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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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Protein Networks02:26

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Alternative RNA Splicing02:18

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Alternative RNA splicing is the regulated splicing of exons and introns to produce different mature mRNAs from a single pre-mRNA. Unlike in constitutive splicing where a single gene produces a single type of mRNA, alternative splicing allows an organism to produce multiple proteins from a single gene and plays an important role in protein diversity.
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Network Covalent Solids02:18

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Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
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RNA Structure01:23

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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.
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RNA Polymerase II Accessory Proteins02:36

RNA Polymerase II Accessory Proteins

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Proteins that regulate transcription can do so either via direct contact with RNA Polymerase or through indirect interactions facilitated by adaptors, mediators, histone-modifying proteins, and nucleosome remodelers. Direct interactions to activate transcription is seen in bacteria as well as in some eukaryotic genes. In these cases, upstream activation sequences are adjacent to the promoters, and the activator proteins interact directly with the transcriptional machinery. For example, in...
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Updated: Feb 13, 2026

Nuclei Isolation from Fresh Frozen Brain Tumors for Single-Nucleus RNA-seq and ATAC-seq
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netSmooth: Network-smoothing based imputation for single cell RNA-seq.

Jonathan Ronen1, Altuna Akalin1

  • 1Scientific Bioinformatics Platform, Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Berlin, 13125, Germany.

F1000Research
|July 20, 2018
PubMed
Summary
This summary is machine-generated.

Single cell RNA-seq (scRNA-seq) data often contains technical biases. netSmooth, a novel network-diffusion method, effectively smooths gene expression values, improving analysis of cell populations and cancer genomics.

Keywords:
genomicsimputationnetworksscRNA-seqsingle-cell

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Identification of Footprints of RNA:Protein Complexes via RNA Immunoprecipitation in Tandem Followed by Sequencing RIPiT-Seq
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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Single cell RNA sequencing (scRNA-seq) is a powerful tool for analyzing cellular heterogeneity.
  • scRNA-seq data is prone to technical biases like dropouts and high variance, complicating downstream analysis.
  • Existing imputation methods can amplify inherent data biases.

Purpose of the Study:

  • To introduce netSmooth, a novel network-diffusion based method for smoothing scRNA-seq data.
  • To address technical biases in scRNA-seq experiments.
  • To improve the accuracy of scRNA-seq data analysis.

Main Methods:

  • netSmooth utilizes network diffusion to leverage prior knowledge of gene expression covariance structure.
  • The method smooths gene expression values by accounting for relationships between genes.
  • Implementation is provided as an R package.

Main Results:

  • netSmooth significantly improves clustering results in scRNA-seq experiments.
  • Demonstrated effectiveness across distinct cell populations, time-course experiments, and cancer genomics datasets.
  • The smoothing approach mitigates technical biases inherent in scRNA-seq data.

Conclusions:

  • netSmooth offers a robust solution for handling technical biases in scRNA-seq data.
  • The network-diffusion approach enhances the reliability of gene expression analysis.
  • Improved clustering and data interpretation are key benefits for various biological applications.