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.

The Next Generation Dilemma: Large Scale Data Analysis

Next week is the AGBT genome conference in Marco Island, Florida. At the conference we will present a poster on work we have been doing with Next Gen Sequencing data analysis. In this post we present the abstract. We'll post the poster when we return from sunny Florida.

Abstract

The volumes of data that can be obtained from Next Generation DNA sequencing instruments make several new kinds of experiments possible and new questions amenable to study. The scale of subsequent analyses, however, presents a new kind of challenge. How do we get from a collection of several million short sequences of bases to genome-scale results? This process involves three stages of analysis that can be described as primary, secondary, and tertiary data analyses. At the first stage, primary data analysis, image data are converted to sequence data. In the middle stage, secondary data analysis, sequences are aligned to reference data to create application-specific data sets for each sample. In the final stage, tertiary data analysis, the data sets are compared to create experiment-specific results. Currently, the software for the primary analyses is provided by the instrument manufacturers and handled within the instrument itself, and when it comes to the tertiary analyses, many good tools already exist. However, between the primary and tertiary analyses lies a gap.

In RNA-Seq, the process of determining relative gene expression means that sequence data from multiple samples must go through the entire process of primary, secondary, and tertiary analysis. To do this work, researchers must puzzle through a diverse collection of early version algorithms that are combined into complicated workflows with 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 a solution to these challenges that closes the gaps between primary, secondary, and tertiary analysis by showing results from a complete workflow system that includes data collection, processing and analysis for RNA-seq.

And, if you cannot be in sunny Florida, join us in Memphis where we will help kick off the ABRF conference with a workshop on Next Generation DNA Sequencing. I'm kicking the workshop off with a talk entitled "From Reads to Data Sets, Why Next Gen is Not Like Sanger Sequencing."

The Experts Agree

It depends what you are trying to do. That is the take home message in Genome Technology’s (GT) trouble-shooting guide on picking assembly and alignment algorithms for Next-Gen sequence data.

In the guide, the GT team asked nine Next-Gen sequencing and bioinformatics experts to answer six questions:

1. How do you choose which alignment algorithm to use?
2. How do you optimize your alignment algorithm for both high speed and low error rate?
3. What approach do you use to handle mismatches or alignment gaps?
4. How do you choose which assembly algorithm to use?
5. Do you use mate-paired reads for de novo assembly? how?
6. What impact does the quality of raw read data have on alignment or assembly? how do your algorithms enhance this?
   

Even a quick look at the questions shows us that many factors need to be considered in setting up a Next-Gen sequencing lab. Questions 1 and 4 point out that aligning sequences is different from assembling them. Other questions address issues related to the size of the data sets being compared, the quality of the data being analyzed, the kinds of information that can be obtained, and the computational approaches being used for different problems.

What the experts said

First, they all agree that different problems require different approaches and have different requirements. In the first question about which aligner to use, the most common response was “for what application and which instrument?” Fundamentally, SOLiD data are different from Illumina GA data which are different from 454 data. While the end results may all be sequences of A's, G's, C's, and T's; the data are derived in different ways because of the platform-specific twists in collecting the data (recall “Color Space, Flow Space, Sequence Space, or Outer Space). Not only are there platform-specific methods for interpreting raw data, multiple programs have been developed for each instrument with their own strengths and weaknesses in terms of speed, sensitivity, the kinds of data they use (color, base, or flow spaces, quality values, and paired end data), and the information that is finally produced. Hence, in addition to choosing a sequencing platform you also have to think about the sequencing application, or the kind of experiment, that will be performed. In gene expression studies, for example, an RNA-Seq experiment has different requirements in terms of aligning the data and interpreting the output than an experiment with Tag Profiling.

Overall the trouble-shooting guide discussed 17 total algorithms, eight for alignment, and nine for assembly (two of which were for Sanger methods). Even this selection wasn't a comprehensive list. When other sites [1, 2] and articles [3] are included and proprietary methods are factored in, over 20 algorithms are available. So what to do? Which is best?

That depends

Yes, the choice of algorithm ultimately depends on what you are trying to do. While we can agree that there is no best solution, we also know that is not a helpful response. What is needed is a way to test the suitability of different algorithms for different kinds of experiments and to represent data in standard ways so that the features of specific algorithms can be evaluated. Also, as this is a new field, standard requirements for how data should be aligned, defining a correct alignment, and what kinds of information are the most informative in describing alignments are still emerging. Some of the early programs are helping to define these requirements.

