DOE's 2011 Sequencing, Finishing, Analysis in the Future Meeting

Cactus at Bandelier
National Monument 

Last week, June 1-3, the Department of Energy held their annual Sequencing, Finishing, Analysis in the Future (SFAF) meeting in Santa Fe, New Mexico.  SFAF, also sponsored b the Joint Genome Institute, and Los Alamos National Laboratory and was attended by individuals from the major genome centers, commercial organizations, and smaller labs.

In addition to standard presentations and panel discussions from the genome centers and sequencing vendors (Life Technologies, Illumina, Roche 454, and Pacific Biosciences), and commercial tech talks, this year's meeting included a workshop on hybrid sequence assembly (mixing Illumina and 454 data, or Illumina and PacBio data). I also presented recent work on how 1000 Genomes and Complete Genomics data are changing our thinking about genetics (abstract below).

John McPherson from the Ontario Cancer Research Institute (OICR, a Geospiza client) gave the kickoff keynote. His talk focused on challenges in cancer sequencing. One of those being that DNA sequencing costs are now predominated by instrument maintenance, sample acquisition, preparation, and informatics, which are never included in the $1000 genome conversation. OICR is now producing 17 trillion bases per month and as they, and others, learn about cancer's complexity, the idea of finding single biochemical targets for magic bullet treatments is becoming less likely.

McPherson also discussed how OICR is getting involved clinical cancer sequencing. Because cancer is a genetic disease, measuring somatic mutations and copy number variations will be best for developing prognostic biomarkers. However, measuring such biomarkers in patients in order to calibrate treatments requires a fast turnaround time between tissue biopsy, sequence data collection, and analysis. Hence, McPherson sees IonTorrent and PacBio as the best platforms for future assays. McPherson closed his presentation stating that data integration is the grand challenge.  We're on it!

The remaining talks explored several aspects of DNA sequencing ranging from high throughput single cell sample preparation, to sequence alignment and de novo sequence assembly, to education and interesting biology. I especially liked Dan Distal's (New England Biolabs) presentation on the wood eating microbiome of shipworms. I learned that shipworms are actually little clams that use their shells as drills to harvest the wood. Understanding how the bacteria eat wood is important because we may be able to harness this ability for future energy production.

Finally, there was my presentation for which I've included the abstract.

What's a referenceable reference?

The goal behind investing time and money into finishing genomes to high levels of completeness and accuracy is that they will serve as a reference sequences for future research. Reference data are used as a standard to measure sequence variation, genomic structure, and study gene expression in microarray and DNA sequencing assays. The depth and quality of information that can be gained from such analyses is a direct function of the quality of the reference sequence and level of annotation. However, finishing genomes is expensive, arduous work. Moreover, in the light of what we are learning about genome and species complexity, it is worthwhile asking the question whether a single reference sequence is the best standard of comparison in genomics studies.

The human genome reference, for example, is well characterized, annotated, and represents a considerable investment. Despite these efforts, it is well understood that many gaps exist in even the most recent versions (hg19, build 37) [1], and many groups still use the previous version (hg18, build 36). Additionally, data emerging from the 1000 Genomes Project, Complete Genomics, and others have demonstrated that the variation between individual genomes is far greater than previously thought. This extreme variability has implications for genotyping microarrays, deep sequencing analysis, and other methods that rely on a single reference genome. Hence, we have analyzed several commonly used genomics tools that are based on the concept of a standard reference sequence, and have found that their underlying assumptions are incorrect. In light of these results, the time has come to question the utility and universality of single genome reference sequences and evaluate how to best understand and interpret genomics data in ways that take a high level of variability into account.

