| Current
Bioinformatics
ISSN: 1574-8936

Current Bioinformatics
Volume 5, Number 2, June 2010
Contents
A Tutorial for Microarray Data Analysis with SAS-STAT Software
Pp. 89- 108
Corrado Dimauro and Nicolò P.P. Macciotta
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Article]
Development of Genomics-Based Gene Expression
Signature Biomarkers in Oncology and Toxicology to Facilitate
Drug Discovery and Translational Medicine Pp.
109-117
Tao Wei and Shuyu Li
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Article]
Machine Learning for Childhood Acute Lymphoblastic
Leukaemia Gene Expression Data Analysis: A Review
Pp. 118-133
Amphun Chaiboonchoe, Sandhya Samarasinghe and Don
Kulasiri
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Article]
High Conservation of Amino Acids with Anomalous
Protonation Behavior Pp. 134-140
David G.C. Hildebrand, Huyuan Yang, Mary Jo Ondrechen and
Ronald J. Williams
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Article]
Matching up Phosphosites to Kinases: A Survey
of Available Predictive Programs Pp. 141-152
Stefano Toppo, Lorenzo A. Pinna and Mauro Salvi
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Article]
Abstracts

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A Tutorial for Microarray Data Analysis with SAS-STAT
Software
Corrado Dimauro and Nicolò P.P. Macciotta
In recent years, microarrays have become a key experimental
tool, enabling the analysis of genome-wide patterns of gene
expression. Numerous complex statistical problems arise during
the analysis of microarray data and, therefore, only specialized
statisticians are able to extract the expressed genes. This
tutorial is aimed to help biologists to conduct some, also
if rough, analysis of their microarray data immediately afterwards
they are produced. After a short review on statistical challenges
in microarray studies, complete data analysis, from raw data
to differentially expressed genes, was developed using both
simulated and real data set. The referring microarray platform
was the spotted cDNA microarray and all the computations were
developed using the SAS-STAT software.
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Development of Genomics-Based Gene Expression
Signature Biomarkers in Oncology and Toxicology to Facilitate
Drug Discovery and Translational Medicine
Tao Wei and Shuyu Li
Biomarker identification has been a critical component
in drug discovery and development. Biomarkers have been utilized
in several areas such as disease diagnosis, pharmacokinetics-pharmacodynamics
(PK-PD) modeling, evaluation of efficacy and toxicity to guide
both pre-clinical compound selection and clinical study of
candidate compounds. Recent advances in genomics and bioinformatics
have created a new paradigm in the development of robust gene
expression signature biomarkers, particularly for predicting
toxicity and cancer drug responses. In this article, we present
an overview of current progress in this arena including methodology
development and applications in the drug discovery process.
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Machine Learning for Childhood Acute Lymphoblastic
Leukaemia Gene Expression Data Analysis: A Review
Amphun Chaiboonchoe, Sandhya Samarasinghe and Don
Kulasiri
Among childhood cancer, acute lymphoblastic leukaemia
(ALL) has been the most extensively studied propelled by the
desire to improve survival rate. DNA microarray technology
has expanded rapidly providing an extensive source of data
that promise to pave the way for better prognosis and diagnosis
of cancer and identify key targets for drug development. DNA
microarray data analysis has been carried out using statistical
analysis as well as machine learning and data mining approaches.
In this paper, we present a comprehensive review of machine
learning approaches that have been used on ALL microarray
data. Followed by the research conducted by biological and
medical childhood leukaemia research groups, machine learning
has been used to enhance cancer diagnosis and subtype classification,
development of novel therapeutic approaches and accurate identification
of risk stratification of patients. These methods have been
used in four major areas of microarray data analysis: gene
selection, clustering, classification and pathway analysis.
Each machine learning algorithm has its own advantages and
drawbacks. Highlights of these as well as some outstanding
future research and challenges are summarized in this paper.
This review aims to serve as a starting point for those interested
in microarray analysis in general and cancer research in particular.
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High Conservation of Amino Acids with Anomalous
Protonation Behavior
David G.C. Hildebrand, Huyuan Yang, Mary Jo Ondrechen and
Ronald J. Williams
The determination of a protein’s biochemical function
from its 3D structure has proved more difficult than anticipated
for structural genomics proteins, most of which are of unknown
or uncertain function. Functional annotations typically have
been assigned using the closest sequence or structure match,
a practice that has resulted in large numbers of misannotated
proteins. Recently it was reported that computed protonation
properties can be used to predict the residues with catalytic
and binding activity, thus providing clues about the function
of the protein. We show that residues with anomalous computed
protonation behavior constitute a small fraction of the protein’s
highly conserved residues. Results for a test set of 61 proteins
reveal that the average conservation scores are high for residues
with unusual protonation behavior, even for many not annotated
as functionally important in the literature. Two enzymes,
protein tyrosine phosphatase from Yersinia enterocolitica
and glucosamine-6-phosphate deaminase from Escherichia
coli, are described in detail as examples to illustrate
the relationship between anomalous protonation behavior and
conservation. We conclude that the residues with anomalous
protonation behavior are generally highly conserved, but are
fewer in number and more spatially localized than the set
of all highly conserved residues in a given protein.
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Matching up Phosphosites to Kinases: A Survey
of Available Predictive Programs
Stefano Toppo, Lorenzo A. Pinna and Mauro Salvi
Over the past few years, research in phosphoproteomic
has assisted a tremendous revolution thanks to instrumental
technologies advances in mass spectrometry combined with innovative
experimental strategies. This has allowed the identification
of thousands of high confidence phosphosites. Presently, almost
60000 non-redundant phosphosites have been identified from
~10000 non-redundant proteins and about 80% of these phosphosites
have been identified from high throughput experiments in the
last six years. The vast majority of phosphosites are still
functionally uncharacterized and the kinases responsible of
their generation are almost unknown.
Several computational approaches have been developed to link
kinase families with putative substrates and although these
are powerful tools, they are not commonly used.
Here we discuss about the present approaches and tools developed
for predicting the functional link between the kinases and
their substrates.
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