Conducting Massively Parallel Sequencing

One of the major breakthroughs in modern biology is the development of massively parallel sequencing, also called next generation sequencing (NGS), which enabled the complete delineation of the human genome more than a decade ago. Since then many more species’ genomes have been sequenced, and the cost per genome has dropped from billions to mere thousands of dollars. New discoveries are being made as a result of the capability many research teams now possess to not only sequence chromosomal DNA, but also to identify which regions a protein of interest specifically binds (Chip-seq), analyze a whole transcriptome of a cell population under investigation (RNA-seq), or find out which RNA regions an RNA binding protein resides (CLIC-seq).

While it is inevitable that many PIs will seriously consider the inclusion of deep sequencing in their next grant proposal, it is not necessarily easy to take the first step and get their feet wet, so to speak. Knowing what format (e.g. 454 for longer reads, HighSeq for higher accuracy, or Ion Torren for bench top convenience) to use and how much to pay requires a vast amount of knowledge and experience. Even when you are done with sample prep, amplification and sequencing, to handle such massive amount of data is not trivial—transporting data alone can be a headache. A database server for storage and analysis requires another layer of expertise. There is no easy solution but to get started somehow. However, be prepared to deal with these issues.

Whether the cost on a type of next generation service is justifiable depends on whether it is required for your purposes. For example, when analyzing a person’s propensity of developing a disease by using known, disease-relevant genetic information, often times exome sequencing is sufficient. This costs anywhere between $1,000 to $3,000 with 100X coverage, significantly less than sequencing a complete genome which typically costs ~$5,000 at ~20x coverage.

High coverage sequencing of maternal blood DNA has been developed into clinically approved prenatal diagnosis of trisomy in Down’s syndrome and other chromosomal abnormalities. Transcriptome analysis helped the understanding of how reprogramming works when iPSCs are. Looking forward, with more routine use of deep sequencing we can predict with much more certainty the “off-target” effects of RNAi or cellular toxicity of chromosomal modifications enabled by ZFN, TALEN, or CRISPR. As a matter of fact, we believe that transcriptome sequencing should be required after each RNAi event to prove a specific linkage between knockdown and functions; similarly, whole genome sequencing results need to be provided after making a site directed chromosomal change in the future for high level publications.

*This blog partially resulted from discussions between Jiwu Wang and his colleagues, who are NGS experts at UCSD’s Cellular and Molecular Medicine, Moore Cancer Center, and BGI Americas.

Tags: CRISPR, deep sequencing, exome sequencing, footprint free, genomic modification, iPSCs, Massively Parallel Sequencing, NSG, TALEN, transcriptome, whole genome sequencing, ZFN