Databases of databases

To kick off 2011, let’s talk about databases. Our ability to collect ever increasing amounts of data at faster rates is driving a corresponding increase in specialized databases that organize data and information. A recent editorial publication in the journal Database, yes we now have a journal called Database, proposed a draft information specification for biological databases.

Why?

Because we derive knowledge by integrating information in novel ways, hence we need ways to make specialized information repositories interoperate.

Why do we need standards?

Because the current and growing collection of specialized databases are poorly characterized with respect their mission, categorization, and practical use. 

The Nucleic Acids Research (NAR) database of databases illustrates the problem well. For the past 18 years, every Jan 1, NAR has published a database issue in which new databases are described along with others that have been updated. The issues typically contain between 120 and 180 articles representing a fraction of the databases listed by NAR. When one counts the number of databases, explores their organization, and reads the accompanying editorial introductions several interesting observations can be made.

First, over the years there has been a healthy growth of databases designed to capture many different kinds of specialized information. This year’s NAR database was updated to include 1330 databases, which are organized into 14 categories and 40 subcategories. Some categories like Metabolic and Signaling Pathways, or Organelle Databases look highly specific whereas others such as Plant Databases, or Protein Sequence Databases are general. Subcategories have similar issues. In some cases categories and subcategories make sense. In other cases it is not clear what the intent of categorization is. For example the category RNA sequence databases lists 73 databases that appear to be mostly rRNA and smallRNA databases. Within the list are a couple of RNA virus databases like the HIV Sequence Database and Subviral RNA Database. These are also listed under subcategory Viral Genome Databases in the Genomics Databases (non-vertebrates) category in an attempt to cross reference the database under different category. OK, I get that. HIV is in RNA sequences because it is an RNA virus. But what about the hepatitis, influenza, and other RNA viruses listed under Viral genome databases, why aren’t they in RNA sequences? While I’m picking on RNA sequences, how come all of the splicing databases are listed in a subcategory in Nucleotide Sequence Databases? Why isn’t RNA Sequences a subcategory under Nucleotide Sequence Databases? Isn’t RNA composed of Nucleotides? It makes one wonder how the databases are categorized.

Categorizing databases to make them easy to find and quickly grock their utility is clearly challenging. This issue becomes more profound when the level of database redundancy, determined from the databases’ names, is considered. This analysis is, of course, limited to names that can be understood. The Hollywood Database for example does not store Julia Roberts' DNA, rather it is an exon annotation database. Fortunately, many databases are not named so cleverly. Going back to our RNA Sequence category we can find many ribosomal sequence databases, several tRNA databases, and general RNA sequence databases. There is even a cluster of eight microRNA databases all starting with an “mi” or “miR” prefix. There are enough rice (18) and Arabidopsis databases (28) that they get their own subcategories. Without too much effort one can see there are many competing resources, yet choosing the ones best suited for your needs would require a substantial investment of time to understand how these databases overlap, where they are unique, and, in many cases, navigate idiosyncratic interfaces and file formats to retrieve and utilize their information. When maintenance and overall information validity is factored in, the challenge compounds.

How do things get this way?

Evolution. Software systems, like biological systems change over time. Databases like organisms evolve from common ancestors. In this way, new species are formed. Selective pressures enhance useful features and increase their redundancy, and cause extinctions. We can see these patterns in play for biological databases by examining the tables of contents and introductory editorials for the past 16 years of the NAR database issue. Interestingly, in the past six or seven years, the issue’s editor has made the point of recording the issue’s anniversary making 2011 the 18th year of this issue. Yet, easily accessible data can be obtained only to 1996, 16 years ago. History is hard and incomplete, just like any evolutionary record.

We cannot discuss database diversity without trying to count the number of species. This is hard too. NAR is an easily assessable site with a list that can be counted. However, one of the databases in the 1999 and 2000 NAR database issues was DBcat, a database of databases. At that time it tracked more than 400 and 500 databases, while NAR tracked 103 and 227 databases, respectively, a fairly large discrepancy. DBcat eventually went extinct due to lack of funding, a very significant selective pressure. Speaking of selective pressure it would be interesting to understand how databases are chosen to be included in NAR’s list. Articles are submitted, and presumably reviewed, so there is self selection and some peer review. The total number of new databases, since 1996, is 1,308, which is close to 1,330, so the current list likely an accumulation of database entries submitted over the years. From the editorial comments, the selection process is getting more selective as statements have changed from databases that might be useful (2007) to "carefully selected" (2010, 2011). Back in 2006, 2007, and 2008 there was even mention of databases being dropped due to obsolescence.

