Tidak ada kategori

The Human Genome Project has led to a massive outpouring of genomic data, which has in turn fueled the rapid developments of high-throughput biotechnologies. Computational molecular biology/bioinformatics is interdisciplinary by nature and calls upon expertise in many di¤erent disciplines—biology, mathematics, statistics, physics, chemistry, computer science, and engineering; and is ubiquitous at the heart of all large-scale and high-throughput biotechnologies.This book covers most of the important topics in computational molecular biology, ranging from traditional ones such as protein structure modeling and sequence alignment, to the recently emerged ones such as expression data analysis and comparative genomics. It also contains a general introduction to the field, as well as a chapter on general statistical modeling and computational techniques in molecular biology. The 19 chapters are grouped into four sections. The introductory section is a chapter by Temple Smith, who attempts to set bioinformatics into a useful historical context. For over half a century, mathematics and even computer-based analyses have played a fundamental role in bringing our biological understanding to its current level. To a very large extent, what is new is the type and sheer volume of new data. The birth of bioinformatics was a direct result of this new data explosion. As this interdisciplinary area matures, it is providing the data and computational support for functional genomics, which is defined as the research domain focused on linking the behavior of cells, organisms, and populations to the information encoded in the genomes. The second of the four sections consists of six chapters on computational methods for comparative sequence and genome analyses.Liu’s chapter presents a systematic development of the basic Bayesian methods alongside contrasting classical statistics procedures, emphasizing the conceptual importance of statistical modeling and the coherent nature of the Bayesian methodology. Huang’s chapter focuses on methods for comparing two sequences and their applications in the analysis of DNA and protein sequences. He presents a global alignment algorithm for comparing two sequences that are entirely similar. He also describes a local alignment algorithm for comparing sequences that contain locally similar regions. The chapter gives e‰cient computational techniques for comparing two long sequences and comparing two sets of sequences, and it provides real applications to illustrate the usefulness of sequence alignment programs in the analysis of DNA and protein sequences.The chapter by Jiang and Wang provides a survey on computational methods for multiple sequence alignment, which is a fundamental and challenging problem in computational molecular biology. Algorithms for multiple sequence alignment are routinely used to find conserved regions in biomolecular sequences, to construct viii Preface family and superfamily representations of sequences, and to reveal evolutionary histories of species (or genes). Kearney’s chapter illustrates the basic concepts in phylogenetics, the design and development of computational tools for evolutionary analyses, using the quartet method as an example. Quartet methods have recently received much attention in the research community. This chapter begins by examining the mathematical, computational, and biological foundations of the quartet method.Sanko¤ and El-Mabrouk’s chapter describes the basic concepts of genome rearrangement and applications. Genome structure evolves through a number of nonlocal rearrangement processes that may involve an arbitrarily large proportion of a chromosome. The formal analysis of rearrangements di¤ers greatly from DNA and protein comparison algorithms.The chapter by Li describes the author’s work on compressing DNA sequences and applications. The chapter concentrates on two programs the author has developed: a lossless compression algorithm, GenCompress, which achieves the best compression ratios for benchmark sequences; and an entropy estimation program, GTAC, which achieves the lowest entropy estimation for benchmark DNA sequences. The third section covers computational methods for mining biological data and discovering patterns hidden in the data. The chapter by Xu presents an overview of the major statistical techniques for quantitative trait analysis. Quantitative traits are defined as traits that have a continuous phenotypic distribution.The chapter by Solovyev describes statistically based methods for the recognition of eukaryotic genes. Computational gene identification is an issue of vital importance as a tool of identifying biologically relevant features (protein coding sequences), which often cannot be found by the traditional sequence database searching technique. Zhang’s chapter gives an overview of computational methods currently used for identifying eukaryotic PolII promoter elements and the transcriptional start sites. Promoters are very important genetic elements. Shamir and Sharan describe some of the main algorithmic approaches to clustering gene expression data, and briefly discuss some of their properties. DNA chip technologies allow for the first time a global, simultaneous view of the transcription levels of many thousands of genes, under various cellular conditions