One program we've used, at Geopsiza, for identifying requirements is MAQ, a program for sequence alignment. As noted in previous blogs [MAQ attack], MAQ is a great general purpose tool. It provides comprehensive information about the data being aligned and details about alignments. MAQ works well for many applications including RNA-Seq, Tag Profiling, ChIP-Seq, and resequencing assays focused on SNP discovery. In performance tests, MAQ is slower than some of the newer programs, one of which is being developed by MAQ’s author, but MAQ is a good model for getting the right kinds of information, formatted in a decent way. Indeed MAQ was the most cited program in the GT guide.

Let’s return to the bigger issue. That is, how can we easily compare between algorithms? For that we need a system where one can easily define a standardized dataset and reference sequence, and have a platform where a new algorithm can be added and run from a common interface. Standard reports that present features of the alignments could then be used to compare programs and parameters.

The laboratory edition of GeneSifter supports these kinds of comparisons. The distributed system architecture allows one to quickly develop control scripts to run programs and format their output in figures and tables that make comparisons possible. With this kind of system in place, the challenges move from which program to run and how to run it, to how to get the right kinds of information and best display the data. To address these issues, Geospiza’s research and development team is working on projects focused on using technologies like HDF5 to create scalable standardized data models for storing information from alignment and assembly programs. Ultimately this work will make it easy to optimize Next-Gen sequencing applications and assays and compare between assorted programs.

References
1. http://en.wikipedia.org/wiki/Sequence_alignment_software,
2. http://www.massgenomics.org/2009/01/short-read-aligners-update-at-agbt.html
3. Shendure J., Ji H., 2008. Next-generation DNA sequencing. Nat Biotechnol 26, 1135-1145.

From Reads to Data Sets, Why Next Gen is not like Sanger Sequencing

Time is running out! Be sure to register for the ABRF 2009 Next Generation DNA Sequencing Workshop to be held in Memphis TN Sat. Feb. 7, 2009. The day will include discussions from core lab directors and others about how to implement new sequencing technologies in a core lab environment. We'll consider how next generation sequencing differs from Sanger sequencing and devote much of the day learning about the practical impact of the differences.

An introduction:

Initially, DNA sequencing was performed to learn about sequences and structure of cloned genes. The first widely-used sequencing systems were based on the “Sanger” method. DNA was synthesized in the presence of chain terminating radioactive dideoxy-nucleotides (1). Mixtures of DNA fragments were separated by size using gel electrophoresis and the bases were identified and entered into a computer through manual techniques. Automated DNA sequencing instruments arrived later. These instruments made DNA sequencing thousands of times more efficient by detecting fluorescently labeled fragments and sending the information directly to a computer (2). For the first time, it became possible to sequence entire human genomes (3,4).

While highly successful, Sanger sequencing is cost prohibitive when it comes to deeper investigations of biological systems. Just as the questions investigated by Sanger sequencing shifted from single genes to entire genomes, the questions being asked by Next Generation techniques are changing as well. Questions related to transcription, for example, and promoter occupancy, can now be answered by using a massively parallel format to sample large collections of individual molecules. In effect, every RNA or DNA molecule might be sampled and counted. Not only are we looking at a “Next Generation” of DNA sequencing, we are looking a next generation of experimental techniques that answer different kinds of questions than those we asked before. These new technologies require fundamental changes in terms of experiment design and data analysis systems.


1) Sanger F., Nicklen S., Coulson A.R., 1977. “DNA sequencing with chain-terminating inhibitors.” Proc Natl Acad Sci U S A 74, 5463-5467.

2) Smith L.M., Sanders J.Z., Kaiser R.J., Hughes P., Dodd C., Connell C.R., Heiner C., Kent S.B., Hood L.E., 1986. “Fluorescence detection in automated DNA sequence analysis.” Nature 321, 674-679.

3) International Human Genome Sequencing Consortium, 2001. “Initial sequencing and analysis of the human genome.” Nature 409, 860-921.

4) Venter J.C., Adams M.D., Myers E.W., et. al. 2001. “The sequence of the human genome.” Science 291, 1304-1351.

Closing 2008

As we bring 2008 to a close, it is a good time to reflect on our progress and think about the new year ahead. Despite the world economic news, both the genomics field in general and Geospiza specifically have many positive accomplishments to show for the year.

In February, we introduced FinchLab for Next Gen Sequencing at the AGBT and ABRF conferences. At these shows, it was clear that Next Gen Sequencing was going to change the ways we think about applying DNA sequencing to interrogate a multitude of genetic and functional genomics problems. Over the course of 2008, many papers were published demonstrating the value of the massively parallel sequencing technology. MassGenomics dubbed 2008: Year of the Cancer Genome. Other blogs are following suit with articles on personal genomics and other advancements provided largely through Next Gen Sequencing.

