Bloginar: Making cancer transcriptome sequencing assays practical for the research and clinical scientist

A few weeks back we (Geospiza and Mayo Clinic) presented a research poster at BioMed Central’s Beyond the Genome conference. The objective was to present GeneSifter’s analysis capabilities and discuss the practical issues scientists face when using Next Generation DNA Sequencing (NGS) technologies to conduct clinically orientated research related to human heath and disease.

Abstract
NGS technologies are increasing in their appeal for studying cancer. Fully characterizing the more than 10,000 types and subtypes of cancer to develop biomarkers that can be used to clinically define tumors and target specific treatments requires large studies that examine specific tumors in 1000s of patients. This goal will fail without significantly reducing both data production and analysis costs so that the vast majority of cancer biologists and clinicians can conduct NGS assays and analyze their data in routine ways.

While sequencing costs are now inexpensive enough for small groups and individuals, beyond genome centers, to conduct the needed studies, the current data analysis methods need to move from large bioinformatics team approaches to automated methods that employ established tools in scalable and adaptable systems to provide standard reports and make results available for interactive exploration by biologists and clinicians. Mature software systems and cloud computing strategies can achieve this goal.

Poster Layout

Excluding the title, the poster has five major sections. The first section includes the abstract (above) and study parameters. In the work, we examined the RNA from 24 head and neck cancer biopsies from 12 individuals' tumor and normal cells.

The remaining sections (2-5), provide a background of NGS challenges, applications, high-level data analysis workflows, the analysis pipeline used in the work, the comparative analyses that need to be conducted, and practical considerations for groups seeking to do similar work. Much of section 2 has been covered in previous blogs and research papers.

Section 3: Secondary Analysis Explores Single Samples
NGS challenges are best known for the amount of data produced by the instruments. While this challenge should not be undervalued, it is over discussed. A far greater challenge lies in the complexity of data analysis. Once the first step (primary analysis, or basecalling) is complete, the resulting millions of reads must be aligned to several collections of reference sequences. For human RNA samples, these include the human genome, splice junction databases, and others to measure biological processes and filter out reads arising from artifacts related to sample preparation. Aligned data are further processed to create tables that annotate individual reads and compute quantitative values related to how the sample’s reads align (or cover) regions of the genome or span exon boundaries. If the assay measures sequence variation, alignments must be further processed to create variant tables.

Secondary analysis produces a collection of data in forms that can be immediately examined to understand overall sample quality and characteristics. High-level summaries indicate how many reads align to things we are interested in and not interested in. In GeneSifter, these summaries are linked to additional reports that show additional detail. Gene List reports, for example, show how the sample reads align within a gene’s boundary. Pictures in these reports are linked to Genesifter's Gene Viewer reports that provide even greater detail about the data with respect to each read’s alignment orientation and observed variation.

An important point about secondary analysis, however, is that it focuses on single sample analyses. As more samples are added to the project, the data from each sample must be processed through an assay specific pipeline. This point is often missed in the NGS data analysis discussion. Moreover, systems supporting this work must not only automate 100s of secondary analysis steps, they must also provide tools to organize the input and output data in project-based ways for comparative analysis.

Section 4: Tertiary Analysis in GeneSifter Compares Data Between Samples
The science happens in NGS when data are compared between samples in statistically rigorous ways. RNA sequencing makes it possible to compare gene expression, exon expression, and sequence variation between samples to identify differentially expressed genes, their isoforms, and whether certain alleles are differentially expressed. Additional insights are gained when gene lists can be examined in pathways and by ontologies. GeneSifter performs these activities in a user-friendly web-environment.

The poster's examples show how gene expression can be globally analyzed for all 24 samples, how a splicing index can distinguish gene isoforms occurring in tumor, but not normal cells, and how sequence variation can be viewed across all samples. Principal component analysis shows that genes in tumor cells are differentially expressed relative to normal cells. Genes highly expressed in tumor cells include those related to cell cycle and other pathways associated with unregulated cell growth. While these observations are not novel, they do confirm our expectations about the samples and being able to make such an observation with just a few clicks prevents working on costly misleading observations. For genes showing differential exon expression, GeneSifter provides ways to identify those genes and navigate to the alignment details. Similarly reports that show differential variation between samples can be filtered by multiple criteria in reports that link to additional annotation details and read alignments.

