Next Gen DNA Sequencing Is Not Sequencing DNA

In the old days, we used DNA sequencing primarily to learn about the sequence and structure of a cloned gene. As the technology and throughput improved, DNA sequencing became a tool for investigating entire genomes. Today, with the exception of de novo sequencing, Next Gen sequencing has changed the way we use DNA sequences. We're no longer looking for new DNA sequences. We're using Next Gen technologies to perform quantitative assays with DNA sequences as the data points. This is a different way of thinking about the data and it impacts how we think about our experiments, data analysis, and IT systems.

In de novo sequencing, the DNA sequence of a new genome, or genes from the environment is elucidated. De novo sequencing ventures into the unknown. Each new genome brings new challenges with respect to interspersed repeats, large segmented gene duplications, polyploidy and interchromosomal variation. The high redundancy samples obtained from Next Gen technology lower the cost and speed this process because less time is required for getting additional data to fill in gaps and finish the work.

The other ultra high throughput DNA sequencing applications, on the other hand, focus on collecting sequences from DNA or RNA molecules for which we already have genomic data. Generally called "resequencing," these applications involve collecting and aligning sequence reads to genomic reference data. Experimental information is obtained by tabulating the frequency, positional information, and variation of the reads in the alignments. Data tables from samples that differ by experimental treatment, environment, or in populations, are compared in different ways to make discoveries and draw conclusions.

DNA sequences are information rich data points

EST (expressed sequence tag) sequencing was one of the first applications to use sequence data in a quantitative way. In EST applications, mRNA from cells was isolated, converted to cDNA, cloned, and sequenced. The data from an EST library provided both new and quantitative information. Because each read came from a single molecule of mRNA, a set of ESTs could be assembled and counted to learn about gene expression. The composition and number of distinct mRNAs from different kinds of tissues could be compared and used to identify genes that were expressed at different time points during development, in different tissues, and in different disease states, such as cancer. The term "tag" was invented to indicate that ESTs could also be used to identify the genomic location of mRNA molecules. Although the information from EST libraries was been informative, lower cost methods such as microarray hybridization and real time-PCR assays replaced EST sequencing over time, as more genomic information became available.

Another quantitative use of sequencing has been to assess allele frequency and identify new variants. These assays are commonly known as "resequencing" since they involve sequencing a known region of genomic DNA in a large number of individuals. Since the regions of DNA under investigation are often related to health or disease, the NIH has proposed that these assays be called "Medical Sequencing." The suggested change also serves to avoid giving the public the impression that resequencing is being carried out to correct mistakes.

Unlike many assay systems (hybridization, enzyme activity, protein binding ...) where an event or complex interaction is measured and described by a single data value, a quantitative assay based on DNA sequences yields a greater variety of information. In a technique analogous to using an EST library, an RNA library can be sequenced, and the expression of many genes can be measured at once, by counting the number of samples that align to a given position or reference. If the library is prepared from DNA, a count of the aligned reads could measure the copy number of a gene. The composition of the read data itself can be informative. Mismatches in aligned reads can help discern alleles of a gene, or members of a gene family. In a variation assay, reads can both assess the frequency of a SNP and discover new variation. DNA sequences could be used in quantitative assays to some extent with Sanger sequencing, but the cost and labor requirements prevented wide spread adoption.

Next Gen adds a global perspective and new challenges

The power of Next Gen experiments comes from sequencing DNA libraries in a massively parallel fashion. Traditionally, a DNA library was used to clone genes. The library was prepared by isolating and fragmenting genomic DNA, ligating the pieces to a plasmid vector, transforming bacteria with the ligation products, and growing colonies of bacteria on plates with antibiotics. The plasmid vector would allow a transformed bacterial cell to grow in the presence of an antibiotic so that transformed cells could be separated from other cells. The transformed cells would then be screened for the presence of a DNA insert or gene of interest through additional selection, colorimetric assay (e.g. blue / white), or blotting. Over time, these basic procedures were refined and scaled up in factory style production to enable high throughput shotgun sequencing and EST sequencing. A significant effort and cost in Sanger sequencing came from the work needed to prepare and track large numbers of clones, or PCR-products, for data linking and later retrieval to close gaps or confirm results.

In Next Gen sequencing, DNA libraries are prepared, but the DNA is not cloned. Instead other techniques are used to "separate," amplify, and sequence individual molecules. The molecules are then sequenced all at once, in parallel, to yield large global data sets in which each read represents a sequence from an individual molecule. The frequency of occurrence of a read in the population of reads can now be used to measure the concentration of individual DNA molecules. Sequencing DNA libraries in this fashion significantly lowers costs, and makes previously cost prohibitive experiments possible. It also changes how we need to think about and perform our experiments.

The first change is that preparing the DNA library is the experiment. Tag profiling, RNA-seq, small RNA, ChIP-seq, DNAse hypersensitivity, methylation, and other assays all have specific ways in which DNA libraries are prepared. Starting materials and fragmentation methods define the experiment and how the resulting datasets will be analyzed and interpreted. The second change is that large numbers of clones no longer need to be prepared, tracked, and stored. This reduces the number of people needed to process samples, and reduces the need for robotics, large number of thermocyclers, and other laboratory equipment. Work that used to require a factory setting can now be done in a single laboratory, or mailroom if you believe the ads.

Attention to details counts

Even though Next Gen sequencing gives us the technical capabilities to ask detailed and quantitative questions about gene structure and expression, successful experiments demand that we pay close attention to the details. Obtaining data that are free of confounding artifacts and accurately represent the molecules in a sample, demands good technique and a focus on detail. DNA libraries no longer involve cloning, but their preparation does require multiple steps performed over multiple days. During this process, different kinds of data ranging from gel images to discrete data values, may be collected and used later for trouble shooting. Tracking the experimental details requires that a system be in place that can be configured to collect information from any number and kind of process. The system also needs to be able to link data to the samples, and convert the information from millions of sequence data points to tables, graphics and other representations that match the context of the experiment and give a global view of how things are working. FinchLab is that kind of system.