MAR 15, 2016 10:00 AM PDT
Gene Expression Analysis Using 3'-RNA Sequencing
SPONSORED BY: Lexogen
CONTINUING EDUCATION (CME/CE/CEU) CREDITS: P.A.C.E. CE
15 56 8321

Speakers:
  • Assistant Professor, University of Delaware
    Biography
      Behnam Abasht joined University of Delaware's faculty as an Assistant Professor in 2011. He received his PhD in Quantitative and Molecular Genetics in France from INRA-Agrocampus Rennes in 2006. He then completed a postdoctoral fellowship in Quantitative Genetics at Iowa State University. Dr. Abasht was a Research Geneticist and Genomics Project Leader at Perdue Farms, Inc. from 2008 until his appointment at the University of Delaware. His area of research is integrative avian biology with emphasis on fine-mapping and functional characterization of quantitative trait loci (QTL) in chickens.

    Abstract:
    10:00AM PT, 12:00PM CT, 1:00PM ET

    RNA sequencing (RNA-seq) has revolutionized the study of gene expression in animals, plants and microorganisms. However, because of its high cost, this technology has been mainly used in experiments with limited number of samples. To examine a cost-effective alternative, we used a method, which confines sequencing to the 3’-end of mRNA and produces just one fragment per transcript, resulting in a dramatic decrease in sequencing cost. Total RNA isolated from chicken adipose tissue samples was used for cDNA library preparation using QuantSeq 3’mRNA-seq library Prep Kit. Sixty-one uniquely indexed cDNA libraries were pooled and sequenced on one lane on the Illumina Hiseq 2500. On average, 2.24 million reads per sample were generated, 90.1% of which were mapped to the chicken reference genome (Ensembl Galgal4). For more than 70% of the genes with detectable expression, we redefined the 3’-end and identified alternative polyadenylation sites within the 3’-untranslated regions. To compare gene expression measures between 3’-RNA-seq and RNA-seq technologies, we used data from a subset of 20 samples that were previously used in a RNA-seq study of feed efficiency. The correlation of the log10(fold-change) for gene expression (high- vs. low-feed efficiency birds) between these two methods was 0.90. In conclusion, 3’-RNA-seq is a cost effective method amenable to global gene expression studies at population-level, e.g., expression QTL (eQTL) mapping.  Also, it allows for accurate detection of the 3’-end of transcripts, enabling verification of the current gene model annotations and global characterization of alternative polyadenylation.

    Learning objectives:

    1) Gain knowledge about 3’-RNA-seqencing and its application for global gene expression studies at population-level
     
    2) Explore how 3’-RNA-seqencing can help improve current gene model annotations and characterize alternative polyadenylation

     

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