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Submitted tosubmitted by
MR .PRASHANT RICHABHATIA
SEC K77B1ROLLN
O 18
BTECH BIOTECH INTEGRATED MBA
EXPRESSED SEQUENCE TAGS
BIOINFORMATICS
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Acknowledgement
Gratitude is the hardest emotion to express and often
one doesnot find adequate words to convey that entire
one feels .those who help me to attain some thing better all the time deserve my highest attention
,thankfulness and deep sincere regards.it gives me
tremendous pleasure in acknowledging the valuable
assistance extended to me by various personalities in
success of this term paper.iam indebeted to my teacher
MR. PRASHANTreativeness, interest and enthusiasmgave a new dimension to my work with a motto to
seek ,to strive and not to yield.HE encouraged me to
make project and helped to solve my problems .Also I would like to express my gratitude to parents who were present in this project visiblyall I
cannot be mentioned but none is forgotten.
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INDEX
PAGE NO TOPIC
4 INTRODUCTION
5 SOURCES OF DATA
6 TISSUE INFORMATION
7 HOW EST ARE MADE
10 Hitchhiker's guide
11 EST INDEX
11 ADVANTAGES/DISADVANTAGES OF EST
12 APPLICATIONS
13 CONCLUSION
14 REFRENCES
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INTRODUCTION
The expressed sequence tag (EST) is a short sub-sequence of a transcribed cDNA sequence. It may
be used to identify various gene transcripts, and are important in gene discovery and gene sequence
determination. The identification of ESTs has proceeded rapidly, with approximately 65.9 million
ESTs now available in public databases .
The EST is mainly produced by a one-shot sequencing of a cloned mRNA (i.e. sequencing of
several hundred base pairs from an end of a cDNA clone taken from a cDNA library). The resulting
sequence is a relatively low quality fragment whose length is limited by current technology toapproximately 500 to 800 nucleotides. Because these clones consist of DNA, that is complementary
to mRNA, the ESTs represent actually the portions of an expressed genes. They may be present in
the database as either cDNA/mRNA sequence or as the reverse complement of the mRNA, the
template strand.
The ESTs can be mapped to various specific chromosome locations usingphysical mapping
techniques, such as radiation hybrid mapping, Happy mapping, orFISH.Or, if the genome of the
organism that originated the EST have been sequenced, one can align the EST sequence to that
genome using an computer.
The current understanding of the human set of genes ,includes the existence of thousands of genes
based solely on EST evidence. ESTs has become a important tool to refine the predicted transcripts
for those genes, which leads to the prediction of their protein products and ultimately 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 forDNA microarrays that then can be used to
determine the gene expression.
Sources of data and annotations
dbEST
The dbEST is an division of Genbank established in 1992. As forGenBank, data in dbEST is directly
submitted by various laboratories worldwide. Scientists of NCBI created dbEST to organize,
retrive,store, and provide access to public EST data that has already accumulated and that continues
to grow daily. Using dbEST, a scientist can access not only data on human ESTs but information on
ESTs from over 300 other organisms as well. Whenever possible, the NCBI scientists make
annotation the EST record with any known information. For example, if an EST matches a DNA
sequence that codes for a known gene with a known function, that gene's name and function are
placed on the EST record. The Annotation produced EST records allows public to utilize dbEST as
for gene discovery. By using a database search tool, such as NCBIs BLAST, any interested person
can conduct sequence similarity searches against dbEST.
