2009 Review

The end of the year is a good time to reflect, review accomplishments, and think about the year to come. 2009 was a good year for Geospiza’s customers, with many exciting accomplishments for the company. Highlights are reviewed below.

Two products form a complete genetic analysis system

Geospiza’s two core products, GeneSifter Laboratory Edition (GSLE) and GeneSifter Analysis Edition (GSAE), help laboratories do their work and scientists analyze their data. GSLE is the LIMS (Laboratory Information Management System) that laboratories, from service labs to high-throughput data production centers, use to collect information about samples, track and manage laboratory procedures, organize and process data, and deliver data and results back to researchers. GSLE supports traditional DNA sequencing (Sanger), fragment analysis, genotyping, microarrays, Next Generation Sequencing (NGS) and other technologies.

In 2008, Geospiza released the third version of the platform (back then it was known as FinchLab). This version launched a new way of providing LIMS solutions. Traditional LIMS systems require extensive programming and customization to meet a laboratory’s specific requirements. They include a very general framework designed to support a wide range of activities. Their advantage is that they are highly customizable. However, this advantage comes at the expense of very high acquisition costs accompanied by lengthy requirements planning and programming before they become operational.

In contrast, GSLE contains default settings that support genetic analysis out-of-the-box, while allowing laboratories to customize operations without programmer support. Default settings in GSLE suppport DNA sequencing, microarray, and genotyping services. The GSLE abstraction layer supports extensive configuration to meet specific needs as they arise. Through this design, the costs of acquiring and operating a high-quality advanced LIMS system are significantly reduced.

Throughout 2009, 100’s of features were added to GSLE to increase support for instruments and data types, and improve how laboratory procedures (workflows) are created, managed, and shared. Enhancements were made to features like experiment ordering, organization, and billing. We also added new application programming interfaces (APIs) to enable integration with enterprise software. Specific highlights included:

• Extending microarray support to include sample sheet generation and automate uploading files
• Improving NGS file and data browsing to simplify the process of searching and viewing the 1000’s of files produced in Next Gen sequencing runs
• Making NGS data downloads, of very large gigabase files, robust and easy
• Adding worksets to group DNA and RNA samples in customized ways that facilitate laboratory processing
• Creating APIs to utilize external password servers and programmatically receive data using GSLE form objects
• Enhancing ways for groups to add HTML to pages to customize their look and feel

In addition to the above features, we’ve also completed development on methods to multiplex NGS samples and track MIDs (molecular identifiers and molecular barcodes), enter laboratory data like OD values and bead counts in batches, create orders with multiple plates, and access SQL queries through an API. Look for these great features and more in the early part of 2010.

GSAE

As noted, GSAE is Geospiza’s data analysis product. While GSLE is capable of running of running advanced data analysis pipelines, the primary focus of data analysis in GSLE is to provide quality control. Thus its data analyses and presentation focus on single samples. GSAE provides the infrastructure and tools to compare the results between samples. In the case of NGS, GSAE also provides more reports and data interactions. GSAE began as a web-based microarray data analysis platform making it well suited for NGS-based gene expression assays. Over 2009 many new features were added to extend its utility to NGS data analysis with a focus on whole transcriptome analysis. Highlights included:

• Developing data analysis pipelines for RNA-Seq, Small RNA, ChIP-Seq, and other kinds of NGS assays
• Adding tools to visualize and discover alternatively spliced transcripts in gene expression assays
• Extending expression analysis tools to include interactive volcano plots, unbalanced two-way ANOVA computations
• Increasing NGS transcriptome analysis capabilities to include variation detection and visualization

The above features fulfill the requirements needed to make a platform complete for both NGS and microarray-based gene expression analysis. And, the addition of variation detection and visualization lays the groundwork for GSAE to extend its market leadership to resequencing data analysis.

Geospiza Research

In 2009 Geospiza won two research awards in the form of Phase II STTR and Phase I SBIR grants. The STTR project is researching new ways to organize, compress, and access NGS data by adapting HDF technologies to bioinformatics. Through this work we are developing a robust data management infrastructure that supports our NGS sequencing analysis pipelines and interactive user interfaces. The second award targets NGS-based variation detection. This work began in the last quarter of the year, but is already delivering new ways to identify and visualize variants in RNA-Seq and whole transcriptome analysis.

To learn more about our progress in 2009, visit our news page. It includes our press releases and reports in the news, publications citing our software, and webinars where we have presented our latest and greatest.

