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Related Concept Videos

RNA-seq03:21

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. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
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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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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 (lncRNA)...
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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

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Updated: Jun 24, 2026

A Rapid High-throughput Method for Mapping Ribonucleoproteins (RNPs) on Human pre-mRNA
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Published on: December 2, 2009

Finding non-coding RNAs through genome-scale clustering.

Huei-Hun Tseng1, Zasha Weinberg, Jeremy Gore

  • 1Department of Computer Science & Engineering, University of Washington, Seattle, WA 98195-2350, USA. lachesis@cs.washington.edu

Journal of Bioinformatics and Computational Biology
|April 3, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient method for identifying bacterial non-coding RNAs (ncRNAs) by clustering genomic sequences. The approach successfully recovers known ncRNA families, promising new discoveries.

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Area of Science:

  • Microbiology
  • Molecular Biology
  • Bioinformatics

Background:

  • Non-coding RNAs (ncRNAs) are crucial regulatory molecules in microbial genomes.
  • RNA-mediated regulation impacts a significant portion of bacterial gene expression.
  • Identifying novel ncRNAs is essential for understanding microbial biology.

Purpose of the Study:

  • To develop an efficient computational method for discovering bacterial ncRNAs.
  • To leverage both primary sequence and secondary structure for homology inference.
  • To assess the method's efficacy in identifying known and potentially novel ncRNA families.

Main Methods:

  • Genomic sequence clustering based on homology.
  • Inference of homology using both primary sequence and predicted secondary structure.
  • Evaluation on a dataset of bacterial sequences, primarily from Firmicutes.

Main Results:

  • The clustering method effectively identifies ncRNA families, outperforming primary sequence homology alone.
  • The approach successfully recovered most known ncRNA family members.
  • Identified motifs suggest potential for discovering new ncRNA families.

Conclusions:

  • The developed method is efficient for finding bacterial non-coding RNAs.
  • Combining sequence and structure information enhances ncRNA detection accuracy.
  • This approach holds significant promise for the discovery of novel ncRNA families in bacteria.