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Related Concept Videos

RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
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Microsoft Excel is a cornerstone tool for data analysis and statistical operations, offering a wide array of functionalities to manage, analyze, and visualize data efficiently. Recognized for its versatility, Excel facilitates the performance of basic to complex statistical operations, serving as an indispensable asset for analysts, researchers, and students alike. Excel's significance in data analysis emanates from its spreadsheet environment, where data can be organized in rows and...
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RNA Stability01:53

RNA Stability

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Intact DNA strands can be found in fossils, while scientists sometimes struggle to keep RNA intact under laboratory conditions. The structural variations between RNA and DNA underlie the differences in their stability and longevity. Because DNA is double-stranded, it is inherently more stable. The single-stranded structure of RNA is less stable but also more flexible and can form weak internal bonds. Additionally, most RNAs in the cell are relatively short, while DNA can be up to 250 million...
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RNA Structure01:23

RNA Structure

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Overview
The basic structure of RNA consists of a five-carbon sugar and one of four nitrogenous bases. Although most RNA is single-stranded, it can form complex secondary and tertiary structures. Such structures play essential roles in the regulation of transcription and translation.
Different Types of RNA Have the Same Basic Structure
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Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
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Nuclei Isolation from Fresh Frozen Brain Tumors for Single-Nucleus RNA-seq and ATAC-seq
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Single-Cell RNA-Seq Technologies and Related Computational Data Analysis.

Geng Chen1, Baitang Ning2, Tieliu Shi1

  • 1Center for Bioinformatics and Computational Biology, and Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, Shanghai, China.

Frontiers in Genetics
|April 27, 2019
PubMed
Summary
This summary is machine-generated.

Single-cell RNA sequencing (scRNA-seq) offers gene expression insights at single-cell resolution. This review covers scRNA-seq methods and analysis techniques, highlighting challenges and future directions for transcriptomic studies.

Keywords:
allelic expressionalternative splicingcell clusteringcell trajectorysingle-cell RNA-seq

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Single-cell RNA sequencing (scRNA-seq) provides unprecedented resolution for transcriptomic studies.
  • scRNA-seq data is characterized by high noise and complexity compared to bulk RNA-seq.
  • Existing bioinformatics tools face challenges in accurately analyzing scRNA-seq data.

Purpose of the Study:

  • To provide a comprehensive overview of scRNA-seq technologies and protocols.
  • To discuss various computational methods for scRNA-seq data analysis.
  • To outline future prospects and applications of scRNA-seq.

Main Methods:

  • Review of single-cell isolation techniques.
  • Examination of diverse scRNA-seq protocols.
  • Discussion of bioinformatics pipelines for scRNA-seq data analysis.

Main Results:

  • Detailed overview of scRNA-seq technologies and isolation methods.
  • Comprehensive discussion of analysis techniques: quality control, mapping, quantification, batch correction, normalization, imputation, dimensionality reduction, feature selection, clustering, trajectory inference, differential expression, splicing, allelic expression, and network reconstruction.
  • Identification of computational challenges and areas for algorithmic development.

Conclusions:

  • scRNA-seq is a revolutionary technology for transcriptomic research.
  • Accurate analysis of scRNA-seq data requires sophisticated bioinformatics approaches.
  • Further development of novel algorithms is crucial for advancing scRNA-seq applications.