Where do we go from here?

From the current data, one would expect that 2011 will see between 60 and 120 new databases appear. Groups like BioDBcore and other committees and trying to encourage standardization with respect to a database’s meta data (name, URL, contacts, when established, data stored, standards used, and much more). This may be helpful, but when I read the list of proposed standards, I am less optimistic because the standards do not address the hard issues. Why, for example, do we need 18 different rice databases or eight “mi*” databases. For that matter, why do we have a journal called Database? NAR does a better job listing databases than Database and being a journal about databases wouldn't it be a good idea if Database tracked databases? And, critical information, like usage, last updated, citations, and user comments, which would help one more easily evaluate whether to invest time investigating the resource, are missing.

Perhaps we should think about better ways to communicate a database’s value to the community it serves, and in the case where several databases do similar things, standards committees should discuss how to bring the individual databases together to share their common features and accentuate their unique qualities to support research endeavors. After all, no amount of annotation is useful if I still have too many choices to sort through.

Genomics and Public Health: Epidemiology in Haiti

While there is debate and discussion about how genomics will be used in public health at a personalized medicine level, it is clear that rapid high-troughput DNA sequencing has immediate utility in epidemiological applications that seek to understand the origins of disease outbreaks.

In the most recent application, published this month, researchers at Pacific Biosciences (PacBio) used their PacBio RS sequencing system to identify the origins of the cholera outbreak in Haiti.  According to the article, cholera as been present in Latin America since 1991, but is had not been epidemic in Haiti for at least 100 years.  When the recent outbreak began in October this year, it was important to determine the origins of the disease, especially since it was concluded that Haiti had a low cholera risk following the earthquake. Understanding the origins of a disease can help define virulence and resistance mechanisms to guide therapeutic approaches.

Sequencing organisms to discover their origins in outbreaks is not new. What is new is the speed at which this can be done.  For example, it took two months for the SARS virus to be sequenced after the epidemic started. In the recent work, the sequencing was completed in four days.  And, it was not just one isolate that was sequenced, but two, with 40 times larger genomes.

When the Haiti sequences were compared to the sequences of 23 other V. cholera strains, the data indicated that the Haiti strain matched strains from South Asia more closely than the endemic strains from Latin America. This finding tells us that the stain was likely introduced, perhaps by aid workers.  Additional sequence analysis of the colera toxin genes also confirmed that the strain causing the epidemic produces more severe disease. From a public health perspective this is important because the less virulent, easier to treat, endemic stains can be displaced by more aggressive strains. The good news is that the new strain is sensitive to tetracycline, a first line antibiotic.

The above work clearly demonstrates how powerful DNA sequencing is in strain identification. The authors favored single molecule sequencing on PacBio because its cycle time is shorter than second generation technologies like Illumina, SOLiD, and 454 and its long read lengths better handle repeats.While these points may be argued by the respective instrument vendors, it is clear is that we are entering an era where we can go very quickly from isolating infectious agents to classifying them at very high resolution in unprecedented ways.  DNA sequencing will have a significant role in diagnosing infectious agents.

Further reading:

Scientists Trace Origin of Recent Cholera Epidemic in Haiti, HHMI News
The Origin of the Haitian Cholera Outbreak Strain, NEJM 2010

GeneSifter Lab Edition 3.16

Recently, we released GeneSifter Laboratory Edition (GSLE) 3.16. Over 40 new features were added to improve laboratory workflow support, enhance multiplexing, make orders and their data easier to manage, and extend the system's application programming interfaces (APIs).

Laboratory Workflow Support

Several new features will make working in the lab easier and improve data tracking and how GSLE is used. Workflows now include the ability to validate data using data ranges, and use laboratory defined lists to track the details about how samples are processed.  For Next Gen Sequencing (NGS), sample multiplexing has been extended to make it possible to define multiplexes and associate Barcodes, MIDs, or Indexes (or whatever else you like to call the little adapter tags) at any step of your process. Also, for those who use specific naming conventions to track process steps, sample naming conventions can be defined for process steps.  Finally, to use the above features and view data, the workflow editor has been streamlined to simplify data entry, workflows can be versioned to track your lab's process improvements, and a new report builder makes workflow data easy to access and view.

