Review Article |
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Open Access |
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Glycomics Data Mining |
V Srinivasa Rao 1,*, S K Das 2, E Kusuma Umari 3 |
1Professor in Computer Science and Engineeing, Pvp Siddhrtha Institute of
Technology, Vijayawada-520 007, India, E-mail: akrgvsr@gmail.com |
2Deparment of Computer Science, Berhampur University, Berhampur, Orissa, India |
| 3Associate Profesor in Electronics and Communication Engg, Nova College of Engineering, Jangareddygudem, India |
| *Corresponding author: |
Dr. V Srinivasa Rao,
Professor in Computer
Science and Engineeing,
Pvp Siddhrtha Institute of Technology,
Vijayawada-520 007, India,
E-mail: akrgvsr@gmail.com |
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| Received September 17, 2009; Accepted October 21, 2009; Published
October 25, 2009 |
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Citation: Rao VS, Das SK, Umari EK (2009) Glycomics Data Mining. J
Comput Sci Syst Biol 2: 262-265. doi:10.4172/jcsb.1000040 |
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Copyright: © 2009 Rao VS, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author
and source are credited. |
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The amount of glycomics data being generated is rapidly
increasing as a result of improvements in analytical
and computational methods. Correlation and analysis of
this large, distributed data set requires an extensible and
flexible representational standard that is also ‘understood’
by a wide range of software applications. An XML-based
data representation standard that faithfully captures essential
structural details of a glycan moiety along with
additional information (such as data provenance) to aid
the interpretation and usage of glycan data, will facilitate
the exchange of glycomics data across the scientific community.
We reviewed importance of data warehouse, showing
a method of applying data mining techniques using
XML, and some of the data issues, analysis problems, and
results.
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Introduction |
Carbohydrates are considered as the third class of information
encoding biological macro molecules.Glycomics is the scientific
attempt to characterize and carbohydrates,for which
informatics is just beginning. Glycomics requires sophisticated
algorethemic approaches.several algorithms and models heve
been developed in this field.The four essential molecular building
blocks of cells are proteins,nucleic acids,lipids and carbohydrates
are often referred to as glycans.Nucleotide and protein
sequences are very important for all bioinformatics applications
and research ,whereas glycan and lipid structures have been
widely neglected in bioinformatics.
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Glycans are the most abundant and structurally diverse
biopolymers found in nature.These glycans bound to proteins
as glycoproteins and are known to affect the function of
proteins.More than half of the protein sequences deposited in
the swiss-prot database include potential glycosylation sites and
thus may be glycoproteins.Based on an analysis of well annotated
and characterized glycoproteins in swiss-prot ,it was known
that half of the proteins are glycosylated.development in this
field is still in the early stages.many algoriyhms are being
developed.major glcosylated projects such as consortium for
functional glycomics, KEGG glycan, GLYCOSCIENCES.de are
being developed and well structured glycol-related data that are
awaiting for data mining and analysis.Complex carbohydrates
are often called as glycans and are often found attached ally to
proteins and lipids.glycoproteins are usually on the cell surface
,where they are recognized by bacteria, viruses,and other proteins,such as lectins,in order to facilitate various functions. It
is also known that glycans are involvd in various biological functions
such as protein folding and signaling events.It is known
that glycan specific pathways are responsible for diseases such
as congential disorders of glycosylation are caused due to the
defects in these pathways.There are reports on glycan biomarkers
related to human diseases such as cancer and autoimmune diseases. |
Carbohydrates are composed of monosacharides that are covalently
linked by glycocidic bonds, either in alpha or beta
form.in order to formulate a standardized notation to glycans
,the Consortium for Functional Glycomics(CFG) proposed a
standard symbolic representation for those monosacharides
which are found in nature.carbohydrates are most classically
drawn as a tree in a two-dimensional plane,with the root
monosaccharide placed at the right most position children
branching towards the left.each node represents a
monosaccharide,and each edge represents a glycocidic
linkage,which includes the carbon numbers that are bond and
the conformation. |
The IUPAC-IUBMB has specified the nomenclature of carbohydrates
to uniquely describe complex oligosaccharides based
on a three letter code to represent monosacharides.each monosaccharide
code is preceded by the anomeric descriptor and the
configuration symbol.the ring size is indicated by an italic f for
furanose and p for pyranose.There are other formats also like
KEGG Chemical Function(KCF) format,which represents glycans
using a connected graph LINCUS,which provides a unique
and l inear notation for glycans ,and linear codes by
glycominds,which provides a commercial carbohydrate database.
