Microarray data of genome-wide gene expression profile in osmads6-1
Ontology highlight
ABSTRACT:
ORGANISM(S): Oryza sativa
PROVIDER: GSE29349 | GEO | 2014/05/12
SECONDARY ACCESSION(S): PRJNA138607
REPOSITORIES: GEO
Similar Datasets
Project description:to investigate gene expression profile in osmads6-1 flowers at stage Sp6, unsing the wild type as control. 6 samples: 3 osmads6-1 and 3 wild-type
2014-05-12 | E-GEOD-29349 | biostudies-arrayexpress
Project description:Organogenesis in plants occurs during all stages of the life cycle. Although previous studies have identified many genes important for either vegetative or reproductive development at RNA level, global scale information of translational and post-translational regulatory levels is still limited. Here, six Arabidopsis stages/organs were analyzed using quantitative proteomics and phosphoproteomics, identifying 2,187 non-redundant proteins and 1,194 phosphoproteins. Compared to that in cauline leaves, expression of 1,445, 1,644, and 1,377 proteins showed greater than 1.5-fold alteration in stage 1-9 flowers, stage 10-12 flowers, and open flowers, respectively. In addition, 294 phosphoproteins with 472 phosphorylation sites were newly discovered, including 275 showing differential expression patterns, providing possible candidates for functional studies or molecular markers. In addition, more phosphorylated proteins were identified in reproductive stages (1,149) than in the vegetative process (995). 701 of the proteins differentially phosphorylated between flowers and leaves were predicted to localize to the nucleus, including 22 transcription factors. Moreover, phosphorylation of CDPK6 and MAPK6 and their interacting proteins, were enriched in reproductive tissues. Overall, our study yielded extensive data on protein expression and phosphorylation at six stages and provides an important resource for understanding regulatory mechanisms during plant development.
2021-09-09 | PXD019691 | Pride
Project description:In this study, whole-genome ChIP-seq analysis was performed to identify Sp6 regulatory targets from postnatal mouse molars. Enriched sequence reads (peaks) from Sp6 immunoprecipitations were located predominately adjacent to transcriptional start sites. Notably enriched for Sp6 peaks were genes highly-expressed in the tooth. Bioinformatic analysis of two pools of 1400 Sp6 enriched sequences identified a consensus nine-nucleotide DNA pulldown sequence of CTg/aTAATTA from mesenchyme and the nearly identical sequence, CTa/gTAATTA, from epithelium. Sp6 AT-rich interaction sequences are nearly identical to the ChIP-seq enriched sequences reported for Sp7 and Sp9. ChIP-seq analysis indicates Sp6 regulates a significant number of genes expressed in the tooth. GSE145909 is the original submission. GSE151391 is also from the same study. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE151391
2021-11-23 | GSE145909 | GEO
Project description:A method providing absolute transcript concentrations from spotted microarray intensity data is presented. Number of transcripts per microg total RNA, mRNA or per cell, are obtained for each gene, enabling comparisons of transcript levels within and between tissues. The method is based on Bayesian statistical modelling incorporating available information about the experiment from target preparation to image analysis, leading to realistically large confidence intervals for estimated concentrations. The method was validated in experiments using transcripts at known concentrations, showing accuracy and reproducibility of estimated concentrations, which were also in excellent agreement with results from quantitative real-time PCR. We determined the concentration for 10,157 genes in cervix cancers and a pool of cancer cell lines and found values in the range of 10(5)-10(10) transcripts per microg total RNA. The precision of our estimates was sufficiently high to detect significant concentration differences between two tumours and between different genes within the same tumour, comparisons that are not possible with standard intensity ratios. Our method can be used to explore the regulation of pathways and to develop individualized therapies, based on absolute transcript concentrations. It can be applied broadly, facilitating the construction of the transcriptome, continuously updating it by integrating future data.
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Project description:The aim of this study was to examine the contribution of ARF6 and ARF8 to flower gene expression. Flowers from arf6 arf8 plants undergo a developmental arrest at approximately stage 12, just prior to flower opening. Flowers from wild-type, ARF6/arf6 arf8/arf8, and arf6 arf8 plants were separated into stage 1-10 flowers, stage 11+12 flowers, and stage 13-14 flowers to define the developmental stages at which ARF6 and ARF8 are required for gene expression. Keywords: comparison of wild type and arf6 arf8 mutants
2005-06-30 | GSE2848 | GEO
Project description:Gene expression profile of flower buds at stage 13, open flowers at stage 14 and siliques at stages 15/16, according to Smyth et al., 1990) of ABAP1 overexpressing (ABAP1OE) plants compared to wild type flower buds, open flowers and siliques (Col-0) using a a whole-genome oligonucleotide array (Operon) (platform accession number accession number GPL1077). ABAP1 is a negative regulator of the cell cycle that binds to transcription factors and represses their target gene expression, including pre-replication complex genes. All experiments were performed in triplicate.
