ABRF 2013

The annual Association of Biomedical Research Facilities begins this weekend (March 2 - 5).  We [PerkinElmer] will be busy at the conference as participants and as a vendor supporting this great organization and our many customers. From client presentations to our own work we will share our latest and greatest.

Highlights:

Saturday: 3/2 "Breaking the Data Analysis Bottleneck: Solutions That Work for RNA and Exome Sequencing." Rebecca Laborde, Mayo Clinic will present on how the teams she works with use GeneSifter for they're NGS data analysis. This is part of Satellite Workshop 1: Applications of NGS.

Monday: 3/4 "Oyster Transcriptome Analysis by Next Gen Sequencing." Natalia Reyero, Genetics and Development Biology Center, NHLBI will give a presentation based on her award nominated poster (below) during the (RG5) Genomics Research Group. GeneSifter had a role in the data analysis.

Saturday and Monday Posters:

#7 "Identifying Mutations in Transcriptionally Active Regions on Genomes Using Next Generation Sequencing." Eric Olson, PerkinElmer, presents ways in which RNA-seq can be used to define transcripts to identify functional mutations in organisms that have sparsely annotated reference genomes. 

#11 "What Does It Take to Identify the Signal from the Noise in Molecular Profiling of Tumors?" Eric Olson, PerkinElmer presents ways to use RNA sequencing and bioinformatic approaches to filter the vast numbers of variants observed in DNA sequence data obtained from tumors to a manageable number that are most likely to be the drivers of tumor growth.

Award Nominee
#119 "Elucidating the Effects of the Deepwater Horizon Oil Spill on the Atlantic Oyster Using Global Transcriptome Analysis" Natalia Reyero, Genetics and Development Biology Center, NHLBI. If you are interested in learning about the aftermath of the Gulf of Mexico oil spill, you will find Natalia's work interesting. 

And that's not all. The the booth will be hopping. We will have meet the speaker opportunities on Sunday and Tuesday and as well as many demos of our GeneSifter Analysis and LIMS products.  PerkinElmer Informatics will have individuals to show new features in PerkinElmer's Electronic Laboratory Notebook and other products, and Caliper and Chemagen reps will be on hand to talk about the great things we do for sample prep.  

Check us out a Booth 522 to get schedules and see what's new.

Sneak Peek: Identifying Mutations in Expressed Regions of Genomes Using NGS

Join us Wednesday, January 30th at 1 PM (EST), 10 AM (PST) to learn how to use NGS to identify mutations in expressed regions of genomes.

Abstract:

The pace at which genome references are being generated for plants and animal species is rapidly increasing with Next Generation Sequencing technologies. While this is a major step forward for researchers studying species that previously did not have sequenced genomes, it is only the beginning of the process toward defining the biology underlying the genome. As long as a reference is available, DNA variants can be readily identified on a genome wide scale, often producing lists of 100s of thousands or even millions of variants. Frequently these variants that occur in expressed genes are of the most interest; however, if annotation defining where genes exist within a genome is not available or poorly defined, identifying which mutations might affect protein coding may not be possible. To address this challenge we will describe a method whereby RNA-Seq can be readily used to identify transcriptionally active regions which creates transcript annotation for un-annotated or enhanced annotation for any organism. This annotation can then be used in conjunction with whole genome sequencing to annotate variants as to whether they fall within transcriptionally active regions thus facilitating the identification of mutations in larger repertoire of expressed regions of a genome.

Eric Olson, Ph.D., and  Hugh Arnold, Ph.D., from Geospiza will present. 

REGISTER HERE

Product Updates: GeneSifter Lab and GeneSifter Analysis Editions

Spring is here and so are new releases of the GeneSifter products. GeneSifter Lab Edition (GSLE) has been bumped up to 3.17 and GeneSifter Analysis Edition (GSAE) is now at 3.7.

What's New?

GSLE - This release includes big features along and a host of improvements. For starters, we added comprehensive inventory tracking. Now, when you configure forms to track your laboratory processes, you can add and track the use of inventory items.

Inventory items are those reagents, kits, tubes, and other bits that are used to prepare samples for analysis. GSLE makes it easy to add these items and their details like barcodes, lot numbers, vendor data, and expiration dates. Items contain arbitrary units so you can track weights as easily as volumes.

When inventory items are used in the laboratory, they can be included in steps. Each time the item is used, the amount to use can be preconfigured and GSLE will do the math for you. When the inventory item's amount drops below a threshold, GSLE can send an email that includes a link for reordering.

In addition to inventory items we increased support for the PacBio RS, and have made Sample Sheet template design completely user configurable. Sample Sheets are those files that contain the samples' names and other information needed for a data collection run. While GSLE always had good sample sheet support, vendor's frequently change formatting and needed data requirements. In some cases a new software release could be required.

The new sample sheet configuration interface eliminates the above problem, and makes it easy for labs to adapt their sheets to changes. A simple web form is used to define formatting rules and the data that will be added. GSLE tags are used to specify data fields and a search interface provides access to all fields in the database. At run time, the sample sheet is filled with the appropriate data.

View the product sheet to see the interfaces and other features.

GSAE - The new release continues to advance GSAE's data analysis capabilities. Specific features include paired-end data analysis for RNA-Seq and DNA re-sequencing applications. We've also improved the ways in which large datasets can be searched, filtered, and queried. Additional improvements include new dashboards to simplify data access and setting up analysis pipelines.

Finally, for those participating in our partner program, we continue to increase the integration between GSLE and GSAE with single sign on and data transfer features.

More details can be found in the product sheet.

Sneak Peak: RNA-Sequencing Applications in Cancer Research: From fastq to differential gene expression, splicing and mutational analysis

Join us next Tuesday, April 12 at 10:00 am PST for a webinar focused on RNA-Seq applications in breast cancer research.

