DNAStar also tackles the data dilemma. “If you go back five years, getting data was expensive, and analysis was base by base,” says Tom Schwei, vp and GM. “Now, sequencing has become so inexpensive that it’s easy to produce a lot of data. Helping scientists filter through that data is crucial.”
DNAStar provides an array of tools to help scientists rein in their data. Their latest version of visualization software is GenVision v2.0, with expanded capabilities for creating publication-quality images depicting large quantities of genomic information. According to Schwei, “NGS volumes are trending toward shorter read lengths, which is a whole different animal, and we handle these aspects well.”
The company’s desktop software is designed to work with its Lasergene software to permit easy flow of larger data sets and to make real-time visual and data edits. “The analysis of data is a field that’s wide open,” says Schwei. “Templated and de novo assemblies are both challenging, each with its own issues. But developing solutions for both types of projects and more (e.g., transcriptome analysis) is complex. We develop tools to keep up with the trends.”
NGS vs. Microarrays
NGS improvements have enabled more detailed views on classic gene-expression experiments typically run on microarrays. “Typically, microarray experiments produce lists of genes with differential expression across a collection of samples,” says Michael Lelivelt, Ph.D., vp, genomics, Partek.
“However, several technical developments enable analysis of NGS mRNA-seq data to produce estimates of levels of individual transcripts differentially expressed rather than simply differentially expressed genes.”
Partek is a desktop application that compares different genomes and transcriptomes. “Instead of mapping sequencing reads to genes, we map the reads to specific transcripts and then define global changes in the transcriptome across various conditions,” explains Dr. Lelivelt.
“With NGS, one can look at gene expression in a less biased way than with microarrays. The ability to correlate changes in RNA abundance with changes in DNA template abundance helps corroborate scientific findings. Researchers want more integration of multiple genomic technologies.”
Giving researchers a good desktop tool to answer these questions makes personalized medicine more feasible. “More researchers use large-scale genomic technologies, because it is so much cheaper now to sequence transcriptomes and easier to define a set of differentially expressed transcripts through streamlined analysis. Lowering both economic and technological barriers will increase NGS’ impact on personalized medicine,” notes Dr. Lelivelt.