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.

Sneak Peak: Data Analysis Methods for Whole Transcriptome Sequencing Applications – Challenges and Solutions

RNA sequencing is one of the most popular Next Generation Sequencing (NGS) applications. Next Thursday, February 11 at 10:00 A.M. PDT (1:00 P.M. EDT), we kick off our 2010 webinar series with a presentation designed to help you understand whole transcriptome data analysis and what can be learned in these experiments. In addition, we will show off some of our latest tools and interfaces that can be used to discover new RNAs, new splice forms of transcripts, and alleles of expressed genes.

Summary

RNA sequencing applications such as Whole Transcriptome Analysis, Tag Profiling and Small RNA Analysis allow whole genome analysis of coding as well as non-coding RNA at an unprecedented level. Current technologies allow for the generation of 500 million data points in a single instrument run. In addition to allowing for the complete characterization of all known RNAs in a sample (gene level expression summaries, exon usage, splice junction, single nucleotide variants, insertions and deletions), these applications are also ideal for the identification of novel RNAs as well as novel splicing events.

This presentation will provide an overview of Whole Transcriptome data analysis workflows with emphasis on calculating gene and exon level expression values as well as identifying splice junctions and variants from short read data. Comparisons of multiple groups to identify differential gene expression as well as differential splicing will also be discussed. Using data drawn from the GEO data repository and Short Read Archive (SRA), analysis examples will be presented for both Illumina’s GA and Lifetech’s SOLiD instruments.

Register Today!

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.

GeneSifter in Current Protocols

This month we are pleased to report Geospiza's publication of the first standard protocols for analyzing Next Generation Sequencing (NGS) data. The pulication, appearing in the September issue of Current Protocols, addresses how to analyze data from both microarray, and NGS experiments. The abstract and links to the paper and our press release are provided below.

Abstract

Transcription profiling with microarrays has become a standard procedure for comparing the levels of gene expression between pairs of samples, or multiple samples following different experimental treatments. New technologies, collectively known as next-generation DNA sequencing methods, are also starting to be used for transcriptome analysis. These technologies, with their low background, large capacity for data collection, and dynamic range, provide a powerful and complementary tool to the assays that formerly relied on microarrays. In this chapter, we describe two protocols for working with microarray data from pairs of samples and samples treated with multiple conditions, and discuss alternative protocols for carrying out similar analyses with next-generation DNA sequencing data from two different instrument platforms (Illumina GA and Applied Biosystems SOLiD).

In the chapter we cover the following protocols:

• Basic Protocol 1: Comparing Gene Expression from Paired Sample Data Obtained from Microarray Experiments
• Alternate Protocol 1: Compare Gene Expression from Paired Samples Obtained from Transcriptome Profiling Assays by Next-Generation DNA Sequencing
• Basic Protocol 2: Comparing Gene Expression from Microarray Experiments with Multiple Conditions
• Alternate Protocol 2: Compare Gene Expression from Next-Generation DNA Sequencing Data Obtained from Multiple Conditions

Links

To view the abstract, contents, figures, and literature cited online visit: Curr. Protoc. Bioinform. 27:7.14.1-7.14.34

To view the press release visit: Geospiza Team Publishes First Standard Protocol for Next Gen Data Analysis

Sneak Peak: Sequencing the Transcriptome: RNA Applications for Next Generation Sequencing

Join us this coming Wednesday, September 16, 2009 10:00 am Pacific Daylight Time (San Francisco, GMT-07:00), for a webinar on whole transcriptome analysis. In the presentation you will learn about how GeneSifter Analysis Edition can be used to identify novel RNAs and novel splice events within known RNAs.

Abstract:

Next Generation Sequencing applications such as RNA-Seq, Tag Profiling, Whole Transcriptome Sequencing and Small RNA Analysis allow whole genome analysis of coding as well as non-coding RNA at an unprecedented level. Current technologies allow for the generation of 200 million data points in a single instrument run. In addition to allowing for the complete characterization of all known RNAs in a sample, these applications are also ideal for the identification of novel RNAs and novel splicing events for known RNAs.

This presentation will provide an overview of the RNA applications using data from the NCBI's GEO database and Short Read Archive with an emphasis on converting raw data into biologically meaningful datasets. Data analysis examples will focus on methods for identifying differentially expressed genes, novel genes, differential splicing and 5’ and 3’ variation in miRNAs.

To register, please visit the event page.