Todd Smith(1), Jeffrey Rosenfeld(2), Christopher Mason(3). (1) Geospiza Inc. Seattle, WA 98119, USA (2) Sackler Institute for Comparative Genomics, American Museum of Natural History, New York, NY 10024, USA (3) Weill Cornell Medical College, New York, NY 10021, USA

Kidd JM, Sampas N, Antonacci F, Graves T, Fulton R, Hayden HS, Alkan C, Malig M, Ventura M, Giannuzzi G, Kallicki J, Anderson P, Tsalenko A, Yamada NA, Tsang P, Kaul R, Wilson RK, Bruhn L, & Eichler EE (2010). Characterization of missing human genome sequences and copy-number polymorphic insertions. Nature methods, 7 (5), 365-71 PMID: 20440878

You can obtain abstracts for all of the presentations at the SFAF website.

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

Levels of Quality

Next Generation Sequencing (NGS) data can produce more questions than answers. A recent LinkedIn discussion thread began with a simple question. “I would like to know how to obtain statistical analysis of data in a fastq file? number of High quality reads, "bad" reads....” This simple question opened a conversation about quality values, read mapping, and assembly. Obviously there is more to NGS data quality than simply separating bad reads from good ones.

Different levels of quality
Before we can understand data quality we need to understand what sequencing experiments measure and how the data are collected. In addition to sequencing genomes, many NGS experiments focus on measuring gene function and regulation by sequencing the fragments of DNA and RNA isolated and prepared in different ways. In these assays, complex laboratory procedures are followed to create specialized DNA libraries that are then sequenced in a massively parallel format.

Once the data are collected, they need to be analyzed in both common and specialized ways as defined by the particular application. The first step (primary analysis) converts image data, produced by different platforms into sequence data (reads). This step, specific to each sequencing platform, also produces a series of quality values (QVs), one value per base in a read, that define a probability that the base is correct. Next (secondary analysis), the reads and bases are aligned to known reference sequences, or, in the case of de novo sequencing, the data are assembled into contiguous units from which a consensus sequence is determined. The final steps (tertiary analysis) involve comparing alignments between samples or searching databases to get scientific meaning from the data. 

In each step of the analysis process, the data and information produced can be further analyzed to get multiple levels of quality information that reflect how well instruments performed, if processes worked, or whether samples were pure. We can group quality analyses into three general levels: QV analysis, sequence characteristics, and alignment information.

Quality Value Analysis

Many of the data quality control (QC) methods are derived from Sanger sequencing where QVs could be used to identify low quality regions that could indicate mixtures of molecules or define areas that should be removed before analysis. QV correlations with base composition could also be used to sort out systematic biases, like high GC content, that affect data quality. Unlike Sanger sequencing, where data in a trace represent an average of signals produced by ensemble of molecules, NGS provides single data points collected from individual molecules arrayed on a surface. NGS QV analysis uses counting statistics to summarize the individual values collected from the several million reads produced by each experiment.

Examples of useful counting statistics include measuring average QVs by base position, box and whisker (BW) plots, histogram plots of QV thresholds, and overall QV distributions. Average QVs by base, BW plots, and QV thresholds are used to see how QVs trend across the reads. In most cases, these plots show the general trend that data quality decreases toward the 3’ ends of reads. Average QVs by base show each base’s QV with error bars indicating the values that are within one standard deviation of the mean. BW plots provide additional detail to show the minimum and maximum QV, the median QV and the lower and upper quartile QV values for each base. Histogram plots of QV thresholds count the number of bases below threshold QVs (10, 20, 30). This methods provides information about potential errors in the data and its utility in applications like RNA-seq or genotyping. Finally distributions of all QVs or the average QV per read can give additional indications of dataset quality.

QV analysis primarily measures sequencing and instrument run quality. For example, sharp drop offs in average QVs can identify systematic issues related to the sequencing reactions. Comparing data between lanes or chambers within a flowcell can flag problems with reagent flow or imaging issues within the instrument. In more detailed analyses, the coordinate positions for each read can be used to reconstruct quality patterns for very small regions (tiles) within a slide to reveal subtle features about the instrument run.