The field of cancer genomics is advancing quickly. News reports from the annual American Association of Cancer Research meeting are indicating that whole genome sequencing studies such as the 50 breast cancer genomes (WashU) are providing more clues about the genes that may be affected in cancer. Meanwhile, the ACLU/Myriad Genetics legal action over genetic testing for breast cancer mutations and disease predisposition continues to move towards the supreme court.

Breast cancer, like many other cancers, is complex.  Sequencing genomes is one way to interrogate cancer biology. However, the genome sequence data in isolation does not tell the complete story. The RNA, representing expressed genes, their isoforms, and non-coding RNA molecules, needs to be measured too. In this webinar, Eric Olson, Geospiza's VP of product development and principal designer of GeneSifter Analysis Edition, will explore the RNA world of breast cancer and present how you can explore existing data to develop new insights.

Abstract
Next Generation Sequencing applications allow biomedical researchers to examine the expression of tens of thousands of genes at once, giving researchers the opportunity to examine expression across entire genomes. RNA Sequencing applications such as Tag Profiling, Small RNA and Whole Transcriptome Analysis can identify and characterize both known and novel transcripts, splice junctions and non-coding RNAs. These sequencing based-applications also allow for the examination of nucleotide variant. Next Generation Sequencing and these RNA applications allow researchers to examine the cancer transcriptome at an unprecedented level. This presentation will provide an overview of the gene expression data analysis process for these applications with an emphasis on identification of differentially expressed genes, identification of novel transcripts and characterization of alternative splicing as well as variant analysis and small RNA expression. Using data drawn from the GEO data repository and the Short Read Archive, NGS Tag Profiling, Small RNA and NGS Whole Transcriptome Analysis data will be examined in Breast Cancer.

You can register at the webex site, or view the slides after the presentation.

Sneak Peak: The Next Generation Challenge: Developing Clinical Insights Through Data Integration

Next week (March 14-18, 2011) is CHI's X-Gen Congress & Expo. I'll be there presenting a poster on the next challenge in bioinformatics, also known as the information bottleneck.

You can follow the tweet by tweet action via @finchtalk or #XGenCongress.

In the meantime, enjoy the poster abstract.

The next generation challenge: developing clinical insights through data integration

Next generation DNA sequencing (NGS) technologies hold great promise as tools for building a new understanding of health and disease. In the case of understanding cancer, deep sequencing provides more sensitive ways to detect the germline and somatic mutations that cause different types of cancer as well as identify new mutations within small subpopulations of tumor cells that can be prognostic indicators of tumor growth or drug resistance. Intense vendor competition amongst NGS platform and service providers are commoditizing data collection costs making data more assessable. However, the single greatest impediment to developing relevant clinical information from these data is the lack of systems that create easy access to the immense bioinformatics and IT infrastructures needed for researchers to work with the data. 

In the case of variant analysis, such systems will need to process very large datasets, and accurately predict common, rare, and de novo levels of variation. Genetic variation must be presented in an annotation-rich, biological context to determine the clinical utility, frequency, and putative biological impact. Software systems used for this work must integrate data from many samples together with resources ranging from core analysis algorithms to application specific datasets to annotations, all woven into computational systems with interactive user interfaces (UIs). Such end-to-end systems currently do not exist, but the parts are emerging. 

Geospiza is improving how researchers understand their data in terms of its biological context, function and potential clinical utility, by develop methods that combine assay results from many samples with existing data and information resources from dbSNP, 1000 Genomes, cancer genome databases, GEO, SRA and others. Through this work, and follow on product development, we will produce integrated sensitive assay systems that harness NGS for identifying very low (1:1000) levels of changes between DNA sequences to detect cancerous mutations, emerging drug resistance, and early-stage signaling cascades.

Authors: Todd M. Smith(1), Christoper Mason(2) 

(1). Geospiza Inc. Seattle WA 98119, USA. 

(2). Weil Cornell Medical College, NY NY 10021, USA

Sneak Peak: ABRF and Software Systems for Clinical Research

The Association for Biomedical Research Facilities conference begins this weekend (2/19) with workshops on Saturday and sessions Sunday through Tuesday.  This year's theme is: Technologies to Enable Personalized Medicine, and appropriately a team from Geospiza will be there at our booth and participating in scientific sessions. 

I will be presenting a poster entitled, "Clinical Systems for Cancer Research" (abstract below). In addition to great science and technology ABRF has a large number of tweeting participants including @finchtalk. You can follow along using the #ABRF and (or) #ABRF2011.  

Abstract

By the end of 2011 we will likely know the DNA sequences for 30,000 human genomes. However, to truly understand how the variation between these genomes affect phenotype at a molecular level, future research projects need to analyze these genomes in conjunction with data from multiple ultra-high throughput assays obtained from large sample populations. In cancer research, for example, studies that examine 1000s of specific tumors in 1000s of patients are needed to fully characterize the more than 10,000 types and subtypes of cancer and develop diagnostic biomarkers. These studies will use high throughput DNA sequencing to characterize tumor genomes and their transcriptomes. Sequencing results will be validated with non-sequencing technologies and putative biomarkers will be examined in large populations using rapid targeted assay approaches.

Geospiza is transforming the above scenario from vision into reality in several ways. The Company’s GeneSifter platform utilizes scalable data management technologies based on open-source HDF5 and BioHDF technologies to capture, integrate, and mine raw data and analysis results from DNA, RNA, and other high-throughput assays. Analysis results are integrated and linked to multiple repositories of information that include variation, expression, pathway, and ontology databases to enable discovery process and support verification assays. Using this platform and RNA-Sequencing and Genomic DNA sequencing from matched tumor/normal samples, we were able to characterize differential gene expression, differential splicing, allele specific expression, RNA editing, somatic mutations and genomic rearrangements as well as validate these observations in a set of patients with oral and other cancers.

