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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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Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations
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Published on: August 4, 2016

Bellerophontes: an RNA-Seq data analysis framework for chimeric transcripts discovery based on accurate fusion model.

Francesco Abate1, Andrea Acquaviva, Giulia Paciello

  • 1Department of Control and Computer Engineering, Politecnico di Torino, Torino 10129, Italy. francesco.abate@polito.it

Bioinformatics (Oxford, England)
|June 20, 2012
PubMed
Summary

Bellerophontes accurately detects gene fusions from short paired-end reads by integrating alignment and abundance analysis. This framework identifies known and novel fusion transcripts, including those in chronic myelogenous leukemia (CML) samples.

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Oncogenic Gene Fusion Detection Using Anchored Multiplex Polymerase Chain Reaction Followed by Next Generation Sequencing
09:49

Oncogenic Gene Fusion Detection Using Anchored Multiplex Polymerase Chain Reaction Followed by Next Generation Sequencing

Published on: July 5, 2019

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Next-generation sequencing (NGS) enables detection of genomic variations, novel genes, and transcript isoforms.
  • Identifying fusion transcripts is crucial for understanding genetic diseases and developing targeted therapies.
  • Existing methods may lack accuracy in junction discovery and fail to identify non-annotated transcripts.

Purpose of the Study:

  • To introduce Bellerophontes, a novel computational framework for detecting fusion transcripts using short paired-end reads.
  • To improve the accuracy of junction discovery and read support for fusion transcripts.
  • To identify both annotated and non-annotated fusion transcripts, including those in chronic myelogenous leukemia (CML).

Main Methods:

  • Integration of splicing-driven alignment and abundance estimation analysis.
  • Utilizes a gene fusion model for selecting putative junctions.
  • Developed as a free and available JAVA/Perl/Bash software implementation.

Main Results:

  • Bellerophontes successfully detected fusion genes in experimentally validated CML samples and public NCBI datasets.
  • The framework identified the exact junction sequences for known fusion genes.
  • Compared to state-of-the-art methods, Bellerophontes demonstrated higher selectivity and provided a more accurate set of spanning reads.
  • Fusion transcripts involving non-annotated transcripts were identified in CML samples.

Conclusions:

  • Bellerophontes offers a more accurate and selective approach for fusion transcript detection from NGS data.
  • The framework's ability to identify non-annotated fusion transcripts expands the scope of genomic analysis.
  • Bellerophontes is a valuable tool for cancer genomics research and the discovery of novel therapeutic targets.