Expressed sequence tag

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In genetics, an expressed sequence tag (EST) is a short sub-sequence of a cDNA sequence. [1] ESTs may be used to identify gene transcripts, and were instrumental in gene discovery and in gene-sequence determination. [2] The identification of ESTs has proceeded rapidly, with approximately 74.2 million ESTs now available in public databases (e.g. GenBank 1 January 2013, all species). EST approaches have largely been superseded by whole genome and transcriptome sequencing and metagenome sequencing.

Contents

An EST results from one-shot sequencing of a cloned cDNA. The cDNAs used for EST generation are typically individual clones from a cDNA library. The resulting sequence is a relatively low-quality fragment whose length is limited by current technology to approximately 500 to 800 nucleotides. Because these clones consist of DNA that is complementary to mRNA, the ESTs represent portions of expressed genes. They may be represented in databases as either cDNA/mRNA sequence or as the reverse complement of the mRNA, the template strand.

One can map ESTs to specific chromosome locations using physical mapping techniques, such as radiation hybrid mapping, Happy mapping, or FISH. Alternatively, if the genome of the organism that originated the EST has been sequenced, one can align the EST sequence to that genome using a computer.

The current understanding of the human set of genes (as of 2006) includes the existence of thousands of genes based solely on EST evidence. In this respect, ESTs have become a tool to refine the predicted transcripts for those genes, which leads to the prediction of their protein products and ultimately of their function. Moreover, the situation in which those ESTs are obtained (tissue, organ, disease state - e.g. cancer) gives information on the conditions in which the corresponding gene is acting. ESTs contain enough information to permit the design of precise probes for DNA microarrays that then can be used to determine gene expression profiles.

Some authors use the term "EST" to describe genes for which little or no further information exists besides the tag. [3]

History

In 1979, teams at Harvard and Caltech extended the basic idea of making DNA copies of mRNAs in vitro to amplifying a library of such in bacterial plasmids. [4]

In 1982, the idea of selecting random or semi-random clones from such a cDNA library for sequencing was explored by Greg Sutcliffe and coworkers. [5]

In 1983, Putney et al. sequenced 178 clones from a rabbit muscle cDNA library. [6]

In 1991, Adams and co-workers coined the term EST and initiated more systematic sequencing as a project (starting with 600 brain cDNAs). [2]

Sources of data and annotations

dbEST

The dbEST is a division of Genbank established in 1992. As for GenBank, data in dbEST is directly submitted by laboratories worldwide and is not curated.

EST contigs

Because of the way ESTs are sequenced, many distinct expressed sequence tags are often partial sequences that correspond to the same mRNA of an organism. In an effort to reduce the number of expressed sequence tags for downstream gene discovery analyses, several groups assembled expressed sequence tags into EST contigs. Example of resources that provide EST contigs include: TIGR gene indices, [7] Unigene, [8] and STACK [9]

Constructing EST contigs is not trivial and may yield artifacts (contigs that contain two distinct gene products). When the complete genome sequence of an organism is available and transcripts are annotated, it is possible to bypass contig assembly and directly match transcripts with ESTs. This approach is used in the TissueInfo system (see below) and makes it easy to link annotations in the genomic database to tissue information provided by EST data.

Tissue information

High-throughput analyses of ESTs often encounter similar data management challenges. A first challenge is that tissue provenance of EST libraries is described in plain English in dbEST. [10] This makes it difficult to write programs that can unambiguously determine that two EST libraries were sequenced from the same tissue. Similarly, disease conditions for the tissue are not annotated in a computationally friendly manner. For instance, cancer origin of a library is often mixed with the tissue name (e.g., the tissue name "glioblastoma" indicates that the EST library was sequenced from brain tissue and the disease condition is cancer). [11] With the notable exception of cancer, the disease condition is often not recorded in dbEST entries. The TissueInfo project was started in 2000 to help with these challenges. The project provides curated data (updated daily) to disambiguate tissue origin and disease state (cancer/non cancer), offers a tissue ontology that links tissues and organs by "is part of" relationships (i.e., formalizes knowledge that hypothalamus is part of brain, and that brain is part of the central nervous system) and distributes open-source software for linking transcript annotations from sequenced genomes to tissue expression profiles calculated with data in dbEST. [12]

See also

Related Research Articles

Bioinformatics Computational analysis of large, complex sets of biological data

Bioinformatics is an interdisciplinary field that develops methods and software tools for understanding biological data, in particular when the data sets are large and complex. As an interdisciplinary field of science, bioinformatics combines biology, chemistry, physics, computer science, information engineering, mathematics and statistics to analyze and interpret the biological data. Bioinformatics has been used for in silico analyses of biological queries using mathematical and statistical techniques.