By: Todd Smith (1), N. Eric Olson (1), Rebecca Laborde (3), Christopher E Mason (2), David Smith (3):  (1) Geospiza, Inc., Seattle WA. (2) Weil Cornell Medical College, NY NY.(3) Mayo Clinic, Rochester MN.

Variant Analysis and Sequencing Labs

Yesterday and two weeks ago, Geospiza released two important news items.  The first announced PerkinElmer's section of the GeneSifter® Lab and Analysis systems to support their new DNA sequencing service. The second was an announcement of our new SBIR award to improve variant detection software.

Why are these important?

The PerkinElmer news is another validation of the fact that software systems need to integrate laboratory operations and scientific data analysis in ways that go deeper than sending computing jobs to a server (links below).  To remain competitive, service labs can no longer satisfy customer needs by simply delivering data. They must deliver a unit of information that is consistent with the experiment, or assay, that is being conducted by their clients.  PerkinElmer recognizes this fact along with our many other customers who participate in our partner program and work with our LIMS (GSLE) and Analysis (GSAE) systems to support their clients doing Sanger sequencing, Next Gen sequencing, or microarray analysis.

The SBIR news communicates our path toward making the units of information that go with whole genome sequencing, targeted sequencing, exome sequencing, allele analysis, and transcriptome sequencing as rich as possible. Through the project, we will add new applications to our analysis capabilities that solve the three fundamental problems related to data quality analysis, information integration, and the user experience.

• Through advanced data quality analysis we can address the question, if we see a variant, can we understand wether the difference is random noise, a systematic error, or biological signal? 
• Information integration will help scientists and clinical researchers quickly add biological context to their data. 
• New visualization and annotation interfaces will help individuals explore the high-dimensional datasets resulting from the 100's and 1000's of samples needed to develop scientific and clinical insights. 

Further Reading

Geospiza wins $1.2 M grant to add new DNA variant application to GeneSifter software.

PerkinElmer selects Geospiza for Next Generation Sequencing.

Keeping Your DNA Sequencing, Genotyping, and Microarray Laboratory Competitive in a New Era of Genomics .

Bloginar: Next Gen Laboratory Systems for Core Facilities

Sneak Peak: AGBT and the Next Gen Software Challenge

The Advances in Genome Biology and Technology meeting begins Thursday (2/3) morning with pre-conference workshops on Wed.  I will be there representing Geospiza and presenting a poster entitled, "The Next Gen Software Challenge: Integrating Diverse Assay-Seq and Validation Systems" (abstract below)

In addition to great science and technology AGBT has a large number of tweeting participants including @finchtalk. You can follow along using the #AGBT and (or) #AGBT2011.  

Abstract

By the end of 2011 we will know the DNA sequences for 30,000 human genomes. Understanding how the variation between these genomes affects phenotype at a molecular level, requires that future research projects integrate genome sequences with data from multiple ultra-high throughput assays obtained from large sample populations. Further insights are gained when these data are combined with additional information from external databases and data resources.

Geospiza is meeting the above challenges with its GeneSifter® platform and underlying open-source HDF5 and BioHDF technologies. Our approach centralizes and structures data in a scalable way so that features can be queried across many samples and between different assays to quickly add context to genome sequences as they are collected. Unlike approaches that rely on collections of flat files, specialized software tool kits, isolated genome-browsers, and independent statistical tool environments, GeneSifter integrates common components into a system that provides researchers with a rich, easy to use, environment to explore their data and develop clinical insights.

By: Todd Smith (1), N. Eric Olson (1), Rebecca Laborde (3), Christopher E Mason (2), David Smith (3):  (1) Geospiza, Inc., Seattle WA. (2) Weil Cornell Medical College, NY NY.(3) Mayo Clinic, Rochester MN.

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

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.

Increasing the Scale of Deep Sequencing Data Analysis with BioHDF

Last month, at the Department of Energy's Sequencing, Finishing and Analysis in the Future meeting, I presented Geospiza's product development work and how BioHDF is contributing to scalable infrastructures. The abstract, presentation, and link to the presentation are posted below.

Abstract

Next Generation DNA Sequencing (NGS) technologies are powerful tools for rapidly sequencing genomes and studying functional genomics. Presently, the value of NGS technology has been largely demonstrated on individual sample analyses. The full potential of NGS will be realized when it can be used in multisample experiments that involve different measurements and include replicates, and controls to make valid statistical comparisons. Arguably, improvements in current technology, and soon to be available “third” generation systems, will make it possible to simultaneously measure 100’s to1000’s of individual samples in single experiments to study transcription, alternative splicing, and how sequences vary between individuals and within expressed genes. However, several bioinformatics systems challenges must be overcome to effectively manage both the volumes of data being produced and the complexity of processing the numerous datasets that will be generated.

Future bioinformatics applications need to be developed on common standard infrastructures that can reduce overall data storage, increase data processing performance, integrate information from multiple sources and are self-describing. HDF technologies meet all of these requirements, have a long history, and are widely used in data-intensive science communities. They consist of general data file formats, software libraries and tools for manipulating the data. Compared to emerging standards such as the SAM/BAM formats, HDF5-based systems demonstrate improved I/O performance and improvedmethods to reduce data storage. HDF5 isalso more extensible and can support multiple data indexes and store multiple data types. For these reasons, HDF5 and its BioHDF implementation are well qualified as standards for implementing data models in binary formats to support the next generation of bioinformatics applications. Through this presentation we will demonstrate BioHDF's latest features in NGS applications that target transcription analysis and resequencing.