EST contigs
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http://en.wikipedia.org/wiki/CDNAhttp://en.wikipedia.org/wiki/Transcription_(genetics)http://en.wikipedia.org/wiki/Sequencinghttp://en.wikipedia.org/wiki/MRNAhttp://en.wikipedia.org/wiki/CDNAhttp://en.wikipedia.org/wiki/CDNA_libraryhttp://en.wikipedia.org/wiki/Nucleotidehttp://en.wikipedia.org/wiki/Template_strandhttp://en.wikipedia.org/wiki/Gene_mappinghttp://en.wikipedia.org/w/index.php?title=Radiation_hybrid_mapping&action=edit&redlink=1http://en.wikipedia.org/wiki/Happy_mappinghttp://en.wikipedia.org/wiki/Fluorescent_in_situ_hybridizationhttp://en.wikipedia.org/wiki/Human_genomehttp://en.wikipedia.org/wiki/Cancerhttp://en.wikipedia.org/wiki/DNA_microarrayhttp://en.wikipedia.org/wiki/Gene_expressionhttp://en.wikipedia.org/wiki/GenBankhttp://en.wikipedia.org/wiki/CDNAhttp://en.wikipedia.org/wiki/Transcription_(genetics)http://en.wikipedia.org/wiki/Sequencinghttp://en.wikipedia.org/wiki/MRNAhttp://en.wikipedia.org/wiki/CDNAhttp://en.wikipedia.org/wiki/CDNA_libraryhttp://en.wikipedia.org/wiki/Nucleotidehttp://en.wikipedia.org/wiki/Template_strandhttp://en.wikipedia.org/wiki/Gene_mappinghttp://en.wikipedia.org/w/index.php?title=Radiation_hybrid_mapping&action=edit&redlink=1http://en.wikipedia.org/wiki/Happy_mappinghttp://en.wikipedia.org/wiki/Fluorescent_in_situ_hybridizationhttp://en.wikipedia.org/wiki/Human_genomehttp://en.wikipedia.org/wiki/Cancerhttp://en.wikipedia.org/wiki/DNA_microarrayhttp://en.wikipedia.org/wiki/Gene_expressionhttp://en.wikipedia.org/wiki/GenBank -
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As ,because of the way ESTs are sequenced, many distinct expressed sequence tags are partial
sequences that correspond to the same mRNA of the organism. In the effort to reduce the number of
expressed sequence tags for downstream gene discovery analyses, several groups assembled
expressed sequence tags into EST contigs. Different example of resources that provide EST contigs
include:
TIGR gene indices Unigene
Because an gene can be expressed as mRNA ,many times, ESTs ultimately derived from this mRNA
may be redundant. That is, there may be many identical, or similar, copies of the same EST. Such
an redundancy and overlapping means that when someone searches dbEST for a particular EST, they
may retrieve a list of tags, many of which represent the same gene. Searching through all of these
identical ESTs can be very time consuming. To resolve the redundancy and the overlapping problem
NCBI investigators developed the UniGene database UniGene automatically partitions GenBank
sequences into a non-redundant set of gene-oriented clusters.
Although, it is widely recognized that the production of ESTs constitutes an efficient strategy forthe identification of genes, it is important to acknowledge that despite its advantages, there are
several limitations associated with the EST approach. One is that it is very difficult to isolate mRNA
from different tissues and cell types.
Second, is that important gene regulatory sequences are found within an intron. Because ESTs are
small segments of cDNA, generated from a mRNA in which the introns have been removed, much
valuable information may be lost by focusing only on cDNA sequencing. Despite of the limitations,
ESTs continue to be very valuable in characterizing the human genome, and genomes of other
organisms. They have enabled the mapping of many genes to chromosomal sites and have also
assisted in the discovery of many new genes.
STACK
Constructing EST contigs is not trivial and may yield artifacts i.e (contigs that contain two distinct
gene products). When the complete genome sequence of an organism is available and transcripts are
annotated, it is easy to bypass contig assembly and directly match the founded transcripts with ESTs.
This approach is used in the TissueInfo system and makes it easy to link annotations in the genomic
database to tissue information provided by EST data.
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http://en.wikipedia.org/wiki/Contighttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=unigenehttp://en.wikipedia.org/wiki/Contighttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=unigene -
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from MOCCAdb is a curated, open-access, marker data resource for researchers working on Coffea
genus,the Rubiaceaefamily or related species of the Asterid clade (Solanaceae).
It recently has , keepsdata about molecular markers, mainly microsatelite (SSR) markers, developed
on sequences from the two cultivated coffee species, C. canephora (Robusta) or C.arabica.
It gives the easy access to data such as PCR assays conditions, cross amplification within related
species, locus position on different linkage maps and diversity parameters. It also contains the
sequences, both from genomic DNA and ESTs (expressed sequence tags) which were used to design
the markers. All data has been validated by published studies/ongoing research.
Our goal is to facilitate the study of cross-species homology relationships using information derived
from public projects involved in genomic and EST sequencing and to provide a tool for comparative
genomic approaches such as genome mapping and genetic diversity studies.
