Automated DNA Sequencing and Analysis

Mark D. Adams, Chris Fields, J. Craig Venter

• Publisher: Academic Press
• Year: 2012
• Print ISBN:
  9780127170107
• eBook ISBN:
  9780080926391
• Topic: Biological Sciences
• Subtopic: Genetics & Genomics
• Index: Biological Sciences

A timely book for DNA researchers, Automated DNA Sequencing and Analysis reviews and assesses the state of the art of automated DNA sequence analysis-from the construction of clone libraries to the developmentof laboratory and community databases. It presents the methodologies and strategies of automated DNA sequence analysis in a way that allows them to be compared and contrasted. By taking a broad view of the process of automated sequence analysis, the present volume bridges the gap between the protocols supplied with instrument and reaction kits and the finalized data presented in the research literature. It will be an invaluable aid to both small laboratories that are interested in taking maximum advantageof automated sequence resources and to groups pursuing large-scale cDNA and genomic sequencing projects. - The field of automation in DAN sequencing and analysis is rapidly moving, this book fulfils those needs, reviews the history of the art and provides pointers to future development.

PART I

SEQUENCING INSTRUMENTS AND STRATEGIES

CHAPTER ONE

The Efficiency of Automated DNA Sequencing

E.Y. CHEN, Advanced Center for Genetic Technology, Applied Biosystems, Division of Perkin-Elmer Corp., 850 Lincoln Centre Drive, Foster City, CA 94404, USA

1.1 INTRODUCTION

The GenBank genetic sequence database, which contained only 15 million nucleotides in 1987, has nearly doubled its size in each of the subsequent 5 years. By 1992 it reached over 120 million, with progressively more data obtained utilizing automated DNA sequencers. The historic progress leading to current DNA sequencing technology is reviewed in Fig. 1.1.

Figure 1.1 A historical view of DNA sequencing efficiency.

While the three-dimensional structure of DNA was known in 1953, the major sequencing work performed prior to 1965 was on RNAs such as tRNAs which were small and easy to obtain in substantial quantities. The RNAs were usually broken into smaller pieces (a few nucleotides long) and each fragment was sequenced by chromatographic methods. The first sequence of an intact molecule, an 80-base yeast tRNA, was published in 1965 (Holley et al., 1965).

DNA sequencing first became possible with the discovery of restriction enzymes and DNA polymerases around 1970. For the first time well-defined fragments could be derived from a larger molecule. Methods based on primed synthesis (Wu & Taylor, 1971) and gel electrophoresis separation led to the first sequence of a genome, the 5.4 kb of bacteriophage ϕX, in 1976 (Sanger et al., 1977a).

A breakthrough in the rate of sequencing came when the dideoxy chain termination (Sanger et al., 1977b) and chemical degradation (Maxam & Gilbert, 1977) techniques were introduced in 1977. Several years later, the former method was used to sequence the 16.5 kb human mitochondria genome (Anderson et al., 1981) and the latter was employed to achieve the analysis of the 40 kb bacteriophage T7 (Dunn & Studier, 1983). These methods provide the theoretical and practical backgrounds for modern sequencing technology. Since the dideoxy method has been used in most large-scale projects and was adopted in present-day automated fluorescent sequencing instruments, the following discussion will focus on its applications.

1.2 EFFICIENCY AND COST

Along with the major developments, Fig. 1.1 also lists estimated sequencing efficiencies, assessed as the number of nucleotides of finished sequence achieved by a skilled, dedicated person in a year. When the dideoxy method was introduced the rate was only 1.5 kb per person-year, due to the difficulties of obtaining single-stranded templates and useful primers.

In a few years these problems were solved with the development of M13 cloning vectors and the availability of the oligonucleotide synthesizer. The dideoxy method thereby became universally applicable to any DNA fragment. Consequently, the rate increased by an order of magnitude to 15 kb per person-year by the early 1980s.

As a part-time effort in 1983, we set out to sequence the 66.5 kb human growth hormone locus (Chen et al., 1989), using a combination of random shotgun, primer walking and nested deletion assembly strategies after subcloning DNA fragments from two cosmids into 17 plasmids. Employing optimized sequencing conditions and computer-assisted data handling, we were able to complete 98% of the project within about 2.5 person-years and the overall project in 3 person-years. (Accumulating the final 2% of the data took extra time because of the instability of some M13 clones (Chen et al., 1989).) It was therefore estimated that the sequencing rate was about 20–25 kb per person-year. With an estimated cost of approximately $75 000 a year to support one person (including $25 000 salary, $15 000 overhead, $20 000 equipment and $15 000 supplies), the cost per base pair would be approximately $3 to $4. However, because manual sequencing tends to incur a high turnover rate of skilled personnel, the actual sequencing cost is even higher if the training expense is included.

Beginning in 1985, several automated sequencers were developed (Smith et al., 1986; Connell et al., 1987; Ansorge et al., 1987; Brumbaugh et al., 1988; Kambara et al., 1988; Freeman et al., 1990) to automate gel electrophoresis, raw data acquisition, and base-calling. Gradually computer-operated robotic workstations and more sophisticated software have also been applied to handle the sequencing reactions (Wilson et al., 1988; D’Cunha et al., 1990; Cathcart, 1990), prepare template samples (Mardis & Roe, 1989; Smith et al., 1990) and assemble data (Staden, 1987; Chen et al., 1982). It took a few years for these instruments to establish their reliable performances. By the early 1990s the resultant enhanced data throughput has increased the sequencing rate to around 100 kb per person-year. The productivity and consistency are obviously very sensitive to further technical innovation. Particularly notable has been the impact of the polymerase chain reaction (PCR) amplification technique, since it can be used to prepare sequencing samples at a much faster rate (Gyllensten, 1989 and Chen et al., 1991b). Furthermore, the combination of PCR and dideoxy method has allowed the development of ‘cycle sequencing’ (Carothers et al., 1989; Lee, 1991), a process that linearly amplifies the detectable signals, tremendously increasing the sensitivity of sequencing.

In general, it is not easy to assess the precise cost in automated sequencing. This is in part because hidden costs (Pohl & Sulston, 1992) are often overlooked by laboratory scientists, and it may also be difficult to distinguish the time spent in production sequencing from development efforts, especially when projects or methods are new. We can, however, get an estimate from the minimum cost to maintain an operating unit including one person, one sequencing instrument, and a shared robot. The cost would at present be about $150 000 a year. (This includes $50 000 salary plus overhead, $15 000 general supplies, $45 000 for sequencing kits, and $40 000 for machine depreciation over 5 years.) At a current yield of 100 kb per person-year, the net cost would then be about $1.50 per base. Even with a productivity of 150 kb per person-year (Sulston et al., 1992), the minimum cost is still $1 per base; and one would need to increase efficiency to at least 300 kb per year to bring the cost down to $0.50 per base.

Of course, sequencing efficiency and cost are also directly related to the nature of the DNA analyzed. Practically, DNAs that contain more repetitive sequence or homopolymer sequence elements, or higher contents of GC require more work. Within a single project, certain regions will also tend to be more troublesome that the others. To solve specific problems, several modifications of the dideoxy method have been developed in the past decade (for reviews see: Chen et al., 1991b; Hunkapiller et al., 1991). For example, the use of nucleotide analogs such as inosine or deaza compounds can help eliminate ‘gel-compression’ problems (Jensen et al., 1991); use of different cloning vectors provides an alternative when a particular M13 clone is unstable (Chen & Seeburg, 1985) and the addition of single-strand binding proteins to sequencing reactions improves the quality of the data produced from DNA templates enriched in looping structures (Chen et al., 1991b). These variations are also applicable to today’s automated sequencing protocols.

1.3 METHODS IN AUTOMATED SEQUENCING

On the basis of the number of fluorescent dyes used, the commercially available automated sequencers can be divided into two types, as listed in Table 1.1. The first type uses single-label, four-lane separation, and has 10- or 12-channel capacity (Brumbaugh et al., 1988; Freeman et al., 1990); the second type employs four-label, single-lane separation, and currently has 36-channel capacity per run (Connell et al., 1987; Lee et al., 1992; Hawkins et al., 1992). The latter type is often the instrument of choice for large-scale or large-sample-number projects because it offers higher throughput. The results from this instrument also tend to be more consistent among different sam...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• Preface
• Acknowledgements
• PART I: SEQUENCING INSTRUMENTS AND STRATEGIES
• PART II: SAMPLE PREPARATION AND SEQUENCING METHODS
• PART III: INFORMATICS
• Appendix: Sources of Sequence Analysis Software
• Index