Sequence Characteristics
In addition to QV analysis we can look the sequences of the reads to get additional information. If we simply count the numbers of A’s, C’s, G’s, or T’s (or color values), at each base position, we can observe sequence biases in our dataset. Adaptor sequences, for example, will show sharp spikes in the data, whereas random sequences will give us an even distribution, or bias that reflects the GC content of the organism being analyzed. Single stranded data will often show a separation of the individual base lines; double stranded coverage should have equal numbers of AT and GC bases.

We can also compare each read to the other reads in the dataset to estimate the overall randomness, or complexity, of our data. Depending on the application, a low complexity dataset, one with a high number of exactly matching reads, can indicate PCR biases or a large number of repeats in the case of de novo sequencing. In other cases, like tag profiling assays, which measure gene expression by sequencing a small fragment from each gene, low complexity data are normal because highly expressed genes will contribute a large number of identical sequences.

Alignment Information
Additional sample and data quality can be measured after secondary analysis. Once the reads are aligned (mapped) to reference data sources we can ask questions that reflect both run and sample quality. The overall number of reads that can be aligned to all sources can also be used to estimate parameters related to library preparation and deposition of the molecules on the beads or slides used for sequencing. Current NGS processes are based on probabilistic methods for separating DNA molecules. Illumina, SOLiD, and 454 all differ with respect to their separation methods, but share the common feature that the highest data yield occurs when concentration of DNA is just right. The number of mappable reads can measure this property.

DNA concentration measures one aspect of sample quality. Examining which reference sources reads align to gives further information. For example, the goal of transcriptome analysis is to sequence non-ribosomal RNA (rRNA). Unfortunately rRNA is the most abundant RNA in a cell. Hence transcriptome assays involve steps to remove rRNA and a large number of rRNA reads in the data indicates problems with the preparation. In exome sequencing or other methods where certain DNA fragments are enriched, the ratio of exon (enriched) and non-exon (non-enriched) alignments can reveal how well the purification worked.

Read mapping, however, is not a complete way to measure data quality. High quality reads that do not match any reference data in the analysis pipeline could be from unknown laboratory contaminants, sequences like novel viruses or phage, or incomplete reference data. Unfortunately, the former case is the more common, so it is a good idea to include reference data for all ongoing projects in the analysis pipeline. Alignments to adaptor sequences can reveal issues related to preparation processes and PCR, and the positions of alignments can be used to measure DNA or RNA fragment lengths.

So Many Questions
The above examples provide a short tour of how NGS data can be analyzed to measure the quality of samples, experiments, protocol and instrument performances. NGS assays are complex and involve multistep lab procedures and data analysis pipelines that are specific to different kinds of applications. Sequence bases and their quality values provide information about instrument runs and some insights into samples and preparation quality. Additional, information are obtained after the data are aligned to multiple reference data sources. Data quality analysis is most useful when values are computed shortly after data are collected, and systems, like GeneSifter Lab and Analysis Editions, that automate these analyses are important investments if labs plan to be successful with their NGS experiments.

GeneSifter in Current Protocols

This month we are pleased to report Geospiza's publication of the first standard protocols for analyzing Next Generation Sequencing (NGS) data. The pulication, appearing in the September issue of Current Protocols, addresses how to analyze data from both microarray, and NGS experiments. The abstract and links to the paper and our press release are provided below.

Abstract

Transcription profiling with microarrays has become a standard procedure for comparing the levels of gene expression between pairs of samples, or multiple samples following different experimental treatments. New technologies, collectively known as next-generation DNA sequencing methods, are also starting to be used for transcriptome analysis. These technologies, with their low background, large capacity for data collection, and dynamic range, provide a powerful and complementary tool to the assays that formerly relied on microarrays. In this chapter, we describe two protocols for working with microarray data from pairs of samples and samples treated with multiple conditions, and discuss alternative protocols for carrying out similar analyses with next-generation DNA sequencing data from two different instrument platforms (Illumina GA and Applied Biosystems SOLiD).

