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Related Concept Videos

Genome Annotation and Assembly03:36

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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Prokaryotic genomes exhibit a streamlined organization of coding and non-coding regions essential for gene expression and protein synthesis. While coding regions contain the genetic instructions for proteins or functional RNAs, non-coding regions regulate the precise transcription and translation of these genes.Coding Regions: Proteins and RNAsThe primary coding regions, known as structural genes, include sequences transcribed into messenger RNA (mRNA) and ultimately translated into...
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RNA-seq

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. 
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Labeling DNA Probes03:31

Labeling DNA Probes

DNA probes are fragments of DNA labeled with a reporter tag to enable their detection or purification. The resulting labeled DNA probes can then hybridize to target nucleic acid sequences through complementary base-pairing, and may be used to recover or identify these regions.
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Ribosome Profiling02:24

Ribosome Profiling

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Related Experiment Video

Updated: Jun 11, 2026

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)
11:35

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Published on: August 21, 2016

Annotating non-coding regions of the genome.

Roger P Alexander1, Gang Fang, Joel Rozowsky

  • 1Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut 06520, USA.

Nature Reviews. Genetics
|July 15, 2010
PubMed
Summary
This summary is machine-generated.

Researchers are annotating non-protein-coding DNA using functional genomics and comparative sequence analysis. This involves signal processing, segmentation, clustering, and relating findings to evolutionary conservation for a comprehensive genome interpretation.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • The majority of the human genome comprises non-protein-coding DNA.
  • Advances in functional genomics and comparative sequence analysis are crucial for understanding these regions.

Purpose of the Study:

  • To outline a conceptual framework for annotating non-protein-coding genomic regions.
  • To integrate functional genomics data with comparative sequence analysis for enhanced annotation.

Main Methods:

  • Interpreting functional genomics experimental output into base-pair signals.
  • Signal smoothing and segmentation to create initial genomic annotations.
  • Clustering annotations to derive larger structures and networks.
  • Relating functional annotations to conserved genomic units and conservation measures.

Main Results:

  • A stepwise approach to functional genomics analysis has been conceptualized.
  • The integration of signal processing, segmentation, and clustering enables detailed annotation.
  • Comparative sequence analysis provides a framework for understanding evolutionary conservation of functional elements.

Conclusions:

  • A systematic method for annotating non-protein-coding DNA is proposed.
  • Combining functional genomics with comparative genomics offers a powerful approach to genome interpretation.
  • This framework facilitates a deeper understanding of the functional landscape of the human genome.