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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
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
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Related Experiment Video

Updated: Apr 15, 2026

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis
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Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

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[RANDTRAN: random transcriptome sequence generator that accounts for partition specific features in eukaryotic mRNA

E A Borzov, A V Marakhonov, M V Ivanov

    Molekuliarnaia Biologiia
    |April 7, 2015
    PubMed
    Summary

    RANDTRAN generates realistic control transcriptomes for computational biology. This tool accounts for RNA regions and nucleotide composition, improving sequence motif analysis.

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    Area of Science:

    • Computational Biology
    • Bioinformatics
    • Genomics

    Context:

    • Accurate random sequence generation is crucial for bioinformatics.
    • Existing tools lack transparency in probabilistic models.
    • Current methods fail to emulate complex transcriptome structures.

    Purpose:

    • Introduce RANDTRAN, a novel tool for generating control transcriptomes.
    • Address limitations of current random sequence generators.
    • Provide biologically relevant control sequences for RNA analysis.

    Summary:

    • RANDTRAN generates control transcriptomes considering 5'/3' non-translated regions and partition-specific nucleotide compositions.
    • The tool emulates ORFs, UTRs, and non-coding RNA proportions.
    • Outputs are compatible with UCSC Genome Browser for seamless integration.

    Impact:

    • Enhances the reliability of motif frequency evaluations.
    • Facilitates advanced computational analyses of RNA sequences.
    • Supports diverse bioinformatics studies requiring realistic control datasets.