Bloginar: The Next Generation Dilemma: Large Scale Data Analysis

Previous posts shared some the things we learned at the AGBT and ABRF meetings in early February. Now it is time to share the work we presented, starting with the AGBT poster, “The Next Generation Dilemma: Large Scale Data Analysis.”

The goal of the poster was to provide a general introduction to the power of Next Generation Sequencing (NGS) and a framework for data analysis. Hence, the abstract described the NGS general data analysis process; its issues and what we are doing for one kind of transcription profiling, RNA-Seq. Between then and now we learned a few things... And the project grew.

The map below guides my “bloginar” poster presentation. In keeping with the general theme of the abstract we focused on transcription analysis, but instead of focusing exclusively on RNA-Seq, the project expanded to compare three kinds of transcription profiling: RNA-Seq, Tag Profiling, and Small RNA Analysis. A link to the poster is provided at the end.

Section 1 provides a general introduction into NGS by discussing the ways NGS is being used to study different aspects of molecular biology. It also covers how the data are analyzed in thee phases (primary, secondary, tertiary) to convert raw data into biologically meaningful information. The three phase model has emerged as a common framework to describe the process of converting image data into primary sequence data (reads) and then turning the reads into information that be used in comparative analyses. Secondary analysis is the phase where reads are aligned to reference sequences to get gene names, position, and (or) frequency information that can be used to measure changes, like gene expression, between samples.

The remaining sections of the poster use examples from transcription analysis to illustrate and address the multiple challenges (listed below) that must be overcome to efficiently use NGS.

• High end infrastructures are needed to manage and work with extremely large data sets
• Complex, multistep analysis procedures are required to produce meaningful information
• Multiple reference data are needed to annotate and verify data and sample quality
• Datasets must be visualized in multiple ways
• Numerous Internet resources must be used to fill in additional details
• Multiple datasets must be comparatively analyzed to gain knowledge

Section 2 describes the three different kinds of transcription profiling experiments. This section provides additional background on the methods and what they measure. For example, RNA-Seq and Tag Profiling are commonly used to measure gene expression. In RNA-Seq, DNA libraries are prepared by randomly amplifying short regions of DNA from cDNA. The sequences that are produced will generally cover the entire region of the transcripts that were originally isolated. Hence, it is possible to get information about alternative splicing and biased allelic expression. In contrast, Tag Profiling focuses on creating DNA libraries from discrete points within the RNA molecules. With Tag Profiling, one can quickly measure relative gene expression, but cannot get information about alternative splicing and allelic expression. The table in section 2 discusses these and other issues one must consider when running the different assays.

Sections 3, 4, and 5 outline three transcriptome scenarios (RNA-Seq, Tag Profiling, and Small RNA, respectively) using real data examples (references provided in the poster). Each scenario follows a common workflow involving the preparation of DNA libraries from RNA samples, followed by secondary analysis, followed by tertiary analysis of the data in GeneSifter Analysis Edition.

For RNA-Seq, two datasets corresponding to mouse erythroid stem (ES) and body (EB) cells were investigated. DNA libraries were produced from each cell line. Sequences were collected from the library and compared to the RefSeq (NCBI) database according to the pipeline shown. The screen captures (middle of the panel) show how the individual reads map to each transcript along with the total numbers of hits summarized by chromosome. The process is repeated twice, once for each cell line, and the two sets of alignments are converted to Gene Lists for comparative analysis in GeneSifter laboratory edition to observe differential expression (bottom of the panel).

The Tag Profiling panel examines data from a recently published experiment (a reference is provided in the poster) in which gene expression was studied in transgenic mice. I’ll leave out the details of the paper, and only point out how this example shows the differences between Tag Profiling and RNA-Seq data. Because Tag Profiling collects data from specific 3’ sites in RNA, the aligned data (middle of the panel) show alignments as single “spikes” toward the 3’ end of transcripts. Occasionally multiple peaks are observed. The question being, are the additional peaks the result of isoforms (alternative polyA sites) or incomplete restriction enzyme digests? How might this be sorted out? Like RNA-Seq, the bottom panel shows the comparative analysis of replicate samples from the wild type (WT) and transgenic (TG) mice.

Data from a small RNA analysis experiment are analyzed in the third panel. Unlike RNA-Seq and Tag Profiling, this secondary analysis has more comparisons of the reads to different sets of reference sequences. The purpose is to identify and filter out common artifacts observed in small RNA preparations. The pipeline we used, and data produced, are shown in the middle of the panel. Histogram plots of read length distribution, determined from alignments in different reference sources, are created because an important feature of small RNAs is that they are small. Distributions clustered around 22 nt indicate a good library. Finally, data are linked to additional reports and databases, like miRBase (Sanger Center), to explore results further. In the example shown, the first hit was to a small RNA that has been observed in opossums; now we have human counter part. In total, four, samples were studied. Like RNA-Seq and Tag Profiling, we can observe the relative expression of each small RNA by analyzing the datasets together (hierarchical clustering, bottom).

Section 6 presents some of the challenges of scale issues that accompany NGS, and how we are addressing these issues with HDF5 technology. This will be a topic of many more posts in the future.

We close the poster by addressing the challenges listed above with the final points:

• High performance data management systems are being developed through the BioHDF project and GeneSifter system architectures.
• The examples show how each application and sequencing platform requires a different data analysis workflow (pipeline). GeneSifter provides a platform to develop and make bioinformatics pipelines and data readily available to communities of biologists.
• The transcriptome is complex, different libraries of sequence data can filter known sequences (e.g. rRNA) and discover new elements (miRNAs) and isoforms of expressed genes.
• Within a dataset, read maps, tables, and histogram plots are needed to summarize and understand the kinds of sequences present and how they relate to an experiment.
• Links to Entrez Gene, the USCS genome browser, and miRBASE, show how additional information can be integrated into the application framework and used.
• Next Gen transcriptomics assays are similar to microarray assays in many ways, hence software systems like Geospiza’s GeneSifter are useful for comparative analysis.

You can also get the file, AGBT_2009.pdf