Tissue information
High-throughput analyses of ESTs may encounter, similar data management challenges. The first
challenge is that 1) tissue provenance of EST libraries is described in plain English in dbEST. This
makes it difficult to write programs thatcan non ambiguously determine that two EST libraries
were sequenced from the a same tissue. Similarly, the disease conditions for the tissue are not
annotated in a computationally and friendly manner.
For example, cancer origin of a library is often mixed with the tissue name (example, the tissue name
"glioblastoma" indicates that the EST library was sequenced from brain tissue and the disease
condition is cancer). With an 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 about the tissue origin and disease
state , offers a tissue ontology that links tissues and organs by "is part of" relationships , it formalizes
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knowledge that hypothalamus is part of brain, and that brain is part of the central nervous system and
distributes the open-source software for linking transcript annotations from sequenced genomes to
various tissue expression profiles calculated with data in dbEST .
Investigators are working hard to sequence and assemble the genomes of various organisms,
including the mouse and human, for a number of important reasons. Although important goals of any
sequencing project isbe to obtain a genomic sequence and identify a complete set of genes, theultimate goal is to gain an understanding of when, where, and how a gene is turned on, a process
commonly referred to as gene expression.
Once we begin to understand where and how a gene is expressed under normal circumstances, we
can then study what happens in an altered state, such as in disease. To accomplish the latter goal,
however, researchers must identify and study the protein, or proteins, coded for by a gene.
As one can imagine, finding a gene that codes for a protein, or proteins, is not easy. The scientists
would start their search by defining a biological problem and developing a strategy for researching
the problem. Often, a search of the scientific literature provided various clues about how to proceed.
For example, other laboratories may have published data that established a link between a particularprotein and a disease of interest. Researchers would then work to isolate that protein, determine its
function, and locate the gene that coded for the protein.other scientists could conduct what is referred
to as linkage studies to determine the chromosomal location of a particular gene. Once the
chromosomal location was determined, the scientists would use biochemical methods to isolate the
gene and related protein.The either way,of these methods took a great deal of timeyears in some
casesand yielded the location and description of only a small percentage of the genes found in the
human genome.
Now, however, the time required to locate and fully describe a gene is rapidly decreasing, t a
technology used to generate what are called Expressed Sequence Tags, orESTs. ESTs provide
researchers with the quick and inexpensive way for discovering new genes, for obtaining data on
gene expression and regulation, and for constructing genome maps. Today, researchers using ESTs to
study the human genome find themselves riding the wave of scientific discovery, the likes of which
have never been seen .
What Are ESTs and How Are They Made?
ESTs are small pieces of DNA sequence (usually 200 to 500 nucleotides long) that are made by
sequencing either one or both ends of an expressed gene. The sequence bits of DNA that represent
genes expressed in certain cells, tissues, or organs from different organisms and use the "tags" to
gene out of a portion of chromosomal DNA by matching base pairs. The challenge associated with
identifying genes from genomic sequences varies among organisms and is dependent basically upon
genome size and on thethe presence or absence of introns, the intervening DNA sequences
interrupting the protein coding sequence of a gene.
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Separating the Wheat from the Chaff: Using mRNA to Generate cDNA
The gene identification is very difficult in humans, because most of our genome is composed of
many introns interspersed with a relative few DNA coding sequences, or genes. These genes are
expressed as proteins, a complex process composed of two mainly two steps.
1) Each gene must be convertedor transcribed, into messenger RNA (mRNA), RNA that servesas a template for protein synthesis.
2) The resulting mRNA then guides the synthesis of a protein through a process called
translation.The mRNAs in a cell do not contain sequences from the regions between genes,
nor from the non-coding introns that are present within the genes. Therefore, isolating mRNA
is key to the finding of expressed genes in the vast expanse of the human genome.
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Figure 1. An overview of the process of protein synthesis.
Protein synthesis is the process where the DNA codes for the amino acids and proteins. The process
is divided into two parts: transcription and translation. During transcription, one strand of a
DNA,which is the double helix and is used as a template by mRNA polymerase to synthesize a
mRNA. During the progress of this step, mRNA passes through various phases, including called
splicing, where the non-coding sequences are removed. In the next step, translation, the mRNA
guides the synthesis of the protein by adding amino acids, one by one, as dictated by the DNA and
represented by the mRNA.
