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).

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.

Sneak Peak: Genetic Analysis From Capillary Electrophoresis to SOLiD

On October 7, 2008 Geospiza hosted a webinar featuring the FinchLab, the only software product to track the entire genetic analysis process, from sample preparation, through processing to analyzed results.

If you are as disappointed about missing it as we are about you missing, no worries. You can get the presentation here.

If you are interested in:

• Learning about Next Gen sequencing applications
• Seeing what makes the Applied Biosystems SOLiD system powerful for transcriptome analysis, CHiP-Seq, resequenicng experiments, and other applications
  
• Understanding the flow of data and information as samples are converted into results
• Overcoming the significant data management challenges that accompany Next Gen technologies
• Setting up Next Gen sequencing in your core lab
• Creating a new lab with Next Gen technologies

This webinar is for you!

In the webinar, we talked about the general applications of Next Gen sequencing and focused on using SOLiD to perform Digital Gene Expression experiments by highlighting mRNA Tag Profiling and whole transcriptome analysis. Throughout the talk we gave specific examples about collecting and analyzing SOLiD data and showed how the Geospiza FinchLab solves challenges related to laboratory setup and managing Next Gen data and analysis workflows.

ChIP-ing Away at Analysis

ChiP-Seq is becoming a popular way to study the interactions between proteins and DNA. This new technology is made possible by the Next Gen sequencing techniques and sophisticated tools for data management and analysis. Next Gen DNA sequencing provides the power to collect the large amounts of data required. FinchLab is the software system that is needed to track the lab steps, initiate analysis, and see your results.

In recent posts, we stressed the point that unlike Sanger sequencing, Next Gen sequencing demands that data collection and analysis be tightly coupled, and presented our initial approach of analyzing Next Gen data with the Maq program. We also discussed how the different steps (basecalling, alignment, statistical analysis) provide a framework for analyzing Next Gen data and described how these steps belong to three phases: primary, secondary, and tertiary data analysis. Last, we gave an example of how FinchLab can be used to characterize data sets for Tag Profiling experiments. This post expands the discussion to include characterization of data sets for ChIP-Seq.

ChIP-Seq

ChiP (Chromosome Immunoprecipitation) is a technique where DNA binding proteins, like transcription factors, can be localized to regions of a DNA molecule. We can use this method to identify which DNA sequences control expression and regulation for diverse genes. In the ChIP procedure, cells are treated with a reversible cross-linking agent to "fix" proteins to other proteins that are nearby, as well as the chromosomal DNA where they're bound. The DNA is then purified and broken into smaller chunks by digestion or shearing and antibodies are used to precipitate any protein-DNA complexes that contain their target antigen. After the immunoprecipitation step, unbound DNA fragments are washed away, the bound DNA fragments are released, and their sequences are analyzed to determine the DNA sequences that the proteins were bound to. Only few years ago, this procedure was much more complicated than it is today, for example, the fragments had to be cloned before they could be sequenced. When microarrays became available, a microarray-based technique called ChIP-on-chip made this assay more efficient by allowing a large number of precipitated DNA fragments to be tested in fewer steps.

Now, Next Gen sequencing takes ChIP assays to a new level [1]. In ChIP-seq the same cross linking, isolation, immunoprecipitation, and DNA purification steps are carried out. However, instead of hybridizing the resulting DNA fragments to a DNA array, the last step involves adding adaptors and sequencing the individual DNA fragments in parallel. When compared to microarrays, ChiP-seq experiments are less expensive, require fewer hands-on steps and benefit from the lack of hybridization artifacts that plague microarrays. Further, because ChIP-seq experiments produce sequence data, they allow researchers to interrogate the entire chromosome. The experimental results are no longer to the probes on the micoarray. ChIP-Seq data are better at distinguishing similar sites and collecting information about point mutations that may give insights into gene expression. No wonder ChIP-Seq is growing in popularity.

FinchLab

To perform a ChIP-seq experiment, you need to have a Next Gen sequencing instrument. You will also need to have the ability to run an alignment program and work with the resulting data to get your results. This is easier said than done. Once the alignment program runs, you might have to also run additional programs and scripts to translate raw output files to meaningful information. The FinchLab ChIP-seq pipeline, for example, runs Maq to generate the initial output, then runs Maq pileup to convert the data to a pileup file. The pileup file is then read by a script to create the HTML report, thumbnail images to see what is happening and "wig" files that can be viewed in the UCSC Genome Browser. If you do this yourself, you have to learn the nuances of the alignment program, how to run it different ways to create the data sets, and write the scripts to create the HTML reports, graphs, and wig files.

With FinchLab, you can skip those steps. You get the same results by clicking a few links to sort the data, and a few more to select the files, run the pipeline, and view the summarized results. You can also click a single link to send the data to the UCSC genome browser for further exploration.


Reference

ChIP-seq: welcome to the new frontier Nature Methods - 4, 613 - 614 (2007)