GeneSifter Laboratory Edition Update

GeneSifter Laboratory Edition has been updated to version 3.13. This release has many new features and improvements that further enhance its ability to support all forms of DNA sequencing and microarray sample processing and data collection.

Geospiza Products

Geospiza's two primary products, GeneSifter Laboratory Edition (GSLE) and GeneSifter Analysis Edition (GSAE), form a complete software system that supports many kinds of genomics and genetic analysis applications. GSLE is the LIMS (Laboratory Information Management System) that is used by core labs and service companies worldwide that offer DNA sequencing (Sanger and Next Generation), microarray analysis, fragment analysis and other forms of genotyping. GSAE is the analysis system researchers use to analyze their data and make discoveries. Both products are actively updated to keep current with latest science and technological advances.

The new release of GSLE helps labs share workflows, perform barcode-based searching, view new data reports, simplify invoicing, and automate data entry through a new API (application programming interface).

Sharing Workflows

GSLE laboratory workflows make it possible for labs to define and track their protocols and data that are collected when samples are processed. Each step in a protocol can be configured to collect any kind of data, like OD values, bead counts, gel images and comments, that are used to record sample quality. In earlier versions, protocols could be downloaded as PDF files that list the steps and their data. With 3.13, a complete workflow (steps, rules, custom data) can be downloaded as an XML file that can be uploaded into another GSLE system to recreate the entire protocol with just a few clicks. This feature simplifies protocol sharing and makes it possible for labs to test procedures in one system and add them to another when they are ready for production.

Barcode Searching and Sample Organization

Sometimes a lab needs to organize separate tubes in 96-well racks for sample preparation. Assigning each tube's rack location can be an arduous process. However, if the tubes are labeled with barcode identifiers, a bed scanner can be used to make the assignments. GSLE 3.13 provides an interface to upload bed scanner data and assign tube locations in a single step. Also, new search capabilities have been added to find orders in the system using sample or primer identifiers. For example, orders can be retrieved by scanning a barcode from a tube in the search interface.


Reports and Data

Throughout GSLE, many details about data can be reviewed using predefined reports. In some cases, pages can be quite long, but only a portion of the report is interesting. GSLE now lets you collapse sections of report pages to focus on specific details. New download features have also been added to better support access to those very large NGS data files.

GSLE has always been good at identifying duplicate data in the system, but not always as good at letting you decide how duplicate data are managed. Managing duplicate data is now more flexible to better support situations where data need to be reanalyzed and reloaded.

The GSLE data model makes it possible to query the database using SQL. In 3.13, the view tables interface has been expanded so that the data stored in each table can be reviewed with a single click.

Invoices

Core lab's that send invoices will benefit from changes that make it possible to download many PDF formatted orders and invoices into a single zipped folder. Configurable automation capabilities have also been added to set invoice due dates and generate multiple invoices from a set of completed orders.

API Tools

As automation and system integration needs increase, external programs are used to enter data from other systems. GSLE 3.13 supports automated data entry through a novel self-documenting API. The API takes advantage of GSLE's built in data validation features that are used by the system's web-based forms. At each site, the API can be turned on and off by on-site administrators and its access can be limited to specific users. This way, all system transactions are easily tracked using existing GLSE logging capabilities. In addition to data validation and access control, the API is self-documenting. Each API containing form has a header that includes key codes, example documentation, and features to view and manually upload formatted data to test automation programs and help system integrators get their work done. GSLE 3.13 further supports enterprise environments with an improved API that is used to query external password authentication servers.

Maq Attack

Maq (Mapping and Assembly with Quality) is an algorithm, developed at the Sanger center, for assembling Next Gen reads onto a reference sequence. Since Maq is widely used for working with Next Generation DNA sequence data, we chose to include support for Maq in our upcoming release of FinchLab. In this post, we will discuss integrating secondary analysis algorithms like Maq with the primary analysis and workflows in FinchLab.

Improving laboratory processes through immediate feedback

The cost to run Next Generation DNA sequencing instruments and the volume of data produced make it important for labs to be able to monitor their processes in real time. In the last post, I discussed how labs can get performance data and accomplish scientific goals during the three stages of data analysis. To quickly review: Primary data analysis involves converting image data to sequence data. Secondary data analysis involves aligning the sequences from the primary data analysis to reference data to create data sets that are used to develop scientific information. An example of a secondary analysis step would be assembling reads into contigs when new genomes are sequenced. Unlike the first two stages, where much of the data is used to detect errors and measure laboratory performance, the last stage is focused on the science. In the Tertiary data analyses genomes are annotated, and data sets are compared. Thus the tertiary analyses are often the most important in terms of gaining new insights. The data used in this phase must be vetted first. It must be high quality and free from systemic errors.

