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.

Introducing GeneSifter

Today, Geospiza announced the acquisition of the award-winning GeneSifter microarray data analysis product. This news has significant implications for Geospiza’s current and new customers. With GeneSifter and FinchLab, Geospiza will deliver complete end to end systems for data intensive genetic analysis applications like Next Gen sequencing and microarrays.

As an example, let's consider transcriptomics or gene expression. One goal of such experiments is to compare the relative gene expression between cells to see how different genes are up or down regulated as the cells change over time or respond to some sort of treatment.

The general process, whether it involves microarrays or Next Gen sequencing, is to measure the number of RNA molecules for a given gene, either over a period of time or after different treatments. Laboratory processes create the molecules to assay, the molecules are measured, data are collected, and we process the data to produce tables of information. These tables are then compared with one another to identify genes that are differentially expressed. With the gene expression results in hand, one can delve deeper by utilizing other databases like Entrez Gene or pathway sites to learn about gene function and gain insights.

From a systems perspective, you need a LIMS to define sample information and keep track of workflow steps and the data generated at the bench. You will also need to track which samples are on a slide, or lane, or well when the data are collected. You will need to store and organize the data by sample. Then, you will need to analyze the data through multiple programs in a pipelined process (filter, align ...) to produce information, like gene lists, that can be compared for each sample. You may want to review this information to see that your experiments are on track and then, if they are, you will want to compare the gene lists from different experiments to tell a story.

FinchLab, combined with Geospiza’s hosted Software as a Service (SaaS) delivery, solves challenges related to IT, LIMS, and the core data analysis. GeneSifter completes the process by delivering a software solution that lets you compare your gene lists. GeneSifter provides information about the relative gene expression between samples and links gene information to key public resources to uncover additional details.

It's an exciting time for those in the genetic analysis and genomics fields. New high throughput data collection technologies are giving scientists the ability to interrogate systems and understand biology in a whole new way. As we come to the end of 2008 and think about 2009, Geospiza is excited to think about how we will integrate and extend our products to further develop end to end systems for a wide variety of genomics applications that target basic and clinical research to help us improve human health and well being.

Next Gen-Omics

Advances in Next Gen technologies have led to a number of significant papers in recent months, highlighting their potential to advance our understanding of cancer and human genetics (1-3). These and the other 100's of papers demonstrate the value of Next Gen sequencing. The work completed thus far has been significant, but much more needs to be done to make these new technologies useful for a broad range of applications. Experiments will get harder.

While much of the discussion in the press focuses on rapidly sequencing human genomes for low cost as part of the grail of personalized genomics (4), a vast amount of research must be performed at the systems level to fully understand the relationship between biochemical processes in a cell and how the instructions for the processes are encoded in the genome. Systems biology and a plethora of "omics" have emerged to measure multiple aspects of cell biology as DNA is transcribed into RNA and RNA translated into protein and proteins interact with molecules to carry out biochemistry.

As noted in the last post we are developing proposals to further advance the state-of-the-art in working with Next Gen data sets. In one of those proposals, Geospiza will develop novel approaches to work with data from applications of Next Gen sequencing technologies that are being developed study the omics of DNA transcription and gene expression.

Toward furthering our understanding of gene expression, Next Gen DNA sequencing is being used to perform quantitative assays where DNA sequences are used as highly informative data points. In these assays, large datasets of sequence reads are collected in a massively parallel format. Reads are aligned to reference data to obtain quantitative information by tabulating the frequency, positional information, and variation from 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 experimental conclusions. Recall the three phases of data analysis.

However, to be useful these data sets need to come from experiments that measure what we think they should measure. The data must be high quality and free of artifacts. In order to compare quantitative information between samples, the data sets must be refined and normalized so that biases introduced through sample processing are accounted for. Thus, a fundamental challenge to performing these kinds of experiments is working with the data sets that are produced. In this regard numerous challenges exist.

The obvious ones relating to data storage and bioinformatics are being identified in both the press and scientific literature (5,6). Other, less published, issues include a lack of:

• standard methods and controls to verify datasets in the context of their experiments,
  
• standardized ways to describe experimental information and
  
• standardized quality metrics to compare measurements between experiments.

Moreover data visualization tools and other user interfaces, if available, are primitive and significantly slow that pace at which a researcher can work with the data. Finally, information technology (IT) infrastructures that can integrate the system parts dealing with sample tracking, experimental data entry, data management, data processing and result presentation are incomplete.

We will tackle the above challenges by working with the community to develop new data analysis methods that can run independently and within Geospiza's FinchLab. FinchLab handles the details of setting up a lab, managing its users, storing and processing data, and making data and reports available to end users through web-based interfaces. The laboratory workflow system and flexible order interfaces provide the centralized tools needed to track samples, their metadata, and experimental information. Geospiza's hosted (Software as a Service [SaaS]) delivery models remove additional IT barriers.

