Deep sequencing of strand-specific cDNA libraries is now a ubiquitous tool for identifying and quantifying RNAs in diverse sample types. The accuracy of conclusions drawn from these analyses depends on precise and quantitative conversion of the RNA sample into a DNA library suitable for sequencing. Here, we describe an optimized method of preparing strand-specific RNA deep sequencing libraries from small RNAs and variably sized RNA fragments obtained from ribonucleoprotein particle footprinting experiments or fragmentation of long RNAs. Our approach works across a wide range of input amounts (400 pg to 200 ng), is easy to follow and produces a library in 2-3 days at relatively low reagent cost, all while giving the user complete control over every step. Because all enzymatic reactions were optimized and driven to apparent completion, sequence diversity and species abundance in the input sample are well preserved.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4288154 | PMC |
| http://dx.doi.org/10.1093/nar/gku1235 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Utilization of primary tumor samples for cancer neoantigen discovery.
J Immunother Cancer
January 2025
Surgery Branch, National Cancer Institute, Bethesda, Maryland, USA
Background: The use of tumor-infiltrating T lymphocytes (TIL) that recognize cancer neoantigens has led to lasting remissions in metastatic melanoma and certain cases of metastatic epithelial cancer. For the treatment of the latter, selecting cells for therapy typically involves laborious screening of TIL for recognition of autologous tumor-specific mutations, detected through next-generation sequencing of freshly resected metastatic tumors. Our study explored the feasibility of using archived formalin-fixed, paraffin-embedded (FFPE) primary tumor samples for cancer neoantigen discovery, to potentially expedite this process and reduce the need for resections normally required for tumor sequencing.
View Article and Find Full Text PDFKMeansGraphMIL: A Weakly Supervised Multiple Instance Learning Model for Predicting Colorectal Cancer Tumor Mutational Burden.
Am J Pathol
January 2025
The Seventh Affiliated Hospital, Sun Yat-Sen University, 628 Zhenyuan Road, Xinhu Street, Guangming New District, Shenzhen, 518107, Guangdong, China. Electronic address:
Colorectal cancer (CRC) is one of the top three most lethal malignancies worldwide, posing a significant threat to human health. Recently proposed immunotherapy checkpoint blockade treatments have proven effective for CRC, but their use depends on measuring specific biomarkers in patients. Among these biomarkers, Tumor Mutational Burden (TMB) has emerged as a novel indicator, traditionally requiring Next-Generation Sequencing (NGS) for measurement, which is time-consuming, labor-intensive, and costly.
View Article and Find Full Text PDFDeep learning in integrating spatial transcriptomics with other modalities.
Brief Bioinform
November 2024
State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, 2 Sipailou, Xuanwu District, Nanjing 210096, China.
Spatial transcriptomics technologies have been extensively applied in biological research, enabling the study of transcriptome while preserving the spatial context of tissues. Paired with spatial transcriptomics data, platforms often provide histology and (or) chromatin images, which capture cellular morphology and chromatin organization. Additionally, single-cell RNA sequencing (scRNA-seq) data from matching tissues often accompany spatial data, offering a transcriptome-wide gene expression profile of individual cells.
View Article and Find Full Text PDFMangroves increased the mercury methylation potential in the sediment by producing organic matters and altering microbial methylators community.
Sci Total Environ
January 2025
College of Marine Sciences, South China Agricultural University, Guangzhou 510642, China. Electronic address:
Mangrove ecosystem has attracted global attention as a hotspot for mercury (Hg) methylation. Although numerous biotic and abiotic parameters have been reported to influence methylmercury (MeHg) production in sediments, the key factors determining the elevated MeHg levels in mangrove wetlands have not been well addressed. In this study, Hg levels in the sediments from different habitats (mudflats, mangrove fringe, and mangrove interior) in the Futian mangrove wetland were investigated, aiming to characterize the predominant factors affecting the MeHg production and distinguish the key microbial taxa responsible for Hg methylation.
View Article and Find Full Text PDFIntegrated RNA sequencing analysis and machine learning identifies a metabolism-related prognostic signature in clear cell renal cell carcinoma.
Sci Rep
January 2025
Department of Urology, Renmin Hospital of Wuhan University, Wuhan, 430060, China.
The connection between metabolic reprogramming and tumor progression has been demonstrated in an increasing number of researches. However, further research is required to identify how metabolic reprogramming affects interpatient heterogeneity and prognosis in clear cell renal cell carcinoma (ccRCC). In this work, single-cell RNA sequencing (scRNA-seq) based deconvolution was utilized to create a malignant cell hierarchy with metabolic differences and to investigate the relationship between metabolic biomarkers and prognosis.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!