Throughout the year, we also learned that while you can do a lot with a huge amount of data, working with the data is extremely challenging. Conference presentations and editorials in journals frequently made this point. While many of these articles focused on the data management challenge, groups acquiring the technology were also learning that the challenges go beyond data management. Comprehensive software systems are needed to manage all facets of the process, from tracking how samples are prepared for specific experiments to how the data are stored and organized, to analyzing and presenting the data according to the experiment being performed. In short, we learned that Next Gen technologies produce sequence data in different ways and require that we think about DNA sequencing in new ways.

Geospiza’s Version 3 Software Platform and GeneSifter

To address these new challenges, and expand support for existing technologies, Geospiza accomplished two significant milestones in 2008. First, we released the third version of our software platform that supports both laboratory workflows and data analysis automation. Through this system, laboratories are able to set up different interfaces to collect experimental information, assign specific workflows to experiments, track the workflow steps in the laboratory, prepare samples for data collection runs, link data back to the original samples and process data according to the needs of the experiment - without any programming. More importantly, for those who want to develop data analysis pipelines, the system provides a deployable environment that lets you add new pipelines and make them easily accessible.

The second major milestone was our acquisition of GeneSifter. GeneSifter is an award-winning microarray data analysis product. With GeneSifter , Geospiza can deliver complete end to end systems for data intensive genetic analysis applications like microarrays and Next Gen sequencing based transcription. Also, GeneSifter, like Geospiza’s other products is web-based and can be delivered as a Software as a Service (SaaS) product.

SaaS was one of the important themes for 2008. Geospiza understands well that data intensive science requires a significant IT (Information Technology) investment. Throughout 2008, we saw first-hand that groups building their own IT infrastructures were not only challenged by investing heavily in quickly depreciating hardware assets, they experienced basic infrastructure challenges like having enough space, power, and cooling systems for the equipment. If those problems were solved, there were the other challenges with getting systems set up, running, installing software, and having experienced people - and time - to maintain the infrastructure. SaaS solves those problems and off loads the burden of maintaining expensive infrastructures. For a number of groups, locally run systems are the right choice. However, it is a choice that should be carefully thought out and well-planned. In our experience, customers choosing the SaaS option were up and running quicker at a lower cost than our customers who chose to build their systems.

As we close 2008 and look forward to 2009, we want to especially thank our customers for their support and the interesting problems they have invited us to help solve.

Papers, Papers, and more Papers

Next Gen Sequencing is hot, hot, hot! You can tell by the numbers and frequency in which papers are being published.

A few posts ago, I wrote about a couple of grant proposals that we were preparing on methods to detect rare variants in cancer and improve the tools and methods to validate datasets from quantitative assays that utilize Next Gen data, like RNA-Seq, ChIP-Seq, or Other-Seq experiments. Besides the normal challenges of getting two proposals written and uploaded to the NIH, there was an additional challenge. Nearly everyday, we opened the tables-of-contents in our e-mail and found a new papers highlighting Next Gen Sequencing techniques, applications, or biological discoveries made through Next Gen techniques. To date, over 200 Next Gen publications have been produced. During the last two months alone more than 30 papers have been published. Some of these (listed in the figure below) were relevant to the proposals we were drafting.

The papers highlighted many of the themes we've touched on here, including the advantages of Next Gen sequencing and challenges with dealing with the data. As we are learning, these technologies allow us to explore the genome and genomics of systems biology at significantly higher resolutions than previously imagined. In one of the higher profile efforts, teams at the Washington University School of Medical and Genome Center compared a leukemia genome to a normal genome using cells from the same patient. This first intra-person whole genome analysis identified acquired mutations in ten genes, eight of which were new. Interestingly, the eight genes have unknown functions and might be important some day for new therapies.

Next Gen technologies are also confirming that molecular biology is more complicated than we thought. For example, the four most recent papers in Science show us that not only is 90% of the genome actively transcribed, but many genes have both sense and anti-sense RNA expressed. It is speculated that the anti-sense transcripts have a role in regulating gene expression. Also, we are seeing that nearly every gene produces alternatively spiced transcripts. The most recent papers indicate that between 92% and 97% of transcripts are alternatively spliced. My guess is that the only genes, not alternatively spliced are those lacking introns, like olfactory receptors. Although, when alternative transcription starts and alternative polyadenylation sites are considered, we may see that all genes are processed in multiple ways. It will be interesting to see how the products of alternative splicing and anti-sense transcription might interact.

This work has a number of take home messages.

1. Like astronomy, when we can see deeper we see more. Next Gen technologies are giving us the means to interrogate large collections of individual RNA or DNA molecules and speculate more on functional consequences.
2. Our limits are our imaginations. The reported experiments have used a variety of creative approaches to study genomic variation, sample expressed molecules from different strands of DNA, and measure protein DNA/RNA interaction.
   