Section 5: Practical Considerations
Complete NGS data analysis systems seamlessly integrate secondary and tertiary analysis. Presently, no other systems are as complete as GeneSifter. There are several reasons why this is the case. First, a significant amount of software must be produced and tested to create such a system. From complex data processing automation, to advanced data queries, to user interfaces that provide interactive visualizations and easy data access, to security, software systems must employ advanced technologies and take years to develop with experienced teams. Second, meeting NGS data processing requirements demands that computer systems be designed with distributable architectures that can support cloud environments in local and hosted configurations. Finally, scientific data systems must support both predefined and ad hoc query capabilities. The scale of NGS applications means that non-traditional approaches must be used to develop data persistence layers that can support a variety of data access methods and, for bioinformatics, this is a new problem.

Because Geospiza has been doing this kind of work for over a decade and could see the coming challenges, we’ve focused our research and development in the right ways to deliver a feature rich product that truly enables researchers to do high quality science with NGS.

Enjoy the poster.

A Genomics Genealogy

Deep sequencing technologies have radically changed how we study biology. Deciding what technology and software to use can be daunting. Choices become easier when the relationships between different DNA sequencing applications are understood.

A brief history 

DNA sequencing grew from our desire to understand how the instructions for the biochemistry of life are encoded in an organism’s DNA. If we know the precise ordering and organization of an organism’s DNA sequence, we can presumably unlock a code that reveals these instructions. Accomplishing this goal required the creation of a new field, molecular biology, and new technologies to sequence genes.

The first sequencing methods were arduous. They combined nuclease digestion with thin layer chromatography to measure di- and trinucleotides that could be puzzled together. Later, Maxim and Gilbert replaced enzymatic DNA degradation with a chemical fragmentation method that enabled the reading of ordered bases from ³²P labeled fragments separated by electrophoresis.

The Sanger method, which used dideoxynucleotide triphosphates to create ensembles of DNA molecules terminated at each base, soon replaced Maxim Gilbert sequencing. The next innovation was to color code DNA with fluorescent dyes so that molecules could be interrogated with a laser and camera coupled to a computer. This innovation automated “high-throughput” DNA sequencing systems, initially with polyacrylamide gels and later with capillary electrophoresis, and made it possible to sequence the human and other genomes. It also created the first transcriptome analysis method, Expressed Tag Sequencing (EST).

Despite 20 years of advances, however, the high-throughput sequencing methods were not high-enough-throughput to realistically interrogate DNA and RNA molecules in creative ways. Big questions (genomes, ESTs, meta-genomes) required large factory-like approaches to automate sample preparation and collect sequences because a fundamental problem had yet to be solved. Specially, each sequence was obtained from an individual purified DNA clone or PCR product.

Real high-throughput is massively parallel throughput 

The next-generation DNA sequencing (NGS) technologies free researchers from the need to clone or purify every molecule. They all share the common innovation that DNA sequencing is performed in a massively parallel format. That is a library, or ensemble of millions of DNA molecules, are simultaneously sequenced. Data collection costs are dramatically decreased through miniaturization and by eliminating the need for warehouses of colony pickers, prep robots, sequencing instruments, and large teams of people.

The new problem is dealing with the data that are produced and increasing computation costs. As NGS opens new possibilities to measure DNA and RNA in novel ways, each application requires a specific laboratory procedure that must be coupled to a specific analysis methodology.

Sequencing genealogy is defined by the questions 

In an evolutionary model, the history of cloning, restriction site mapping, and Sanger sequencing form the trunk of the genomics application tree (top figure) from which branches develop as new applications emerge.

NGS has driven the evolution of three main sequencing branches: De Novo, Functional Genomics, and Variation Assays. The De Novo, or Exploratory, sequencing contains three subbranches that include new genomes (projects that seek to determine a complete genome sequence of an organism), meta-genomes (projects in which DNA fragments are sequenced from environmental samples), or meta-transcriptomes (projects where cDNA fragments are sequenced from environmental samples).

The Functional Genomics branch is growing fast. In these experiments, different collections of RNA or DNA molecules from an organism, tissue, or cells, are isolated and sequenced to measure gene expression and how it is regulated. Three subbranches describe the different kinds of function genomics: Expression, Regulation, and EpiGenomics, and each of these subbranches can be further divided into specific assay groups (DGE, RNA-Seq, small RNA, etc) that can be even further subdivided into specialized procedures (RNA-Seq with strandedness preserved) that are defined by laboratory protocols, kits, and instruments. When the experiments are refined and are made reproducible, they become assays.