The problem, however, is that mRNA is very unstable outside of a cell; as a result of which the ,
scientists use special enzymes to convert it to complementary DNA (cDNA). ThecDNA is a muchmore stable compound and, basically because it was generated from a mRNA, in which the introns
have been removed, cDNA represents only expressed DNA sequence.
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The cDNA is an form of DNA prepared in the laboratory using an enzyme called reverse
transcriptase.The cDNA production is a reverse of the usual process of transcription in cells because
the procedure uses mRNA as a template rather than the DNA. Unlike genomic DNA,/cDNA
contains only expressed DNA sequences/exons.
From cDNAs to ESTs
Once if the cDNA representing an expressed gene has been isolated, scientists can then
sequence a few hundred nucleotides from either end of the molecule to create two different kinds of
ESTs. Sequencing only the beginning portion of the cDNA produces what is called a 5' EST. A 5'
EST is obtained from any portion of a transcript that usually codes for a protein. These regions,
tend to be conserved across the species and do not change much within a gene family.
Sequencing, the ending portion of the cDNA molecule produces what is called a 3' EST. As, these
ESTs are made from the 3' end of a transcript, they fall within non-coding, or untranslated regions
(UTRs), and therefore tend to exhibit less cross-species conservation than do coding sequences.
A "gene family" is a group of closely related genes that produces similar protein products.
A UTR is that part of a gene that is not translated into protein.
Figure 2. An overview of how ESTs are generated.
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A hitchhiker's guide to expressed sequence tag (EST) analysis
Expressed sequence tag (EST) sequencing projects are underway for numerous organisms, generating
millions of short, single-pass nucleotide sequence reads, accumulating in EST databases. Extensive
computational strategies have been developed to organize and analyse both small- and large-scale
EST data for gene discovery, transcript and single nucleotide polymorphism analysis as well as
functional annotation of putative gene products.
They basically ,provide an overview of the significance of ESTs in the genomic era, their properties
and the applications of ESTs. Methods adopted for each step of EST analysis by various research
groups have been compared. Challenges that lie ahead in organizing and analysing the ever
increasing EST data have also been identified.
The most appropriate software tools for EST pre-processing, clustering and assembly, database
matching and functional annotation have been compiled (available online from). We propose a road
map for EST analysis to accelerate the effective analyses of EST data sets. An investigation of EST
analysis platforms reveals that they all terminate prior to downstream functional annotation including
gene ontologies, motif/pattern analysis and pathway mapping.
EST Index ConstructionThe major goal of an EST databases is to organize ,construct and consolidate the largely redundant
EST data to improve maximally the quality of the sequence information so thedata can be used to
extract full-length cDNAs. The process includes a bunch of preprocessingstep that removes vector
contaminants and masks repeats. Vecscreen, introduced in can be used to screen out bacterial vectorsequences. This is followed by a clustering step that associates EST sequences with unique genes.
The next stepis to derive consensus sequences by fusing redundant, overlapping ESTs and to correct
errors, especially frameshift errors. As a result step results in longer EST contigs. Finally, the coding
regions are defined through the use of HMMbasedgene-finding algorithms .it helps to exclude the
potentialintron and 3_-untranslated sequences. Once the coding sequence is identified, it canbe
annotated by translating it into protein sequences for database similarity searching.To go another step
further, compiled ESTs can be used to align with the genomic sequence, if available to identify the
genome locus of the expressed gene as well as intronexon boundaries of the gene. The clustering
process that reduces the EST redundancy and produces a collectionof nonredundant and annotated
EST sequences is known as gene index construction.
DISADVANTAGES OF USING EST
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However, there are number of many drawbacks of using ESTs for te use of expression profile
analysis.ESTsequences are of lowquality because they are automatically generated , without
verification and thus contain high error of rates. the Many bases are ambiguously determined,
represented by Ns. Common errors also include frameshift errors andartifactual stop codons,
resulting in failures of translating the sequences. In addition,there is often contaminationby vector
sequence, introns (from unsplicedRNAs), ribosomalRNA (rRNA), mitochondrialRNA, among
others. ESTs represent only partialsequences of genes. various Gene sequences at the 3_ endtend to be more heavily representedthan those at the 5_ end because reverse transcription is
primed with oligo(dT)primers. Unfortunately, the sequences from the 3_ end are also most error
pronebecause of the low base-call quality at the start of sequence reads. Another problemof ESTs
is the presence of chimeric clones owing to cloning artifacts in libraryconstruction, in which more
than one transcript is ligated in a clone resulting inthe 5_ end of a sequence representing one gene
and the 3_ end another gene. It hasbeen estimated that up to 11% of cDNA clones may be
chimeric. Another fundamental problem with EST profiling is that it predominantly represents
highly expressed,abundant transcripts. Weakly expressed genes are hardly found in a EST
sequencing survey.
