Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Transcription profiling of global gene expression changes during mouse preimplantation development: unfertilized eggs, fertilized egg, 2-cell embryos, 4-cell embryos, 8-cell embryos, morulae, blastocysts

ABSTRACT: Microarray data analysis Intensity of 21,939 gene features per array was extracted from scanned microarray images using Feature Extraction 5.1.1 software (Agilent Technologies), which performs background subtractions and dye normalization. This normalization method is targeted at detecting changes in relative expression of individual genes rather than global expression. Global expression change would require external normalization controls (van de Peppel et al., 2003). Text output was processed using an application developed in-house to perform ANOVA analysis (http://lgsun.grc.nia.nih.gov/ANOVA/). Intensity of features measured with a >50% error were replaced with missing values except features with very low intensity. Surrogate values equal to mean error were inserted for values that were negative or less than the probe error. Data were analyzed using ANOVA with embryonic stage as a factor. The small number of biological replications typical in expression profiling experiments results in a highly variable error variance, and this problem is usually addressed by log-ratio thresholds (Schena et al., 1995) that require subjective decisions about biological significance, or by Bayesian adjustment of error variance (Baldi and Long, 2001), which may still underestimate error variance and result in false positive results. To reduce false-positives, we opted for a very conservative error model in which error variance that is used for estimating F-statistics is the maximum of the actual error variance for this gene and the average error variance in 500 genes with similar average intensity. Statistical significance was determined using the False Discovery Rate (FDR = 10%) method (Benjamini and Hochberg, 1995). Pair-wise mean comparison was done with t-statistics and FDR=10%. Further data processing including scatter plots, hierarchical clustering, and principal component analysis (PCA) were also performed through NIA microarray analysis tool (http://lgsun.grc.nia.nih.gov/ANOVA/). The input file for NIA microarray analysis tool is available at http://lgsun.grc.nia.nih.gov/microarray/data.html

ORGANISM(S): Mus musculus

SUBMITTER: Alexei Sharov

PROVIDER: E-MEXP-73 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Publications

Dynamics of global gene expression changes during mouse preimplantation development.

Hamatani Toshio T Carter Mark G MG Sharov Alexei A AA Ko Minoru S H MS

Developmental cell 20040101 1

Understanding preimplantation development is important both for basic reproductive biology and for practical applications including regenerative medicine and livestock breeding. Global expression profiles revealed and characterized the distinctive patterns of maternal RNA degradation and zygotic gene activation, including two major transient waves of de novo transcription. The first wave corresponds to zygotic genome activation (ZGA); the second wave, named mid-preimplantation gene activation (M ...[more]

PMID: 14723852

Similar Datasets

Project description:Feature Extraction 5.1.1 software (Agilent Technologies) performs background subtractions and dye normalization. This normalization method is targeted at detecting changes in relative expression of individual genes rather than global expression. Global expression change would require external normalization controls (van de Peppel et al., 2003). Text output was processed using an application developed in-house to perform ANOVA analysis (http://lgsun.grc.nia.nih.gov/ANOVA/). Intensity of features measured with a >50% error were replaced with missing values except features with very low intensity. Surrogate values equal to mean error were inserted for values that were negative or less than the probe error. Data were analyzed using ANOVA with embryonic stage as a factor. The small number of biological replications typical in expression profiling experiments results in a highly variable error variance, and this problem is usually addressed by log-ratio thresholds (Schena et al., 1995) that require subjective decisions about biological significance, or by Bayesian adjustment of error variance (Baldi and Long, 2001), which may still underestimate error variance and result in false positive results. To reduce false-positives, we opted for a very conservative error model in which error variance that is used for estimating F-statistics is the maximum of the actual error variance for this gene and the average error variance in 500 genes with similar average intensity. Statistical significance was determined using the False Discovery Rate (FDR = 10%) method (Benjamini and Hochberg, 1995). Pair-wise mean comparison was done with t-statistics and FDR=10%. Further data processing including scatter plots, hierarchical clustering, and principal component analysis (PCA) were also performed through NIA microarray analysis tool (http://lgsun.grc.nia.nih.gov/ANOVA/). Details are described in the publication (Hamatani T. et al., Developmental Cell. Published online Dec. 18, 2003).

Project description:Many classes of noncoding RNAs (ncRNAs) are known to be involved in gene expression regulation; however, long intronic ncRNAs have received little attention. In previous works, our group has provided evidence that intronic regions, transcribed from 80% of all RefSeq gene loci, are key sources of potentially regulatory ncRNAs. Intronic transcripts were shown to be correlated to the degree of prostate cancer differentiation, regulated by physiological stimuli, and highly expressed in genomic loci related to transcriptional regulation. In the present work we aimed at functional characterization of KLHL29 intronic transcript, one of the most highly expressed in human tissues. Although the protein-coding KLHL29 transcripts are still poorly studied, genes from this family are involved in a wide variety of biological processes, including cell cycle regulation mechanisms. We have observed that the long intronic ncRNA is predominantly transcribed from the opposite strand at the KLHL29 locus, and that the ncRNA overexpression does not affect the levels of protein-coding mRNAs from the same locus. Intriguingly, we found 2,002 genes with significant (q< 5.3%) differential expression between HEK293 cells overexpressing KLHL29 sense (S) or antisense (AS) intronic transcript. Within these genes, GO categories such as ‘Regulation of cell cycle’ and ‘Transcription’ were significantly enriched. Furthermore, human tumorigenic cell lines overexpressing KLHL29 AS intronic transcript showed increased proliferation rates in vitro and increased tumorigenic capacity in nude mice. In conclusion, we suggest that KLHL29 long intronic ncRNA controls the cell cycle mechanisms, probably through a fine-tuning regulation of gene expression. Genes were defined as expressed when their intensity signals were above the average negative control value plus three standard deviations; a set of 153 negative controls (Agilent Technologies) was present on the array. Only genes that were detected as expressed in all four replicates of the same HEK293 transfection (or not expressed in the four replicates) were used in further analysis. The intensity values between experiments were normalized by the 40% trimmed mean intensity. For each transfection, one entire replicate experiment was excluded from the analyses, based on evaluating the coefficient of variance among the samples (for S KLHL29 intronic transcript, exclusion of the outlier replicate decreased the coefficient of variance from 0.21-0.24 (obtained by excluding any other replicate) to 0.19; for AS transcript, the coefficient of variance decreased from 0.17-0.18 to 0.12). Differentially expressed genes were identified using three transfection replicates for each construction, employing the Significance Analysis of Microarrays (SAM) tool (Tusher et al., 2001) with the following parameters: two-class response, 100 permutations and K-Nearest Neighbors Imputer. We applied 5.4 or 10.3% FDR (False Discovery Rate) cutoffs, as described in the results. A fold-change filter (1.4-fold cutoff) was applied after identification of significant differentially expressed genes.

Dataset Information

Transcription profiling of global gene expression changes during mouse preimplantation development: unfertilized eggs, fertilized egg, 2-cell embryos, 4-cell embryos, 8-cell embryos, morulae, blastocysts

Publications

Dynamics of global gene expression changes during mouse preimplantation development.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets