In This Issue
Winter Issue of The Bridge on Frontiers of Engineering
December 15, 2010 Volume 40 Issue 4

The Current Status and Future Outlook for Genomic Technologies

Wednesday, December 15, 2010

Author: Jeffrey Fisher and Mostafa Ronaghi

 Genome sequencing will soon become a standard part of medical care, agricultural practices, drug discovery and approval, and a myriad of other applications

Genomics emerged as a scientific field after the invention of the original DNA sequencing technique by Fredrick Sanger (Sanger et al., 1977a,b). Sanger introduced a chemical method for reading about 100 nucleotides, which, at the time, took about six months of preparation. Thanks to a large community of scientists worldwide, Sanger’s technique eventually evolved to become the technology of choice for sequencing. The draft sequencing of the first human genome took about 13 years to complete, and the project cost some $3 billion.

Pyrosequencing, the second alternative technology, is based on sequencing-by-synthesis, which could be parallelized to enable higher throughput by more than 100 fold (Ronaghi et al., 1996, 1998). Pyrosequencing was used to sequence thousands of microbial and larger genomes, including James Watson’s genome.

In 2006, a private company (Illumina) introduced reversible dye-terminator sequencing-by-synthesis (Bentley et al., 2006). This technology has increased throughput by ~10,000 fold in the last four years and reduced the cost of sequencing a human genome to less than $10,000. The most recent system based on this chemistry allows sequencing of several human genomes in a single run.

In this article, we describe dye-terminator sequencing-by-synthesis and efforts to reduce costs even further. In addition, we discuss emerging applications and challenges to bringing genomics into the mainstream.


On the most fundamental level, sequencing the genome consists of just a handful of basic biochemical steps. The challenge is posed by the enormous scale of molecularly encoded information—two almost identical strands, each consisting of 3.2 billion base pairs of information for the human genome—that must be processed through those steps. Furthermore, a typical genome is read to 30X coverage, which means that each base pair is read on average 30 times (on separate strands of DNA), giving a total throughput per genome of 100 billion base pairs.

The processing and reading of these immense amounts of information has been made possible by the adoption of engineering-based approaches to massive parallelization of the sequencing reactions. All current-generation sequencing platforms coordinate chemical, engineering, and computation subsystems on an unprecedented scale (measured in information throughput) (Figure 1).

Figure 1

DNA Sequencing

Sequencing, which determines the arrangement of the four genetic bases (A, T, C, and G) in a given stretch of DNA, relies on four steps (Metzker, 2010; Pettersson et al., 2009; Shendure and Ji, 2008):

1. Fragmentation—breaking the genome into manageable segments, usually a few hundred base pairs long.

2. Isolation—capturing the segments in a way that keeps the signals they present distinct.

3. Amplification—although single-molecule techniques can theoretically proceed without this step, most systems apply some form of clonal amplification to increase the signal and accuracy of sequencing.

4. Readout—transforming the genetic information base by base into a machine-readable form, typically an optical (fluorescent) signal.

Although the field of genomics has evolved in recent years to include a variety of sequencing systems, including some that do not necessarily follow this exact pattern, the majority of commercial platforms use all four steps in one form or another.

The Genome Analyzer and HiSeq Systems

The Illumina Genome Analyzer and HiSeq systems are examples of the massively parallel nature of the biochemical workflow described in the four steps listed above (Bentley et al., 2008). First a sample of DNA is fragmented into segments ~400 base pairs long, and oligonucleotides of known sequence are ligated to the ends. These ligated adapters function as “handles” for each segment, allowing it to be manipulated in downstream reactions. For example, they provide a means of trapping the DNA segment in the flow cell and later releasing it. They also provide areas where primers can bind for the sequencing reaction.

Figure 2

Next, the sample is injected into a flow cell containing a lawn of oligonucleotides that will bind to the adapters on the DNA segments (Figure 2a). The concentration is carefully controlled so that only one strand is present in a given area of the chip—representing the signal isolation step. The segment is then amplified in place by means of a substrate-bound polymerase chain reaction process (called bridge PCR), until each single segment has grown into a cluster of thousands of identical copies of the sequence (Figure 2b). A single flow cell finally contains several hundred million individual clusters. Although they are now larger than the initial single strand, the clusters remain immobile and physically separated from each other, making it possible to visually distinguish them during the readout step.

The genetic sequence is then transformed into a visual signal by synthesizing a complementary strand, one base at a time, using nucleotides with four separate color tags (Figure 3a). For each cycle (during which a single base per cluster is read), DNA polymerase incorporates a single nucleotide that matches the next base on the template sequence. All four nucleotides, each carrying a different dye, are added in a mixture, but only one nucleotide is incorporated into the growing DNA strand. The incorporated nucleotide has a terminator group that blocks subsequent nucleotides from being added. The entire flow cell is then imaged, and the color of each cluster indicates which base was added for that sequence (Figure 3b). Finally, the terminator group and fluorophore are cleaved (i.e., chemically separated) from the nucleotide, and the cycle begins again.

Figure 3

This process is repeated until each cluster has been read 100 to 150 times. The segment can then be “flipped over,” and another 100 to 150 bases of sequence information can be read from the other end. Thus, the total amount of information that can be garnered from a single flow cell is directly proportional to the number of clusters and the read length per cluster, both of which represent targets for improvement as we continually increase system throughput.

Moore’s Law and Genomics

The often-quoted Moore’s law posits that the number of transistors on an integrated circuit will double every 18 to 24 months, consequently reducing the cost per transistor (Figure 4). Sequencing costs have demonstrated a similar exponential decrease over time, but at an even faster pace.

Figure 4

One factor that has made this possible is that, unlike transistors, which have a density limited to improvements in the two-dimensional efficiency (surface area) of the chip, sequencing density can increase along a “third dimension,” which is the read length. Therefore, each subsystem in Figure 1 can be improved to increase the total throughput of the system. Improvements in the chemistry have resulted in improved accuracy, longer read lengths, and shorter cycle times. In addition, by increasing both the area of the flow cell and the density of clusters, the total number of clusters has also been increased.

The engineering subsystem has doubled the throughput by using both the top and bottom of the flow cell for cluster growth. Cluster density has been increased by improving the optics and the algorithms that detect clusters. Total run time is regularly decreased by using faster chemistries, faster fluidics, faster optical scanning, and faster algorithms for image processing and base calling. On the one hand, improvements in each subsystem independently contribute to increases in throughput. On the other hand, an improvement in one system often becomes the leading driver for advances in the others.

Frontiers in Genomics

The way forward lies in improving the technology so that it can be adapted to a broader range of applications. Three ways to achieve this are: (1) increasing accuracy to enable all diagnostic applications; (2) increasing the sensitivity of the system so that it can more robustly handle lower signal-to-noise ratios; and (3) increasing throughput to drive down costs.

Improving the Accuracy of Diagnoses

Using the methods described above, one can sequence an entire human genome starting with less than one microgram of DNA, about the amount of genetic material in fewer than 150 cells. However, there are other types of samples for which even this relatively modest amount of material is difficult to come by. For example, many researchers are beginning to look at the genomics of single cells—and not just one single cell, but processing small populations individually to evaluate the hetero-geneity in the group (Kurimoto and Saitou, 2010; Taniguchi et al., 2009; Walker and Parkhill, 2008). However, because a cell contains only about 6 picograms (pg) of genomic DNA and 10 pg of RNA, the corresponding signal is many orders of magnitude weaker than normal.

Another sample type that would benefit from improved assay sensitivity is a formalin-fixed, paraffin-embedded (FFPE) sample. FFPEs are histological tissue samples that have been chemically fixed to reduce degeneration, so they can be stained and examined under a microscope. Because there are huge archives of historical samples for which detailed patient outcomes are already known, researchers can use FFPE samples as resources to improve diagnosis by tracking down the genetic markers of disease. In addition, more accurate prognoses and more effective treatments are possible by studying the correlation between disease progression and genetic type in these earlier patients.

Unfortunately, fixing, staining, and storing FFPE samples can break down the genetic material, thus making sequencing or genotyping much more difficult. Nevertheless, the ability to use these samples and perform genomic analysis on them represents an invaluable resource for tracking down genetic contributions to disease and wellness (Bibikova et al., 2004; Lewis et al., 2001; Schweiger et al., 2009; Yeakley et al., 2005).

Increasing Sensitivity to Signal-to-Noise Ratios

In some cases, the signal itself is present at normal levels, but a much higher level of background noise drowns it out. For example, there has recently been a good deal of interest in studying the microbiome of different environments, such as soil, seawater, and the human gut (Gill et al., 2006; Turnbaugh et al., 2007; Woyke et al., 2009). In these cases, the genetic diversity of the sample can make it difficult to separate the components of different organisms.

Genomics also plays a vital role in the study of cancer (Balmain et al., 2003; Jones and Baylin, 2002; Stratton et al., 2009), which is defined by its genetic instability and pathology (Lengauer et al., 1998; Loeb, 1991, 2001). However, cells taken even from the same tumor can exhibit extreme genetic heterogeneity making increased sensitivity and detection key to distinguishing the often subtle differences that lead to one outcome as opposed to another. Sequencing this kind of sample requires much deeper coverage (7,200X read redundancy per base) than the typical 30X coverage for a homogenous sample.

Reducing Costs

Increases in throughput will affect the quantity of genetic information available, and the resultant decrease in cost will open up completely new markets, representing a qualitative shift in the ways in which genomics impacts our daily lives. When the cost of sequencing an entire genome is comparable to the current cost of analyzing a single gene, the market will experience a watershed moment as a flood of new applications for sequencing become possible. Diagnosis, prognosis, pharmacogenomics, drug development, agriculture—all will be changed in a fundamental way.

When whole-genome sequencing is priced in the hundreds of dollars, it will begin to be used all around us. It will become standard to have a copy of one’s own genome. As de novo sequencing brings the genomes of an increasing variety of organisms into the world’s data-bases, the study of biology will change from a fundamentally morphological classification system to genetically based classification. In agriculture, sequencing can act as an analog of a tissue-embedded radio frequency identification device (RFID); but instead of having to tag a sample with an electronic technology, we will simply extract some genetic material from a sample and sequence it, leading back to the very farm from which it came.

Today, it typically takes 12 years to bring a new drug to market, half of which is spent on discovery and half on approval; sequencing plays a role in both stages. During drug discovery, the pathways elucidated by genomic analysis lead to targeted development and shorter discovery cycles. The approval process will be facilitated by using genetic testing to define the patient populations involved in the testing of new drugs. Genetic testing will make it possible to account for genetic variation in a trial subject group when assessing efficacy and side effects. This will also lead to an improvement in treatment after a drug has been approved, as companion genetic tests for drugs will help doctors make informed decisions about how a drug might interact with a patient’s genetic makeup.


Peripheral Systems

Achieving the improvements described above will require overcoming technical obstacles directly related to chemical, engineering, and computation modules of sequencing systems. However, some of the most significant bottlenecks to throughput are found not in sequencing itself, but in the peripheral (or ancillary) systems upstream and downstream of the process.

On the upstream side, for example, the rate at which samples are sequenced now outpaces the rate at which they can be prepared and loaded. At a conference last month, the Broad Institute described its ongoing efforts to increase the number of samples a technician can prepare each week from 12 or 15 to almost one thousand by making sample preparation faster, cheaper, and with higher throughput (Lennon, 2010).

On the downstream end of the system we are beginning to bump up against throughput limits as well. Currently the HiSeq 2000 system produces about 40 GB (represented either as gigabases or gigabytes) of sequence information per day ( hiseq_2000.ilmn). Information generated and accumulated at that rate cannot conveniently be handled by a local desktop computer. The storage, manipulation, and analysis of this information can only be done in the “cloud,” whether by local servers, dedicated off-site servers, or third parties.

Although this amount of information can easily be transferred over network hardware, as sequencing systems advance to more than 1 terabyte per day, the physical infrastructure of data networks will begin to become a limiting factor. New algorithms and standards for non-lossy compression of whole-genome data sets into files recording an individual’s genetic variations (single nucleotide polymorphisms [SNPs], copy number, etc.) from the reference genome will reduce the data burden a thousand-fold. However, even solving these kinds of bandwidth issues will not address the question of how one analyzes and uses the huge amount of information being generated. To put this in perspective, a single machine can now produce the same amount of data in one week as the Human Genome Project produced in 10 years.

Non-technical Challenges

Some of the most significant challenges facing mainstream genomics are decidedly non-technical in nature. Like many information-based fields, the pace of innovation is outstripping the rate at which legislation and regulation can keep up. Laws designed prior to the genomic revolution are being shoehorned to fit technologies and situations for which there are no clear precedents.

The regulatory landscape must be more clearly defined, so companies can move forward with confidence in leveraging innovations to improve people’s lives. Simultaneously, we must raise public awareness of genomic technologies to dispel myths and promote a realistic, more accurate understanding of the importance of genomics to the health of both individuals and society as a whole.


Genomics has emerged as an important tool for studying biological systems. Significant cost reductions in genomic sequencing have accelerated the adaptation of this technology for applications in a variety of market segments (e.g., research, forensics, consumer products, agriculture, and diagnostics). The most important factor in reducing cost is increasing throughput per day. We predict that the cost of sequencing an entire genome will drop to a few hundred dollars in the next few years as throughput rises with increasing density, longer read length, and shorter cycle time.

In a year or so, the cost of genome sequencing will be less than the cost of single-gene testing, which by itself has already brought significant cost savings to health care. We also predict that genome sequencing will soon become a standard part of medical practice and that in the next 15 years everybody in the Western world will be genome-sequenced.


Balmain, A., J. Gray, and B. Ponder. 2003. The genetics and genomics of cancer. Nature Genetics 33(Suppl): 238–244.

Bentley, D.R. 2006. Whole-genome re-sequencing. Current Opinion in Genetics and Development 16(6): 545–552.

Bentley, D.R., et al. 2008. Accurate whole human genome sequencing using reversible terminator chemistry. Nature 456(7218): 53–59.

Bibikova, M., D. Talantov, E. Chudin, J. M. Yeakley, J. Chen, D. Doucet, E. Wickham, D. Atkins, D. Barker, M. Chee, Y. Wang, and J.-B. Fan. 2004. Quantitative gene expression profiling in formalin-fixed, paraffin-embedded tissues using universal bead arrays. American Journal of Pathology 165(5): 1799–1807.

Gill, S.R., M. Pop, R.T. DeBoy, P.B. Eckburg, P.J. Turnbaugh, B.S. Samuel, J.I. Gordon, D.A. Relman, C.M.
Fraser-Liggett, and K. E. Nelson. 2006. Metagenomic analysis of the human distal gut microbiome. Science 312(5778): 1355–1359.

Jones, P.A., and S.B. Baylin. 2002. The fundamental role of epigenetic events in cancer. Nature Reviews. Genetics 3(6): 415–428.

Kurimoto, K., and M. Saitou. 2010. Single-cell cDNA microarray profiling of complex biological processes of differentiation. Current Opinion in Genetics and Development 20(5): 470–477.

Lengauer, C., K.W. Kinzler, and B. Vogelstein. 1998. Genetic instabilities in human cancers. Nature 396(6712): 643–649.

Lennon, N. 2010. Optimization of Sample Preparation for Next-Generation Sequencing. Presented at the Evolution of Next-Generation Sequencing Conference, September 27–29, 2010, Providence, Rhode Island.

Lewis, F., N.J. Maughan, V. Smith, K. Hillan, and P. Quirke. 2001. Unlocking the archive–gene expression in paraffin-embedded tissue. Journal of Pathology 195(1): 66–71.

Loeb, L.A. 1991. Mutator phenotype may be required for multistage carcinogenesis. Cancer Research 51(12): 3075–3079.

Loeb, L.A. 2001. A mutator phenotype in cancer. Cancer Research 61(8): 3230–3239.

Metzker, M.L. 2010. Sequencing technologies—the next generation. Nature Reviews. Genetics 11(1): 31–46.

Pettersson, E., J. Lundeberg, and A. Ahmadian. 2009. Generations of sequencing technologies. Genomics 93(2): 105–111.

Ronaghi, M., S. Karamohamed, B. Pettersson, M. Uhlén, and P. Nyrén. 1996. Real-time DNA sequencing using detection of pyrophosphate release. Analytical Biochemistry 242(1): 84–89.

Ronaghi, M., M. Uhlén, and P. Nyrén. 1998. A sequencing method based on real-time pyrophosphate. Science 281(5375): 363, 365.

Sanger, F., G.M. Air, B.G. Barrell, N.L. Brown, A.R. Coulson, C.A. Fiddes, C.A. Hutchison, P.M. Slocombe, and M. Smith. 1977a. Nucleotide sequence of bacteriophage phi x174 DNA. Nature 265(5596): 687–695.

Sanger, F., S. Nicklen, and A. R. Coulson. 1977b. DNA sequencing with chain-terminating inhibitors. Proceedings of the National Academy of Sciences of the United States of America 74(12): 5463–5467.

Schweiger, M.R., M. Kerick, B. Timmermann, M.W. Albrecht, T. Borodina, D. Parkhomchuk, K. Zatloukal, and H. Lehrach. 2009. Genome-wide massively parallel sequencing of formaldehyde fixed-paraffin embedded (FFPE) tumor tissues for copy-number- and mutation-analysis. PLoS ONE 4(5): e5548.

Shendure, J., and H. Ji. 2008. Next-generation DNA sequencing. Nature Biotechnology 26(10): 1135–1145.

Stratton, M.R., P.J. Campbell, and P.A. Futreal. 2009. The cancer genome. Nature 458(7239): 719–724.

Taniguchi, K., T. Kajiyama, and H. Kambara. 2009. Quantitative analysis of gene expression in a single cell by QPCR. Nature Methods 6(7): 503–506.

Turnbaugh, P.J., R.E. Ley, M. Hamady, C.M. Fraser-Liggett, R. Knight, and J.I. Gordon. 2007. The human microbiome project. Nature 449(7164): 804–810.

Walker, A., and J. Parkhill. 2008. Single-cell genomics. Nature Reviews Microbiology 6(3): 176–177.

Woyke, T., G. Xie, A. Copeland, J.M. González, C. Han, H. Kiss, J.H. Saw, P. Senin, C. Yang, S. Chatterji, J.-F. Cheng, J.A. Eisen, M.E. Sieracki, and R. Stepanauskas. 2009. Assembling the marine metagenome, one cell at a time. PLoS One 4(4): e5299.

Yeakley, J.M., M. Bibikova, E. Chudin, E. Wickham, J.B. Fan, T. Downs, J. Modder, M. Kostelec, A. Arsanjani, and J. Wang-Rodriguez. 2005. Gene expression profiling in formalin-fixed, paraffin-embedded (ffpe) benign and cancerous prostate tissues using universal bead arrays. Journal of Clinical Oncology 23(16S): 9526.


About the Author:Jeffrey Fisher is a scientist in the Applied Genomics Group, Research and Development, and Mostafa Ronaghi is senior vice president and chief technology officer at Illumina in San Diego.