RNA-Seq is a next generation sequencing method with a wide range of applications including single nucleotide polymorphism (SNP) detection, splice junction identification, and gene expression level measurement. However, the RNA-Seq sequence data can be biased during library constructions resulting in incorrect data for SNP, splice junction, and gene expression studies.
Here, researchers from the University of Texas at Austin have developed new library preparation methods to limit such biases. A whole transcriptome library prepared for the SOLiD system displayed numerous read duplications (pile-ups) and gaps in known exons. The pile-ups and gaps of the whole transcriptome library caused a loss of SNP and splice junction information and reduced the quality of gene expression results. Further, they found clear sequence biases for both 5′ and 3′ end reads in the whole transcriptome library. To remove this bias, RNaseIII fragmentation was replaced with heat fragmentation. For adaptor ligation, T4 Polynucleotide Kinase (T4PNK) was used following heat fragmentation. However, its kinase and phosphatase activities introduced additional sequence biases. To minimize them, they used OptiKinase before T4PNK. This study further revealed the specific target sequences of RNaseIII and T4PNK.
These results suggest that the heat fragmentation removed the RNaseIII sequence bias and significantly reduced the pile-ups and gaps. OptiKinase minimized the T4PNK sequence biases and removed most of the remaining pile-ups and gaps, thus maximizing the quality of RNA-Seq data.
- Lee C, Harris RA, Wall JK, Mayfield RD, Wilke CO. (2013) RNaseIII and T4 Polynucleotide Kinase sequence biases and solutions during RNA-seq library construction. Biol Direct 8(1),16. [article]