To better track samples organized in tubes, plates, slides, flowcells, freezers, buildings, and the universe, we have introduce a new tool called containers. Containers can be defined by the laboratory and are used to link samples to specific actions like sample storage or a data collection run. For example, Illumina flowcells can be created and have samples added. The flowcell can be run immediately or stored following cluster generation.  When samples are added to a flowcell, a new visual interface simplifies adding mixtures of multiplexed samples, single samples in individual lanes, or single samples spread over several lanes. Other kinds of containers track sample storage and can be updated with bed scanners to track the arrangement of samples as they move through the lab. And, containers can contain workflows to manage more complex procedures.

Order and Sample Management

Details matter, and GSLE 3.16 improves how you view them. Lab samples (templates) views now show all laboratory data associated with a sample on a single page. These data are organized in sections that can be collapsed so you can see the big picture, or focus on specific details. We also updated views of orders to make it easier to view their progress and made the orders easier to access when viewing data for release.

API Support

Some groups use GSLE as a stand alone system, others use GSLE in conjunction with external systems in commercial and very high-throughput production environments.  To support the later case, GSLE has a set of rich data-driven APIs that enable system to system communication. In 3.16, the APIs have been extended to edit certain kinds of stored data, add large batches of laboratory samples, upload container maps  to track samples, and manipulate charge codes with accounting systems.

Contact sales@geospiza.com to learn more.

Samples to Knowledge

Today Geospiza and Ingenuity announced a collaboration to integrate our respective GeneSifter Analysis Edition (GSAE) and Ingenuity Pathway Analysis (IPA) software systems. 

Why is this important?

Geospiza has always been committed to providing our customers the most complete software systems for genetic analysis. Our LIMS [GeneSifter Laboratory Edition] and GSAE have worked together to form a comprehensive samples to results platform. From core labs, to individual research groups, to large scale sequencing centers, GSLE is used for collecting sample information, tracking sample processing, and organizing the resulting DNA sequences, microarray files, and other data. Advanced quality reports keep projects on track and within budget.  

For many years, GSAE has provided a robust and scalable way to scientifically analyze the data collected for many samples. Complex datasets are reduced and normalized to produce quantitative values that can be compared between samples and within groups of samples. Additionally, GSAE has integrated open-source tools like Gene Ontologies and KEGG pathways to explore the biology associated with lists of differentially expressed genes. In the case of Next Generation Sequencing, GSAE has had the most comprehensive and integrated support for the entire data analysis workflow from basic quality assessment to sequence alignment and comparative analysis. 

With Ingenuity we will be able to take data-driven biology exploration to a whole new level.  The IPA system is a leading platform for discovering pathways and finding the relevant literature associated with genes and lists of genes that show differential expression in microarray analysis. Ingenuity's approach focuses on combining software curation with expert review to create a state-of-the-art system that gets scientists to actionable information more quickly than conventional methods.  

Through this collaboration two leading companies will be working together to extend their support for NGS applications. GeneSifter's pathway analysis capabilities will increase and IPA's support will extend to NGS. Our customers will benefit by having access to the most advanced tools for turning vast amounts of data into biologically meaningful results to derive new knowledge.

Samples to Results^TM becomes Samples to Knowledge^TM

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

Geospiza in the News

Our release that Eurofins MWG Operon has standardized their worldwide DNA sequencing operations on the GeneSifter LIMS is important for several reasons.

Most significantly, as noted in the press release, we were chosen because of our deep support for both Sanger and Next Generation DNA sequencing (NGS). Our support for Sanger has been developed and improved on since 1997. No other LIMS product provides a comparable depth when it comes to understanding how Sanger sequencing (and fragment analysis) services are supported in the laboratory, or how Sanger data should be summarized in groups, by instruments, or runs to make quality control assessments and decisions. Finally, GeneSifter is the only Sanger-based product that allows sequence editing and tracks versions of those edits. This feature is made possible through integration with our popular FinchTV program that also provides a jumping off point for analyzing individual sequences with BLAST.  