Outline of steps to process EST sequences for construction of the UniGene
database
A UniGene is the NCBI EST cluster database.Each cluster is a set of overlapping EST sequences
that are computationally processedto represent a single expressed gene. The database is constructed based on combinedinformationfromdbEST,GenBankmRNAdatabase,andelectronically spliced
genomic DNA. Only ESTs with 3_ poly-A ends are clustered to minimize the the problemof
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chimerism. The resulting 3_ EST sequences provide more unique representation of the transcripts.
The next step is to remove contaminant sequences that includebacterial vectors and linker sequences.
The cleaned ESTs are used to search against adatabase ofknownunique genes(EGADdatabase) with
theBLASTprogram.Thecompilingstep identifies sequence overlaps and derives sequence consensus
using theCAP3program.During this step, errors in individual ESTs are corrected; the sequencesare
then partitioned into clusters and assembled into contigs. The final result is a set of nonredundant,
gene-oriented clusters known as UniGene clusters.
ADVANTAGES Various advantages are-: 1) EST technology is still widely used. Thisis because ESTlibraries can be easily generated from various cell lines, tissues, organs, and at
variousdevelopmental stages. ESTs can also facilitate the unique identification of a genefrom
a cDNA library; a short tag can lead to a cDNA clone. 2)Although individual ESTs\are proneto error, an entire collection of ESTs contains valuable information. Often,after consolidation
of multiple EST sequences, a full-length cDNA can be derived.3)By searching a
nonredundant EST collection, one can identify potential genes ofinterest.The rapidaccumulation of EST sequences has prompted the establishment of
public and
APPLICATIONS
Gene Discovery through Expressed Sequence Tag
Sequencing in Trypanosoma cruzi
The analysis of expressed sequence tags basially constitutes a useful approach for gene identificationthat, in that case of human pathogens, may result in the identification of new targets for
chemotherapy and vaccine development. As the part of the Trypanosoma cruzi genome project, we
have partially sequenced the 5 ends of 1,949 clones to generate ESTs. The clones were randomly
selected from the normalized , CL Brener epimastigote cDNA library. the total of which in 14.6% of
the clones were homologous to previously identified T. cruzi genes, while 18.4% of which have had
significant matches to genes from other organisms in the database. The overall total of the 67% of
the ESTs had no matches with the database, and thus, some of them might be T. cruzi-specific genes.
The different Functional groups of those sequences which might match in the database were made
according to their basic biological functions. The two largest categories were protein synthesis
(23.3%) and cell surface molecules (10.8%).
Expressed sequence tags and their application for plantresearch
The Expressed Sequence Tags (ESTs) are short in length, and are usually containing unedited
sequences obtained by single-pass sequencing of cDNA clones from any cDNA library. After
analysis and comparison of ESTs, they can provide information on gene expression, function and
evolution. the Large-scale EST sequencing has become an attractive alternative to plant genome
sequencing.
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Recently, in the plant EST collections have in general as many as 3.8 million sequences from about
200 species. They have proved to be important tool for gene discovery and plant metabolism
analysis. Several plant-specific EST databases have been created which provide access to sequence
data and bioinformatics-based tools for data mining.
Searching EST collections allows pre-selection of genes for the prepration of the cDNA arrays,
targeted to bring about the maximum information on the various specialized processes, like stressresponse, symbiotic nitrogen fixation etc. Also, ESt-based molecular markers such as SNP, SSR, and
indels are fast developing tools for breeders and researchers.
CONCLUSION
The EST is mainly produced by a one-shot sequencing of a cloned mRNA (i.e. sequencing ofseveral hundred base pairs from an end of a cDNA clone taken 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 actually the portions of an expressed genes. They may be present in
the database as either cDNA/mRNA sequence or as the reverse complement of the mRNA, the
template strand.