In the chapter we cover the following protocols:

• Basic Protocol 1: Comparing Gene Expression from Paired Sample Data Obtained from Microarray Experiments
• Alternate Protocol 1: Compare Gene Expression from Paired Samples Obtained from Transcriptome Profiling Assays by Next-Generation DNA Sequencing
• Basic Protocol 2: Comparing Gene Expression from Microarray Experiments with Multiple Conditions
• Alternate Protocol 2: Compare Gene Expression from Next-Generation DNA Sequencing Data Obtained from Multiple Conditions

Links

To view the abstract, contents, figures, and literature cited online visit: Curr. Protoc. Bioinform. 27:7.14.1-7.14.34

To view the press release visit: Geospiza Team Publishes First Standard Protocol for Next Gen Data Analysis

Sneak Peak: Small RNA Analysis with Geospiza

Join us this Wednesday, March 4th at 10:00 A.M. PST (1:00 P.M. EST), for a webinar focusing on small RNA analysis. Eric Olson, our VP of Product Development and principle designer of Geospiza’s GeneSifter Analysis Edition will present our latest insights on analyzing large Next Generation Sequencing datasets to study small RNA biology.

Follow the link to register for this interesting presentation.

Abstract

Next Generation Sequencing allows whole genome analysis of small RNAs at an unprecedented level. Current technologies allow for the generation of 200 million data points in a single instrument run. In addition to allowing for the complete characterization of all known small RNAs in a sample, these applications are also ideal for the identification of novel small RNAs. This presentation will provide an overview of micro RNA expression analysis from raw data to biological significance using examples from publicly available datasets and Geospiza’s GeneSifter software.

The Next Generation Dilemma: Large Scale Data Analysis

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

Abstract

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

In RNA-Seq, the process of determining relative gene expression means that sequence data from multiple samples must go through the entire process of primary, secondary, and tertiary analysis. To do this work, researchers must puzzle through a diverse collection of early version algorithms that are combined into complicated workflows with steps producing complicated file formats. Command line tools such as MAQ, SOAP, MapReads, and BWA, have specialized requirements for formatted input and output and leave researchers with large data files that still require additional processing and formatting for tertiary analyses. Moreover, once reads are aligned, datasets need to be visualized and further refined for additional comparative analysis. We present a solution to these challenges that closes the gaps between primary, secondary, and tertiary analysis by showing results from a complete workflow system that includes data collection, processing and analysis for RNA-seq.

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

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.

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.

Finch 3: Getting Information Out of Your Data

Geospiza's tag line "From Sample to Results" represents the importance of capturing information from all steps in the laboratory process. Data volumes are important and lots of time is being spent discussing the overwhelming volumes of data produced by new data collection technologies like Next Gen sequencers. However, the real issue is not how you are going to store the data, rather it is what are you going to do with it? What do your data mean in the context of your experiment?

The Geospiza FinchLab software system supports the entire laboratory and data analysis workflow to convert sample information into results. What this means is that the system provides a complete set of web-based interfaces and an underlying database to enter information about samples and experiments, track sample preparation steps in the laboratory, link the resulting data back to samples, and process the data to get biological information. Previous posts have focused on information entry, laboratory workflows, and data linking. This post will focus on how data are processed to get biological information.

The ultra-high data output of Next Gen sequencers allows us to use DNA sequencing to ask many new kinds of questions about structural and nucleotide variation and measure several indicators of expression and transcription control on a genome-wide scale. The data produced consists of images, signal intensity data, quality information, and DNA sequences and quality values. For each data collection run, the total collection of data and files can be enormous and can require significant computing resources. While all of the data have to be dealt with in some fashion, some of the data have long-term value while other data are only needed in the short term. The final scientific results will often be produced by comparing data sets created from the DNA sequences and their comparison to reference data.

Next Gen data are processed in three phases.

