Transcriptome profiling by high throughput RNA Sequencing

Information

  • Research Project
  • 8455625
  • ApplicationId
    8455625
  • Core Project Number
    R43GM105178
  • Full Project Number
    1R43GM105178-01
  • Serial Number
    105178
  • FOA Number
    PA-12-088
  • Sub Project Id
  • Project Start Date
    4/15/2013 - 11 years ago
  • Project End Date
    4/14/2015 - 9 years ago
  • Program Officer Name
    MAAS, STEFAN
  • Budget Start Date
    4/15/2013 - 11 years ago
  • Budget End Date
    4/14/2015 - 9 years ago
  • Fiscal Year
    2013
  • Support Year
    01
  • Suffix
  • Award Notice Date
    4/15/2013 - 11 years ago

Transcriptome profiling by high throughput RNA Sequencing

DESCRIPTION (provided by applicant): 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.

IC Name
NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES
  • Activity
    R43
  • Administering IC
    GM
  • Application Type
    1
  • Direct Cost Amount
  • Indirect Cost Amount
  • Total Cost
    292795
  • Sub Project Total Cost
  • ARRA Funded
    False
  • CFDA Code
    859
  • Ed Inst. Type
  • Funding ICs
    NIGMS:292795\
  • Funding Mechanism
    SBIR-STTR RPGs
  • Study Section
    ZRG1
  • Study Section Name
    Special Emphasis Panel
  • Organization Name
    BIOO SCIENTIFIC CORPORATION
  • Organization Department
  • Organization DUNS
    611930244
  • Organization City
    AUSTIN
  • Organization State
    TX
  • Organization Country
    UNITED STATES
  • Organization Zip Code
    787443202
  • Organization District
    UNITED STATES