Complementary DNA Single-stranded DNA synthesized from RNA

In genetics, complementary DNA (cDNA) is DNA synthesized from a single-stranded RNA template in a reaction catalyzed by the enzyme reverse transcriptase. cDNA is often used to clone eukaryotic genes in prokaryotes. When scientists want to express a specific protein in a cell that does not normally express that protein, they will transfer the cDNA that codes for the protein to the recipient cell. In molecular biology, cDNA is also generated to analyze transcriptomic profiles in bulk tissue, single cells, or single nuclei in assays such as microarrays and RNA-seq.

A contig is a set of overlapping DNA segments that together represent a consensus region of DNA. In bottom-up sequencing projects, a contig refers to overlapping sequence data (reads); in top-down sequencing projects, contig refers to the overlapping clones that form a physical map of the genome that is used to guide sequencing and assembly. Contigs can thus refer both to overlapping DNA sequence and to overlapping physical segments (fragments) contained in clones depending on the context.

Library (biology)

In molecular biology, a library is a collection of DNA fragments that is stored and propagated in a population of micro-organisms through the process of molecular cloning. There are different types of DNA libraries, including cDNA libraries, genomic libraries and randomized mutant libraries. DNA library technology is a mainstay of current molecular biology, genetic engineering, and protein engineering, and the applications of these libraries depend on the source of the original DNA fragments. There are differences in the cloning vectors and techniques used in library preparation, but in general each DNA fragment is uniquely inserted into a cloning vector and the pool of recombinant DNA molecules is then transferred into a population of bacteria or yeast such that each organism contains on average one construct. As the population of organisms is grown in culture, the DNA molecules contained within them are copied and propagated.

The transcriptome is the set of all RNA transcripts, including coding and non-coding, in an individual or a population of cells. The term can also sometimes be used to refer to all RNAs, or just mRNA, depending on the particular experiment. The term transcriptome is a portmanteau of the words transcript and genome; it is associated with the process of transcript production during the biological process of transcription.

Serial analysis of gene expression Molecular biology technique

Serial Analysis of Gene Expression (SAGE) is a transcriptomic technique used by molecular biologists to produce a snapshot of the messenger RNA population in a sample of interest in the form of small tags that correspond to fragments of those transcripts. Several variants have been developed since, most notably a more robust version, LongSAGE, RL-SAGE and the most recent SuperSAGE. Many of these have improved the technique with the capture of longer tags, enabling more confident identification of a source gene.

In the fields of bioinformatics and computational biology, Genome survey sequences (GSS) are nucleotide sequences similar to expressed sequence tags (ESTs) that the only difference is that most of them are genomic in origin, rather than mRNA.

ChIP-sequencing, also known as ChIP-seq, is a method used to analyze protein interactions with DNA. ChIP-seq combines chromatin immunoprecipitation (ChIP) with massively parallel DNA sequencing to identify the binding sites of DNA-associated proteins. It can be used to map global binding sites precisely for any protein of interest. Previously, ChIP-on-chip was the most common technique utilized to study these protein–DNA relations.

RNA-Seq Lab technique in cellular biology

RNA-Seq is a sequencing technique which uses next-generation sequencing (NGS) to reveal the presence and quantity of RNA in a biological sample at a given moment, analyzing the continuously changing cellular transcriptome.

Paired-end tags (PET) are the short sequences at the 5’ and 3' ends of a DNA fragment which are unique enough that they (theoretically) exist together only once in a genome, therefore making the sequence of the DNA in between them available upon search or upon further sequencing. Paired-end tags (PET) exist in PET libraries with the intervening DNA absent, that is, a PET "represents" a larger fragment of genomic or cDNA by consisting of a short 5' linker sequence, a short 5' sequence tag, a short 3' sequence tag, and a short 3' linker sequence. It was shown conceptually that 13 base pairs are sufficient to map tags uniquely. However, longer sequences are more practical for mapping reads uniquely. The endonucleases used to produce PETs give longer tags but sequences of 50–100 base pairs would be optimal for both mapping and cost efficiency. After extracting the PETs from many DNA fragments, they are linked (concatenated) together for efficient sequencing. On average, 20–30 tags could be sequenced with the Sanger method, which has a longer read length. Since the tag sequences are short, individual PETs are well suited for next-generation sequencing that has short read lengths and higher throughput. The main advantages of PET sequencing are its reduced cost by sequencing only short fragments, detection of structural variants in the genome, and increased specificity when aligning back to the genome compared to single tags, which involves only one end of the DNA fragment.

