Bioinformatics: Sequence and Genome Analysis

Book Preface

THE DEVELOPMENT OF SEQUENCE ANALYSIS METHODS has depended on the contributions of many individuals from varied scientific backgrounds. This chapter provides a brief historical account of the more significant advances that have taken place, as well as an overview of the chapters of this book. Because many contributors cannot be mentioned due to space constraints, additional references to earlier and current reference books, articles, reviews, and journals provide a broader view of the field and are included in the reference lists to this chapter.

THE FIRST SEQUENCES TO BE COLLECTED WERE THOSE OF PROTEINS

The development of protein-sequencing methods (Sanger and Tuppy 1951) led to the sequencing of representatives of several of the more common protein families such as cytochromes from a variety of organisms. Margaret Dayhoff (1972, 1978) and her collaborators at the National Biomedical Research Foundation (NBRF), Washington, DC, were the first to assemble databases of these sequences into a protein sequence atlas in the 1960s, and their collection center eventually became known as the Protein Information Resource (PIR, formerly Protein Identification Resource; http://watson.gmu.edu:8080/pirwww/index. html). The NBRF maintained the database from 1984, and in 1988, the PIR-International Protein Sequence Database (http://www-nbrf.georgetown.edu/pir) was established as a collaboration of NBRF, the Munich Center for Protein Sequences (MIPS), and the Japan International Protein Information Database (JIPID). Dayhoff and her coworkers organized the proteins into families and superfamilies based on the degree of sequence similarity. Tables that reflected the frequency of changes observed in the sequences of a group of closely related proteins were then derived. Proteins that were less than 15% different were chosen to avoid the chance that the observed amino acid changes reflected two sequential amino acid changes instead of only one. From aligned sequences, a phylogenetic tree was derived showing graphically which sequences were most related and therefore shared a common branch on the tree. Once these trees were made, they were used to score the amino acid changes that occurred during evolution of the genes for these proteins in the various organisms from which they originated (Fig. 1.1).

Subsequently, a set of matrices (tables)—the percent amino acid mutations accepted by evolutionary selection or PAM tables—which showed the probability that one amino acid changed into any other in these trees was constructed, thus showing which amino acids are most conserved at the corresponding position in two sequences. These tables are still used to measure similarity between protein sequences and in database searches to find sequences that match a query sequence. The rule used is that the more identical and conserved amino acids that there are in two sequences, the more likely they are to have been derived from a common ancestor gene during evolution. If the sequences are very much alike, the proteins probably have the same biochemical function and three-dimensional structural folds. Thus, Dayhoff and her colleagues contributed in several ways to modern biological sequence analysis by providing the first protein sequence database as well as PAM tables for performing protein sequence comparisons. Amino acid substitution tables are routinely used in performing sequence alignments and database similarity searches, and their use for this purpose is discussed in Chapters 3 and 7.

DNA SEQUENCE DATABASES

DNA sequence databases were first assembled at Los Alamos National Laboratory (LANL), New Mexico, by Walter Goad and colleagues in the GenBank database and at the European Molecular Biology Laboratory (EMBL) in Heidelberg, Germany. Translated DNA sequences were also included in the Protein Information Resource (PIR) database at the National Biomedical Research Foundation in Washington, DC. Goad had conceived of the GenBank prototype in 1979; LANL collected GenBank data from 1982 to 1992. GenBank is now under the auspices of the National Center for Biotechnology Information (NCBI) (http://www.ncbi.nlm.nih.gov). The EMBL Data Library was founded in 1980 (http://www.ebi.ac.uk). In 1984 the DNA DataBank of Japan (DDBJ), Mishima, Japan, came into existence (http://www.ddbj.nig.ac.jp). GenBank, EMBL, and DDBJ have now formed the International Nucleotide Sequence Database Collaboration (http://www. ncbi.nlm.nih.gov/collab), which acts to facilitate exchange of data on a daily basis. PIR has made similar arrangements. Initially, a sequence entry included a computer filename and DNA or protein sequence files. These were eventually expanded to include much more information about the sequence, such as function, mutations, encoded proteins, regulatory sites, and references. This information was then placed along with the sequence into a database format that could be readily searched for many types of information. There are many such databases and formats, which are discussed in Chapter 2. The number of entries in the nucleic acid sequence databases GenBank and EMBL has continued to increase enormously from the daily updates. Annotating all of these new sequences is a time-consuming, painstaking, and sometimes error-prone process. As time passes, the process is becoming more automated, creating additional problems of accuracy and reliability. In December 1997, there were 1.26 109 bases in GenBank; this number increased to 2.57 109 bases as of April 1999, and 1.0 1010 as of September 2000. Despite the exponentially increasing numbers of sequences stored, the implementation of efficient search methods has provided ready public access to these sequences. To decrease the number of matches to a database search, non-redundant databases that list only a single representative of identical sequences have been prepared. However, many sequence databases still include a large number of entries of the same gene or protein sequences originating from sequence fragments, patents, replica entries from different databases, and other such sequences.