Recently Herget et al., (2008) carefully analyzed the encoding
capabilities of all existing carbohydrate sequence formats
and the content of publically available structure databases
as part of the EUROCarbDB project (www.eurocarbdb.org) . |
Recently Tamaki et al., (2009) applied a balancing technique
to obtain more appropriate and robust models, and compared their accuracy with that of the conventional model. To construct
new models, they randomly sampled the same number of subjects
with and without new dental caries. Hashimoto et al., (2008) developed an efficient method for mining motifs or significant
subtrees from glycans. |
There are three major databases for the complex carbohydrates,
KEGG GLYCAN, and the database developed by the consortium
for functional glycomics. All three databases are based on
the carb bank database developed in 1990s. The major issue
that was facing by the glycol-inforamatics community was that
each of these databases represented their glycan structures in
different formats, a workshop was held at the National Institutes
of Health(NIH), united states ,at this meeting, the GLYDE-
2 XML format for glycans and glycoconjugates, developed by
the CCRC, was agreed upon as the standard format for exchanging
carbohydrate . |
Apart from the development of these various databases, several
methods for analyzing the structures have been developed.
They are classifies into 6 types.
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| 1 |
Glycosylation analysis |
| 2 |
Glycomics |
| 3 |
Glycan biomarker prediction |
| 4 |
Glycan structure analysis |
| 5 |
Glyco-gene structure analysis |
| 6 |
Glycomics data mining |
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Glycoproteomics |
Glycosylation is a post translational modification.Half of the
proteins are glycosylated. Role of the glycans depends on N- or
O-linked forms. Glycan is required for biological function of
proteins, such as the FC-efector function of IgG, the regulated
clearance of glycochrome lutropin. Glycans also serve as recognition
targets for their complimentary binding proteins, the
lectins.lectins involves in fertilization, development, differentiation
and lymphocyte circulation and also in the development
of pathological conditions. Glycans are also used for the attachment
of microbes,toxins and constitute the first point of interaction
between microbes and glycans. The function of glycan depends
on which the protein is attached to.so,it is very difficult to
document about glycoproteins. The integrated structure of compiling structure and funct ion of glycans is called
glycoproteins.Biosynthesis of N-Glycans and O-Glycans involves
many glycotransferases. In the early steps of glycotansfer
many steps are obligatory but there will be competition of steps
in the later stages.some well known changes in glycan structures
occurs during the neoplastic transformation result from
changes in the balances of transferases. Various aspects of structure
and function relationship of glycoproteins were discussed,
[refer Glycoproteomics paper]. |
HIV virus contains gp120/gp40 glycan protein complexes on
the outer surface. The surface of g120 contains several clusters
of mannose. Scientists discovered an antibody G12 which can
neutralize the infection. This antibody narrows the distance between
binding pockets so that the virus cannot get attached to
the host cd4. Another antiviral protein, actinohivin which is a
lectin that contains 3-mannose binding sites and it is active
against simian immunodeficiency virus and HIV virus. N-glycans
of gP120 can also be exploited as targets for therapeutics. Many glycoprotein therapeutics have been expressed using various
production vehicles such as cell lines, transgenic animals
and plants. |
Mass spectroscopy strategies are suitable to asses the type of
glycan present and their position of attachment on a particular
protein.Multi-ms strategy is used for the analysis of simple or
complex mixtures from biological extract. For a detailed analysis
several fluroscence-based methods as well as direct analysis
by dionex system is available. |
Glycome db |
It is the integration of all available carbohydrate sequences
into one central database called Glycome db. Approximately
100000 database records with 73341 sequences are accessible
in the public domain. The biggest obstacle for data integration
was the use of various sequence encoding formats by different
initiatives.so, they found it necessary to develop a new sequence
format, called GlycoCT, which is a superset of the structural
encoding capabilities inherent in all other formats developed so
far. They have implemented a translation library which can read
all of the carbohydrate sequence formats and translate them to
GlycoCT. GlycoUpdateDB is the application program which
have to be designed to carry out the integration of the interpretable
data obtained from the resources. It is a JAVA application,
Depending on a PostgreSQL database which can be configured
by an XML file. The configuration file contains settings for the
local database and instructions for the download and data integration
process. GlycomeDB consists of several database schemata
with tables that store all downloaded and generated datasets.