The companies producing Next Gen systems recognize the need to automate primary and secondary analysis. Consequently, they provide some basic algorithms along with the Next Gen instruments. Although these tools can help a lab get started, many labs have found that significant software development is needed on top of the starting tools if they are to fully automate their operation, translate output files into meaningful summaries, and give users easy access to the data. The starter kits from the instrument vendors can also be difficult to adapt when performing other kinds of experiments. Working with Next Gen systems typically means that you will have deal with a lot of disconnected software, a lack of user interfaces, and diverse new choices for algorithms when it comes to getting your work done.

FinchLab and Maq in an integrated system

The Geospiza FinchLab integrates analytical algorithms such as Maq into a complete system that encompasses all the steps in genetic analysis. Our Samples to Results platform provides flexible data entry interfaces to track sample meta data. The laboratory information management system is user configurable so that any kind of genetic analysis procedure can be run and tracked and most importantly provides tight linkage between samples, lab work, and their resulting data. This system makes it easy to transition high quality primary results to secondary data analysis.

One of the challenges with Next Gen sequencing has been choosing an algorithm for secondary analysis. Secondary data analysis needs to be adaptable to different technology platforms and algorithms for specialized sequencing applications. FinchLab meets this need because it can accommodate multiple algorithms when it comes to secondary and tertiary analysis. One of these algorithms is Maq. Maq attractive because it can be used in diverse applications where reads are aligned to a reference sequence. Among these are Transcriptomics (Tag Profiling, EST analysis, small RNA discovery), Promoter Mapping (CHiP-Seq, DNAase hypersensitivity), Methylation analysis, and Variation Analyses (SNP, CNV). Maq offers a rich set of output files so it can be used to quickly provide an overview of your data and help you verify that your experiment is on track before you invest serious time in tertiary work. Finally Maq is being actively developed and improved and is open-source so it is easy to access and use regardless of affiliation.

Maq and other algorithms are integrated into FinchLab through the FinchLab Remote Analysis Server (RAS). RAS is a lightweight job tracking system that can be configured to run any kind of program in different computing environments. RAS communicates with FinchLab to get the data and return the results. Data analyses are run in FinchLab by selecting the sequence file(s), clicking a link to go to a page and select the analysis method(s) and reference data sets, and then clicking a button to start the work. RAS tracks the details of data processing and sends information back to FinchLab so that you can always see what happening through the web interface.

A basic FinchLab system includes the RAS and pipelines for running Maq in two ways. The first is Tag Profiling and Expression Analysis. In this operation, Maq output files are converted to gene lists with links to drill down into the data and NCBI references. The second option it to use Maq in a general analysis procedure where all the output files are made available. In the next months, new tools will convert more of these files into output that can be added to genome browsers and other tertiary analysis systems.

A final strength of RAS is that it produces different kinds of log files to track potential errors. These kinds of files are extremely valuable in trouble-shooting and fixing problems. Since Next Gen technology is new and still in constant flux, you can be certain that unexpected issues will arise. Keeping the research on track is easier when informative RAS logging and reports help to diagnose and resolve issues quickly. Not only can FinchLab help with Next Gen assays, help solve those unexpected Next Gen problems, multiple Next Gen algorithms can be integrated into FinchLab to complete the story.

Finch 3: Managing Workflows

Genetic analysis workflows begin with RNA or DNA samples and end with results. In between, multiple lab procedures and steps are used to transform materials, move samples between containers, and collect the data. Each kind of data collected and each data collection platform requires that different laboratory procedures are followed. When we analyze the procedures, we can identify common elements. A large number of unique workflows can be created by assembling these elements in different ways.

In the last post, we learned about the FinchLab order form builder and some of its features for developing different kinds of interfaces for entering sample information. Three factors contribute to the power of Finch orders. First, labs can create unique entry forms by selecting items like pull down menus, check boxes, radio buttons, and text entry fields for numbers or text, from a web page. No programming is needed. Second, for core labs with business needs, the form fields can be linked to diverse price lists. Third, the subject of this post, is that the forms are also linked to different kinds of workflows.

