Overview Transfer RNA (tRNA) is an adaptor molecule that decodes mRNA and translates protein. Recent studies have demonstrated that tRNAs also serve as a major source of small non-coding RNAs having distinct and varied functions [1]. These tRNA-derived ncRNAs are not random degradation products, but rather generated through precise biogenesis processes (Fig. 1). tRNA-derived ncRNAs […]
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The Challenges and Solutions of Small RNA Profiling The Biases of Using TPM in Small RNA Sequencing Data Analysis Simultaneously Profile Multiple Small RNA Classes Accurately Direct End-labeling to Avoid the Biases From Sequencing Library Prep The comparison of small RNA transcript levels between samples using TPM in small RNA-sequencing (small RNA-seq) is actually meaningless. In small
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The Challenges and Solutions of Small RNA Profiling The Biases of Using TPM in Small RNA Sequencing Data Analysis Simultaneously Profile Multiple Small RNA Classes Accurately Direct End-labeling to Avoid the Biases From Sequencing Library Prep To fluorescently label small RNAs for microarray profiling, the total RNA is treated with T4 polynucleotide kinase (T4 PNK) to remove
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The Challenges and Solutions of Small RNA Profiling The Biases of Using TPM in Small RNA Sequencing Data Analysis Simultaneously Profile Multiple Small RNA Classes Accurately Direct End-labeling to Avoid the Biases From Sequencing Library Prep Mature small RNAs like miRNAs and tsRNAs have non-random, precisely cleaved termini and defined RNA sizes during biogenesis. Arraystar Small RNA
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The Challenges and Solutions of Small RNA Profiling The Biases of Using TPM in Small RNA Sequencing Data Analysis Simultaneously Profile Multiple Small RNA Classes Accurately Direct End-labeling to Avoid the Biases From Sequencing Library Prep Small RNA-seq has been used for profiling microRNAs and other small RNAs. However, the use of small RNA-seq data to quantify
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Arraystar GlycoRNA Array Profiling Arraystar GlycoRNA Array combines methods of biochemical capture of glycoRNA and RNA detection by microarray to quantify and profile glycoRNA expression. The integration of these two advanced techniques leverages the strengths of both methods for high specificity, sensitivity, and accuracy. The array covers a wide range of glycosylated small RNA classes,
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GlycoRNAs have emerged as a new area of study in cancer, cardiovascular, neurological, immune, and respiratory diseases, opening up new avenues into novel biomarker and therapeutic applications. Breast Cancer Surface glycoRNAs are inversely related with tumor malignancy and metastasis, i.e. breast non-cancer cells (MCF-10A) have the highest glycoRNA levels, followed by breast cancer cells (MCF-7), while breast
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GlycoRNAs are glycosylated small non-coding RNAs, including small nuclear RNAs (snRNAs), ribosomal RNAs (rRNAs), small nucleolar RNAs (snoRNAs), transfer RNAs (tRNAs), Y-RNAs[1], and microRNAs [2]. Glycosylated mRNAs are not known. Particularly, the N-glycans on glycoRNAs are highly sialylated and fucosylated. GlycoRNAs are displayed on the cell surface and can bind Siglec receptors important in immune
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Ultraconserved regions (UCRs) are DNA segments greater than 200 bp in length that are completely conserved among human, rat, and mouse (Bejerano et al., 2004). 481 UCRs have been identified, 225 of which are classified as transcribed (“exonic”) or possibly transcribed (“possibly exonic”) due to overlap with coding exons. Of the remaining 256 UCRs, termed
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Case Studies of profiling T-UCR expression in Cancer Recent genome-wide expression studies show that a subset of ultraconserved regions (UCRs), known as transcribed ultraconserved regions (T-UCRs), are abnormally expressed in a number of human cancers, such as leukemia, colorectal carcinoma, and hepatocellular carcinoma (Braconi, et al., 2011; Calin, et al., 2007; Lujambio, et al., 2010). In addition, the
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