The ESTs can be mapped to various specific chromosome locations usingphysical mapping
techniques, such as radiation hybrid mapping, Happy mapping, orFISH.Or, if the genome of the
organism that originated the EST have been sequenced, one can align the EST sequence to that
genome using an computer. The analysis of expressed sequence tags basially constitutes a useful
approach for gene identification that, in that case of human pathogens, may result in the identification
of new targets for chemotherapy and vaccine development. As the part of the
Trypanosoma cruzi genome project, we have partially sequenced the 5 ends of 1,949 clones to
generate ESTs. The Expressed Sequence Tags (ESTs) are short in length, and are usually containing
unedited sequences obtained by single-pass sequencing of cDNA clones from any cDNA library.
After analysis and comparison of ESTs, they can provide information on gene expression, function
and evolution. the Large-scale EST sequencing has become an attractive alternative to plant genome
sequencing.Expressed sequence tag (EST) sequencing projects are underway for numerous
organisms, generating millions of short, single-pass nucleotide sequence reads, accumulating in ESTdatabases. Extensive computational strategies have been developed to organize and analyse both
small- and large-scale EST data for gene discovery, transcript and single nucleotide polymorphism
analysis as well as functional annotation of putative gene products. The most appropriate software
tools for EST pre-processing, clustering and assembly, database matching and functional annotation
have been compiled (available online from). We propose a road map for EST analysis to accelerate
the effective analyses of EST data sets. An investigation of EST analysis platforms reveals that they
all terminate prior to downstream functional annotation including gene ontologies, motif/pattern
analysis and pathway mapping.
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http://en.wikipedia.org/wiki/Sequencinghttp://en.wikipedia.org/wiki/MRNAhttp://en.wikipedia.org/wiki/CDNAhttp://en.wikipedia.org/wiki/CDNA_libraryhttp://en.wikipedia.org/wiki/Nucleotidehttp://en.wikipedia.org/wiki/Template_strandhttp://en.wikipedia.org/wiki/Gene_mappinghttp://en.wikipedia.org/w/index.php?title=Radiation_hybrid_mapping&action=edit&redlink=1http://en.wikipedia.org/wiki/Happy_mappinghttp://en.wikipedia.org/wiki/Fluorescent_in_situ_hybridizationhttp://en.wikipedia.org/wiki/Sequencinghttp://en.wikipedia.org/wiki/MRNAhttp://en.wikipedia.org/wiki/CDNAhttp://en.wikipedia.org/wiki/CDNA_libraryhttp://en.wikipedia.org/wiki/Nucleotidehttp://en.wikipedia.org/wiki/Template_strandhttp://en.wikipedia.org/wiki/Gene_mappinghttp://en.wikipedia.org/w/index.php?title=Radiation_hybrid_mapping&action=edit&redlink=1http://en.wikipedia.org/wiki/Happy_mappinghttp://en.wikipedia.org/wiki/Fluorescent_in_situ_hybridization -
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REFRENCES
http://biolinfo.org/EST
www.ncbi.nlm.nih.gov/UniGene/
www.ncbi.nlm.nih.gov/SAGE\/
WWW.EST.BIOINFO.ORG/HTML/
WWW.BIOWORLD.COM
WWW.BIOMFUNCTION.ORG/
WWW.BIOLINE.INFO.GOV/
WWW.BIOGENOME.COM
WWW.HUMANGENOME.ORG/
WWW.EST%20.COM
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http://biolinfo.org/ESThttp://www.ncbi.nlm.nih.gov/SAGE/%5Chttp://www.ncbi.nlm.nih.gov/SAGE/%5Chttp://www.est.bioinfo.org/HTML/http://www.bioworld.com/http://www.biomfunction.org/http://www.bioline.info.gov/http://www.biogenome.com/http://www.humangenome.org/http://www.est.com/http://biolinfo.org/ESThttp://www.ncbi.nlm.nih.gov/SAGE/%5Chttp://www.est.bioinfo.org/HTML/http://www.bioworld.com/http://www.biomfunction.org/http://www.bioline.info.gov/http://www.biogenome.com/http://www.humangenome.org/http://www.est.com/