Three Themes from AGBT and ABRF Part III: The IT Problem

The power of Next Generation DNA Sequencing (NGS) technology come from the fact that a massive amount of data, sampling millions of individual molecules, is collected in a massively parallel format. This power also limits the potential wide-spread adoption of the technology because of the IT (Information Technology) challenges that result from the massive amount of data created with each sequencer run.

IT challenges form the third technical theme from the AGBT and ABRF conferences. The previous two posts underscored the need for good laboratory practices and rich bioinformatics support to make NGS experiments successful. This post discusses the experiences communicated by the early adopters of NGS technology with respect to the computing infrastructure.

Surprises

Throughout the literature and NGS presentations, the data management issues created by NGS play a central role. Recent editorials in Nature Methods [1] and Nature Biotechnology [2] speak to the problem and express researchers' frustrations in dealing with the lack of IT infrastructures. At the ABRF workshop, we had two presentations specifically focused on the IT challenges, describing two different experiences.

In the first case, the group implementing NGS had a number of surprises after the NGS system was installed and running. They learned that these systems not only require a lot of storage and computing support, they also use up a lot of bandwidth when data are transferred. The bandwidth problem led to the need for a revised network architecture to isolate the NGS data flow from other network activity.

This talk brought similar surprises to mind. In other labs, NGS “surprises” have led to groups needing to upgrade server rooms by installing backup power, air conditioning, and other equipment. Of course these surprises are manageable if you have an IT group and a server room in the first place. In some cases, groups start with even less and find that the IT costs makes the NGS endeavor very expensive. Even with support and space the IT costs for bringing in NGS can quickly grow into six figures (above $100,000) for infrastructure alone.

The second presentation was given by a group who was well prepared for NGS. Their university had made a previous commitment to building an IT infrastructure to support data intensive genomics research, so adding NGS was a step up in their view. Their experience allowed them to develop a strong implementation plan that called for a number of systems upgrades that included upgrading network hardware. While total costs were less than the six figure surprises others experienced, they did spend many tens of thousands of dollars on new file servers, CPUs, network switches, and server room upgrades.

The conclusion from both of the presentations was that if you are going to set up an NGS infrastructure three things are important: planning, planning, planning. Also, institutional support is critically important since renovations and new building may need to ramp up too. Personnel with network, systems administration, and unix experience are also essential. Finally, as the second speaker put it, you need to encourage researchers to invest in the infrastructure. If they are not involved in the process and contributing time and money, the endeavor can quickly fail.

These talks bring me to my favorite marketing slogan where one of Illumina’s customers put an NGS instrument in their mail room. Whenever I hear that, or see the ad, it makes me think, “yes, you can turn a mail room into a genome center, but where will you put the data center?”

There is a solution

For those thinking about NGS technology, or running an NGS experiment where the samples are submitted to a lab, and the data returned, even contemplating the IT requirements can be discouraging. But, it does not have to be this way. Over the past ten years, an immense infrastructure of data centers has emerged . Today, there are many options and price points available for storage, computing, and backup systems. Groups can save significant time and money using on-line services because costs scale with need. Moreover, on-line services eliminate the need for dedicated systems and data administrators putting more money in the budget for experiments. You have a choice. Jump in and do some interesting science or work hard to have your campus facilities remodeled.

Geospiza is taking advantage of the Internet’s infrastructure to offer our clients cost effective ways to get NGS running in their lab. GeneSifter Laboratory Edition can be delivered through a SaaS (Software as a Service) model to get labs up and running quickly. Just sign up, get access, and you are ready to go. GeneSifter Analysis Edition solves the IT problem for research groups who get their sequencing done through core labs or other service providers. In these cases, you upload you data and with a few clicks, process your data and analyze the results. Because the infrastructure is built, overall costs for IT and bioinformatics are much lower, and you do not have to experience a remodeling project.

