Dataset Information

EST-based analysis of gene expression in the porcine brain.

ABSTRACT: Since pig is an important livestock species worldwide, its gene expression has been investigated intensively, but rarely in brain. In order to study gene expression profiles in the pig central nervous system, we sequenced and analyzed 43,122 high-quality 5' end expressed sequence tags (ESTs) from porcine cerebellum, cortex cerebrum, and brain stem cDNA libraries, involving several different prenatal and postnatal developmental stages. The initial ESTs were assembled into 16,101 clusters and compared to protein and nucleic acid databases in GenBank. Of these sequences, 30.6% clusters matched protein databases and represented function known sequences; 75.1% had significant hits to nucleic acid databases and partial represented known function; 73.3% matched known porcine ESTs; and 21.5% had no matches to any known sequences in GenBank. We used the categories defined by the Gene Ontology to survey gene expression in the porcine brain.

SUBMITTER: Zhang B

PROVIDER: S-EPMC5187415 | biostudies-literature | 2004 Nov

REPOSITORIES: biostudies-literature

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EST-based analysis of gene expression in the porcine brain.

Zhang Bing B Jin Wu W Zeng Yanwu Y Su Zhixi Z Hu Songnian S Yu Jun J

Genomics, proteomics & bioinformatics 20041101 4

Since pig is an important livestock species worldwide, its gene expression has been investigated intensively, but rarely in brain. In order to study gene expression profiles in the pig central nervous system, we sequenced and analyzed 43,122 high-quality 5' end expressed sequence tags (ESTs) from porcine cerebellum, cortex cerebrum, and brain stem cDNA libraries, involving several different prenatal and postnatal developmental stages. The initial ESTs were assembled into 16,101 clusters and comp ...[more]

PMID: 15901252

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Project description:BACKGROUND: Coffee is one of the world's most important crops; it is consumed worldwide and plays a significant role in the economy of producing countries. Coffea arabica and C. canephora are responsible for 70 and 30% of commercial production, respectively. C. arabica is an allotetraploid from a recent hybridization of the diploid species, C. canephora and C. eugenioides. C. arabica has lower genetic diversity and results in a higher quality beverage than C. canephora. Research initiatives have been launched to produce genomic and transcriptomic data about Coffea spp. as a strategy to improve breeding efficiency. RESULTS: Assembling the expressed sequence tags (ESTs) of C. arabica and C. canephora produced by the Brazilian Coffee Genome Project and the Nestlé-Cornell Consortium revealed 32,007 clusters of C. arabica and 16,665 clusters of C. canephora. We detected different GC3 profiles between these species that are related to their genome structure and mating system. BLAST analysis revealed similarities between coffee and grape (Vitis vinifera) genes. Using KA/KS analysis, we identified coffee genes under purifying and positive selection. Protein domain and gene ontology analyses suggested differences between Coffea spp. data, mainly in relation to complex sugar synthases and nucleotide binding proteins. OrthoMCL was used to identify specific and prevalent coffee protein families when compared to five other plant species. Among the interesting families annotated are new cystatins, glycine-rich proteins and RALF-like peptides. Hierarchical clustering was used to independently group C. arabica and C. canephora expression clusters according to expression data extracted from EST libraries, resulting in the identification of differentially expressed genes. Based on these results, we emphasize gene annotation and discuss plant defenses, abiotic stress and cup quality-related functional categories. CONCLUSION: We present the first comprehensive genome-wide transcript profile study of C. arabica and C. canephora, which can be freely assessed by the scientific community at http://www.lge.ibi.unicamp.br/coffea. Our data reveal the presence of species-specific/prevalent genes in coffee that may help to explain particular characteristics of these two crops. The identification of differentially expressed transcripts offers a starting point for the correlation between gene expression profiles and Coffea spp. developmental traits, providing valuable insights for coffee breeding and biotechnology, especially concerning sugar metabolism and stress tolerance.

Project description:EST expression profiling provides an attractive tool for studying differential gene expression, but cDNA libraries' origins and EST data quality are not always known or reported. Libraries may originate from pooled or mixed tissues; EST clustering, EST counts, library annotations and analysis algorithms may contain errors. Traditional data analysis methods, including research into tissue-specific gene expression, assume EST counts to be correct and libraries to be correctly annotated, which is not always the case. Therefore, a method capable of assessing the quality of expression data based on that data alone would be invaluable for assessing the quality of EST data and determining their suitability for mRNA expression analysis. Here we report an approach to the selection of a small generic subset of 244 UniGene clusters suitable for identification of the tissue of origin for EST libraries and quality control of the expression data using EST expression information alone. We created a small expression matrix of UniGene IDs using two rounds of selection followed by two rounds of optimisation. Our selection procedures differ from traditional approaches to finding "tissue-specific" genes and our matrix yields consistency high positive correlation values for libraries with confirmed tissues of origin and can be applied for tissue typing and quality control of libraries as small as just a few hundred total ESTs. Furthermore, we can pick up tissue correlations between related tissues e.g. brain and peripheral nervous tissue, heart and muscle tissues and identify tissue origins for a few libraries of uncharacterised tissue identity. It was possible to confirm tissue identity for some libraries which have been derived from cancer tissues or have been normalised. Tissue matching is affected strongly by cancer progression or library normalisation and our approach may potentially be applied for elucidating the stage of normalisation in normalised libraries or for cancer staging.

Dataset Information

EST-based analysis of gene expression in the porcine brain.

Publications

EST-based analysis of gene expression in the porcine brain.

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