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The transcriptome is the set of all RNA molecules, including mRNA, rRNA, tRNA, and other non-coding RNA produced in one or a population of cells.

[edit] Scope

The term can be applied to the total set of transcripts in a given organism, or to the specific subset of transcripts present in a particular cell type. Unlike the genome, which is roughly fixed for a given cell line (excluding mutations), the transcriptome can vary with external environmental conditions. Because it includes all mRNA transcripts in the cell, the transcriptome reflects the genes that are being actively expressed at any given time, with the exception of mRNA degradation phenomena such as transcriptional attenuation. The study of transcriptomics, also referred to as expression profiling, examines the expression level of mRNAs in a given cell population, often using high-throughput techniques based on DNA microarray technology. The use of next-generation sequencing technology to study the transcriptome at the nucleotide level is known as RNA-Seq.^[1]

[edit] Analysis

DNA microarrays can provide a method for comparing on a genome-wide basis the abundance of DNAs in the same samples. The DNA in spots can only be PCR products that are specific for individual genes. A DNA copy of RNA is made using the enzyme reverse transcriptase. Gene arrays can only be used to investigate genes in eukaryotic cells. Sequencing is now being used instead of gene arrays to quantify DNA levels, at least semi quantitatively.

A number of organism-specific transcriptome databases have been constructed and annotated to aid in the identification of genes that are differentially expressed in distinct cell populations or subtypes.

[edit] Applications

The transcriptomes of stem cells and cancer cells are of particular interest to researchers who seek to understand the processes of cellular differentiation and carcinogenesis.

Analysis of the transcriptomes of human oocytes and embryos is used to understand the molecular mechanisms and signaling pathways controlling early embryonic development, and could theoretically be a powerful tool in making proper embryo selection in in vitro fertilisation.^[2]

[edit] Relation to proteome

The transcriptome can be seen as a precursor for the proteome, that is, the entire set of proteins expressed by a genome.

However, the analysis of relative mRNA expression levels can be complicated by the fact that relatively small changes in mRNA expression can produce large changes in the total amount of the corresponding protein present in the cell. One analysis method, known as Gene Set Enrichment Analysis, identifies coregulated gene networks rather than individual genes that are up- or down-regulated in different cell populations.^[3]

Although microarray studies can reveal the relative amounts of different mRNAs in the cell, levels of mRNA are not directly proportional to the expression level of the proteins they code for. The number of protein molecules synthesized using a given mRNA molecule as a template is highly dependent on translation-initiation features of the mRNA sequence; in particular, the ability of the translation initiation sequence is a key determinant in the recruiting of ribosomes for protein translation. The complete protein complement of a cell or organism is known as the proteome.

A study of 158,807 mouse transcripts revealed that 4,520 of these transcripts form antisense partners that are base pair complementary to the exons of genes.^[4] These results raise the possibility that significant numbers of "antisense RNA-coding genes" might participate in the regulation of the levels of expression of protein-coding mRNAs.

[edit] References

1. ^ Wang Z, Gerstein M, Snyder M. (2009). RNA-Seq: a revolutionary tool for transcriptomics. Nature Rev. Genetics 10(1): 57-63.
2. ^ Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP. (2005). Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 102(43):15545-50.
3. ^ "Antisense Transcription in the Mammalian Transcriptome" by the RIKEN Genome Exploration Research Group and Genome Science Group (Genome Network Project Core Group) and the FANTOM Consortium: S. Katayama et al. in Science, Vol 309, Issue 5740, 1564-1566 , 2 September 2005.
4. ^ Velculescu VE, Zhang L, Zhou W, Vogelstein J, Basrai MA, Bassett DE Jr, Hieter P, Vogelstein B, Kinzler KW. Characterization of the yeast transcriptome. Cell. 1997 Jan 24;88(2):243-51.
5. ^ Laule O, Hirsch-Hoffmann M, Hruz T, Gruissem W, and P Zimmermann. (2006) Web-based analysis of the mouse transcriptome using Genevestigator. BMC Bioinformatics 7:311
6. ^ Stinchcomb, B. L.; Echols, D. J.; Haouzi, D.; Anahory, T.; Dechaud, H.; De Vos, J.; Hamamah, S. (1966). "Missouri Upper Cambrian Monoplacophora Previously Considered Cephalopods". Journal of Paleontology 40 (3): 647¨C650. doi:10.1093/humupd/dmq036.  edit

[edit] See also

• Expression profiling
• List of omics topics in biology
• Bioinformatics
• DNA microarrays (a.k.a. DNA chips)
• Gene expression
• Serial analysis of gene expression
• RNA-Seq
• Metabolomics
• Proteome
• Functional genomics

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