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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: Mar 26, 2026

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
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Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

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An Annotation Agnostic Algorithm for Detecting Nascent RNA Transcripts in GRO-Seq.

Joseph G Azofeifa, Mary A Allen, Manuel E Lladser

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |February 2, 2016
    PubMed
    Summary
    This summary is machine-generated.

    We developed Fast Read Stitcher (FStitch), a new algorithm for detecting nascent RNA transcription using global nuclear run-on sequencing (GRO-seq). This method accurately identifies transcribed genomic regions, offering new insights into gene expression without needing prior genomic annotation.

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    Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
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    Area of Science:

    • Genomics
    • Molecular Biology
    • Bioinformatics

    Background:

    • Global nuclear run-on sequencing (GRO-seq) directly measures active transcription by capturing nascent RNA.
    • Traditional RNA sequencing (RNA-seq) measures steady-state RNA levels, influenced by transcription, processing, and stability.
    • GRO-seq data analysis presents unique challenges not yet fully addressed.

    Purpose of the Study:

    • To introduce a fast and simple algorithm for detecting nascent RNA transcription from GRO-seq data.
    • To provide a robust method for identifying transcribed genomic regions.
    • To facilitate novel discoveries in transcription by enabling annotation-agnostic analysis.

    Main Methods:

    • Development of the Fast Read Stitcher (FStitch) algorithm.
    • Utilizing machine learning techniques: hidden Markov models and logistic regression.
    • Classification of transcribed genomic regions based on user-defined training sets.

    Main Results:

    • FStitch demonstrates accuracy and robustness across varying sequencing read depths.
    • The algorithm is annotation-agnostic, requiring no prior genomic annotation.
    • Analysis of GRO-seq data with FStitch reveals new insights into transcription processes.

    Conclusions:

    • FStitch offers an efficient and effective approach for analyzing GRO-seq data.
    • The algorithm's annotation-agnostic nature enables unbiased discovery of transcriptional activity.
    • This tool advances the study of bona fide transcription and gene regulation.