2021-01-09 | GSE164480 | GEO
Project description:We report genome-wide binding targets of REF6, a H3K27me3 demethylase and also high-throughput profiling of histone modifications of H3K27me3 in Arabidopsis flowers. Using chromatin-immunoprecipitation followed by high-throughput sequencing, we mapped thousands of binding targets of REF6 in flowers and in addition, we used a second fixative, DSG to capture more indirect targets of REF6. In order to investigate the function of indirect binding of REF6 represented H3K27me3 demethylase, the binding targets of DNA binding domain deleted REF6 were identified in the triple mutant background in which REF6 and its two homologs were mutated. Meanwhile, the H3K27me3 marked regions in both wild type and triple mutant flowers were defined. By analyzing the differentially enriched H3K27me3 region and REF6 binding profile, we found that REF6 functions to determine the boundary of H3K27me3 regions. Comparison of binding profiles of REF6 in flowers and in seedlings (ref) revealed a tissue-specific binding manner. More importantly, we found that the indirect binding of REF6 is mediated by trans factors and this indirect binding is crucial for REF6 function, especially in reproductive organs in Arabidopsis. Our results provide novel molecular insights into REF6 functions.
2018-07-09 | GSE106942 | GEO
Project description:BACKGROUND: Microarray gene expression data are accumulating in public databases. The expression profiles contain valuable information for understanding human gene expression patterns. However, the effective use of public microarray data requires integrating the expression profiles from heterogeneous sources. RESULTS: In this study, we have compiled a compendium of microarray expression profiles of various human tissue samples. The microarray raw data generated in different research laboratories have been obtained and combined into a single dataset after data normalization and transformation. To demonstrate the usefulness of the integrated microarray data for studying human gene expression patterns, we have analyzed the dataset to identify potential tissue-selective genes. A new method has been proposed for genome-wide identification of tissue-selective gene targets using both microarray intensity values and detection calls. The candidate genes for brain, liver and testis-selective expression have been examined, and the results suggest that our approach can select some interesting gene targets for further experimental studies. CONCLUSION: A computational approach has been developed in this study for combining microarray expression profiles from heterogeneous sources. The integrated microarray data can be used to investigate tissue-selective expression patterns of human genes.
| S-EPMC2975411 | biostudies-literature
Project description:We determined the genome-wide binding profile of CTCF and cohesin subunits Smc1a and Smc3 in wild type mouse ES cells and compared them to a double FLAG-Halo-mCTCF/mRad21-SNAPf-V5 knock-in mES line, generated by CRISPR/Cas9-mediated genome editing to perform imaging experiments.
2024-02-01 | GSE104000 | GEO
Project description:BACKGROUND: Understanding how genes are expressed specifically in particular tissues is a fundamental question in developmental biology. Many tissue-specific genes are involved in the pathogenesis of complex human diseases. However, experimental identification of tissue-specific genes is time consuming and difficult. The accurate predictions of tissue-specific gene targets could provide useful information for biomarker development and drug target identification. RESULTS: In this study, we have developed a machine learning approach for predicting the human tissue-specific genes using microarray expression data. The lists of known tissue-specific genes for different tissues were collected from UniProt database, and the expression data retrieved from the previously compiled dataset according to the lists were used for input vector encoding. Random Forests (RFs) and Support Vector Machines (SVMs) were used to construct accurate classifiers. The RF classifiers were found to outperform SVM models for tissue-specific gene prediction. The results suggest that the candidate genes for brain or liver specific expression can provide valuable information for further experimental studies. Our approach was also applied for identifying tissue-selective gene targets for different types of tissues. CONCLUSIONS: A machine learning approach has been developed for accurately identifying the candidate genes for tissue specific/selective expression. The approach provides an efficient way to select some interesting genes for developing new biomedical markers and improve our knowledge of tissue-specific expression.
| S-EPMC3552705 | biostudies-literature