Transcriptome
Transcriptome
The transcriptome is the set of a cell's mRNA molecules. Microarray technology - the quantitative, sequence-specific hybridization of nucleotides - was the first domain of massively parallel, high-throughput biology. Quantifying gene expression levels in a tissue-, development-, or response-specific has yielded detailed insight into cellular function at the molecular level. Yet, while the questions remain, high-throughput sequencing methods are rapidly supplanting microarrays to provide the data. Moreover, we realize that the transcriptome is not just a passive buffer of expressed information: an entire, complex, intrinsic level of regulation through hybridization of small nuclear RNAs has been discovered.
Introductory reading
| Malone & Oliver (2011) Microarrays, deep sequencing and the true measure of the transcriptome. BMC Biol 9:34. (pmid: 21627854) |
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[ PubMed ] [ DOI ] Microarrays first made the analysis of the transcriptome possible, and have produced much important information. Today, however, researchers are increasingly turning to direct high-throughput sequencing -- RNA-Seq -- which has considerable advantages for examining transcriptome fine structure -- for example in the detection of allele-specific expression and splice junctions. In this article, we discuss the relative merits of the two techniques, the inherent biases in each, and whether all of the vast body of array work needs to be revisited using the newer technology. We conclude that microarrays remain useful and accurate tools for measuring expression levels, and RNA-Seq complements and extends microarray measurements. |
Contents
- Microarray standards and databases
- Working with expression data
- Interpretation
Further reading and resources
| Carninci (2007) Constructing the landscape of the mammalian transcriptome. J Exp Biol 210:1497-506. (pmid: 17449815) |
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[ PubMed ] [ DOI ] The principal route to understanding the biological significance of the genome sequence comes from discovery and characterization of that portion of the genome that is transcribed into RNA products. We now know that this ;transcriptome' is unexpectedly complex and its precise definition in any one species requires multiple technical approaches and an ability to work on a very large scale. A key step is the development of technologies able to capture snapshots of the complexity of the various kinds of RNA generated by the genome. As the human, mouse and other model genome sequencing projects approach completion, considerable effort has been focused on identifying and annotating the protein-coding genes as the principal output of the genome. In pursuing this aim, several key technologies have been developed to generate large numbers and highly diverse sets of full-length cDNAs and their variants. However, the search has identified another hidden transcriptional universe comprising a wide variety of non-protein coding RNA transcripts. Despite initial scepticism, various experiments and complementary technologies have demonstrated that these RNAs are dynamically transcribed and a subset of them can act as sense-antisense RNAs, which influence the transcriptional output of the genome. Recent experimental evidence suggests that the list of non-protein coding RNAs is still largely incomplete and that transcription is substantially more complex even than currently thought. |
| Chuang et al. (2007) Network-based classification of breast cancer metastasis. Mol Syst Biol 3:140. (pmid: 17940530) |
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[ PubMed ] [ DOI ] Mapping the pathways that give rise to metastasis is one of the key challenges of breast cancer research. Recently, several large-scale studies have shed light on this problem through analysis of gene expression profiles to identify markers correlated with metastasis. Here, we apply a protein-network-based approach that identifies markers not as individual genes but as subnetworks extracted from protein interaction databases. The resulting subnetworks provide novel hypotheses for pathways involved in tumor progression. Although genes with known breast cancer mutations are typically not detected through analysis of differential expression, they play a central role in the protein network by interconnecting many differentially expressed genes. We find that the subnetwork markers are more reproducible than individual marker genes selected without network information, and that they achieve higher accuracy in the classification of metastatic versus non-metastatic tumors. |
| Hubble et al. (2009) Implementation of GenePattern within the Stanford Microarray Database. Nucleic Acids Res 37:D898-901. (pmid: 18953035) |
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[ PubMed ] [ DOI ] Hundreds of researchers across the world use the Stanford Microarray Database (SMD; http://smd.stanford.edu/) to store, annotate, view, analyze and share microarray data. In addition to providing registered users at Stanford access to their own data, SMD also provides access to public data, and tools with which to analyze those data, to any public user anywhere in the world. Previously, the addition of new microarray data analysis tools to SMD has been limited by available engineering resources, and in addition, the existing suite of tools did not provide a simple way to design, execute and share analysis pipelines, or to document such pipelines for the purposes of publication. To address this, we have incorporated the GenePattern software package directly into SMD, providing access to many new analysis tools, as well as a plug-in architecture that allows users to directly integrate and share additional tools through SMD. In this article, we describe our implementation of the GenePattern microarray analysis software package into the SMD code base. This extension is available with the SMD source code that is fully and freely available to others under an Open Source license, enabling other groups to create a local installation of SMD with an enriched data analysis capability. |
| Xie & Ahn (2010) Statistical methods for integrating multiple types of high-throughput data. Methods Mol Biol 620:511-29. (pmid: 20652519) |
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[ PubMed ] [ DOI ] Large-scale sequencing, copy number, mRNA, and protein data have given great promise to the biomedical research, while posing great challenges to data management and data analysis. Integrating different types of high-throughput data from diverse sources can increase the statistical power of data analysis and provide deeper biological understanding. This chapter uses two biomedical research examples to illustrate why there is an urgent need to develop reliable and robust methods for integrating the heterogeneous data. We then introduce and review some recently developed statistical methods for integrative analysis for both statistical inference and classification purposes. Finally, we present some useful public access databases and program code to facilitate the integrative analysis in practice. |
| Parkinson et al. (2011) ArrayExpress update--an archive of microarray and high-throughput sequencing-based functional genomics experiments. Nucleic Acids Res 39:D1002-4. (pmid: 21071405) |
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[ PubMed ] [ DOI ] The ArrayExpress Archive (http://www.ebi.ac.uk/arrayexpress) is one of the three international public repositories of functional genomics data supporting publications. It includes data generated by sequencing or array-based technologies. Data are submitted by users and imported directly from the NCBI Gene Expression Omnibus. The ArrayExpress Archive is closely integrated with the Gene Expression Atlas and the sequence databases at the European Bioinformatics Institute. Advanced queries provided via ontology enabled interfaces include queries based on technology and sample attributes such as disease, cell types and anatomy. |
| Zheng & Tao (2011) Stochastic analysis of gene expression. Methods Mol Biol 734:123-51. (pmid: 21468988) |
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[ PubMed ] [ DOI ] In this chapter, stochasticity in gene expression is investigated using Ω-expansion technique. Two theoretical models are considered here, one concern the stochastic fluctuations in a single-gene network with negative feedback regulation, and the other the additivity of noise propagation in a protein cascade. All of these theoretical analyses may provide a basic framework for understanding stochastic gene expression. |
| Han et al. (2011) SnapShot: High-throughput sequencing applications. Cell 146:1044, 1044.e1-2. (pmid: 21925324) |