SEQUENCE RETRIEVAL FROM PUBLIC DATABASES

An important step in providing sequence database access was the development of Web pages that allow queries to be made of the major sequence databases (GenBank, EMBL, etc.). An early example of this technology at NCBI was a menu-driven program called GENINFO developed by D. Benson, D. Lipman, and colleagues. This program searched rapidly through previously indexed sequence databases for entries that matched a biologist’s query. Subsequently, a derivative program called ENTREZ (http://www.ncbi.nlm.nih.gov/Entrez) with a simple window-based interface, and eventually a Web-based interface, was developed at NCBI. The idea behind these programs was to provide an easy-to-use interface with a flexible search procedure to the sequence databases. Sequence entries in the major databases have additional information about the sequence included with the sequence entry, such as accession or index number, name and alternative names for the sequence, names of relevant genes, types of regulatory sequences, the source organism, references, and known mutations. ENTREZ accesses this information, thus allowing rapid searches of entire sequence databases for matches to one or more specified search terms. These programs also can locate similar sequences (called “neighbors” by ENTREZ) on the basis of previous similarity comparisons. When asked to perform a search for one or more terms in a database, simple pattern search programs will only find exact matches to a query. In contrast, ENTREZ searches for similar or related terms, or complex searches composed of several choices, with great ease and lists the found items in the order of likelihood that they matched the original query. ENTREZ originally allowed straightforward access to databases of both DNA and protein sequences and their supporting references, and even to an index of related entries or similar sequences in separate or the same databases. More recently, ENTREZ has provided access to all of Medline, the full bibliographic database of the National Library of Medicine (NLM), Washington, DC. Access to a number of other databases, such as a phylogenetic database of organisms and a protein structure database, is also provided. This access is provided without cost to any user—private, government, industry, or research—a decision by the staff of NCBI that has provided a stimulus to biomedical research that cannot be underestimated. NCBI presently handles several million independent accesses to their system each day.

A note of caution is in order. Database query programs such as ENTREZ greatly facilitate keeping up with the increasing number of sequences and biomedical journals. However, as with any automated method, one should be wary that a requested database search may not retrieve all of the relevant material, and important entries may be missed. Bear in mind that each database entry has required manual editing at some stage, giving rise to a low frequency of inescapable spelling errors and other problems. On occasion, a particular reference that should be in the database is not found because the search terms may be misspelled in the relevant database entry, the entry may not be present in the database, or there may be some more complicated problem. If exhaustive and careful attempts fail, reporting such problems to the program manager or system administrator should correct the problem.

SEQUENCE ANALYSIS PROGRAMS

Because DNA sequencing involves ordering a set of peaks (A, G, C, or T) on a sequencing gel, the process can be quite error-prone, depending on the quality of the data. As more DNA sequences became available in the late 1970s, interest also increased in developing computer programs to analyze these sequences in various ways. In 1982 and 1984, Nucleic Acids Research published two special issues devoted to the application of computers for sequence analysis, including programs for large mainframe computers down to the then-new microcomputers. Shortly after, the Genetics Computer Group (GCG) was started at the University of Wisconsin by J. Devereux, offering a set of programs for analysis that ran on a VAX computer. Eventually GCG became commercial (http://www.gcg.com/). Other companies offering microcomputer programs for sequence analysis, including Intelligenetics, DNAStar, and others, also appeared at approximately the same time. Laboratories also developed and shared computer programs on a no-cost or low-cost basis. For example, to facilitate the collection of data, the programs PHRED (Ewing and Green 1998; Ewing et al. 1998) and PHRAP were developed by Phil Green and colleagues at the University of Washington to assist with reading and processing sequencing data. PHRED and PHRAP are now distributed by CodonCode Corporation (http://www.codoncode.com). These commercial and noncommercial programs are still widely used. In addition, Web sites are available to perform many types of sequence analyses; they are free to academic institutions or are available at moderate cost to commercial users. Following is a brief review of the development of methods for sequence analysis.

THE DOT MATRIX OR DIAGRAM METHOD FOR COMPARING SEQUENCES

In 1970, A.J. Gibbs and G.A. McIntyre (1970) described a new method for comparing two amino acid and nucleotide sequences in which a graph was drawn with one sequence written across the page and the other down the left-hand side. Whenever the same letter appeared in both sequences, a dot was placed at the intersection of the corresponding sequence positions on the graph (Fig. 1.2). The resulting graph was then scanned for a series of dots that formed a diagonal, which revealed similarity, or a string of the same characters, between the sequences. Long sequences can also be compared in this manner on a single page by using smaller dots. The dot matrix method quite readily reveals the presence of insertions or deletions between sequences because they shift the diagonal horizontally or vertically by the amount of change. Comparing a single sequence to itself can reveal the presence of a repeat of the same sequence in the same (direct repeat) or reverse (inverted repeat or palindrome) orientation. This method of self-comparison can reveal several features, such as similarity between chromosomes, tandem genes, repeated domains in a protein sequence, regions of low sequence complexity where the same characters are often repeated, or self-complementary sequences in RNA that can potentially base-pair to give a double-stranded structure. Because diagonals may not always be apparent on the graph due to weak similarity, Gibbs and McIntyre counted all possible diagonals and these counts were compared to those of random sequences to identify the most significant alignments. Maizel and Lenk (1981) later developed various filtering and color display schemes that greatly increased the usefulness of the dot matrix method. This dot matrix representation of sequence comparisons continues to play an important role in analysis of DNA and protein sequence similarity, as well as repeats in genes and very long chromosomal sequences, as described in Chapter 3 (p. 59).