The transcriptome is the set of all RNA molecules, including mRNA, rRNA, tRNA, and other non-coding RNA transcribed in one cell or a population of cells. It differs from the exome in that it includes only those RNA molecules found in a specified cell population, and usually includes the amount or concentration of each RNA molecule in addition to the molecular identities.


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]

Methods of construction

There are two general methods of inferring transcriptomes. One approach maps sequence reads onto a reference genome, either of the organism itself (whose transcriptome is being studied) or of a closely related species. The other approach, de novo transcriptome assembly, uses software to infer transcripts directly from short sequence reads.


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.

RNA-seq is emerging (2013) as the method of choice for measuring transcriptomes of organisms, though the older technique of DNA microarrays is still used.[citation needed]


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]

Transcriptomics is an emerging and continually growing field in biomarker discovery for use in assessing the safety of drugs or chemical risk assessment.[1]

Relation to proteome

Further information: Proteome

The transcriptome can be seen as a precursor[dubious ] 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.[2] 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.

See also

Lua error in package.lua at line 80: module 'Module:Portal/images/aliases' not found.


  1. ^ Szabo, David (2014). Transcriptomic biomarkers in safety and risk assessment of chemicals. In Ramesh Gupta, editors:Gupta - Biomarkers in Toxicology, Oxford:Academic Press. pp. 1033–1038. ISBN 978-0-12-404630-6. 
  2. ^ Schwanhäusser, Björn et al. (May 2011). "Global quantification of mammalian gene expression control". Nature 473 (7347): 337–342. PMID 21593866. doi:10.1038/nature10098. 
  • ^ Wang Z, Gerstein M, Snyder M. (2009). RNA-Seq: a revolutionary tool for transcriptomics. Nature Rev. Genetics 10(1): 57-63.
  • ^ 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.
  • ^ "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.
  • ^ 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.
  • ^ 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
  • ^ Assou, S.; Boumela, I.; Haouzi, D.; Anahory, T.; Dechaud, H.; De Vos, J.; Hamamah, S. (2010). "Dynamic changes in gene expression during human early embryo development: From fundamental aspects to clinical applications". Human Reproduction Update 17 (2): 272–290. PMC 3189516. PMID 20716614. doi:10.1093/humupd/dmq036.  edit