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

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.

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.

Managing Digital Gene Expression Workflows with FinchLab

Last Wed (4/23) Illumina hosted a Geospiza presentation featuring how FinchLab supports mRNA tag profiling experiments. We had a great turnout and the presentation is posted on the Illumina web site.

In the webninar we talked about:

• Next Gen sequencing applications
• How the Illumina Genome Analyzer makes mRNA Tag Profiling more sensitive by looking at some features of mRNA Tag Profiling data sets with FinchLab
  
• Setting up and tracking laboratory workflows with FinchLab
• Why it is important to link the laboratory work and data analysis work
• Setting up data analysis and reviewing results with FinchLab
  
• Using hosted solutions to overcome the significant data management challenges that accompany Next Gen technologies

Over the coming weeks and months we'll explore the above points through multiple posts. In the meantime, get the presentation and enjoy.

From Sample to Results: Managing Illumina Data Workflow with FinchLab

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.

Digital Gene Expression with Next Gen Sequencing

Next Gen Sequencing is changing how we approach problems ranging from whole genome shotgun sequencing, to variation analysis, to gene expression, to structural genomics. Next week, April 23rd, Geospiza will present a webinar on managing Digital Gene Expression experiments and data with FinchLab. The webinar is hosted by Illumina as part of their ongoing webinar series on Next Gen sequencing.

Abstract

Next Gen sequencers enable researchers to perform new and exciting experiments like digital gene expression. Next Gen sequencers, however, also expose researchers to unprecedented experimental data volume and the need for new tools to support these projects. A single run of the Illumina Genome Analyzer, for example, can generate terabytes of data and 100s of thousands of files. To manage these projects effectively, researchers will need new software systems to quickly track samples, access and analyze the key results files produced by these runs and focus on the science, rather than IT.

In this webinar, Geospiza will demonstrate how the FinchLab Next Gen Edition workflow software can be used track samples, quality review data, and characterize the biological significance of an Illumina dataset while streamlining the entire process from sample to result for a Digital Gene Expression experiment.

Hope to see you there.

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.

What's a Bustard?

For that matter, what's a Firecrest? or a Gerald? Many with an Illumina Genome Analyzer are now learning these are the directories that have the data they may be interested in.

What's in those directories?

In this post, we explore some of the data in the directories, talk about what data might be important, and use FinchLab Next Gen Edition (FinchLab NG) to look at some of the files. In the Next Gen world we are also going to be learning about the data life cycle. When you are thinking about how to store three or four or ten terabytes (TB) of data for each run, and considering that you might run your instrument 40 or 50 times or more in the next year, you might stop and ask the question, "how much of that data is really important and for how long?" That's the data life cycle. It's going to be important.

To begin our understanding, let's look at the data being created in a run. When an Illumina Genome Analyzer (aka Solexa) collects data, many things happen. First, images are collected for each cycle in a run and tile in a lane on a slide. They're pretty small, but there are a lot, maybe 360,000 or so and they add up to the terabytes we talk about. These images are analyzed to create tens of thousands (about 100 gigabytes [GB] worth) of "raw intensity files" that go in the Firecrest directory. Next, a base-calling algorithm reads the raw intensity files to create sequence, quality and other files (about 80 GB worth) that go in the Bustard directory. The last step is the Eland program pipleline. It reads the Bustard files, aligns their data to reference sequences, makes more quality calculations, and creates more files. These data go in the Gerald directory to give about 25 or 30 GB of sequence and quality data.

So, what's the best data to work with? That depends on what problem you are trying to solve. Specialists developing new basecalling tools or alignment tools might focus on the data in Firecrest and Bustard. Most researchers, however, are going to work with data in the Gerald directory. That reduces our TB problem down to a tens of GB problem. That's a big difference!

FinchLab NG can help.

FinchLab NG gives you the LIMS capabilities to run your Next Gen laboratory workflows and track which samples go on which slides and where on the slide the samples go. We call this part the run. When a run is complete you can link the data on your filesystem to FinchLab NG and use the web interfaces to explore the data. You can also link specific data files to samples. So, if you are sharing data or operating a core lab your researchers can easily access their data through their Finch account.

The screen shot below gives an example of how the HTML quality files can be explored. It shows two windows, the one on the left is the FinchLab NG with data for a Solexa run. You can see that the directory has 3606 files and a number are htm summary files. You can find these 12 files in that directory of 3606 files entering "htm" in the Finch Finder.The window on the right was obtained by clicking the "Intensity Plots" link that is directly below the info table and just above the data list. In this example the intensity plots are shown for each tile of the 8 lanes on the slide. To see this better click the image and zoom in with your browser.