SciVee Video

Acknowledgments

Contributing Authors: Todd Smith (1), Christopher E Mason (2), Paul Zumbo (2), Mike Folk (3), Dana Robinson (3), Mark Welsh (1), Eric Smith (1), N. Eric Olson (1),

1. Geospiza, Inc. 100 West Harrison N. Tower 330, Seattle WA 98119 2. Department of Physiology and Biophysics, Weil Cornell Medical College, 1305 York Ave., New York NY, 10021 3. The HDF Group, 1901 S. First St., Champaign IL 61820

Funding: NIH: STTR HG003792

Levels of Quality

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

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

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

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

Quality Value Analysis

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

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

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

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

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

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

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

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

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

Journal Club: GeneSifter Aids Stem Cell Research

Last week’s Nature featured an article entitled “Aberrant silencing of imprinted genes on chromosome 12qF1 in mouse induced pluripotent stem cells [1]” in which GeneSifter Analysis Edition (GSAE) was used to compare gene expression between genetically identical mouse embryonic stem (ES) cells and induced pluripotent stem cells (iPSCs).

Stem Cells 

Stems cells are undifferentiated, pluripotent, cells that later develop into the specialized cells of tissues and organs. Pluripotent cells can divide essentially without limit, become any kind of cell, and have been found to naturally repair certain tissues. They are the focus of research because of their potential for treating diseases that damage tissues. Initially stem cells were isolated from embryonic tissues. However, with human cells, this approach is controversial. In 2006 researchers developed ways to “reprogram” somatic cells to become pluripotent cells [2]. In addition to being less controversial, iPSCs have other advantages, but there are open questions as to their therapeutic safety due to potential artifacts introduced during the reprogramming process. 

Reprogramming cells to become iPSCs involves the overexpression of a select set of transcription factors by viral transfection, DNA transformation, and other methods. To better understand what happens during reprogramming, researchers have examined gene expression and DNA methylation patterns between ES cells and iPSCs and have noted major differences in mRNA and microRNA expression as well as DNA methylation patterns. As noted in the paper, a problem with previous studies is that they compared cells with different genetic backgrounds. That is, the iPSCs harbor viral transgenes that are not present in the ES cells, and the observed differences could likely be due to factors unrelated to reprogramming. Thus, a goal of this paper's research was to compare genetically identical cells to pinpoint the exact mechanisms of reprogramming. 

GeneSifter in Action 

Comparing genetically similar cells requires that both ES cells and iPSCs have the same transgenes. To accomplish this goal, Stadtfeld and coworkers devised a clever strategy whereby they created a novel inducible transgene cassette and introduced it into mouse ES cells. The modified ES cells were then used to generate cloned mice containing the inducible gene cassette in all of their cells. Somatic cells could be converted to iPSCs by adding the appropriate inducing agents to the tissue culture media.

Even though ES cells and iPSCs were genetically identical, ES cells were able to generate live mice whereas iPSCs could not. To understand why, the team looked at gene expression using microarrays. The mRNA profiles for six iPSC and four ES cell replicates were analyzed in GeneSifter. Unsupervised clustering showed that global gene expression was similar for all cells. When the iPSC and ES cell data were compared using correlation analysis, the scatter plot identified two differentially expressed transcripts corresponding to a non-coding RNA (Gtl2) and small nucleolar RNA (Rian). The transcripts’ genes map to the imprinted Dlk1-Dio3 gene cluster on mouse chromosome 12qF1. While these genes were strongly repressed in iPSC clones, the expression of housekeeping and pluripotentency cells was unaffected as demonstrated using GeneSifter’s expression heat maps.

Subsequent experiments that looked at gene expression from over 60 iPSC lines produced from different types of cells and chimeric mice that were produced from mixtures of iPSCs and stem cells showed that the gene silenced iPSCs had limited development potential. Because the Dlk3-Dio cluster imprinting is regulated by methylation, methylation patterns revealed that the Gtl2 allele had acquired an aberrant silent state in the iPSC clones. Finally, by knowing that Dlk3-Dio cluster imprinting is also regulated by histone acetylation, the authors were able to treat their iPSCs with a histone deacetylase inhibitor and produce live animals from the iPSCs. Producing live animals from iPSCs in a significant milestone for the field.

While histone deacetylase inhibitors have multiple effects, and more work will need to be done, the authors have completed a tour de force of work in this exciting field, and we are thrilled that our software could assist in this important study. 

Further Reading

1. Stadtfeld M., Apostolou E., Akutsu H., Fukuda A., Follett P., Natesan S., Kono T., Shioda T., Hochedlinger K., 2010. "Aberrant silencing of imprinted genes on chromosome 12qF1 in mouse induced pluripotent stem cells." Nature 465, 175-181.

2. Takahashi K., Yamanaka S., 2006. "Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors." Cell 126, 663-676.

Stem Cell Basics: http://stemcells.nih.gov/info/basics 

iPSCs: http://en.wikipedia.org/wiki/IPS_cells

Bloginar: RNA Deep Sequencing: Beyond Proof of Concept

RNA-Seq is a powerful method for measuring gene expression because you can use the deep sequence data to measure transcript abundance and also determine how transcripts are spliced and whether alleles of genes are expressed differentially.  

At this year’s ABRF (Association for Biomedical Research Facilities) conference, we presented a poster, using data from published study, to demonstrate how GeneSifter Analysis Edition (GSAE) can be used in next generation DNA sequencing (NGS) assays that seek to compare gene expression and alternative splicing between different tissues, conditions, or species.