A software tool-GlycanBuilder for building and displaying glycan
structures using a chosen symbolic notation was reported
by Ceroni et al., (2007). The tool uses an automatic rendering
algorithm to draw the saccharide symbols and to place them on
the drawing board. The information about the symbolic notation
is derived from a configurable graphical model as a set of
rules governing the aspect and placement of residues and linkages.
The algorithm is able to represent a structure using only
few traversals of the tree and is inherently fast. The tool uses an
XML format for import and export of encoded structures. |
Carbohydrates are involved in a variety of
fundamentalbiological processes (cellular differentiation, embryonic
development, fertilization) and pathological situations
(bacterial and viral infections, inflammatory diseases,cancer).
They therefore have a large pharmaceutical and diagnostic potential.
Protein-carbohydrate interactions are intensively investigated
using a variety of experimental methods. Among these,
X-ray and NMR measurements provide a detailed 3D picture of
the spatial location of the ligand as well as the protein. It is
estimated that more than half of all the proteins in the human
body have carbohydrate molecules attached. Structural data
taken from X-ray crystallography does not necessarily mean that
a potential glycosylation site is unoccupied; but the presence of
carbohydrate residues in the 3D structures provides direct and
unambiguous evidence for the occupancy of a glycosylation site. |
Therefore, these data have been intensively used to gain deeper
insights into factors that regulate glycan attachment to a Nglycosylation
site. Among the 25667 PDB entries structures were
detected which contain a total of 6714 carbohydrate chains. About half of them are covalently attached, the other half belongs
to non-covalently bound ligands. It was found that about
30% of all PDB entries containing carbohydrates comprise one
or several errors in glycan description, which are mainly due to
wrong assignment of saccharide units. The reason for this unacceptable
high rate of errors is obvious. Sequences for complex
carbohydrates differ significantly from the simple linear oneletter
code used to describe genes and proteins: |
| a |
the number of naturally occurring residues is much larger
for glycans |
| b |
each pair of monosaccharide residues can be linked in
severalways, |
| c |
a residue can be connected to three or four others (branching). |
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Sugar resides present in the PDB are defined in the so-called
HET Group Dictionary. Since a three-letter code is used to
uniquely assign carbohydrates, a new residue name is required
for each stereochemically different sugar unit and each substitution.
This procedure makes correct assignment of sugar units
tedious, complicated and obviously error-prone. Unfortunately,
no software is currently available which automatically aligns
the 3D information contained in PDB and the given assignments.
It is the focus of pdb-care to fill this gap. The pdb-care program
is based on the carbohydrate detection software pdb2linucs that
is able to identify and assign carbohydrate structures using only
the reported atom types and their 3D atom coordinates. To be
able to compare the detected carbohydrate structures in LINUCS
notation to the residue assignments as reported in the PDB HET
Group Dictionary a translation table in XML format was created,
where both descriptions are confronted. Three types of
residues are included: monosaccharides, oligosaccharides and
combine residues consisting of a carbohydrate moiety and a noncarbohydrate
part. The translation table actually contains 141
monosaccharides, 31 oligosaccharides and 77 combined residues
and is still growing. The pdb-care protocol reports the type
of problems, inconsistencies and errors detected. pdb-care is
written in C. Interaction with the user is done through a webinterface,
which is implemented in PHP. The pdb-care service
is hosted at the central spectroscopic department of the German
Cancer Research Centre in Heidelberg, Germany. The pdb-care
web interface allows either to analyse a file obtained directly
from PDB using the PDB-ID, or to provide a pdb-file located
on the local computer by upload or by copy paste into the provided
input window. Since carbohydrate structures are described
as so called hetero atoms within the HETATM records, all data
assigned to the ATOM records (amino acids, nucleotides) are
neglected. Sahoo et al., (2005) introduced GLYcan Data Exchange
(GLYDE) standard as an XML-based representation
format to enable interoperability and exchange of glycomics data.