FinchLab's data management and analysis server make the system scalable through a distributed architecture. The current implementation of the analysis server creates a complete platform to rapidly prototype new data analysis workflows and will allow us to quickly devise and execute feasibility tests, experiment with new data representations, and iteratively develop the needed data models to integrate results with experimental details.

References

1. Ley, T. J., Mardis, E. R., Ding, L., Fulton, B., et al. DNA sequencing of a cytogenetically normal acute myeloid leukaemia genome. Nature 456, 66-72 (2008).

2. Wang, J., Wang, W., Li, R., Li, Y., et al. The diploid genome sequence of an Asian individual. Nature 456, 60-65 (2008).

3. Bentley, D. R., Balasubramanian, S., Swerdlow, H. P., Smith, G. P., et al. Accurate whole human genome sequencing using reversible terminator chemistry. Nature 456, 53-59 (2008).

4. My genome. So what? Nature 456, 1 (2008).

5. Prepare for the deluge. Nature Biotechnology 26, 1099 (2008).

6. Byte-ing off more than you can chew. Nature Methods 5, 577 (2008).

November is about elections and grants

Halloween is over, and today is election day, go vote! Then, come back and read about what we planning for our next steps in Next Gen sequencing.

This month we are preparing SBIR proposals to target some of the real challenges researchers face when working with next-generation (Next Gen) sequence data.

The first will deal with issues related to detecting rare variants in cancer. With our collaborators we plan to develop control samples to detect different kinds of mutations and use the samples and data produced to develop new software tools and interfaces for measuring results. While the work will focus on cancer research, detecting rare variants in large datasets is a common problem with many applications.

The second proposal will deal with improving tools and methods to validate datasets from quantitative assays that utilize Next Gen data. When you run an RNA-Seq, ChIP-Seq, or Other-Seq experiment, where you collect numerous molecular tags from RNA or DNA in your research samples, how do you know that your data represent interesting biology and are free of artifacts? Pulling the relevent features, that distinguish biological reality from experimental artifact, out of datasets comprised of millions and millions of reads can be a real problem.

The above projects will leverge Geospiza's, Geospiza customers, and community experience to develop novel features and integrated resources that will be added to FinchLab, new products, and open-source contributions to make your work easier. If you are interested in learning more about these projects and (or) participating in working with us to takle the next-generation of hard problems contact us.

Journal Club: Focus on Next Gen Sequencing

Yesterday I received my issue of Nature Biotechnology. This month it features Next-Generation (Next Gen) Sequencing. One editorial, one profile, three news features, a commentary, two perspectives, and two reviews discuss the origins, trials, tribulations and what’s coming next in Next Gen. For now, I'll focus on the editorial.

Bioinformatics is a big big issue

“If the data problem is not addressed, ABI’s SOLiD, 454’s GS FLX, Illumina’s GAII or any of the other deep sequencing platforms will be destined to sit in their air-conditioned rooms like a Stradivarius without a bow” was the closing statement in the lead editorial “Prepare for the deluge.”

Reminds me of something I said a few months back.

In the editorial, Nature Biotechnology (NBT) makes a number of important points starting with how the launch of the Roche/454 pyrosequencer in 2005 could generate as much data as more than 50 ABI capillary sequencers. Since that launch, we have seen new instruments emerge that are producing ever increasing amounts of data by orders of magnitude. Or as NBT put it “The overwhelming amounts of data being produced are the equivalent of taking a drink from a fire hose.”

It's like they read our web site (we ran the image below at the beginning of the year).


The volumes of data and new ways in which it must be worked with are creating many challenges. To begin, there is the conundrum of what to keep; do you keep raw images and processed reads? Or do you just keep the reads? If you keep raw images, the costs are significant. The cost of storing all that information must be considered in the context of the likelihood of whether you will ever need to go back to these data. We call this the data life cycle.

From raw images, the next challenge is the computational infrastructure needed to process reads and obtain meaningful information. This is a complex process that involves many steps and high performance computers. NBT made the accurate and important point that the instrument manufacturer only provide the software to analyze what comes off of the machine for common applications. A great deal of bioinformatics support is needed for downstream analysis once the initial data alignments or assemblies are completed. Also, standards for comparing data between instrument platforms are lacking. This makes it difficult to compare results from different instruments.

While more is needed in terms of bioinformatics support, being able to get tools for alignment and assembly is a good starting point and NBT lauded ABI’s SOLiD community program as a step in the right direction. This kind of approach is also needed by the other instrument vendors. Presently Illumina and Roche include their tools with an instrument purchase. This is fine for the laboratory, but it makes a hard problem harder for any researchers who might be getting data sets from different labs. This could lead to threads of frustration.