The following map guides the presentation. The poster has a title and four main sections, which cover background information, introduction to the published work and data, ways to observe alternative splicing and global gene expression differences between samples, and ways to observe sex specific gene expression differences. The last section also identifies a mistake made by the authors.  

Section 1. The first section begins with the abstract and lists five specific challenges created by NGS: 1) high end computing infrastructures are needed to work with NGS data, 2) NGS data analysis involves complex multistep processes, 3) NGS data need to be compared to many reference databases, 4) the resulting datasets of alignments must be visualized in different ways, and 5) scientific knowledge is gained when several aligned datasets are compared. 

Next, we are reminded that NGS data are analyzed in three phases: primary analysis, secondary analysis and tertiary analysis. Primary analysis is the step that converts images to reads consisting of basecalls (or colors, or flowgrams), and quality values. In secondary analysis, reads are aligned to reference data (mapped) or amongst themselves (assembled). Secondary analysis produces tables of alignments that must be compared to one and other, in tertiary analysis, to gain scientific insights. 

Finally, GSAE is introduced as a platform for scalable data analysis. GSAE’s key features and advantages are listed along with several screen shots to show the many ways in which analyzed data can be presented to gain scientific insights.  

Section 2 introduces the RNA-Seq data used for the presentation. These data, from a study that set out to measure sex and lineage specific alternative splicing in primates [1], were obtained from the Gene Expression Omnibus (GEO) database at NCBI, transferred into GSAE, and processed through GSAE’s RNA-Seq analysis pipelines.  We chose this study because it models a proper expression analysis using replicated samples to compare different cases.

All steps of the process, from loading the data to processing the files and viewing results were executed through GSAE’s web-based interfaces. The four general steps of the process are outlined in the box labeled “Steps.” 

The section ends with screen shots from GSAE showing how the primary data can be viewed and a list of the reports showing different alignment results for each sample in the list. The reports are accessed from a “Navigation Panel” that contains links to Alignment Summaries, a Filter Report, and a Searchable Sortable Gene List (shown), and several other reports (not shown). 

The Alignment Summary provides information about the numbers of reads mapping to different reference data sources that are used in the analysis to understand sample quality and study biology. For example, in RNA-Seq, it is important to measure and filter reads matching ribosomal RNA (rRNA) because the amount of rRNA present indicates how well clean up procedures work. Similarly, the number of reads matching adaptors indicates how well the library was prepared. Biological, or discovery based, filters include reads matching novel exon junctions and intergenic regions of the genome.

Other reports like the Filter Report and Gene List provide additional detail. The Filter Report clusters alignments and plots base coverage (read density) across genomic regions. Some regions, like mitochondrial DNA, and rRNA genes, or transcripts, are annotated. Others are not. These regions can be used to identify areas of novel transcription activity.

The Gene List provides the most detail and gives a comprehensive overview of the number of reads matching a gene, numbers of normalized reads, and the counts of novel splices, single nucleotide variants (SNVs), and small insertions and deletions (indels). Read densities are plotted as small graphs to reveal each gene’s exon/intron structure. Additional columns provide the gene’s name, chromosome, and contain links to further details in Entrez. The graphs are linked to the Integrated Gene Viewer to explore the data further. Finally, the Gene LIst is an interactive report that can searched, sorted, and filtered in different ways, so you can easily view the details of your gene or chromosome of interest.

Section 3 shows how GSAE can be used to measure global gene expression and examine the details for a gene that is differentially expressed between samples. In the case of RNA-Seq, or exon arrays, relative exon levels can be measured to observe genes that are spliced differently between samples. The presented example focuses on the arginosuccinate synthetase 1 (ASS1) gene and compares the expression levels of its transcripts and exons between the six replicated human and primate male samples.

The Gene Summary report shows that ASS1 is down regulated by 1.38 times in the human samples. More interestingly, the exon usage plot shows that this gene is differentially spliced between the species. Read alignment data, supporting this observation, are viewed by clicking the “View Exon Data” link that is below the Exon Usage heat map. This link brings up the Integrated Gene Viewer (IGV) for all six samples. In addition to showing read densities across the gene, IGV also shows the numbers of reads that span exon junctions as loops with heights proportional to the number of reads mapping to a given junction. In these plots we see that the human samples are missing the second exon whereas the primate samples show two forms of the transcript. IGV also includes the Entrez annotations and known isoforms for the gene and the positions of known SNPs from dbSNP.  And, IGV is interactive; controls at the top of the report and regions within the gene map windows are used to navigate to new locations and zoom in or out of the data presented.  When multiple genes are compared, the data are updated for all genes simultaneously. 

Section 3 closes with heat map representing global gene expression for the samples being compared. Expression data are clustered using a 2-way ANOVA with 5% false discovery filter (FDR). The top half of the hierarchical cluster groups genes that are down regulated in humans and up regulated in primates and the bottom half groups genes that are expressed in the opposite fashion. The differentially expressed genes can also be viewed in Pathway Reports which show how many genes are up or down regulated in a particular Gene Ontology (GO) pathway.  Links in these reports navigate to lists of the individual genes or their KEGG pathways.  When a KEGG pathway is displayed, the genes that are differentially expressed are highlighted.

Section 4 focuses on the sex specific differences in gene expression between the human and primate samples. In this example, 12 samples are being compared: three replicates for the male and female samples of two species. When these data were reanalyzed in GSAE, we were able to note that an obvious mistake. By examining Y chromosome gene expression, it was clear that one of the human male samples (M2-2) was lacking expression of these genes. Similarly, when the X (inactive)-specific transcript (XIST) was examined, M2-2 showed high expression like the other female samples. The simplest explanations for these observations are that either M2-2 is a female, or a dataset was copied and mislabeled in GEO. However, given that the 12 datasets show subtle differences, it is likely that they are all different and the first explanation is more likely. 

