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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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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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Genes usually encode proteins necessary for the proper functioning of a healthy cell. Mutations can often cause changes to the gene expression pattern, thereby altering the phenotype.
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Genes usually encode proteins necessary for the proper functioning of a healthy cell. Mutations can often cause changes to the gene expression pattern, thereby altering the phenotype.
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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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Quantitative Trait Loci Identify Functional Noncoding Variation in Cancer.

Holger Heyn1

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This summary is machine-generated.

Noncoding alterations in cancer genomes are increasingly recognized as drivers of oncogenesis. Quantitative trait loci (QTL) analysis offers a framework to identify these causal variants and understand cancer gene deregulation.

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Area of Science:

  • Genomics
  • Cancer Biology
  • Bioinformatics

Background:

  • Interpreting noncoding genetic alterations in cancer remains a challenge.
  • Somatic variations in protein-coding regions are well-understood, but noncoding aberrations are often overlooked.
  • Advances in genome-wide profiling highlight the role of noncoding alterations in gene deregulation and cancer development.

Purpose of the Study:

  • To review the role of noncoding alterations in oncogenesis.
  • To propose a theoretical framework for identifying causal somatic variants in noncoding regions.
  • To emphasize the utility of quantitative trait loci (QTL) analysis in cancer research.

Main Methods:

  • Literature review of noncoding alterations in cancer.
  • Theoretical framework development for variant identification.
  • Application of quantitative trait loci (QTL) analysis principles.

Main Results:

  • Noncoding alterations can act as cancer-driving events through gene deregulation.
  • Somatic QTL studies provide a strategy to pinpoint functional noncoding variants.
  • This approach assumes functional noncoding alterations impact quantifiable regulatory processes.

Conclusions:

  • Understanding noncoding alterations is crucial for a comprehensive view of cancer biology.
  • Quantitative trait loci (QTL) analysis is a valuable tool for identifying cancer-driving noncoding variants.
  • Integrated analysis of coding and noncoding alterations will advance cancer research.