The news also points out that Sanger sequencing continues to play an important role in the DNA sequencing ecosystem. No other sequencing technology is suitable for doing a large number of small things. Thus, researchers access to Sanger systems will continue to play an important role in confirming clones, sequencing individual PCR products, and validating the results of NGS experiments. As many labs sunset their Sanger equipment in favor of NGS equipment, services such as those provided by Eurofins MWG Operon will continue to grow in importance, and we are thrilled to help. 

Of course, the GeneSifter LIMS (GSLE) does much more than Sanger sequencing. Our NGS support is highly regarded, along with our microarray support as discussed in recent blogs announcing the current GSLE release and our recent news about PacBio.

Other product strengths include GSLE's configurability and application programming interfaces (APIs). Supporting worldwide deployment means non-english speakers within labs need to be able to communicate and use their words in forms and instructions. Using GSLE's standard web interfaces groups can internationalize parts of the system with their own language terms and comments to help their work. Finally, using GSLE's APIs Eurofins MWG Operon has easily incorporated the system into their web site to provide their customers with a smooth experience. 

The Interface Needs an Interface

A recent publication on Galaxy proposes that it is the missing graphical interface for genomics. Let’s find out.

The tag line of Michael Schatz’s article inGenome Biology states, “The Galaxy package empowers regular users to perform rich DNA sequence analysis through a much-needed and user-friendly graphical web interface.” I would take this description and Schatz’s later comment, “the ambitious goal of Galaxy is to empower regular users to carry out their own computational analysis without having to be an expert in computational biology or computer science” to mean that someone, like a biologist, who does not have much bioinformatics or computer experience could use the system to analyze data from a microarray or next gen sequencing experiment.

The Galaxy package is a software framework running bioinformatics programs and assembling those programs into complex pipelines referred to as workflows. It employs a web-interface and ships with a collection of tools to get a biologist quickly up and running, with examples. Given that Galaxy targets bioinformatics, it is reasonable to assume that its regular users are biologists. So, the appropriate question would be, how much does a biologist have to know about computers to use the system?

To test this question I decided to install the package. I have a Mac, and as a biologist, whether I’m on a Mac or PC, I expect that, if I’m given a download option, the software will easy to download and can be installed using a double click installer program. Galaxy does not have this. Instead, it uses a command line tool (oh, I need to use terminal) that requires Mercurial (hg). Hmm, what’s Mercurial? Mercurial is a version control system that supports distributed development projects. This not quite what I expected, but I’ll give it a try. I go to the hg (someone has a chemistry sense of humor) site and without too much trouble find a Mac OS X package, which uses a double click installer program. I’m in luck - of course I’ll ignore the you might have to add export LC_ALL=en_US.UTF-8, and export LANG=en_US.UTF-8 to your ~/.profile file - hg installs and works.

Now back to my terminal, I ignore the python version check and path setup commands, and type hg clone http://www.bx.psu.edu/hg/galaxy galaxy_dist; things happen. I follow the rest of the instructions - cd galaxy_dist; sh setup.sh - finally I start galaxy with the sh run.sh command. I go to my web browser and type http://localhost:8080 and galaxy is running! Kudos to the galaxy team for making a typically complicated process relatively simple. I’m also glad that I had none of the possible documented problems. However, to get this far, I had to tap into my unix experience.

With Galaxy running, I can now see if Schatz’s claims stand up. What should I do? The left hand menu gives me a huge number of choices. There are 31 categories that organize input/output functions, file manipulation tools, graphing tools, statistical analysis tools, analysis tools, NGS tools, and SNP tools, perhaps 200 choices of things to do. I’ll start with something simple like displaying the quality values in an Illumina NGS file. To do this, I click on “upload file” under the get data menu. Wow! There are 56 choices of file formats - and 17 have explanations. Fortunately there is an auto-detect. I leave that option, go the choose file button to select an NGS file on my hard drive and load it in. I’ll ignore the comment that files greater than 2GB should be uploaded by an http/ftp URL, because I don’t know what they are talking about. Instead I’ll make a small test file with a few thousand reads. I’ll also ignore the URL/text box and choice to convert spaces to tabs and the genome menu that seems to have hundreds of genomes loaded as these options have nothing to do with a fastq file. I’ll assume “execute” means “save” and click it.

