepared independently owing to failing initial quality control. Keywords = NOD, diabetes, congenic Keywords: other
2001-11-21 | GSE11 | GEO
Project description:We performed a series of microarray expression profile experiments in central brain tissue from 3rd instar larvae, interrogating expression differences between wild type and In(3LR)sep strains. Since our 4C data (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE23166), demonstrated dramatic changes in nuclear organization of PcG target genes residing on In(3LR)sep. Keywords: mRNA expression profiling by oligonucleotide arrays
2011-05-09 | GSE26693 | GEO
Project description:This series represents 180 lymph-node negative relapse free patients and 106 lymph-node negate patients that developed a distant metastasis. Please see attached patient clinical parameters sheet for more information (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?view=data&acc=GSE2034&id=40089&db=GeoDb_blob26). Keywords: other
2005-02-22 | E-GEOD-2034 | biostudies-arrayexpress