Thursday, October 28, 2010

Bloginar: Making cancer transcriptome sequencing assays practical for the research and clinical scientist

A few weeks back we (Geospiza and Mayo Clinic) presented a research poster at BioMed Central’s Beyond the Genome conference. The objective was to present GeneSifter’s analysis capabilities and discuss the practical issues scientists face when using Next Generation DNA Sequencing (NGS) technologies to conduct clinically orientated research related to human heath and disease.

NGS technologies are increasing in their appeal for studying cancer. Fully characterizing the more than 10,000 types and subtypes of cancer to develop biomarkers that can be used to clinically define tumors and target specific treatments requires large studies that examine specific tumors in 1000s of patients. This goal will fail without significantly reducing both data production and analysis costs so that the vast majority of cancer biologists and clinicians can conduct NGS assays and analyze their data in routine ways.

While sequencing costs are now inexpensive enough for small groups and individuals, beyond genome centers, to conduct the needed studies, the current data analysis methods need to move from large bioinformatics team approaches to automated methods that employ established tools in scalable and adaptable systems to provide standard reports and make results available for interactive exploration by biologists and clinicians. Mature software systems and cloud computing strategies can achieve this goal.

Poster Layo
Excluding the title, the poster has five major sections. The first section includes the abstract (above) and study parameters. In the work, we examined the RNA from 24 head and neck cancer biopsies from 12 individuals' tumor and normal cells.

The remaining sections (2-5), provide a background of NGS challenges, applications, high-level data analysis workflows, the analysis pipeline used in the work, the comparative analyses that need to be conducted, and practical considerations for groups seeking to do similar work. Much of section 2 has been covered in previous blogs and research papers.

Section 3: Secondary Analysis Explores Single Samples
NGS challenges are best known for the amount of data produced by the instruments. While this challenge should not be undervalued, it is over discussed. A far greater challenge lies in the complexity of data analysis. Once the first step (primary analysis, or basecalling) is complete, the resulting millions of reads must be aligned to several collections of reference sequences. For human RNA samples, these include the human genome, splice junction databases, and others to measure biological processes and filter out reads arising from artifacts related to sample preparation. Aligned data are further processed to create tables that annotate individual reads and compute quantitative values related to how the sample’s reads align (or cover) regions of the genome or span exon boundaries. If the assay measures sequence variation, alignments must be further processed to create variant tables.

Secondary analysis produces a collection of data in forms that can be immediately examined to understand overall sample quality and characteristics. High-level summaries indicate how many reads align to things we are interested in and not interested in. In GeneSifter, these summaries are linked to additional reports that show additional detail. Gene List reports, for example, show how the sample reads align within a gene’s boundary. Pictures in these reports are linked to Genesifter's Gene Viewer reports that provide even greater detail about the data with respect to each read’s alignment orientation and observed variation.

An important point about secondary analysis, however, is that it focuses on single sample analyses. As more samples are added to the project, the data from each sample must be processed through an assay specific pipeline. This point is often missed in the NGS data analysis discussion. Moreover, systems supporting this work must not only automate 100s of secondary analysis steps, they must also provide tools to organize the input and output data in project-based ways for comparative analysis.

Section 4: Tertiary Analysis in GeneSifter Compares Data Between Samples
The science happens in NGS when data are compared between samples in statistically rigorous ways. RNA sequencing makes it possible to compare gene expression, exon expression, and sequence variation between samples to identify differentially expressed genes, their isoforms, and whether certain alleles are differentially expressed. Additional insights are gained when gene lists can be examined in pathways and by ontologies. GeneSifter performs these activities in a user-friendly web-environment.

The poster's examples show how gene expression can be globally analyzed for all 24 samples, how a splicing index can distinguish gene isoforms occurring in tumor, but not normal cells, and how sequence variation can be viewed across all samples. Principal component analysis shows that genes in tumor cells are differentially expressed relative to normal cells. Genes highly expressed in tumor cells include those related to cell cycle and other pathways associated with unregulated cell growth. While these observations are not novel, they do confirm our expectations about the samples and being able to make such an observation with just a few clicks prevents working on costly misleading observations. For genes showing differential exon expression, GeneSifter provides ways to identify those genes and navigate to the alignment details. Similarly reports that show differential variation between samples can be filtered by multiple criteria in reports that link to additional annotation details and read alignments.

Section 5: Practical Considerations
Complete NGS data analysis systems seamlessly integrate secondary and tertiary analysis. Presently, no other systems are as complete as GeneSifter. There are several reasons why this is the case. First, a significant amount of software must be produced and tested to create such a system. From complex data processing automation, to advanced data queries, to user interfaces that provide interactive visualizations and easy data access, to security, software systems must employ advanced technologies and take years to develop with experienced teams. Second, meeting NGS data processing requirements demands that computer systems be designed with distributable architectures that can support cloud environments in local and hosted configurations. Finally, scientific data systems must support both predefined and ad hoc query capabilities. The scale of NGS applications means that non-traditional approaches must be used to develop data persistence layers that can support a variety of data access methods and, for bioinformatics, this is a new problem.

Because Geospiza has been doing this kind of work for over a decade and could see the coming challenges, we’ve focused our research and development in the right ways to deliver a feature rich product that truly enables researchers to do high quality science with NGS.

Enjoy the poster.