References
1. 2008. Byte-ing off more than you can chew. Nat Methods 5, 577.
2. 2008. Prepare for the deluge. Nat Biotechnol 26, 1099.

Three Themes from AGBT and ABRF Part II: The Bioinformatics Bottleneck

In my last post, I summarized the presentations and conversations regarding Next Gen Sequencing (NGS) challenges in terms of three themes: You have to pay attention to details in the laboratory, bioinformatics is a bottleneck, and the IT burden is significant. In that post, I discussed the issues related to the laboratory and how GeneSifter Lab Edition overcomes those challenges.

This post tackles the second theme: the bioinformatics bottleneck.

In the Sanger days, bioinformatics was really a challenge for only the highest throughput facilities like genome centers. In these labs, streamlined workflows (pipelines) were developed for the different kinds of sequencing (genomes, ESTs[expressed sequence tags, 1], SAGE [serial analysis of gene expression, 2]). Because, Sanger sequencing was high cost and low throughput, compared to NGS, the cost of developing the bioinformatics pipelines was low relative to the cost of collecting the data. Thus, large-scale projects such as whole genome shotgun sequencing, ESTs, or resequencing studies, could be supported by a handful of pipelines that changed infrequently. In addition, small-scale Sanger projects could be handled well by desktop software and services like NCBI BLAST.

NGS breaks the Sanger paradigm. A single NGS instrument has the same throughput as an entire warehouse of Sanger instruments. To illustrate this point in a striking way, we can look at dbEST - NCBI’s database of ESTs. Today, there are approximately 59 million reads in this database, representing the total accumulation of sequencing projects over a 10 year period. Considering that one run of an Illumina GA or SOLiD can produce between 80 and 180 million reads in week or two we can now, in a single week, produce up to three times more ESTs than we have seen deposited over the past 10 years. These numbers also dwarf the amount of data collected from other gene expression analysis systems like microarrays and sequencing techniques like SAGE.


The emergence of the bioinformatics bottleneck

The bioinformatics bottleneck is related to the fact that NGS platforms are general purpose; they collect sequence data. That’s it. Because they collect a lot of data very quickly, we can use sequences as data points for many different kinds of measurements. When we think this way, an extremely wide range of experiments can be conceived.

From sequencing complete genomes, to sampling genes in the environment, to measuring mutations in cancer, to understanding epigenomics, to measuring gene expression and the transcriptome, NGS applications are proliferating at a rapid pace. However, each experiment requires a specialized bioinformatics pipeline and the algorithms used within a bioinformatics pipelines must be tuned for the data produced from the different sequencing platforms and questions being asked. When these considerations are combined with other issues like what reference data to use for sequence comparisons the number of bioinformatics pipelines can grow in a combinatorial fashion.

The early recommendation is that each lab wanting to do NGS work needs to have a dedicated bioinformatics professional. In more than one talk, presenters even quantified bioinformatics support in terms of FTEs (full time equivalents) per instrument. Bioinformatics is needed in both the sequencing laboratory, to develop and maintain quality control pipelines, and in the research environment, to process (align) the data, mine the output for interesting features, and perform comparative analyses between datasets.

But this won’t work

It is clear that bioinformatics is critical to understanding the data being produced. However, the current recommendation that any group planning NGS experiments should also have a dedicated bioinformatician is impractical for several reasons.

First, the model of a bioinformatician for every lab is simply not scalable. Fundamentally, there are not enough people that understand the science, programming, statistics, and other resources such as different forms of reference data, algorithms, and data types needed to make sense of NGS data. We see plenty of evidence, in the literature and presentations, that there are many outstanding people doing this work and contributing to the community, the problem is that they already have jobs!

Even if we consider that the above model is workable, hiring people takes significant time, is expensive, and ongoing costs are going to be high. These time and cost investments only become reasonable when a significant number of experiments are planned. One or two instruments will produce between 25 and 50 runs worth of data per year. If you calculate instrument costs, reagents, salary, and overhead costs, you are quickly into many thousands of dollars per sample. Indeed, a theme expressed in the bioinformatics bottleneck is that bioinformatics is becoming the single largest ongoing cost of NGS. Add in the IT computer support (next post) and you better have a plan for running a lot more than 50 runs per year. Remember the first issue - good bioinformaticians with NGS analysis experience have jobs.