An online tool for the conversion of other representations to
GLYDE format has been developed. |
KEGG |
| The KEGG database was initiated in 1995. After 10 years of
development the KEGG project has entered a new phase in accordance
with the chemical genomics activity. KEGG database
is a resource for understanding the higher order functions and
the utility of the biological systems such as cell or the organism from genomics and molecular information.KEGG is considered
as the computer representation of the biological system consists
of building blocks and wiring diagrams which can be used as
simulation and modelling as well as for browsing and
retrieval.originally, the wiring diagrams involved endogenous
molecules, both those that are directly encoded in the genome
and those that are indirectly encoded through biosynthetic/biodegradation
pathways. These wiring diagrams also include exogenous
molecules.this will help to understand the interactions
between environment and the biological system.there are four
types of databases in KEGG. They are gene database,brite database,
ligand database and pathway database. KEGG system can
be represented in two types of graphs. Nest graph in which nodes
can themselves be graphs line graph which is derived by interchanging
nodes and edges of other graphs. |
KEGG is the major component of the Japanese GenomeNet,
which is served by the Kyoto University Bioinformatics Center.
The other GenomeNet services including DBGET and BLAST/
FASTA searches are now primarily developed and used to support
KEGG. The KEGG API service has become an increasingly
popular mode of access. It is the SOAP/WSDL interface
to KEGG, enabling users to write their own programs to access,
customize and utilize KEGG. KegArray and KegDraw are
standalone Java applications that make use of the KEGG resources.
KegArray is for microarray data analysis in conjunction
with KEGG pathways and genomes. KegDraw is for drawing
glycanstructures and chemical compound structures, which
can then be used to query against KEGG and PubChem databases. |
Mass Spectrometry |
High resolution mass spectrometry is one of those biotechnologies
that are highly promising to improve health outcome.
Proteomics biomarkers that can distinguish that can distinguish
healthy patients from cancer patients have been identified using
MS data. Ms study is demonstrated which uses glycomics to
identify ovarian cancer.mass spectrometry is used for protein
profiling in cancer on the peptide or protein abundance from
the MS data. Recent literatures on cancer classification using
MS have identified some potential protein biomarkers in serum
to distinguish cancer from normal samples.since glycans play
important roles in cell communication and signaling events they
may be implicated in cancer.Clinical glycomics is used to identify
potential biomarkers for the early learning algorithms to
classify high dimensional MS cancer data. Artificial neural networks
are used to discriminate different tumor states. Decision
tree based esembled methods were proposed to identify
biomarkers for inflammatory diseases.All these studies aim to
discover the potential MS biomarkers that can distinguish from
one group to another.stastical method is used to combine the
high dimensional MS measurements into a single score to classify
cancer status jointly with suitable preprocessing of the data.
There are several studies on combining biomarkers. Su and Liu
studied the case where markers follow a multivariate normal
distribution.they gave a closed form of optimal solution to the
linear parameters. Normality is not suitable for mass spectrometry
data because measurements of relative abundance are always
positive. Pepe and Thompson considered linearly combining
two biomarkers by optimizing the area under the ROC curve. The method was developed only for low dimensional
situation. Ma and Huang applied Thompsons idea of optimizing
AUC to microarray experiment. They used multivariate normal
distribution in the simulation study and assumed independence
between biomarkers which is not true for mass spectrometry
data. The implementation is for real mass spectrometry
ovarian cancer data analysis. TGDR-AUC method is applied to
low dimensional and high dimensional ovarian cancer data. It is
concluded that TGDR-AUC algorithm is appropriate in the
analysis of mass spectrometry glycomic data. |
Conclusion |
| Data mining is an appropriate and sufficiently sensitive method
to analyze avalable glycomics data. Diversi ty in
conceptualization of tools and diversity of common format used
for outcome measurements could have hampered actual discovery
of significant output. |
References |
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