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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 Profiling02:24

Ribosome Profiling

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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.
Applications of ribosome profiling
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The technique...
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Related Experiment Video

Updated: Apr 5, 2026

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis
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The identification and characterization of novel transcripts from RNA-seq data.

Tyler Weirick, Giuseppe Militello, Raphael Müller

    Briefings in Bioinformatics
    |August 19, 2015
    PubMed
    Summary

    Next-generation sequencing (NGS) generates vast RNA sequencing (RNA-seq) data. Careful filtering of novel transcripts is crucial for accurate analysis and biological validation.

    Keywords:
    RNA-seqgene expressionlncRNAnovel transcriptstranscriptome assembly

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    Area of Science:

    • Genomics
    • Molecular Biology
    • Bioinformatics

    Background:

    • Next-generation sequencing (NGS) technologies, particularly RNA sequencing (RNA-seq), are increasingly prevalent in biological research, largely replacing older microarray techniques.
    • The analysis of the vast amounts of RNA-seq data generated presents a significant bottleneck in research.
    • A substantial portion of the genome is transcribed into noncoding RNAs (ncRNAs), but their annotations are often incomplete, complicating RNA-seq data analysis.

    Purpose of the Study:

    • To examine the limitations of current RNA sequencing (RNA-seq) data analysis.
    • To focus on the detection and characterization of novel RNA transcripts.
    • To validate the identification of novel transcripts through biological experiments.

    Main Methods:

    • Case studies were employed to investigate RNA-seq analysis limitations.
    • Focus was placed on identifying and characterizing novel RNA transcripts.
    • Biological experiments were used to validate the findings from RNA-seq data analysis.

    Main Results:

    • Novel transcripts can be accurately identified from RNA-seq data when appropriate filtering strategies are applied.
    • The study highlights the challenges associated with analyzing the complex landscape of RNA transcripts, including noncoding RNAs.
    • Validation confirmed the feasibility of detecting novel transcripts through rigorous data analysis.

    Conclusions:

    • The accurate identification of novel transcripts from RNA-seq data is achievable with careful application of analytical filters.
    • Thorough examination of identified novel transcripts is essential before initiating downstream biological experiments.
    • Improved annotation of noncoding RNAs and refined data analysis pipelines are critical for advancing RNA-seq applications.