Variation Assays form the third main branch of the tree. Genomic sequences are compared within and between populations to link genotype and phenotype. In special cases like cancer and immunology research, variation assays are used to observe changes within an organism’s somatic genomes over time. Today, variation, or resequencing, assays measure nucleotide and small insertions and deletions in whole genomes and exomes. If linked sequence strategies (mate-pairs, paired-ends) are used, larger structural changes including copy number variations can also be measured.

Why is this important?

As a software provider with both deep lab and analysis experience, we [Geospiza] are often asked questions about what instrument platform is the best or how our software stacks up against other available options. The answer, of course, depends on what you want to do. De Novo applications benefit from long reads offered by platforms like 454. Many of the assay-based applications demand ultra-deep sequencing with very high numbers of sequences (reads) as provided by the short-read platforms (Illumina, SOLiD). New single molecule sequencing platforms like PacBio's are targeting a wide rage of applications but have best been demonstrated, thus far, for long-read uses and novel methylation assays.

From an informatics perspective, the exploratory and assay-based branches have distinct software requirements. Exploratory applications require that reads be assembled into contigs that must be further ordered into scaffolds to get to the complete sequence. In meta-genomics or meta-transcriptomics applications, data are assembled to obtain gene sequences. These projects are further complicated by orthologous and paralogous sequences and highly expressed genes that over represent certain sequences. In these situations, specialized hardware or complex data reduction strategies are needed to make assembly practical. Once data are assembled, they are functionally annotated in a second computational phase using tools like BLAST.

Assay-based data analysis also has two distinct phases, but they are significantly different from De Novo sequencing. The first phase involves aligning (or mapping) reads to reference data sources and then reducing the aligned data into quantitative values. At least one reference is required and the better it is annotated the more informative the initial results will be. Alignment differs from assembly in that reads are separately compared to a reference rather than amongst themselves. Alignment processing capacity can be easily scaled with multiple inexpensive computers whereas assembly processing cannot.

The second phase of Assay-based sequencing is to produce a discrete output as defined by a diagnostic application, or compare the quantitative values computed from the alignments from several samples, obtained from different individuals and (or) treatments relative to controls. This phase requires statistical tools to normalize data, filter false positives and negatives, and measure differences. Assay-based applications become more informative when large numbers of samples and replicates are included in a study.

Connecting the dots 

While the sequencing applications can be grouped and summarized in different ways, they are also interrelated. For example, De Novo projects are open-ended and exploratory, but their end product, a well-annotated reference sequence, is the foundation for Functional Genomics and Variation applications. Variation analysis is only useful if we can assign function to specific genotypes. Functional assignments come, in part, from previous experiments and genomic annotations, but are increasingly being produced by sequencing assays, so the new challenge is integrating that data obtained from different assays into coherent datasets that can link many attributes to a set of genotypes.

NGS clearly opens new possibilities for studying and characterizing biological systems. Different applications require different sequencing platforms, laboratory procedures, and software systems that can organize analysis tools and automate data processing. On this last point, as one evaluates their projects and their options for being successful, they need to identify informatics groups that have deep experience, available solutions, and strong capabilities to meet the next challenges. Geospiza is one such group.

Further Reading

DNA Sequencing History

Gilbert W, Maxam A (1973) The nucleotide sequence of the lac operator. Proc Natl Acad Sci U S A 70:3581

Maxam AM, Gilbert W (1977) A new method for sequencing DNA. Proc Natl Acad Sci U S A 74:560

Sanger F, Nicklen S, Coulson AR (1977) DNA sequencing with chain-terminating inhibitors. Proc Natl Acad Sci U S A 74:5463-7

Smith LM, Sanders JZ, Kaiser RJ, Hughes P, Dodd C, Connell CR, Heiner C, Kent SB, Hood LE (1986) Fluorescence detection in automated DNA sequence analysis. Nature 321:674-9

Adams MD, Soares MB, Kerlavage AR, Fields C, Venter JC (1993) Rapid cdna sequencing (expressed sequence tags) from a directionally cloned human infant brain cdna library. Nat Genet 4:373-80

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

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

FinchTalks

From Reads to Datasets Why Next Gen is Not Like Sanger
Expeditiously Exponential: Genome Standards in a New Era 

Next Gen DNA Sequencing Is Not Sequencing DNA
Color Space, Flow Space, Sequence Space or Outer Space: Part II, Uncertainty in Next Gen Data

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)