The poster closes with a sidebar showing how GSAE can be used to measure global sequence variation and the take home points for the presentation. The most significant being that if the authors of the paper had used a system like GSAE, they could have quickly observed the problems in their data that we saw and prevented a mistake. 

To see how you can use GSAE for your data sign up for a trial. 

1. Sex-specific and lineage-specific alternative splicing in primates. Blekhman R, Marioni JC, Zumbo P, Stephens M, Gilad Y,. Genome Res. published online December 15, 2009

Bloginar: Standardizing Bioinformatics with BioHDF (HDF5)

Yesterday we (The HDF Group and Geospiza) released the BioHDF prototype software.  To mark the occasion, and demonstrate some of BioHDF’s capabilities and advantages, I share the poster we presented at this year’s AGBT (Advances in Genome Biology and Technology) conference.

The following map guides the presentation. The poster has a title and four main sections, which cover background information, specific aspects of the general Next Generation Sequencing (NGS) workflow, and HDF5’s advantages for working with large amounts of NGS data.

 

Section 1.  The first section introduces HDF5 (Hierarchical Data Format) as a software platform for working with scientific data.  The introduction begins with the abstract and lists five specific challenges created by NGS: 1) high end computing infrastructures are needed to work with NGS data, 2) NGS data analysis involves complex multi-step processes that, 3) compare NGS data to multiple reference sequence databases, 4) the resulting datasets of alignments must be visualized in multiple ways, and 5) scientific knowledge is gained when many datasets are compared. 

Next, choices for managing NGS data are compared in a four category table.  These include text and binary formats. While text formats (delimited and XML) have been popular for bioinformatics, they do not scale well and binary formats are gaining in popularity. The current bioinformatics binary formats are listed (bottom left) along with a description of their limitations. 

The introduction closes with a description of HDF5 and its advantages for supporting NGS data management and analysis. Specifically, HDF5 is platform for managing scientific data. Such data are typically complex and consist of images, large multi-dimensional arrays, and meta data. HDF5 has been used for over 20 years in other data intensive fields; it is robust, portable, and tuned for high performance computing. Thus HDF5 is well suited for NGS. Indeed, groups from academic researchers to NGS instrument vendors, and software companies are recognizing the value of HDF5.

Section 2. This section illustrates how HDF5 facilitates primary data analysis. First we are reminded that NGS data are analyzed in three phases: primary analysis, secondary analysis and tertiary analysis. Primary analysis is the step that converts images to reads consisting of basecalls (or colors, or flowgrams), and quality values. In secondary analysis, reads are aligned to reference data (mapped) or amongst themselves (assembled). In many NGS assays, secondary analysis produces tables of alignments that must be compared to one and other, in tertiary analysis, to gain scientific insights. 

The remaining portion of section 2 shows how Illumina GA and SOLiD primary data (reads and quality values) can be stored in BioHDF and later reviewed using the BioHDF tools and scripts.  The resulting quality graphs are organized into three groups (left to right) to show base composition plots, quality value (QV) distribution graphs, and other summaries.

Base composition plots show the count of each base (or color) that occurs at a given position in the read. These plots are used to assess overall randomness of a library and observe systematic nucleotide incorporation errors or biases.

Quality value plots show the distribution of QVs at each base position within the ensemble of reads. As each NGS run produces many millions of reads, it is worthwhile summarizing QVs in multiple ways. The first plots, from the top, show the average QV per base with error bars indicating QVs that are within one standard deviation of the mean. Next, box and whisker plots show the overall quality distribution (median, lower and upper quartile, minimum and maximum values) at each position. These plots are followed by “error” plots which show the total count of QVs below certain thresholds (red, QV < 10; green QV < 20; blue, QV < 30). The final two sets of plots show the number of QVs at each position for all observed values and the number of bases having each quality value.

The final group of plots show overall dataset complexity, GC content (base space only), average QV/read, and %GC vs average QV (base space only).  Dataset complexity is computed by determining the number of times a given read exactly matches other reads in the dataset. In some experiments, too many identical reads indicates a problem like PCR bias. In other cases, like tag profiling, many identical reads are expected from highly expressed genes. Errors in the data can artificially increase complexity.

Section 3.  Primary data analysis gives us a picture of how well the samples were prepared or how well the instrument ran with some indication about sample quality. Secondary and tertiary analysis tell us about sample quality and more importantly, provides biological insights. The third section focuses on secondary and tertiary analysis and begins with a brief cartoon showing a high level data analysis workflow using BioHDF to store primary data, alignment results, and annotations. BioHDF tools are used to query these data and other software within GeneSifter is used to compare data between samples and display the data in interactive reports to examine the details from single or multiple samples.

The left side of this section illustrates what is possible with single samples. Beginning with a simple table that indicates how many reads align to each reference sequence, we can drill into multiple reports that provide increasing detail about the alignments. For example, the gene list report (second from top) uses gene model annotations to summarize the alignments for all genes identified in the dataset. Each gene is displayed as a thumbnail graphic that can be clicked to see greater detail, which is shown in the third plot. The Integrated Gene View not only shows the density of reads across the gene's genomic region, but also shows evidence of splice junctions, and identified single base differences (SNVs) and small insertions and deletions (indels). Navigation controls provide ways to zoom into and out of the current view of data, and move to new locations. Additionally, when possible, the read density plot is accompanied by an Entrez gene model and dbSNP data so that data can be observed in a context of known information. Tables that describe the observed variants follow. Clicking on a variant drills into the alignment viewer to show the reads encompassing the point of variation.