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De novo transcriptome assembly is the de novo sequence assembly method of creating a transcriptome without the aid of a reference genome.

Digital transcriptome subtraction

Digital transcriptome subtraction (DTS) is a bioinformatics method to detect the presence of novel pathogen transcripts through computational removal of the host sequences. DTS is the direct in silico analogue of the wet-lab approach Representational Difference Analysis (RDA), and is made possible by unbiased high-throughput sequencing and the availability of a high-quality, annotated reference genome of the host. The method specifically examines the etiological agent of infectious diseases and is best known for discovering Merkel cell polymavirus, the suspect causative agent in Merkel cell carcinoma.

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FANTOM

FANTOM is an international research consortium first established in 2000 as part of the RIKEN research institute in Japan. The original meeting gathered international scientists from diverse backgrounds to help annotate the function of mouse cDNA clones generated by the Hayashizaki group. Since the initial FANTOM1 effort, the consortium has released multiple projects that look to understand the mechanisms governing the regulation of mammalian genomes. Their work has generated a large collection of shared data and helped advance biochemical and bioinformatic methodologies in genomics research.

References

  1. ↑ ESTs Factsheet. National Center for Biotechnology Information.
  2. 1 2 Adams MD, Kelley JM, Gocayne JD, et al. (Jun 1991). "Complementary DNA sequencing: expressed sequence tags and human genome project". Science. 252 (5013): 1651–6. Bibcode:1991Sci...252.1651A. doi:10.1126/science.2047873. PMID   2047873. S2CID   13436211.
  3. ↑ dbEST
  4. ↑ Sim GK, Kafatos FC, Jones CW, Koehler MD, Efstratiadis A, Maniatis T (December 1979). "Use of a cDNA library for studies on evolution and developmental expression of the chorion multigene families". Cell. 18 (4): 1303–16. doi: 10.1016/0092-8674(79)90241-1 . PMID   519770.
  5. ↑ Sutcliffe JG, Milner RJ, Bloom FE, Lerner RA (August 1982). "Common 82-nucleotide sequence unique to brain RNA". Proc Natl Acad Sci U S A. 79 (16): 4942–6. Bibcode:1982PNAS...79.4942S. doi: 10.1073/pnas.79.16.4942 . PMC   346801 . PMID   6956902.
  6. ↑ Putney SD, Herlihy WC, Schimmel P (1983). "A new troponin T and cDNA clones for 13 different muscle proteins, found by shotgun sequencing". Nature. 302 (5910): 718–21. Bibcode:1983Natur.302..718P. doi:10.1038/302718a0. PMID   6687628. S2CID   4364361.
  7. ↑ Lee Y, Tsai J, Sunkara S, et al. (Jan 2005). "The TIGR Gene Indices: clustering and assembling EST and known genes and integration with eukaryotic genomes". Nucleic Acids Res. 33 (Database issue): D71–4. doi:10.1093/nar/gki064. PMC   540018 . PMID   15608288.
  8. ↑ Stanton JA, Macgregor AB, Green DP (2003). "Identifying tissue-enriched gene expression in mouse tissues using the NIH UniGene database". Appl Bioinform. 2 (3 Suppl): S65–73. PMID   15130819.
  9. ↑ Christoffels A, van Gelder A, Greyling G, Miller R, Hide T, Hide W (Jan 2001). "STACK: Sequence Tag Alignment and Consensus Knowledgebase". Nucleic Acids Res. 29 (1): 234–8. doi:10.1093/nar/29.1.234. PMC   29830 . PMID   11125101.
  10. ↑ Skrabanek L, Campagne F (Nov 2001). "TissueInfo: high-throughput identification of tissue expression profiles and specificity". Nucleic Acids Res. 29 (21): E102–2. doi:10.1093/nar/29.21.e102. PMC   60201 . PMID   11691939.
  11. ↑ Campagne F, Skrabanek L (2006). "Mining expressed sequence tags identifies cancer markers of clinical interest". BMC Bioinformatics. 7: 481. doi:10.1186/1471-2105-7-481. PMC   1635568 . PMID   17078886.
  12. ↑ :institute for computational biomedicine::TissueInfo Archived June 4, 2008, at the Wayback Machine

Tissue Info


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