Next Gen data workflows involve three distinct phases of work: 1. Data are collected from control and experimental samples. 2. Sequence data obtained from each sample are aligned to reference sequence data, or data sets to produce aligned data sets 3. Summaries of the alignment information from the aligned data sets are compared to produce scientific understanding. Each phase has a discrete analytical process and we, and others, call these phases primary data analysis, secondary data analysis and tertiary data analysis.

Primary data analysis involves converting image data to sequence data. The sequence data can be in familiar "ACTG" sequence space or less familiar color space (SOLiD) or flow space (454). Primary data analysis is commonly performed by software provided by the data collection instrument vendor and it is the first place where quality assessment about a sequencing run takes place.

Secondary data analysis creates the data sets that will be further used to develop scientific information. This step involves aligning the sequences from the primary data analyses to reference data. Reference data can be complete genomes, subsets of genomic data like expressed genes, or individual chromosomes. Reference data are chosen in an application specific manner and sometimes multiple reference data sets will be used in an iterative fashion.

Secondary data analysis has two objectives. The first is to determine the quality of the DNA library that was sequenced, from a biological and sample perspective. The primary data analysis supplies quality measurements that can used to determine if the instrument ran properly, or whether the density of beads or clusters were at their optimum to deliver the highest number of high quality reads. However, those data do not tell you about the quality of the samples. Answering questions about sample quality, such as did the DNA library contain systematic artifacts such as sequence bias? Were there high numbers of ligated adaptors or incomplete restriction enzyme digests, or any other factors that would interfere with interpreting the data? These kinds of questions are addressed in the secondary data analysis by aligning your reads to the reference data and seeing that your data make sense.

The second objective of secondary data analysis is to prepare the data sets for tertiary analysis where they will be compared in an experimental fashion. This step involves further manipulation of alignments, typically expressed in very large hard to read algorithm specific tables, to produce data tables that can be consumed by additional software. Speaking of algorithms, there is a large and growing list to choose from. Some are general purpose and others are specific to particular applications, we'll comment more on that later.

Tertiary data analysis represents the third phase of the Next Gen workflow. This phase may involve a simple activity like viewing a data set in a tool like a genome browser so that the frequency of tags can be used to identify promoter sites, patterns of variation, or structural differences. In other experiments, like digital gene expression, tertiary analysis can involve comparing different data sets in a similar fashion to microarray experiments. These kinds of analyses are the most complex; expression measurements need to be normalized between data sets and statistical comparisons need to be made to assess differences.

To summarize, the goal of primary and secondary analysis is to produce well-characterized data sets that can be further compared to obtain scientific results. Well-characterized means that the quality is good for both the run and the samples and that any biologically relevant artifacts are identified, limited, and understood. The workflows for these analyses involve many steps, multiple scientific algorithms, and numerous file formats. The choices of algorithms, data files, data file formats, and overall number of steps depend the kinds of experiments and assays being performed. Despite this complexity there are standard ways to work with Next Gen systems to understand what you have before progressing through each phase.

The Geospiza FinchLab system focuses on helping you with both primary and secondary data analysis.

Finch 3, Linking Samples and Data

One of the big challenges with Next Gen sequencing is linking sample information with data. People tell us: "It's a real problem." "We use Excel, but it is hard." "We're losing track."

Do you find it hard to connect sample information with all the different types of data files? If so you should look at FinchLab.

A review:

About a month ago, I started talking about our third version of the Finch platform and introduced the software requirements for running a modern lab. To review, labs today need software systems that allow them to:

1. Set up different interfaces to collect experimental information
2. Assign specific workflows to experiments
3. Track the workflow steps in the laboratory
4. Prepare samples for data collection runs
5. Link data from the runs back to the original samples
6. Process data according to the needs of the experiment

In FinchLab, order forms are used to first enter sample information into the system. They can be created for specific experiments and the samples entered will, most importantly, be linked to the data that are produced. The process is straightforward. Someone working with the lab, a customer or collaborator, selects the appropriate form and fills out the requested information. Later, an individual in the lab reviews the order and, if everything is okay, chooses the "processing" state from a menu. This action "moves" the samples into the lab where the work will be done. When the samples are ready for data collection they are added to an "Instrument run." The instrument run is Finch's way of tracking which samples go in what well of a plate or lane/chamber on a slide. The samples are added to the instrument and data are collected.

