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Alternative RNA Splicing02:18

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Alternative RNA splicing is the regulated splicing of exons and introns to produce different mature mRNAs from a single pre-mRNA. Unlike in constitutive splicing where a single gene produces a single type of mRNA, alternative splicing allows an organism to produce multiple proteins from a single gene and plays an important role in protein diversity.
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Splicing is the process by which eukaryotic RNA is edited before its translation into protein. The RNA strand transcribed from eukaryotic DNA is called the primary transcript. The primary transcripts that become mRNAs are called precursor messenger RNAs (pre-mRNAs). Eukaryotic pre-mRNA contains alternating sequences of exons and introns. Exons are nucleotide sequences that code for proteins, whereas introns are the non-coding regions. In RNA splicing, introns are removed and exons are bonded...
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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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Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models
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TransRef enables accurate transcriptome assembly by redefining accurate neo-splicing graphs.

Ting Yu1, Renmin Han1, Zhaoyuan Fang2

  • 1Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, China.

Briefings in Bioinformatics
|July 13, 2021
PubMed
Summary
This summary is machine-generated.

TransRef accurately assembles transcriptomes from RNA sequencing data using a novel graph model. This computational algorithm outperforms existing tools, especially for novel and low-expression transcripts.

Keywords:
RNA-seq readsgenome-guided assemblyneo-splicing graphsreference annotationstranscriptome assembly

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • RNA sequencing (RNA-seq) is crucial for transcriptome analysis.
  • Identifying all expressed transcripts from short reads is computationally challenging.

Purpose of the Study:

  • Introduce TransRef, a novel computational algorithm for accurate transcriptome assembly.
  • Improve the identification of novel and low-expression transcripts.

Main Methods:

  • Developed a new graph model called the neo-splicing graph.
  • Applied iterative constrained dynamic programming for transcript reconstruction.

Main Results:

  • TransRef demonstrated superior performance compared to StringTie2, Cufflinks, and Scallop on real and simulated datasets.
  • TransRef excelled at identifying novel transcripts and those with low expression levels.

Conclusions:

  • TransRef offers a significant advancement in transcriptome assembly accuracy.
  • The neo-splicing graph model and dynamic programming approach enhance transcript identification capabilities.