What are Workflows?

A workflow is a series of series of steps that must be performed to complete a task. In genetic analysis, there are two kinds of workflows: those that involve laboratory work, and those that involve data processing and analysis. The laboratory workflows prepare sample materials so that data can be collected. For example, in gene expression studies, RNA is extracted from a source material (cells, tissue, bacteria), and converted to cDNA for sequencing. The workflow steps may involve purification, quality analysis on agarose gels, concentration measurements, and reactions where materials are further prepared for additional steps.

The data workflows encompass all the steps involved in tracking, processing, managing, and analyzing data. Sequence data are processed by programs to create assemblies and alignments that are edited or interrogated to create genomic sequences, discover variation, understand gene expression, or perform other activities. Other kinds of data workflows such as microarray analysis, or genotyping involve developing and comparing data sets to gain insights. Data workflows involve file manipulations, program control, and databases. The challenge for the scientist today, and the focus of Geospiza's software development is to bring the laboratory and data workflows together.

Workflow Systems

Workflows can be managed or unmanaged. Whether you work at the bench or work with files and software, you use a workflow any time you carry out a procedure with more than one step. Perhaps you wite the steps in your notebook, check them off as you go, and tape in additional data like spectrophotometer readings or photos. Perhaps you write papers in Word and format the bibliography with Endnote or resize photos with Photoshop before adding them to a blog post. In all these cases you performed unmanaged workflows.

Managing and tracking workflows becomes important as the number of activities and number of individuals performing them increase in scale. Imagine your lab bench procedures performed multiple times a day with different individuals operating particular steps. This scenario occurs in core labs that perform the same set of processes over and over again. You can still track steps on paper, but it's not long before the system becomes difficult to manage. It takes too much time to write and compile all of the notes, and it's hard to know which materials have reached which step. Once a system goes beyond the work of a single person, paper notes quit providing the right kinds of overviews. You now need to manage your workflows and track them with a software system.

A good workflow system allows you to define the steps in your protocols. It will provide interfaces to move samples through the steps and also provide ways to add information to the system as steps are completed. If the system is well-designed, it will not allow you do things at inappropriate times or require too much "thinking" as the system is operated. A well-designed system will also reduce complexity and allow you to build workflows through software interfaces. Good systems give scientists the ability to manage their work, they do not require their users to learn arcane programming tools or resort to custom programming. Finally, the system will be flexible enough to let you create as many workflows as you need for different kinds of experiments and link those workflows to data entry forms so that the right kind of information is available to right process.

FinchLab Workflows

The Geospiza FinchLab workflow system meets the above requirements. The system has a high level workflow that understands that some processes require little tracking (a quick test) and other's require more significant tracking ("I want to store and reuse DNA samples"). More detailed processes are assigned workflows that consist of thee parts: A name, a "State," and a "Status." The "State" controls the software interfaces and determines which information are presented and accessed at different parts of a process. A sequencing or genotyping reaction, for example, cannot be added to a data collection instrument until it is "ready." The other part specifies the steps of the process. The steps of the process (Statuses) are defined by the lab and added to a workflow using the web interfaces. When a workflow is created, it is given a name, as many steps as needed, and it is assigned a State. The workflows are then assigned to different kinds of items so that the system always knows what to do next with the samples that enter.

A workflow management system like FinchLab makes it just as easy to track the steps of Sanger DNA sequencing, as it is to track the steps of a Solexa, SOLiD, or 454 sequencing processes. You can also, in the same system, run genotyping assays and other kinds of genetic analysis like microarrays and bead assays.


Next time, we'll talk about what happens in the lab.

New Finch Best Practices Guides Available!

The Geospiza Support Team has released 2 more Best Practice Guides!

These guides are created for the IT professionals and system administrators responsible for your Finch server.

The 2 new guides are:

1) Stopping and Starting your Finch Server's Services. This guide reviews the proper way to start and stop your Finch server's services and discusses some known problems that you might encounter. It's a great reference tool to if your server isn't responding to http, or if you're writing documentation for backup procedures.

2) The Finch Suite Installation and Configuration Guide: This document is aimed towards fresh installations of Finch, however, however, it should serve as an excellent reference guide for your existing server as well.

You can download these documents from the Online Support Documentation website under the 'Best Practice Guides' section, located at the bottom of the page.