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Chapter

Cover Introduction to Protein Science

Bioinformatics of protein sequence and structure  

This chapter examines bioinformatics, a new field which is a hybrid of biology and computer science. Biology, especially high-throughput data streams such as DNA sequencing and structural genomics projects, provides its input. Computer science permits the effective use of information-processing equipment to support research based on these data. Databases organize knowledge and make it accessible. Algorithms then allow analysis of the information, thereby producing additional data streams to be incorporated into the repository. The chapter then looks at the concept of sequence alignment, the methods for aligning sequences, and facilities for sequence database searching based on them, notably BLAST (Basic Local Alignment Search Tool) and PSI-BLAST. It also considers the characteristics of pairwise sequence alignment, multiple sequence alignment, and structural alignment.

Chapter

Cover Introduction to Bioinformatics

Alignments and phylogenetic trees  

This chapter examines the concept of sequence alignment, which is the identification of residue-residue correspondences. It is the basic tool of bioinformatics. The chapter presents a comparison of pairwise sequence alignments and multiple sequence alignments. Multiple sequence alignments are much more informative than pairwise sequence alignments, in terms of revealing patterns of conservation. The chapter then looks at the process of constructing and interpreting dot plots, before considering the use of the Hamming distance and Levenshtein distance as measures of dissimilarity of character strings. It also explains the basis of scoring schemes for string alignment, including substitution matrices and gap penalties. Finally, the chapter studies the applications of multiple sequence alignments to database searching, before exploring the contents and significance of phylogenetic trees, and the methods available for deriving them.

Chapter

Cover Concepts in Bioinformatics and Genomics

Basic Local Alignment Search Tool (Blast) and Multiple Sequence Alignment  

This chapter look at the topic of pairwise sequence comparison by describing the Basic Local Alignment Search Tool (BLAST). It discusses multiple sequence alignment programs with an emphasis on the first popular program of this class–Cluster Alignment Weighted (ClustalW). In the 1990s, advances in DNA sequencing technology led to a significant expansion of the number of sequences deposited in databases. The chapter examines the logic of how BLAST aligns a query sequence with a subject sequence from a database. It emphasizes the significant advantages of BLAST over other sequence alignment programs: its increased speed and its use of a statistical measurement, the E-value, to assess the significance of the similarity score. The chapter then shifts to look into major BLAST programs available through NCBI. It then analyses the logic of how ClustalW aligns multiple sequences, then considers other multiple sequence alignment programs.

Chapter

Cover Concepts in Bioinformatics and Genomics

Developing a Bioinformatics Tool  

This chapter guides through the development of a pairwise sequence alignment tool that implements global, ends-free global, and local alignment. It focuses on the algorithms needed to implement the tool. However, because Python is a commonly used programming language for bioinformatics, the chapter encourages you to apply the Python concepts to implement your pairwise sequence alignment tool. The chapter begins by analysing the output report of an existing local sequence alignment tool, EMBOSS Water, to familiarize ourselves with its inputs, outputs, and functionality. It then looks at an overview of simple pairwise alignment (SPA), and introduces the concept of algorithms–taking a look at different ways to express algorithms. Towards the end, the chapter explains the longest common subsequence (LCS) algorithm and how it can be extended to implement local and global pairwise alignment. It then assesses the complexity of algorithms–that is, how much memory and time they require.

Book

Cover Introduction to Bioinformatics

Arthur M. Lesk

Introduction to Bioinformatics starts off by introducing the topic. It then looks at genetics and genomes. It moves on to consider the panorama of life. The text also considers alignments and phylogenetic trees. There is a chapter on structural bioinformatics and drug discovery. The text also examines scientific publications and archives, particularly media, content, access, and presentation. Artificial intelligence is considered as well, in addition to machine learning. There is an introduction to systems biology that follows towards the end. The book's final chapters look at metabolic pathways and control of organization.

Book

Cover Concepts in Bioinformatics and Genomics

Jamil Momand, Alison McCurdy, Silvia Heubach, and Nancy Warter-Perez

Concepts in Bioinformatics and Genomics starts with a review of molecular biology and looks at its relevance to the topic. It then goes on to consider information organization and sequence databases, molecular evolution, substitution matrices, and pairwise sequence alignment. Other topics covered include the basic local alignment sequence tool and multiple sequence alignment, protein structure prediction, phylogenetics, genomics, transcript and protein expression analysis, and basic probability. There are also chapters on advanced probability for bioinformatics applications, programming basics and applications to bioinformatics, and how to develop a basic bioinformatics tool.

Chapter

Cover Concepts in Bioinformatics and Genomics

Pairwise Sequence Alignment  

This chapter focuses on amino acid substitution matrices and pairwise sequence comparison programs. It begins by discussing the nuts and bolts of algorithms that use data from evolution and protein domain conservation to infer whether two genes are homologs. The chapter then explores the details of how pairwise sequence alignment is performed by computer programs originally written in the 1970s and 1980s and gradually improved. To approach the pairwise sequence alignment programs, the chapter first discusses the sliding window. The sliding window accumulates information or data about the properties of a segment of amino acid residues in a window of specific length within a long polypeptide. It then investigates how the sliding window program is used to create dot plots. Next, the chapter delves into the Needleman-Wunsch global alignment program and the Smith-Waterman local alignment program. It also considers the two updated versions of the Needleman-Wunsch global alignment programs.

Chapter

Cover Concepts in Bioinformatics and Genomics

Review of Molecular Biology  

This chapter offers an overview of molecular biology. It presents the essential biology vocabulary for understanding bioinformatics, then introduces the important molecule, the p53 protein (sometimes referred to as ‘p53’, for short), which plays a significant role in preventing cancer. The chapter also discusses the relationship between genes, transcripts, proteins, and some functions carried out by proteins. It then examines how DNA alterations can lead to protein alterations that affect protein function. The chapter next explains the one-letter code for nucleotides and amino acids. Towards the end, the chapter looks at the term ‘sequence alignment’. It also investigates how the first experiment demonstrating the relationship between a mutation and a disease was carried out.

Chapter

Cover Concepts in Bioinformatics and Genomics

Advanced Probability for Bioinformatics Applications  

This chapter starts with the subject of a continuous random variable and then moves to a discussion of the extreme value distribution and its use in analysing the significance of an alignment. It looks into the computation and interpretation of P- and E-values to evaluate sequence alignments. The chapter also discusses the main characteristic of a Markov process (probability of current state dependent only on previous state), then examines how to translate information about a Markov process into a state diagram and the associated transition matrix. It also points out the probability that a particular sequence of states resulting from a Markov process occurs. Next, the chapter analyses the stochastic processes, specifically Markov chains and hidden Markov models, as well as a mathematical derivation of the Jukes-Cantor model.