Project description:R&D project to develop low input library construction methods. .
This dataset contains all the data available for this study on 2019-04-01.
Project description:As the most abundant and best-characterized internal mRNA modification, N6-methyladenosine (m6A) emerges to play a critical regulatory role in wide range of physiological and pathological processes, including gametogenesis, neuronal development, obesity and tumorigenesis. Methylated RNA immunoprecipitation coupled with next-generation sequencing (MeRIP-seq) facilitates transcriptome-wide m6A profiling, also is the most widely used technique to understand the biological significance of m6A. However, it typically requires over 100 μg of total RNA or 107 cells as input materials, hampering its application in limited samples. Here, we develop tMeRIP-seq, a transposase assisted MeRIP-seq method to achieve m6A profiling using ultra-low amount of input RNA. By marrying Tn5 tagmentation to m6A-specific immunoprecipitation, tMeRIP-seq largely improves the efficiency of library construction and reduces the input materials to as little as 60 ng total RNA or 103 cells. We apply this method on a small droplet of human blood and recapitulate the m6A profile previously reported using conventional protocol. We find tMeRIP-seq is a convenient and powerful method to examine m6A in ultra-low input material, potentially providing m6A as a new layer of bio-marker for liquid biopsy.
Project description:We developed a new method on sequencing low-input RNA. This method shows much low-bias with the advantage of semiconductor while competing with smart-seq2. In order to analyze the low-input RNA datasets sensitively, we also develop FANSe2splice with high experimental verification rate as the analysis tool in our method.
Project description:we develop a robut low input cell and high throughput single cell ChIP method used for both native and fixed in vitro cultured cells and intact tissue
Project description:we describe a streamlined RNAseq protocol (EASY RNAseq) for sensitive transcriptome assessment starting from low amount of input materials. EASY RNAseq is technically robust enough for sequencing small pools of homogenous and heterogeneous cells, recovering higher numbers of genes and with a more even expression distribution pattern than other commonly used methods.