The data

Now comes the fun part. If you have a Next Gen system you'll ultimately end up with 1000's of files scattered in multiple directories. The primary organization for the data will be in unix-style directories, which are like Mac or Windows folders. Within the directories you will find a mix of sequence files, quality files, files that contain information about run metrics and possibly images. You'll have to make decisions about what to save for long-term use and what to archive, or delete.

As noted, the instrument software organizes the data by the instrument run. However, a run can have multiple samples, and the samples can be from different experiments. A single sample can be spread over multiple lanes and chambers of a slide. If you are running a core lab, the samples will come from different customers and your customers often belong to different lab groups. And there is the analysis. The programs that operate on the data require specific formats for input files and produce many kinds of output files. Your challenge is to organize the data so that it is easy to find and access in a logical way. So what do you do?

Organizing data the hard way

If you do not have a data management system, you'll need to write down which samples go with which person, group or experiment. That's pretty simple. You can tape a piece of paper on the instrument and write this down, or you can diligently open a file, commonly an Excel spreadsheet, and record the info there. Not too bad, after all there are only a handful of partitions on a slide (2, 8, 16) and you only run the instrument once or twice a week. If you never upgrade your instrument, or never try and push too many samples through, then you're fine. Of course the less you run your instrument the more your data cost and the goal is to get really good at running your instrument, as frequently as possible. Otherwise you look bad at audit time.

Let's look at a scenario where the instrument is being run at maximal throughput. Over the course of a year, data from between 200 and 1000 slide lanes (chambers) may be collected. These data may be associated with 100's or 1000's of samples and belong to a few or many users in one or many lab groups. The relevant sequence files are between a few hundred megabytes to gigabytes in size; they exist in directories with run quality metrics and possibly analysis results. To sort this out you could have committee meetings to determine whether data should be organized by sample, experiment, user, or group, or you could just pick an organization. Once you've decided on your organization you have to set up access. Does everyone get a unix account? Do you set up SAMBA services? Do you put the data on other systems like Macs and PCs? What if people want to share? The decisions and IT details are endless. Regardless, you'll need a battery of scripts to automate moving data around to meet your organizational scheme. Or you could do something easier.

Organizing data the Finch way

One of FinchLab's many strengths is how it organizes Next Gen data. Because the system tracks samples and users, and has group and permissions models, issues related to data access and sharing are simplified. After a run is complete, the system knows which data files go to what samples. It also knows which samples were submitted by each user. Thus data can be maintained in the run directories that were created by the instrument software to simplify file-based organization. When a run is complete in FinchLab a data link is made to the run directory. The data link informs the system which files go with a run. Data processing routines in the system sort the data into sequences, quality metric files, and other data. At this stage data are associated with samples. Once this is done, the lab has easy access to the data via web pages. The lab can also make decisions about access to data and how to analyze the data. These last two features make FinchLab a powerful system for core labs and research groups. With only few clicks your data are organized by run, user, group, and experiment - and you didn't have to think about it.

Finishing in the Future

"The data sets are astronomical," "the data that needs to be attached to sequences is unbelievable," and "browsing [data] is incomprehensible." These are just three of the many quotes I heard about the challenges associated with DNA sequencing last week at the "Finishing in the Future Meeting" sponsored by the Joint Genome Institute (JGI) and Los Alamos National Laboratory (LANL).

