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Related Concept Videos

lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

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In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
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lncRNA - Long Non-coding RNAs02:39

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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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Types of RNA01:20

Types of RNA

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Three main types of RNA are involved in protein synthesis: messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). These RNAs perform diverse functions and can be broadly classified as protein-coding or non-coding RNA. Non-coding RNAs play important roles in regulating gene expression in response to developmental and environmental changes. Non-coding RNAs in prokaryotes can be manipulated to develop more effective antibacterial drugs for human or animal use.
RNA Performs Diverse...
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Types of RNA01:23

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Overview
Three main types of RNA are involved in protein synthesis: messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). These RNAs perform diverse functions and can be broadly classified as protein-coding or non-coding RNA. Non-coding RNAs play important roles in the regulation of gene expression in response to developmental and environmental changes. Non-coding RNAs in prokaryotes can be manipulated to develop more effective antibacterial drugs for human or animal use.
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Ribosome Profiling02:24

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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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Computational recognition for long non-coding RNA (lncRNA): Software and databases.

Sohiya Yotsukura, David duVerle, Timothy Hancock

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    |February 4, 2016
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    Summary
    This summary is machine-generated.

    This review covers tools for identifying long non-coding RNAs (lncRNAs), which regulate gene expression and disease. It highlights challenges in lncRNA discovery and annotation, offering guidance for biologists and tool developers.

    Keywords:
    algorithmsbioinformaticsdatabaseslong non-coding RNA

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    Area of Science:

    • Genomics
    • Bioinformatics
    • Molecular Biology

    Background:

    • Most DNA does not code for proteins; these regions regulate gene expression and disease.
    • Long non-coding RNAs (lncRNAs) are crucial regulators but difficult to identify due to lack of conserved motifs.
    • lncRNAs play key roles in transcriptional and post-transcriptional processes, influencing disease onset.

    Purpose of the Study:

    • To review current databases, platforms, and bioinformatics tools for lncRNA discovery and annotation.
    • To discuss the challenges associated with lncRNA diversity and complex interactions.
    • To provide guidance for biologists using lncRNA analysis tools and to inform the development of new computational methods.

    Main Methods:

    • Integration of lncRNA detection with miRNA, RNA-binding protein, and chromatin interaction data.
    • Utilizing characteristic features of lncRNAs for bioinformatics-based identification.
    • Reviewing existing databases and computational tools for cataloging and annotating lncRNAs.

    Main Results:

    • Current bioinformatics tools can recognize and functionally annotate numerous lncRNAs by integrating various data types.
    • Databases and platforms exist for cataloging and annotating lncRNAs.
    • Novel lncRNA sequences can be discovered using specialized tools.

    Conclusions:

    • Despite challenges like lncRNA diversity and lack of unified nomenclature, bioinformatics approaches are advancing lncRNA identification.
    • Existing resources can aid biologists in analyzing experimental data related to lncRNAs.
    • Addressing limitations in current methods will drive the development of improved computational tools for lncRNA research.