If you have access to bioinformatics support, or can hire an individual, that person will quickly become overwhelmed with work. The biggest reason is that the software infrastructures needed to quickly develop new pipelines, automate them, and deliver data in ways that can be consumed by non-programming scientists are typically lacking. The result is that scientific programming efforts generally turn into lengthy software development projects because without an infrastructure, the numbers and kinds of experiments quickly grow past beyond the capacity of a single individual.

So, What can be done?

Geospiza solves the bioinformatics challenge in multiple ways. GeneSifter Lab and Analysis editions provide a platform that delivers the complete infrastructures needed to deploy NGS data processing pipelines and deliver results through web-based interfaces. These systems include pipelines for many of the common NGS applications such as transcription analysis, small RNA detection, ChIP-Seq and other assays. The system architecture and accompanying API creates a framework to quickly add new pipelines and make the results available to biologists running the experiments.

For those with access to bioinformatics help, GeneSifter will make your team more productive because developers will be freed of the burden of having to create the automation and delivery infrastructure, enabling them to focus on new scientific programming problems. For those without access to such resources, we have many pipelines ready to go. Moreover, because we have a platform and the infrastructure already built, as well as deep bioinformatics experience, we can create and deliver new analysis pipelines quickly. Finally, our product development roadmap is well-aligned with the most common NGS assays which means we you can probably do your bioinformatics analysis today!

References: 

1. Adams M.D., Soares M.B., Kerlavage A.R., Fields C., Venter J.C., 1993. Rapid cDNA sequencing (expressed sequence tags) from a directionally cloned human infant brain cDNA library. Nat Genet 4, 373-380.

2. Velculescu V.E., Zhang L., Vogelstein B., Kinzler K.W., 1995. Serial analysis of gene expression. Science 270, 484-487.

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.

Next Gen Laboratory Software Systems for Core Facilities

Do you have a core lab? Considering adding Next Generation DNA sequencing capacity to your lab? Then you will be interested in visiting our both and checking out our poster at the annual Association for Biomolecular Research Facilities (ABRF) meeting next week in Memphis TN. We'll be at booth 408, and presenting poster number V27-S1.

Poster Abstract

Throughout the past year, as next generation sequencing (NGS) technologies have emerged in the marketplace, their promise of what can be done with massive amounts of sequence data has been tempered with the reality that performing experiments and working with the data is extremely challenging. As core labs contemplate acquiring NGS technologies, they must consider how the new technologies will affect their current and future operations. The old model of collecting and delivering data is likely to change to one where the core lab becomes an active participant in advising and helping clients set up experiments and analyze the data. However, while many labs want to utilize NGS, few have the Information Technology (IT) infrastructures and procedures in place to successfully make use of these systems.

In the case of gene expression, NGS technologies are being evaluated as complementary or replacement technologies for microarrays. Assays like RNA-Seq and tag profiling, that focus on measuring relative gene expression, require that researchers and core labs must puzzle through a diverse collection of early version algorithms that are combined into complicated workflows with many steps producing complicated file formats. Command line tools such as MAQ, SOAP, MapReads, and BWA, have specialized requirements for formatted input and output and leave researchers with large data files that still require additional processing and formatting for tertiary analyses. Moreover, once reads are aligned, datasets need to be visualized and further refined for additional comparative analysis. We present solutions to these challenges by showing results from a complete workflow system that includes data collection, processing, and analysis for RNA-seq suited for the core laboratory.

In the poster we'll walk through the laboratory and data analysis issues one needs to think about to perform a two cell expression comparison with RNA-Seq. Below is a snippet from the poster. I'll post the full presentation when I return.