Metagenomics

The two and half day conference, focused on finishing genomic sequences, kicked off with a session on metagenomics. Metagenomics is about isolating DNA from environments and sequencing random molecules to "see what's out there." Excitement for metagenomics is being driven by Next Gen sequencing throughput, because so many sequences can be collected relatively inexpensively. A benefit of being able to collect such large data sets is that we can interrogate organisms that can cannot be cultured. The first talk, "Defining the Human Microbiome: Friends or Family," was presented by Bruce Birren from the Broad Institute of MIT & Harvard. In this talk, we learned about the HMP (Human Microbiome Project), a project dedicated to characterizing the microbes that live on our bodies. It is estimated that microbial cells out number our cells by ten to one. It has long been speculated that our microbiomes are involved in our health and sickness and recent studies are confirming these ideas.

Sequencing technologies continue to increase data throughput

The afternoon session opened with presentations from Roche (454), Illumina, and Applied Biosystems on their respective Next Gen sequencing platforms. Each company presented the strengths of their platform and new discoveries that are being made by virtue of having a lot of data. Each company also presented data on improvements designed to produce even more data and road maps for future improvement to produce even more data. As Haley Fiske from Illumina put it, "we're in the middle of an arms race!" Finally, all the companies are working on molecular barcodes, so that multiple samples can be analyzed within an experiment. So, we started with a lot of data from a sample and are going to a lot of data from a lot of samples. That should add some very nice complexity to sample and data tracking.

A unique perspective

Sydney Brenner opened the second day with a talk on "The Unfinished Genome." The thing I like most about a Sydney Brenner talk is how he puts ideas together. In this talk he presented how one could look at existing data and literature to figure things out or make new discoveries. In one example, he speculated on when the genes for eye development may have first appeared. From the physiology of the eye you can use the biochemistry of vision to identify the genes that encode the various proteins involved in the process. These proteins are often involved in other process, but differ slightly. They arise from gene duplication and modification. So, you can look at gene duplications and measure the age of a duplication by looking at neighboring genes. If a duplication event is old, neighboring genes will be unequal distances apart. You can use this information, along with phylogenetic data, to estimate when the events occurred. Of course this kind of study benefits from more sequence data. Sydney encouraged everyone to keep sequencing.

Sydney closed his talk by making a fun analogy where genomics is like astronomy and thus should have been called "genomy." He supported his analogy by noting that astronomy has astro physic and genomics has genetics. Both are quantitative and measure history and evolution. Astronomy also has astrology, the prediction of an individual's future from the stars. Similarly, folks would like to predict an individual's future from their genes and suggested we call this work "Genology," since it has the same kind of scientific foundation as astrology.

Challenges and solutions

The rest of the conference and posters focused on finishing projects. Today the genome centers are making use of all the platforms to generate large data sets and finish projects. A challenge for genomics is lowering finishing costs. The problem being that generating "draft" data has become so inexpensive and fast that finishing has become a signifiant bottleneck. Finishing is needed to produce the high quality referece sequences that will inform our genomic science, so investigarting ways to lower finishing costs is a worthwhile endeavour. Genome centers are approaching this problem by looking at ways to mix data from different technologies such as 454 and Illumina or SOLiD. They are also developing new and mixed software approaches such as combining multiple assembly algorithms to improve alignments. These efforts are being conducted in conjunction with experiments where mixtures of single pass and paired read data sets are tested to determine optimal approaches for closing gaps.

The take home from this meeting is that, over the coming years, a multitude of new approaches and software programs will emerge to enable genome scale science. The current technology providers are aggressively working to increase data throughput, data quality and read length to make their platforms as flexible as possible. New technology providers are making progress on even higher throughput platforms. Computer scientists are working hard on new algorithms and data visualizations to handle the data. Molecular barcodes will allow for greater numbers of samples per data collection event and increase sample tracking complexity.

The bottom line

Individual research groups will continue to have increasing access to "genome center scale" technology. However, the challenges with sample tracking, data management, and data analysis will be daunting. Research groups with interesting problems will be cut off from these technologies unless they have access to cost-effective, robust informatics infrastructures. They will need help setting up their labs, organizing the data, and making use of new and emerging software technologies.