After clicking execute some activity appears in the right hand menu indicating that my file is being uploaded. After a few minutes, my NGS file is in the system. To look at quality information, I select the “NGS: QC and manipulation” menu to find a tool. There are 18 options for tools to split files, join files, convert files, and convert quality data in files; this stuff is complicated. Since all I want to do is start with creating some summary statics, I find and select "FASTQ summary statistics." This opens a page in the main window where I can select the file that I uploaded and click the execute button to generate a big 20 column table that contains one row per base in the reads. The columns contain information about the frequency of bases and statistical values derived from the quality values in the file. These data are displayed in a text table that is hard to read, so the next step is to graphically view the data in histogram and box plots.

Graphing tools are listed under a different menu, “Graph/Display Data.” I like box plots, so I’ll select that choice. In the main window I select my summary stats file, create a title for the plot, set the plot’s dimensions (in pixels), define x and y axes titles, and select the columns from the big table that contains the appropriate data. I click the execute button to create files containing the graphs. Oops, I get an error message. It says “/bin/sh gnuplot command not found.” I have to install gnuplot. To get gnuplot going I have to download source, compile the package, and install. To do this I will need developer tools installed along with gnuplot’s other dependencies for image drawing. This is getting to be more work than I bargained for ...

When Schatz said “regular user” he must have meant unix savvy biologist that understands bioinformatics terminology, file formats, and other conventions, and can install software from source code.

Alternatively, I can upload my data into GeneSifter, select the QC analysis pipeline, navigate to the file summary page, and click the view results link. After all, GeneSifter was designed by biologists for biologists.

Geospiza, SAIC, and PacBio

On Tue. 8/31, we release news about our collaboration with SAIC whereby we will enhance GeneSifter's support for the PacBio RS, Pacific Biosciences’ Single Molecule Real Time (SMRT™) sequencing system.

Geospiza is excited to work with the group at SAIC-Frederick. SAIC-Frederick is the operations and technical support contractor for the National Cancer Institute's research, so the effort will ultimately benefit researchers at NCI. Because several of our customers are part of PacBio's early access effort we will be able to get a broad view of PacBio RS platform's strengths and applications to support our many other customers who are interested in getting the system.

Most importantly, this news demonstrates our commitment to supporting all of the sequencing platforms. We already have great support for the Illumina GA and HiSeq, SOLiD, and 454. PacBio, IonTorrent can be supported through our configuration interfaces, and new releases will include specialized features to enhance our customers' experiences working with each instrument's unique qualities.

Look for announcements as new releases of GeneSifter Lab Edition and Analysis Edition roll out over the coming months. In the meantime, check out the full release including a nice quote from Eric Schadt, PacBio's CSO.

Sneak Peak: SOLiD RNA-Seq Data Analysis featuring Geospiza

Next Tue. Aug. 31, Life Technologies (LT) will host webinar featuring Geospiza.  The abstract is posted below.  To register, contact your LT rep.

SOLiD RNA-Seq Data Analysis featuring Geospiza

Presented by: Hugh Arnold, PhD.
Whole Transcriptome Analysis (WTA) allows researchers to assess the whole genome for coding as well as non-coding RNAs at an unprecedented level. In addition to allowing for the complete characterization of all known RNAs in a sample (gene level expression, exon usage, splice junction, single nucleotide variants, insertions and deletions), these applications are also ideal for the identification of novel RNAs as well as novel splicing events. In this talk we will present the capabilities of GeneSifter Analysis Edition (GSAE) in supporting researchers doing WTA on SOLiD™ sequencing platforms. Using data generated from paired-end SOLiD 4™ sequencing runs of Human Brain Reference and Universal Human Reference libraries, we will cover the analysis process used in GSAE for taking raw color space reads and getting to application specific results across multiple samples. More specifically, we will highlight the open-source, best analysis practices from published literature used in GSAE as well as discuss some of the challenges and solutions for WTA analysis. Lastly, we will review a suite of application specific tools available in GSAE that allow researchers to characterize and visualize differential transcript expression, differential splicing, and identify single nucleotide variants (SNVs) in a biologically meaningful context, across many whole transcriptomes.