Recent advances in massively parallel cDNA sequencing (RNA-seq) have paved the way for comprehensive analysis of the transcriptome, a set of all RNA molecules including mRNA, rRNA, tRNA and other non-coding RNAs in one or more populations of cells. RNA-Seq can identify the precise location of transcription boundaries, show how exons are connected and reveal sequence variations in transcribed regions. Taken together, RNA-Seq is the first sequencing based method that allows the entire transciptome to be surveyed in a very high- throughput and quantitative manner, offering both single-base resolution for annotation and digital gene expression levels at the genome scale. However, this technology is under active development and there are several enzymatic steps during library preparation that can contribute to sequence dependent bias, hindering comparisons between genomic regions and adversely affecting transriptome profiling. Ligases used to add on adapter sequences in RNA- Seq have structure based preferences, reverse transcriptases that make the first cDNA strand are prone to copy errors and rearrangements, and polymerases used for amplification often stumble when approaching GC / AT rich regions. The goal of this project is to create a library preparation kit for making minimally biased, highly indexed RNA-Seq libraries for deep sequencing that are constructed in a way to allow transcriptome profiles to be easily and fairly interrogated. Our proposal to develop technology to study the transcriptome in an unbiased, high-throughput manner should make future clinical applications a reality and propel research in comparative tissue disease profiles, further un-locking transcriptional regulation.
Advances in massively parallel cDNA sequencing (RNA-Seq) have paved the way for comprehensive analysis of the transcriptome, a set of all RNA molecules including mRNA, rRNA, tRNA and other non-coding RNAs in one or more populations of cells. Our proposal to develop technology to study the transcriptome in an unbiased and quantitative manner should propel research in comparative tissue disease profiles and further un-lock the diagnostic potential of transcript profiling.