That's where Geospiza can help.

Sneak Peak: Managing Next Gen Digital Gene Expression Workflows

This Wednesday, April 23rd, Illumina will host a webinar featuring the Geospiza FinchLab.

If you are interested in:

• Learning about Next Gen sequencing applications
• Seeing how the Illumina Genome Analyzer makes mRNA Tag Profiling more sensitive
• 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 will talk about the general applications of Next Gen sequencing and focus on using the Illumina Genome Analyzer to perform Digital Gene Expression experiments by highlighting mRNA Tag Profiling. Throughout the talk we will give specific examples about collecting and analyzing tag profiling data and show how the Geospiza FinchLab solves challenges related to laboratory setup and managing Next Gen data and analysis workflows.

Working with Workflows

Genetic analysis workflows involve both complex laboratory and data analysis and manipulation procedures. A good workflow management system not only tracks processes, but simplifies the work.

In my last post , I introduced the concept of workflows in describing the issues one needs to think about as they prepare their lab for Next Gen sequencing. To better understand these challenges, we can learn from previous experience with Sanger sequencing in particular and genetic assays in general.

As we know, DNA sequencing serves many purposes. New genomes and genes in the environment are characterized and identified by De Novo sequencing. Gene expression can be assessed by measuring Expressed Sequence Tags (ESTs), and DNA variation and structure can be investigated by resequencing regions of known genomes. We also know that gene expression and genetic variation can also be studied with multiple technologies such as hybridization, fragment analysis, and direct genotyping and it is desirable to use multiple methods to confirm results. Within each of these general applications and technology platforms, specific laboratory and bioinformatics workflows are used to prepare samples, determine data quality, study biology, and predict biological outcomes.

The process begins in the laboratory.

Recently I came across a Wikipedia article on DNA sequencing that had a simple diagram showing the flow of materials from samples to data. I liked this diagram, so I reproduced it, with modifications. We begin with the sample. A sample is a general term that describes a biological material. Sometimes, like when you are at the doctor, these are called specimens. Since biology is all around and in us, samples come from anything that we can extract DNA or RNA from. Blood, organ tissue, hair, leaves, bananas, oysters, cultured cells, feces, you-can-image-what-else, can all be samples for genetic analysis. I know a guy who uses a 22 to collect the apical meristems from trees to study poplar genetics. Samples come from anywhere.

With our samples in hand, we can perform genetic analyses. What we do next depends on what we want to learn. If we want to sequence a genome we're going to prepare a DNA library by randomly shearing the genomic DNA and cloning the fragments into sequencing vectors. The purified cloned DNA templates are sequenced and the data we obtain are assembled into larger sequences (contigs) until, hopefully, we have a complete genome. In resequencing and other genetic assays, DNA templates are prepared from sample DNA by amplifying specific regions of a genome with PCR. The PCR products, amplicons, are sequenced and the resulting data are compared to a reference sequence to identify differences. Gene expression (EST and hybridization) analysis follows similar patterns except that RNA is purified from samples and then converted to cDNA using RT-PCR (Reverse Transcriptase PCR, not Real Time PCR - that's a genetic assay).

From a workflow point of view, we can see how the physical materials change throughout the process. Sample material is converted to DNA or RNA (nucleic acids), and the nucleic acids are further manipulated to create templates that are used for the analytical reaction (DNA sequencing, fragment analysis, RealTime-PCR, ...). As the materials flow through the lab, they're manipulated in a variety of containers. A process may begin with a sample in a tube, use a petri plate to isolate bacterial colonies, 96-well plates to purify DNA and perform reactions, and 384-well plates to collect sequence data. The movement of the materials must be tracked, along with their hierarchical relationships. A sample may have many templates that are analyzed, or a template may have multiple analyses. When we do this a lot we need a way to see where our samples are in their particular processes. We need a workflow management system, like FinchLab.