3. Good hands do good science. As pointed out in the paper from the Sanger Center on their implementation of Next Gen sequencing, the processes are complex and technically demanding. You need to have good laboratory practices with strong informatics support for all phases (laboratory, data management, and data analysis) of the Next Gen sequencing processes.

The final point is very important and Geospiza’s lab management and data analysis products will simplify your efforts in getting Next Gen systems running to make your major investment pay off and quickly publish results.

To see how, join us for a webinar next Wednesday, Dec. 17 at 10 am PDT, for RNA Expression Analysis with Geospiza.


Click on the figure to enlarge the text.

Sneak Peak: RNA Expression Analysis with Geospiza

Next Generation DNA sequencing is revolutionizing transcriptome analysis and giving us much deeper insights into the ways in which genes are expressed. Next Wednesday, December 17th, Geospiza will host a webinar on how FinchLab and GeneSifter simplify complex data analyses to turn millions of reads into informative datasets that can yield scientific insights.

Next Gen sequencing is quickly becoming an attractive option for gene expression analysis because the vast numbers of sequences that can be obtained provide a highly sensitive way to evaluate the RNA population inside of a cell. In addition to rRNA, tRNA, and mRNA, new assays are quickly emerging to measuring non-coding RNA and multiple classes of small RNAs as well. Moreover, as we obtain deeper information, largely through Next Gen, we learn that even mRNA is more complicated than previously thought. In yeast, 85% of the genome might be transcribed and new reports indicate that 92-97% of human genes undergo alternative splicing.

Next Gen sequencing applications such as RNA-Seq, Tag Profiling, and Small RNA Analysis allow whole genome analysis of coding as well as non-coding RNA at an unprecedented level. Current technologies can generate 200 million data points in a single instrument and can completely characterize all known RNAs in a sample, and identify novel RNAs and novel splicing events for known RNAs.

Join us next Wed. Dec 17 at 10:00 am (PDT) as we provide an overview of two applications, RNA-Seq and miRNA-Seq, using examples from publicly available datasets. The presentation will include a discussion of the challenges and solutions for how sequence data from the transcriptome can be analyzed in routine ways with Geospiza’s products.

Register Now!

Further reading

Castle J.C., Zhang C., Shah J.K., Kulkarni A.V., Kalsotra A., Cooper T.A., Johnson J.M., 2008. Expression of 24,426 human alternative splicing events and predicted cis regulation in 48 tissues and cell lines. Nat Genet 40, 1416-1425.

David L., Huber W., Granovskaia M., Toedling J., Palm C.J., Bofkin L., Jones T., Davis R.W., Steinmetz L.M., 2006. A high-resolution map of transcription in the yeast genome. Proc Natl Acad Sci U S A 103, 5320-5325.

Mortazavi A., Williams B.A., McCue K., Schaeffer L., Wold B., 2008. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods 5, 621-628.

Seila A.C., Calabrese J.M., Levine S.S., Yeo G.W., Rahl P.B., Flynn R.A., Young R.A., Sharp P.A., 2008. Divergent Transcription from Active Promoters. Science.

Wang E.T., Sandberg R., Luo S., Khrebtukova I., Zhang L., Mayr C., Kingsmore S.F., Schroth G.P., Burge C.B., 2008. Alternative isoform regulation in human tissue transcriptomes. Nature 456, 470-476.

Wold B., Myers R.M., 2008. Sequence census methods for functional genomics. Nat Methods 5, 19-21.

Zamore P.D., Haley B., 2005. Ribo-gnome: the big world of small RNAs. Science 309, 1519-1524.

ABRF 2009 is just around the corner

Karen Jonscher said it best, “Reminder - register now for the [ABRF] Satellite Educational Workshops!”

In her email to the ABRF email forum, Karen reminded us that the ABRF Education Committee is excited to present five new Satellite Educational Workshops at ABRF 2009 in Memphis Tennessee on genomics and proteomics technologies. Of course, I think the most exciting topic is Next Generation DNA Sequencing, subtitled "Massively Parallel Sequencers in the Core Facility: Applications and Computation."

The workshop will have a full day of presentations and discussions. The first part, “Platforms and Applications” will focus on the laboratory perspective of running these systems. We will have three presentations on what is it like to prepare and run samples as well as troubleshoot equipment and how to review quality control data.

The second part, “Computation and Analysis” will tackle heady issues of what to do with the massive amounts of data being produced. Presenters will provide information ranging from an overview of data analysis, to data management infrastructures, to discovery based analysis for SNP and biomarker discovery.

During the day there will be time to meet and speak with the presenters as well as representatives from sponsoring companies. It will be good.

Both general information about all of the workshops and specific information about the next generation sequencing workshop are posted at the ABRF site. Don't wait, you might miss a great opportunity.

Hope to see you in Memphis.