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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. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
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Updated: Sep 25, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
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scTSSR2: Imputing Dropout Events for Single-Cell RNA Sequencing Using Fast Two-Side Self-Representation.

Bo Li, Ke Jin, Le Ou-Yang

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |April 27, 2022
    PubMed
    Summary

    scTSSR2 effectively imputes dropout events in single-cell RNA sequencing (scRNA-seq) data, improving analysis quality. This fast method outperforms existing imputation techniques for scRNA-seq datasets.

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    Area of Science:

    • Genomics
    • Bioinformatics

    Background:

    • Single-cell RNA sequencing (scRNA-seq) offers high-resolution gene expression insights.
    • Dropout events are a common challenge in scRNA-seq data, hindering downstream analysis.

    Purpose of the Study:

    • To develop an efficient imputation method for scRNA-seq data.
    • To address the issue of dropout events and enhance data quality.

    Main Methods:

    • Developed scTSSR2, combining matrix decomposition with fast two-side sparse self-representation.
    • Compared scTSSR2's computational speed and memory usage against existing methods.

    Main Results:

    • scTSSR2 demonstrates significant advantages in computational speed and memory efficiency.
    • Comprehensive experiments show scTSSR2 outperforms state-of-the-art imputation methods.
    • An R package, scTSSR2, was created for denoising scRNA-seq data.

    Conclusions:

    • scTSSR2 is an effective and efficient tool for imputing dropout events in scRNA-seq data.
    • The developed R package facilitates improved scRNA-seq data quality and analysis.