As we close 2009, we especially want to thank our customers and collaborators for their support in making the year successful and we look forward to an exciting year ahead in 2010.

Expeditiously Exponential: Genome Standards in a New Era

One of the hot topics of 2009 has been the exponential growth in genomics and other data and how this growth will impact data use and sharing. The journal Science explored these issues in its policy forum in Oct. In early November, I discussed the first article, which was devoted to sharing data and data standards. The second article, listed under the category “Genomics,” focuses on how genomic standards need to evolve with new sequencing technologies.

Drafting By

The premise of the article “Genome Project Standards in a New Era of Sequencing” was to begin a conversation about how to define standards for sequence data quality in this new era of ultra-high throughput DNA sequencing. One of the “easy” things to do with Next Generation Sequencing (NGS) technologies is create draft genome sequences. A draft genomic sequence is defined as a collection of contig sequences that result from one, or a few, assemblies of large numbers of smaller DNA sequences called reads. In traditional Sanger sequencing a read was between 400 and 800 bases in length and came from a single clone, or sub-clone of a large DNA fragment. NGS reads, come from individual molecules in a DNA library and vary between 36 and 800 bases in length depending on the sequencing platform being used (454, Illumina, SOLiD, or Helicos).

A single NGS run can now produce enough data to create a draft assembly for many kinds of organisms with smaller genomes such as viruses, bacteria, and fungi. This makes it possible to create many draft genomes quickly and inexpensively. Indeed the article was accompanied by a figure showing that the current growth of draft sequences exceeds the growth of finished sequences by a significant amount. If this trend continues, the ratio of draft to finished sequences will grow exponentially into the foreseeable future.

Drafty Standards

The primary purpose for a complete genome sequence is to serve as a reference for other kinds of experiments. A well annotated reference is accompanied by a catalog of genes and their functions, as well as an ordering of the genes, regulatory regions, and the sequences needed for evolutionary comparisons that further elucidate genomic structure and function. A problem with draft sequences is that they can contain a large numbers of errors that range from incorrect base calls to more problematic mis-assemblies that place bases or groups of bases in the wrong order. Because, these holes leave some sequences are more drafty than others, they are less useful in fulfilling their purpose as reference data.

If we can describe the draftiness of a genome sequence we may be able to weight its fitness for a specific purpose. The article went on to tackle this problem by recommending a series of qualitative descriptions that describe levels of draft sequences. Beginning with the Standard Draft, or an assembly of contigs of unfiltered data from one or more sequencing platforms, the terms move through High-Quality Draft, to Improved High-Quality Draft, to Annotation-Directed Improvement, to Noncontiguous Finished, to Finished. Finished sequence is defined as less than 1 error per 100,000 bases and each genomic unit (chromosomes or plasmids that are capable of replication) is assembled into a single contig with a minimal number of exceptions. The individuals proposing these standards are a well respected group in the genome community and are working with the database groups and sequence ontology groups to incorporate these new descriptions into data submissions and annotations for data that may be used by others.

Given the high cost and lengthy time required to finish genomic sequences, finishing every genome to a high standard is impractical. If we are going to work with genomes that are finished to varying degrees, systematic ways to describe the quality of the data are needed . This policy recommendations are a good start, but more needs to be done to make the proposed standards useful.

First, standards need to be quantitative. Qualitative descriptions are less useful because they create downstream challenges when reference data are used in automated data processing and interpretation pipelines. As the numbers of available genomes grow into the thousands and tens of thousands, subjective standards make the data more and more cumbersome and difficult to review. Moreover without quantitative assessment, how will one know when they have an average error rate of 1 in 100,000 bases? The authors intentionally avoided recommending numeric thresholds in the proposed standards because the instrumentation and sequencing methodologies are changing rapidly. This may be true, but future discussions nevertheless should focus on quantitative descriptions for that very reason. It is because data collection methods and instrumentation are changing rapidly that we need measures we can compare. This is the new world.

Second, the article fails to address how the different standards might be applied in a practical sense. For example, what can I expect to do with a finished genome that I cannot do with a nearly finished genome? What is a standard draft useful for? How should I trust my results and what might I expect to do to verify a finding? While the article does a good job describing the quality attributes of the data that genome centers might produce, the proposed standards would have broader impact if they could more specifically set expectations of what could be done with data.

Without this understanding, we still won't know when when our data are good enough.