As the article continued, the "overwhelmed" scale increased to dire.

NBT stated:

“What all of this means is that for the foreseeable future, next-generation sequencing platforms may remain out of the hands of labs lacking the deep pockets needed for bioinformatics support.”

They also added,

“Thus, if the next-generation platforms are to truly democratize sequencing—bringing genomics out of the main sequencing centers and into the laboratories of single investigators or small academic consortia—much more effort needs to be expended in developing cost-effective software and data management solutions.”

NBT offered some solutions, including getting the instrument vendors to develop community based solutions, and encouraging the grant funding organizations to fund bioinformatics as much as they fund sequencing.

Is Next Gen for everyone?

The NBT editors made a lot of great points, but we do not see the world in as dire terms as they do. Yes, a great challenge to Next Gen and getting up and running with this equipment includes preparing for the informatics challenges that await. Next Gen is not Sanger. You cannot look at every read to figure out what your data mean and you will need a serious computational infrastructure to store, organize and work with the data. Also, not mentioned in the article, but incredibly important, you will need a laboratory information management system to organize your experimental information and track the many steps needed to prepare good DNA libraries for sequencing.

And, there are solutions.

Geospiza’s FinchLab combined with our Software as a Service (SaaS) delivery, provides immediate access to the necessary software and hardware infrastructure to run these new instruments.

FinchLab delivers the software infrastructure to support laboratory workflows for all the platforms, links the resulting data to samples, and - through a growing list of data analysis pipelines and visualization interfaces - provides the necessary bioinformatics for a wide range of sequencing applications. Further, our bioinformatics approach is community-based. We are working with the best tools as they emerge and are collaborating with multiple groups to advance additional research and development.

SaaS delivers the computing infrastructure on demand. With our SaaS model, the computer infrastructure is always available and grows with your needs. You do not have to set up a large computer system, or build a new building, or risk over or under investing to deal with the data.

With FinchLab, the vision of next-generation platforms truly democratizing sequencing can be realized.

Uploading your data to iFinch

iFinch is a scaled down version of our V2 Finch system for genetic analysis. 

Unlike our larger, industrial strength systems, iFinch is designed for individual researchers, small labs, or teachers who want a trouble-free system for managing and working with genetic data.  Currently, students and teachers are using iFinch as part of the Bio-Rad Explorer Cloning and Sequencing kit.

I call iFinch "bioinformatics in a box." I've used iFinch in two bioinformatics courses and it's been pretty helpful. iFinch and FinchTV play nicely together and the combination works well for students.

You don't even have to get a computer for storing data or learn how to manage a database. We do all that for you and you use the system through the web. It's nice and painless.

**********NOTE***********

If you received an iFinch account from Bio-Rad, you will need to turn on your data processor before you begin uploading data.

Checking and starting your Finch data processor

1.  Log into your iFinch account.

2.  Find and select the Data Processor link in the System menu.

3. Look at the Data processor status.

4. If the Data processor has stopped, you will need to Restart it by selecting the Restart button.  If you are a student, you will need to have an instructor log in and do this.

Once your data processor has been started, you can go ahead and upload your data as shown in the movie below.

Uploading your data
The first thing we do with iFinch is to put our data into the iFinch database. In the movie, you can see how we upload chromatograms through the web interface.



iFinch can store any kind of file, but it really shines when it comes to working with chromatograms or genotyping data.

If you have lots of files (more than a few 96 well plates), we do have other systems for uploading data. But, that's another post and another movie.

Road Trip: AB SOLiD Users Meeting

Wow! That's the best way to summarize my impressions from the Applied Biosystems (AB) SOLiD users conference last week, when AB launched their V3 SOLiD platform. AB claims that this system will be capable of delivering a human genome's worth of data for about $10,000 US.

Last spring, the race to the $1000 genome leaped forward when AB announced that they sequenced a human genome at 12-fold coverage for $60,000. When the new system ships in early 2009, that same project can be completed for $10,000. Also, this week others have claimed progress towards a $5000 human genome.

That's all great, but what can you do with this technology besides human genomes?

That was the focus of the SOLiD users conference. For a day and a half, we were treated to presentations from scientists and product managers from AB as well as SOLiD customers who have been developing interesting applications. Highlights are described below.

Technology Improvements:

Increasing Data Throughput - Practically everyone is facing the challenge of dealing with large volumes of data, and now we've learned the new version of the SOLiD system will produce even more. A single instrument run will produce between 125 million to 400 million reads depending on the application. This scale up is achieved by increasing the bead density on a slide, dropping the overall cost per individual read. Read lengths are also increasing, making it possible to get between 30 and 40 gigabases of data from a run. And, the amount of time required for each run is shrinking; not only can you get all of these data, you can do it again more quickly.

