Erciyes - Distributed and Sequential Algorithms for Bioinformatics
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This unique textbook/reference presents unified coverage of bioinformatics topics relating to both biological sequences and biological networks, providing an in-depth analysis of cutting-edge distributed algorithms, as well as of relevant sequential algorithms. In addition to introducing the latest algorithms in this area, more than fifteen new distributed algorithms are also proposed. Topics and features: Reviews a range of open challenges in biological sequences and networks, beginning with an informal description of the problem before defining it formally Describes in detail both sequential and parallel/distributed algorithms for each problem, briefly discussing software packages if there are any available Suggests approaches for distributed algorithms as possible extensions to sequential algorithms, when the distributed algorithms for the topic are scarce Proposes a number of new distributed algorithms in each chapter, to serve as potential starting points for further research Concludes each chapter with self-test exercises, a summary of the key points, a comparison of the algorithms described, and a literature review This clearly-written and easy to follow work is ideal as a textbook for graduate and senior undergraduate students of computer science and biology, and as a self-study guide for any interested reader with a basic background in discrete mathematic s and algorithms. Researchers in bioinformatics will also find the book to be a useful reference on this subject. Dr. K. Erciyes is Rector of Izmir University, Turkey, where he also serves as a professor in the Computer Engineering Department. His other publications include the Springer title Distributed Graph Algorithms for Computer Networks.
Erciyes: author's other books
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Ryuhei Uehara
Aubanel
J. R. Parker
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Springer International Publishing Switzerland 2015
K. Erciyes Distributed and Sequential Algorithms for Bioinformatics Computational Biology 10.1007/978-3-319-24966-7_11. Introduction
K. Erciyes 1
(1)
Computer Engineering Department, Izmir University, Uckuyular, Izmir, Turkey
K. Erciyes
Email:
1.1 Introduction
Biology is the science of life and living organisms. An organism is a living entity that may consist of organs that are made of tissues. Cells are the building blocks of organisms and form tissues of organs. Cells consist of molecules and molecular biology is the science of studying the cell at molecular level. The nucleus of a cell contains deoxyribonucleic acid (DNA) which stores all of the genetic information. DNA is a double helix structure consisting of four types of molecules called nucleotides . It consists of a long sequence of nucleotides of about 3 billion pairs. From the viewpoint of computing, DNA is simply a string that has a four-letter alphabet. The ribonucleic acid (RNA) has one strand and also consists of four types of nucleotides like DNA, with one different nucleotide. Proteins are large molecules outside the nucleus of the cell and perform vital life functions. A protein is basically a linear chain of molecules called amino acids. Molecules of the cell interact to perform all necessary functions for living.
Recent technological advancements have provided vast amounts of biological data at molecular level. Analysis and extracting meaningful information from this data requires new methods and approaches. This data comes in two basic forms as sequence and network data. On one hand, we are provided with sequence data of DNA/RNA and proteins, and on the other hand, we have topological information about the connectivity of various networks within the cell. Analysis of this data is a task on its own due to its huge size.
We first describe the problems encountered in the analysis of biological sequences and networks and we then describe why distributed algorithms are imperative as computational methods in this chapter. It seems design and implementation of distributed algorithms are inevitable for these time-consuming difficult problems and their scarcity can be attributed to the relatively recent provision of the biological data and the field being multidisciplinary in nature. We conclude by providing the outline of the book.
1.2 Biological Sequences
The biological sequences in the cell we are referring are the nucleotide sequences in DNA, RNA, and amino acid sequences in proteins. DNA contains four nucleotides in a double-helix-shaped two-strand structure: Adenine (A), Cytosine (C), Guanine (G), and Thymine (T). Adenine always pairs with thymine, and guanine with cytosine. The primary structure of a protein consists of a linear chain of amino acids and the order of these affects its 3D shape. Figure shows a simplified diagram of DNA and a protein. 
Fig. 1.1
a DNA double helix structure. b A protein structure having a linear sequence of amino acids
Analysis of these biological sequences involves the following tasks:
- Comparison of sequences : A basic requirement to analyze a newly discovered sequence is to compare it with the known sequences. The basic assumption here is that the similar structures may indicate similar functions. In the very basic form, we attempt to find the approximately nucleotides in two or more sequences. This process is commonly called sequence alignment and can be solved in polynomial time by dynamic algorithms. A sequence alignment algorithm provides the similarities and distances between a number of input sequences which can be used for further processing.
- Clustering : The grouping of similar sequences is called clustering and this process is at a higher level than sequence alignment as it needs the distances between the sequences as computed by the alignment. Clustering of sequences aims to find their affinities and infer ancestral relationships based on the groupings. The functions of sequences in clusters can be analyzed more easily and also this information can be used for disease analysis of organisms.
- Sequence patterns : DNA and proteins contain repeating subsequences which are called sequence repeats . These repeats may be consecutive or dispersed, and the former is commonly referred to as tandem repeats and the latter as sequence motifs . In various diseases, the number of the repeats is more than expected, and hence discovering them helps to understand the disease mechanism in an organism. They also reside near genes and may be used to find the location of genes. The number and locations of repeats are unique for individuals and can be used in forensics to identify individuals.
- Gene finding : A gene codes for a polypeptide which can form or be a part of a protein. There are over 20,000 genes in human DNA which occupy only about 3 % of human genome. Finding genes is a fundamental step in their analysis. A mutated gene may cause the formation of a wrong protein, disturbing the healthy state of an organism, but mutations are harmless in many cases.
- Genome rearrangements : Mutations of DNA at a coarser level than point mutations of nucleotides involve certain alterations of segments or genes in DNA. These changes include reversals, duplications, and transfer to different locations in DNA. Genome rearrangements may result in the production of new species but in many cases, they are considered as the causes of complex diseases.
- Haplotype inference : The DNA sequencing methods of humans provide the order of DNA nucleotides from two chromosomes as this approach is more cost-effective and practical. However, the sequence information from a single chromosome called a haplotype is needed for disease analysis and also to discover ancestral relationships. Discovering single chromosome data from the two chromosomes is called haplotype inference and is needed for the data to be meaningful
All of the above-described tasks are fundamental areas of research in the analysis of biological sequences. Comparison of sequences, clustering, and finding repeats apply both to DNA/RNA and protein sequences. Protein sequences may also be employed to discover genes as they are the product of genes; however, genome rearrangements and haplotype inference problems are commonly associated with the DNA/RNA sequences. Except for the sequence alignment problem, there are hardly any polynomial algorithms for these problems. Even when there is a solution in polynomial time, the size of data necessitates the use of approximation algorithm if they are available. As we will see, the heuristic algorithms that can be shown to work for a wide range of input combinations experimentally are the only choice in many cases.
1.3 Biological Networks
networks consist of biological entities which interact in some form. The modeling and analysis of biological networks are fundamental areas of research in bioinformatics. The number of nodes in a biological network is large and these nodes have complex relations among them. We can represent a biological network by a graph where an edge between two entities indicates an interaction between them. This way, many results in graph theory and also various graph algorithms become available for immediate use to help solve a number of problems in biological networks.
We can make coarse distinction between the networks in the cell and other biological networks. The cell contains DNA, RNA, proteins, and metabolites at the molecular level. Networks at biological level are gene regulation networks, signal transduction networks, proteinprotein interaction (PPI) networks, and metabolic networks. DNA is static containing the genetic code and proteins take part in various vital functions in the cell. Genes in DNA code for proteins in a process called gene expression .
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