The right side illustrates multi-sample analysis in GeneSifter. In assays like RNA-Seq, alignment tables are converted to gene expression values that can be compared between samples. Volcano (top) and other plots are used visualize the differences between the datasets. Since each point in the volcano plot represents the difference in expression for a gene between two samples (or conditions), we can click on that point to view the expression details for that gene (middle) in the different samples. In the case of RNA-Seq, we can also obtain expression values for the individual exons with the gene, making it possible to observe differential exon levels in conjunction with overall gene expression levels (middle). Clicking the appropriate link in the exon expression bar graph, takes us to the alignment details for the samples being analyzed (bottom), in this example we have two cases and two control replicates. Like the single sample Integrated Gene Views, annotations are displayed with alignment data. When navigation buttons are clicked all of the displayed genes move together so that you can explore the gene's details and surrounding neighborhood for multiple samples in a comparative fashion.

Section 4.  The poster closes with details about BioHDF.  First, the data model is described. An advantage of the BioHDF model is that read data are organized non-redundantly. Other formats, like BAM, tend to store reads with alignments and if a read has multiple alignments in a genome, or is aligned to multiple reference sequences, it gets stored multiple times. This may seem trivial, but anything that can happen a million times, becomes noticeable. This fact is demonstrated in the in table listed in the second panel “High Performance Computing Advantages.”  Other HDF5 advantages are listed below the performance stats table.  Most notably is HDF5’s ability to easily support multiple indexing schemes like nested containment lists (NClists). NClists solve the problem of efficiently accessing reads from alignments that may be contained in other alignments, which I will save for a later post.

Finally, the poster is summarized with a number of take home points. These reiterate the fact that NGS is driving the need to use binary file formats to manage NGS and analysis results and that HDF5 provides an attractive solution because of its long history and development efforts that specifically target scientific programming requirements. In our hands, HDF5 has helped make GeneSifter a highly scalable and interactive web-application with less development effort than would have been needed to implement other technologies.  

If you are software developer and are interested in BioHDF please visit www.biohdf.org.  If you do not want to program and instead, want a way to easily analyze your NGS data to make new discoveries, please contact us. 

RNA Deep Sequencing - Beyond Proof of Concept

ABRF 2010 begins this weekend.  In addition to my LIMS presentation on Sunday, I will present our poster featuring data analysis of sequences from "Sex-specific and lineage-specific alternative splicing in primates" (Blekhman et. al) in GeneSifter Analysis Edition.

The poster number is RP-3. Stop by and see how we learned that not all samples are what they seem to be ...

Abstract 

Next Generation DNA Sequencing (NGS) technologies are powerful tools for rapidly sequencing genomes and studying functional genomics. Presently, the value of NGS technology has been largely demonstrated on individual sample analyses (1-3). The full potential of NGS will be realized when it can be used in multisample experiments that involve different measurements and include replicates, and controls to make valid statistical comparisons. Arguably, improvements in current technology, and soon to be available “third” generation systems, will make it possible to simultaneously measure 100’s to1000’s of individual samples in single experiments to study transcription, alternative splicing, and how sequences vary between individuals and within expressed genes. However, several bioinformatics systems challenges must be overcome to effectively manage both the volumes of data being produced and the complexity of processing the numerous datasets that will be generated.

In this poster we present a system that is used it to verify and further characterize previously published data from a gene expression study that includes both replicates and experimental values comparing sex and lineage specific alternative splicing in primates (4). This system, developed on a high performance computing architecture, stores and organizes the data, aligns millions of reads to different reference sequences, identifies and removes artifacts, executes comparative and statistical analyses, and finally links results to pathway and ontological information for making discoveries and confirming hypotheses. The supporting infrastructure includes intuitive user interfaces for organizing data, executing analytical operations, viewing summarized reports, navigating through details in the results and can be easily operated by biologists.

1. Marioni JC, et. al. (2008) Genome Res.

2. Ramsköld D, et. al. (2009) PLoS Comput Biol.

3. Pleasance ED, et. al.(2010) Nature.

4. Blekhman R, et. al. (2009) Genome Res.

GeneSifter Lab Edition v3.14 - Release Notes

GeneSifter Laboratory Edition (GSLE) 3.14.0 introduces a host of new features and capabilities that make daily laboratory data management work even easier.  Read below to learn why GSLE is a leading LIMS product for all forms of DNA sequencing, microarrays, and other genetic analysis applications.

Orders and Invoices

Multi plate submissions: Order forms have been extended in several ways to further simplify how labs collect sample and project information. A new order form template lets core facilities, managing larger sequencing projects, easily receive samples and their information in a multiple plate format. New order fields specific to the plate format are included to support sample tracking and lab work.

Add data to fields: Orders forms have been further improved by adding the ability to add new values (or terms) to dropdown fields that already exist on published order forms.


Project field: Additionally, labs can add an optional project field to forms. With these improvements, labs can create forms that are easier to use and modify, as well as enable project tracking for their customers.

Sample location and sample selection: Two new features deliver help for labs that provide sample storage (biobanking) services to their clients. First, order forms can include sample location information. This is particularly useful in situations where samples are delivered in 96-well plates that are stored for later use. Second, samples already stored by the lab as purified DNA, RNA or other material (templates) can be selected from specialized search interfaces within order forms. Like all GSLE sample entry forms, these features can be included or not on a case-by-case basis depending on your specific needs. 

Invoice formatting: For labs that have the dreaded chore of sending billing data to accounting departments we have added the ability to modify the invoice number format to include additional characters that are used to distinguish which labs are sending information.

Laboratory Operations

GSLE provides the ability to create, list and follow steps in sample protocols (also called workflows). In 3.14 new features not only expand the capabilities but make it possible to further standardize procedures. 


