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Current
Bioinformatics
ISSN: 1574-8936
Current Bioinformatics
Volume 3, Number 1, January 2008
Contents
Bioinformatics Approaches for Understanding and 1
Predicting Protein Folding Rates Pp. 1-9
M. Michael Gromiha and S. Selvaraj
[Abstract]
Web and Grid Technologies in Bioinformatics, Computational
and Systems Biology: A Review Pp. 10-31
Azhar A. Shah, Daniel Barthel, Piotr Lukasiak, Jacek Blazewicz
and Natalio Krasnogor
[Abstract]
Computational RNA Structure Prediction
Pp. 32-45
Emidio Capriotti and Marc A. Marti-Renom
[Abstract]
Computational Approaches for Predicting Causal Missense
Mutations in Cancer Genome Projects Pp. 46-55
Lawrence S. Hon, Joshua S. Kaminker and Zemin Zhang
[Abstract]
Medical Expert Systems Pp. 56-65
Crina Samarghitean and Mauno Vihinen
[Abstract]
Abstracts
[Back to top]
Bioinformatics Approaches for Understanding and Predicting
Protein Folding Rates
M. Michael Gromiha and S. Selvaraj
Understanding the relationship between amino acid sequences
and protein folding rates is a challenging task similar to
the protein folding problem. In this review, after a brief
definition of protein folding rates, we describe various methods
including contact order, long-range order, total contact distance
etc. for understanding/predicting protein folding rates from
three-dimensional structures of proteins. In addition, the
applications of secondary structure content, length of secondary
structures and solvent accessibility for understanding protein
folding rates will be discussed. Further, the methods based
on amino acid properties, composition, long-range contacts
etc. for predicting protein folding rates from amino acid
sequences will be discussed. The importance of amino acid
properties, hydrophobic cluster formation and long-range contact
network for understanding the transition state structures
of proteins, which are related to protein folding rates, will
be outlined.
[Back to top]
Web and Grid Technologies in Bioinformatics, Computational
and Systems Biology: A Review
Azhar A. Shah, Daniel Barthel, Piotr Lukasiak, Jacek Blazewicz
and Natalio Krasnogor
The acquisition of biological data, ranging from molecular
characterization and simulations (e.g. protein folding dynamics),
to systems biology endeavors (e.g. whole organ simulations)
all the way up to ecological observations (e.g. as to ascertain
climate change’s impact on the biota) is growing at
unprecedented speed. The use of computational and networking
resources is thus unavoidable. As the datasets become bigger
and the acquisition technology more refined, the biologist
is empowered to ask deeper and more complex questions. These,
in turn, drive a runoff effect where large research consortia
emerge that span beyond organizations and national boundaries.
Thus the need for reliable, robust, certified, curated, accessible,
secure and timely data processing and management becomes entrenched
within, and crucial to, 21st
century biology. Furthermore, the proliferation of biotechnologies
and advances in biological sciences has produced a strong
drive for new informatics solutions, both at the basic science
and technological levels. The previously unknown situation
of dealing with, on one hand, (potentially) exabytes of data,
much of which is noisy, has large experimental errors or theoretical
uncertainties associated with it, or on the other hand, large
quantities of data that require automated computationally
intense analysis and processing, have produced important innovations
in web and grid technology. In this paper we present a trace
of these technological changes in Web and Grid technology,
including details of emerging infrastructures, standards,
languages and tools, as they apply to bioinformatics, computational
biology and systems biology. A major focus of this technological
review is to collate up-to-date information regarding the
design and implementation of various bioinformatics Webs,
Grids, Web-based grids or Grid-based webs in terms of their
infrastructure, standards, protocols, services, applications
and other tools. This review, besides surveying the current
state-of-the-art, will also provide a road map for future
research and open questions.
[Back to top]
Computational RNA Structure Prediction
Emidio Capriotti and Marc A. Marti-Renom
The view of RNA as simple information transfer molecule
has been continuously challenged since the discovery of ribozymes,
a class of RNA molecules with enzyme-like function. Moreover,
the recent discovery of tiny RNA molecules such as μRNAs
and small interfering RNA, is transforming our thinking about
how gene expression is regulated. Thus, RNA molecules are
now known to carry a large repertory of biological functions
within cells including information transfer, enzymatic catalysis
and regulation of cellular processes. Similar to proteins,
functional RNA molecules fold into their native three-dimensional
(3D) conformation, which is essential for performing their
biological activity. Despite advances in understanding the
folding and unfolding of RNA, our knowledge of the atomic
mechanism by which RNA molecules adopt their biological active
structure is still limited. In this review, we outline the
general principles that govern RNA structure and describe
the databases and algorithms for analyzing and predicting
RNA secondary and tertiary structure. Finally, we assess the
impact of the current coverage of the RNA structural space
on comparative modeling RNA structures.
[Back to top]
Computational Approaches for Predicting Causal Missense
Mutations in Cancer Genome Projects
Lawrence S. Hon, Joshua S. Kaminker and Zemin Zhang
A central focus of cancer genetics is the study of mutations
that are causally implicated in tumorigenesis. Although missense
variants are commonly identified in genomic sequence, only
a small fraction directly contributes to oncogenesis. The
ability to distinguish those somatic missense changes that
contribute to cancer progression from those that do not is
a difficult problem usually accomplished through functional
in vivo analyses. With the advent of several large-scale
cancer genome projects geared toward identifying mutations
that are causally implicated in cancer, it is becoming increasingly
important to develop methods for distinguishing functionally
relevant mutations from those passenger mutations and other
innocuous polymorphisms. Here we review two general strategies
that are based on either mutation frequency data or the nature
of amino acid substitutions. Frequency-based methods are commonly
used for estimating the enrichment of causal mutations and
for identifying specific mutations under positive selection
pressure. The statistical power of these methods is dependent
on the number of cancer samples being surveyed. The potential
functional consequences of missense mutations can also be
examined by bioinformatics approaches since multiple computational
methods have been developed to estimate the deleterious effect
of amino acid substitutions. It is likely that many of the
existing methods can potentially be applied to large-scale
cancer genome data to detect relevant causal mutations regardless
of their prevalence. Future data analysis of missense somatic
mutations will likely benefit from continual development of
integrated and automated methods for combining all available
information to predict whether a particular mutation is causally
implicated.
[Back to top]
Medical Expert Systems
Crina Samarghitean and Mauno Vihinen
Expert systems, or decision support systems, are artificial
intelligence systems that have been trained with real cases
to perform complicated tasks. They are used in a variety of
areas and are among the most popular application fields in
artificial intelligence. Expert systems have applications
in different areas of medicine. Here we present a short history
of medical expert systems and the characteristics of these
systems. Medical expert systems were initially developed for
academic areas and later for clinical applications also. Health
care systems produce tremendous amounts of information (patient,
demographic, clinical and billing data), which are susceptible
to analysis by intelligent software and need new techniques
to extract new knowledge. A variety of medical expert systems
tools are available and can function as intelligent assistants
to clinicians, helping in diagnostic processes, laboratory
analysis, treatment protocol, and teaching of medical students
and residents. A critical review of the strengths and limitations,
as well as the latest trends in decision support systems,
is discussed. In addition, a model for computer-based medical
diagnoses of primary immunodeficiencies is presented.
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