Three Themes from ABRF and AGBT Part I: The Laboratory Challenge

It's been an exciting week on the road at the AGBT and ABRF conferences. From the many presentations and discussions it is clear that the current and future next generation DNA sequencing (NGS) technologies are changing the way we think about genomics and molecular biology. It is also clear that successfully using these technologies impacts research and core laboratories in three significant areas:

1. The Laboratory: Running successful experiments requires careful attention to detail.
2. Bioinformatics: Every presentation called out bioinformatics as a major bottleneck. The data are hard to work with and different NGS experiments require different specialized bioinformatics workflows (pipelines).
3. Information Technology (IT): The bioinformatics bottleneck is exacerbated by IT issues involving data storage, computation, and data transfer bandwidth.

We kicked off ABRF by participating in the Next Gen DNA Sequencing workshop on Saturday (Feb. 7). It was extremely well attended with presentations on experiences in setting up labs for Next Gen sequencing, preparing DNA libraries for sequencing, and dealing with the IT and bioinformatics.

I had the opportunity to provide the “overview” talk. In that presentation “From Reads to Datasets, Why Next Gen is not Sanger Sequencing,” I focused on the kinds of things you can do with NGS technology, its power, and the high level issues that groups are facing today when implementing these systems. I also introduced one of our research projects on developing scalable infrastructures using HDF5 for Next Gen bioinformatics and high-performing, dynamic, software interfaces. Three themes resufraced again and again throughout the day:  one must pay attention to laboratory details, bioinformatics is a bottleneck, and don't underestimate the impact of NGS systems on IT.

In this post, I'll discuss the laboratory details and visit the other themes in posts to come.

Laboratory Management

To better understand the impact of NGS on the lab, we can compare it to Sanger sequencing. In the table below, different categories ranging from the kinds of samples, to their preparation, to the data, are considered to show how NGS differs from Sanger sequencing. Sequencing samples for example are very different between Sanger and NGS. In Sanger sequencing, one typically works with clones or PCR amplicons. Each sample (clone or PCR product) produces a single sequence read. Overall, sequencing systems are robust, so the biggest challenges to labs has been tracking the samples as they move from tube to plate or between wells within plates.

In contrast, NGS experiments involve sequencing DNA libraries and each sample produces millions of reads. Presently, only a few samples are sequenced at a time so the sample tracking issues, when compared to Sanger, are greatly reduced. Indeed, one of the significant advantages and cost savings of NGS is to eliminate the need for cloning or PCR amplification in preparing templates to sequence.

Directly sequencing DNA libraries is a key ability and a major factor that makes NGS so powerful. It also directly contributes to the bioinformatics complexity (more on that in the next post). Each one of the millions of reads that are produced from the sample corresponds to an individual molecule, present in the DNA library. Thus, the overall quality of the data and the things you can learn are a direct function of the library.



Producing good libraries requires that you have a good handle on many factors. To begin, you will need to track RNA and DNA concentrations, at different steps of the process. You also need to know the “quality” of the molecules in the sample. For example, RNA assays will give the best results when RNA is carefully prepared and free of RNAses. In RNA-Seq, the best results are obtained when the RNA is fragmented prior to cDNA synthesis. To understand the quality of the starting RNA, fragmentation, and cDNA synthesis steps, tools like agarose gels or Bioanalyzer traces are used to evaluate fragment lengths and determine overall sample quality. Other assays and sequencing projects have similar processes. Throughout both conferences, it was stressed that regardless of whether you are sequencing genomes, small RNAs, performing an RNA-Seq, or other “tag and count” kinds of experiments, you need to pay attention to the details of the process. Tools like the NanoDrop, or QPCR procedure need to be routinely used to measure RNA or DNA concentration. Tools like gels and the Bioanalyzer are used to measure sample quality. And, in many cases both kinds of tools are used.

Through many conversations, it became clear that Bioanalyzer images, Nanodrop reports, and other lab data quickly accumulate during these kinds of experiments. While an NGS experiment is in progress, these data are pretty accessible and the links between data quality and the collected data are easy to see. It only takes a few weeks, however,  for these lab data to disperse.  They find their way into paper notebooks, or unorganized folders on multiple computers. When the results from one sample need to be compared to another,  a new problem appears. It becomes harder and harder to find the lab data that correspond to each sample.

To summarize, NGS technology makes it possible to interrogate large ensembles of individual RNA or DNA molecules. Different questions can be asked by preparing the ensembles (libraries) in different ways involving complex procedures. To ensure that the resulting data are useful, the libraries need to be of high and known quality. Quality is measured with multiple tools at different points of the process to produce multiple forms of laboratory data. Traditional methods such as laboratory notebooks, files on computers, and post-it notes however, make these data hard to find when the time comes to compare results between samples.

Fortunately, the GeneSifter Lab Edition solves these challenges. The Lab Edition of Geospiza’s software platform provides a comprehensive laboratory information management system (LIMS) for NGS and other kinds of genetic analysis assays, experiments, and projects. Using web-based interfaces, laboratories can define protocols (laboratory workflows) with any number of steps. Steps may be ordered and required to ensure that procedures are correctly followed. Within each step, the laboratory can define and collect different kinds of custom data (Nanodrop values, Bioanalyzer traces, gel images, ...). Laboratories using the GeneSifter Lab Edition can produce more reliable information because they can track the details of their library preparation and link key laboratory data to sequencing results.