Increasing Sample Scale - Many people like to say, yes, the data is a problem, but at least the sample numbers are low, so sample tracking is not that hard.

Maybe they spoke too soon.

AB and the other companies with Next Gen technologies are working to deliver "molecular barcodes" that allow researchers to combine multiple samples on a single slide. This is called "multiplexing." In multiplexing, the samples are distinguished by tagging each one with a unique sequence, the barcode. After the run, the software uses the sequence tags to sort the data into their respective data sets. The bottom line is that we will go from a system that generates a lot of data from a few samples, to a system that generates even more data from a lot of samples.

Science:

What you can do with 100's of millions of reads: On the science side, there were many good presentations that focused on RNA-Seq and variant detection using the SOLiD system. Of particular interest was Dr. Gail Payne's presentation on the work, recently published in Genome Research, entitled "Whole Genome Mutational Profiling Using Next Generation Sequencing Technology." In the paper, the 454, Illumina, and SOLiD sequencing platforms were compared for their abilities to accurately detect mutations in a common system. This is one of the first head to head to head comparisons to date. Like the presidential debates, I'm sure each platform will be claimed to be the best by its vendor.

From the presentation and paper, the SOLiD platform does offer a clear advantage in its total throughput capacity. 454 showed showed the long read advantage in that approximately 1.5% more of the yeast genome studied was covered by 454 data than with shorter read technology. And, the SOLiD system, with its dibase (color space) encoding, seemed to provide higher sequence accuracy. When the reads were normalized to the same levels of coverage, a small advantage for SOLiD, can be seen.

When false positive rates of mutation detection were compared, SOLiD had zero for all levels of coverage (6x, 8x, 10x, 20x, 30x, 175x [full run of two slides]), Illumina had two false positives at 6x and 13x, and zero false positives for 19x and 44x (full run of one slide) coverage, and 454 had 17, six, and one false positive for 6x, 8x, and 11x (full run) coverage, respectively.

In terms of false negative (missed) mutations, all platforms did a good job. At coverages above 10x, none of the platforms missed any mutations. The 454 platform missed a single mutation at 6x and 8x coverage and Illumina missed two mutations at 6x coverage. SOLiD, on the other hand, missed four and five at 8x and 6x coverage, respectively.

What was not clear from the paper and data, was the reproducibility of these results. From what I can tell, single DNA libraries were prepared and sequenced; but replicates were lacking. Would the results change if each library preparation and sequencing process was repeated?

Finally, the work demonstrates that it is very challenging to perform a clean "apples to apples" comparison. The 454 and Illumina data were aligned with Mosiak and the SOLiD data were aligned with MapReads. Since each system produces different error profiles and the different software programs each make different assumptions about how to use the error profiles to align data and assess variation, the results should not be over interpreted. I do, however, agree with the authors, that these systems are well-suited for rapidly detecting mutations in a high throughput manner.

ChIP-Seq / RNA-Seq: On the second day, Dr. Jessie Gray presented work on combining ChIP-Seq and RNA-Seq to study gene expression. This is important work because it illustrates the power of Next Gen technology and creative ways in which experiments can be designed.

Dr. Gray's experiment was designed to look at this question: When we see that a transcription factor is bound to DNA, how do we know if that transcription factor is really involved in turning on gene expression?

ChIP-Seq allows us to determine where different transcription factors are bound to DNA at a given time, but it does not tell us whether that binding event turned on transcription. RNA-Seq tells us if transcription is turned on, after a given treatment or point in time, but it doesn't tell us which transcription factors were involved. Thus, if we can combine ChiP-Seq and RNA-Seq measurements, we can elucidate a cause and effect model and find where a transcription factor is binding and which genes it potentially controls.

This might be harder than it sounds:

As I listened to this work, I was struck by two challenges. On the computational side, one has to not only think about how to organize and process the sequence data into alignments and reduce those aligned datasets into organized tables that can be compared, but also how to create the right kind of interfaces for combining and interactively exploring the data sets.

On the biochemistry side, the challenges presented with ChIP-Seq reminded me of the old adage of trying to purify disapearase - "the more you purify the less there is." ChIP-Seq and other assays that involve multiple steps of chemical treatments and purification, produce vanishingly small amounts of material for sampling. The later challenge complicates the first challenge, because in systems where one works with "invisible" amounts of DNA, a lot of creative PCR, like "in gel PCR" is required to generate sufficient quantities of sample for measurement.

PCR is good for many things, including generating artifacts. So, the computation problem expands. A software system that generates alignments, reduces them to data sets that can be combined in different ways, and provides interactive user interfaces for data exploration, must also be able to understand common artifacts so that results can be quality controlled. Data visualizations must also be provided so that researchers can distinguish biological observations from experimental error.

These are exactly the kinds of problems that Geospiza solves.