"Sequence and Genome Analysis: Methods and Applications II" is a
continuation of our previous book, entitled "Sequence and Genome
Analysis: Methods and Applications." Chapter 1 covers several
aspects of protein structural motifs, including the procedures and
approaches for detecting and associating protein structural motifs
to functions, as well as the issues and limitations of the
methodologies based on 3D motif for function prediction. Chapter 2
proposes a computationally efficient algorithm for identifying
structural motifs. This algorithm considers the dynamic nature of
the folding process, captures both local and non-local interactions
between residues, and identifies structural motifs in a 3D space.
Chapter 3 discusses the Watson-Crick finite automata. Watson-Crick
automata are devices with two reading heads that can scan the two
strands of a DNA molecule and decide whether or not the molecule is
acceptable. They are essential in the field of DNA computing and
have an important role in formal language and automata theory.
Chapter 4 introduces logoBar, which is a means for annotating and
displaying protein sequence logos. Protein sequence logos represent
information content matrices of stretches of conserved DNA or
protein sequences using a paradigm, in which the height of letters
represents the information contribution of each residue in a
sequence alignment. Chapter 5 presents an extraction and
visualization method of anatomic structures from volumetric medical
images, using a new level set method and hybrid rendering
technique. The extraction of 3D objects and its visualization is
one of the most important steps in the analysis of the preprocessed
medical image data. Such data can help in performing diagnosis,
treatment planning, and treatment delivery. Chapter 6 takes viral
channel forming proteins as an example to outline computational
strategies in developing structurally and functionally optimized
models (with a minimum input of experimental results), with the aim
of understanding the function of a particular protein. Chapter 7
studies hydrophobicity, a topic that has long been considered as
one of the primary driving forces in the folding of proteins. This
chapter focuses on statistical methods based on factor analysis and
filtering to extract cluster-dependent average hydrophobicity
profiles in an aligned family of proteins. Meanwhile, Chapter 8
addresses surface-based DNA computing by proposing an algorithm,
which can solve a hard computational problem, namely, the
Satisfiability problem. This problem is well-known for its
exponential difficulty. Chapter 9 introduces a software
architecture called GAMESSP2, which is used for protein secondary
structure prediction. SSP2 is an abstract software architecture
that follows the guidelines imposed by all information sources
deemed relevant in implementing a successful secondary structure
predictor. GAME stands for Generic Architecture Based on Multiple
Expert, and is a tool for designing, implementing, and tuning
secondary structure predictors. Finally, Chapter 10 describes an
electrical model for fast DNA detection. This model can achieve a
resolution of 0.01% for the capacitive shift, using a differential
reading of the signals detected by two sensors, namely, a dummy
sensor and an active sensor. Similar to Volume I, this volume is
suitable for advanced undergraduate students and postgraduate
students. It takes a practical approach rather than a conceptual
approach. Moreover, it offers a truly reader-friendly way to get to
the subject related to bioinformatics and biotechnology, making it
the ideal resource for any student new to this subject, and
providing a definitive guide to anyone in this vibrant and evolving
discipline. This book is an invaluable companion for students, from
their first encounter with the subject to more advanced studies,
while the full-color artworks are designed to present the key
concepts with simplicity, clarity, and consistency.
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