Multiplexing: In Next Generation Sequencing (NGS) several libraries are often combined into a single lane or region of a slide to increase the number of individual samples analyzed in a sequencing run. As each library is prepared, a specific adaptor sequence is added so sequence reads corresponding to different samples can be identified by their adaptor tag. This procedure, called multiplexing or barcoding, is supported in 3.14 and allows the lab to combine samples and adaptor sequences and group the combination of libraries together (Worksets) for sample processing and instrument runs. Once data are collected, sample naming conventions, combined with adaptor sequence (Multiplex Identifier, MID) stored in sample sheets, are used to separate individual reads into files corresponding to the samples that were in the original workset.

Batch data entry: Some lab processes require that samples are manipulated in groups (batches), but laboratory data are collected for individual samples within the batch. For example, the concentrations of individual DNA samples may need to be measured in a 96-well plate. To improve how the OD values, comments, or other information are entered, workflow steps have been updated to include batch data entry forms that provide spreadsheet like data entry capabilities. Like all GSLE batch data entry forms, data can be entered easily using the form’s column highlight and easy fill controls, or uploaded from an excel spreadsheet.

Subsample processing: GSLE 3.14 also increases sample processing flexibility. As noted above, order forms can now support the ability to select samples that are already stored in the system. This feature is further extended into the laboratory by creating tools that allow many new samples to be created from a “parent” or stock samples. When new samples (templates) are created, options are provided so that each new sample can be entered into a different process. For example, you receive a tissue sample that needs several experiments performed; RNA-Seq, ChIP-Seq and resequencing. Now you can easily pick the sample and create three new sub samples defining which process will be performed on each sample with just a few clicks.

Selecting samples based on custom data: Some labs need to use custom data entered into order forms to sort and filter samples in the lab. For example, an order form may ask a researcher to enter read lengths for their NGS run. A 36 base run is much faster than a 100 base run, and on some platforms costs less. Thus, the lab will sort samples based on read length prior to the data collection event. While always possible to get this information in many GSLE displays, 3.14 adds new capabilities to use any custom data in its specialized sample picker tools.

Other Features

Customer data management: GSLE v3.14 gives labs’ customers increased ability to organize their chromatograms, fragment analysis files and microarray files as needed. Data files can be edited, relabeled, moved or deleted. Projects and folders can be created, modified or deleted to aid in data organization.

Application Programming Interface (Onsite Installations Only)

SQL-API: As automation and system integration needs increase, requirements for supporting programmatic data entry become more important. GSLE has continued to expand the self-documenting Application Programming Interface (API). We have also added an SQL API that can be used to create custom reports that are accessed via a wget style unix command.


Input API enhancements: The Input API now returns success IDs and CGI parameter names have been eliminated. The full documentation can be reviewed by contacting support@geospiza.com for the GSLE SQL API Manual or the GSLE Input API Manual. 


Next Generation Analysis Transfer Tool (Hosted Partners Only)

Simplified data transfers: A data transfer interface has been added to connect GSLE and GeneSifter Analysis Edition (GSAE). Partner Program administrators use the interface to select data files in GSLE and transfer them to their customer’s account in GSAE.

Schema Table update note

There was an update to an existing schema table;  the column "Plate_Label" is now in table om_sample_plate instead of om_order.

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: Data Sharing and Standardization

We can all agree that our ability to produce genomics and other kinds of data is increasing at exponential rates. Less clear, is understanding the consequences for how these data will be shared and ultimately used. These topics were explored in last month's (Oct. 9, 2009) policy forum feature in the journal Science.

The first article, listed under the category "megascience," dealt with issues about sharing 'omics data. The challenge being that systems biology research demands that data from many kinds of instrument platforms (DNA sequencing, mass spectrometry, flow cytometry, microscopy, and others) be combined in different ways to produce a complete picture of a biological system. Today, each platform generates its own kind of "big" data that, to be useful, must be computationally processed and transformed into standard outputs. Moreover, the data are often collected by different research groups focused on particular aspects of a common problem. Hence, the full utility of the data being produced can only be realized when the data are made open and shared throughout the scientific community. The article listed past efforts in developing sharing policies and the central table included 12 data sharing policies that are already in effect.

Sharing data solves half of the problem, the other aspect is being able to use the data once shared. This requires that data be structured and annotated in ways that make it understandable by a wide range of research groups. Such standards typically include minimum information check lists that define specific annotations, and which data should be kept from different platforms. The data and metadata are stored in structured documents that reflect a community's view about what is important to know with respect to how data were collected and the samples the data were collected from. The problem is that annotation standards are developed by diverse groups and, like the data, are expanding. This expansion creates new challenges with making data interoperable; the very problem standards try to address.

The article closed with high-level recommendations for enforcing policy through funding and publication requirements and acknowledged that full compliance requires that general concerns with pre-publication data use and patient information be addressed. More importantly, the article acknowledged that meeting data sharing and formatting standards has economic implications. That is, researches need time-efficient data management systems, the right kinds of tools and informatics expertise to meet standards. We also need to develop the right kind of global infrastructure to support data sharing.

Fortunately complying with data standards is an area where Geospiza can help. First, our software systems rely on open, scientifically valid tools and technologies. In DNA sequencing we support community developed alignment algorithms. The statistical analysis tools in GeneSifter Analysis Edition utilize R and BioConductor to compare gene expression data from both microarrays and DNA sequencing. Further, we participate in the community by contributing additional open-source tools and standards through efforts like the BioHDF project. Second, the GeneSifter Analysis and Laboratory platforms provide the time-effiecient data management solutions needed to move data through its complete life cycle from collection, to intermediate analysis, to publishing files in standard formats.

GeneSifter lowers researcher's economic barriers of meeting data sharing and annotation standards keep the focus on doing good science with the data.