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Related Concept Videos

MicroRNAs01:22

MicroRNAs

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MicroRNA (miRNA) are short, regulatory RNA transcribed from introns (non-coding regions of a gene) or intergenic regions (stretches of DNA present between genes). Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself, forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA...
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MicroRNAs01:22

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MicroRNA (miRNA) are short, regulatory RNA transcribed from introns—non-coding regions of a gene—or intergenic regions—stretches of DNA present between genes. Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After...
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mirMachine: A One-Stop Shop for Plant miRNA Annotation
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Methods and Techniques for miRNA Data Analysis.

Francesca Cristiano1, Pierangelo Veltri2

  • 1Bioinformatic Bioinformatics Laboratory, Department of Surgical and Medical Sciences, University Magna Græcia of Catanzaro, Campus Universitatrio "Salvatore Venuta", Germaneto 88100, Catanzaro, Italy. fcristiano@unicz.it.

Methods in Molecular Biology (Clifton, N.J.)
|June 13, 2015
PubMed
Summary
This summary is machine-generated.

Genomic data analysis extracts vital information from genes using advanced sequencing. This enables biomarker discovery and personalized therapies for inherited disorders and complex diseases.

Keywords:
BioinformaticsDatabasesGene targetNext-generation sequencingOntologiesmicroRNA

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Area of Science:

  • Genomics and Bioinformatics
  • Molecular Biology
  • Computational Biology

Background:

  • Genomic data analysis is crucial for understanding gene functions and variations.
  • Genome sequencing technologies facilitate the characterization of genomic profiles.
  • Identifying gene mutations and phenotypes is essential for disease diagnosis and drug development.

Purpose of the Study:

  • To provide an overview of software platforms for genomic data analysis.
  • To present available databases and their query engines for genomic research.
  • To highlight the importance of genomic data analysis in biomarker identification and therapeutic strategies.

Main Methods:

  • Analysis of gene mutations by comparing fold changes in gene representations.
  • Utilizing high-performance platforms and optimized algorithms for large-scale genomic data.
  • Employing Next-Generation Sequencing (NGS) and microarray technologies for data generation.

Main Results:

  • Genomic data analysis identifies biomarkers and mutations relevant for diagnosis and therapies.
  • The study emphasizes the role of mutations and phenotypes in disease studies and drug design.
  • Software platforms and databases are essential tools for managing and querying genomic information.

Conclusions:

  • Genomic data analysis is indispensable for modern biological research and clinical applications.
  • Advanced tools and databases are required to handle the complexity of genomic data.
  • This overview serves as a guide to available resources for genomic data analysis.