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Related Concept Videos

RNA-seq03:21

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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. 
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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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Proteins are involved in several cellular processes and biochemical reactions. Analyzing a specific protein of interest requires it to be isolated from the other proteins in the cell. This is achieved by overexpressing the specific gene in a suitable host to produce large quantities of the target protein. A tag or label is recombined with the gene to produce a fusion protein containing the target protein and the tag. The tags on these fusion proteins can then be used for easy detection and...
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Fusion-Bloom: fusion detection in assembled transcriptomes.

Readman Chiu1, Ka Ming Nip1,2, Inanc Birol1,3

  • 1Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC V5Z 4S6, Canada.

Bioinformatics (Oxford, England)
|December 3, 2019
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Summary

Fusion-Bloom is a new bioinformatics tool that improves the detection of gene fusions from RNA sequencing data. This method offers a good balance of sensitivity and specificity for cancer research.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Gene fusions are critical diagnostic markers in numerous cancers.
  • RNA sequencing (RNA-seq) is a key technology for fusion detection.
  • Current bioinformatics tools for fusion detection require enhancement.

Purpose of the Study:

  • To introduce Fusion-Bloom, a novel fusion detection method.
  • To evaluate Fusion-Bloom's performance against existing tools.

Main Methods:

  • Fusion-Bloom utilizes de novo transcriptome assembly and assembly-based structural variant calling.
  • The method integrates RNA-Bloom and PAVFinder technologies.
  • Performance was benchmarked against five state-of-the-art fusion detection tools using multiple datasets.

Main Results:

  • Fusion-Bloom demonstrated a favorable balance between detection sensitivity and specificity.
  • The tool showed competitive performance compared to existing methods.

Conclusions:

  • Fusion-Bloom is a valuable new tool for fusion detection in RNA-seq data.
  • The method is expected to be applicable in translational research and clinical genomics pipelines.