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Related Concept Videos

RNA Splicing01:32

RNA Splicing

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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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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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One of the unique features of tRNA is the presence of modified bases. In some tRNAs, modified bases account for nearly 20% of the total bases in the molecule. Altogether, these unusual bases protect the tRNA from enzymatic degradation by RNases.
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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
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HSRA: Hadoop-based spliced read aligner for RNA sequencing data.

Roberto R Expósito1, Jorge González-Domínguez1, Juan Touriño1

  • 1Computer Architecture Group, Universidade da Coruña, Campus de Elviña, 15071 A Coruña, Spain.

Plos One
|August 1, 2018
PubMed
Summary
This summary is machine-generated.

HSRA is a new Big Data tool that accelerates RNA sequencing (RNA-seq) read alignment using MapReduce. This tool enhances gene expression analysis by improving the speed of mapping short reads to reference genomes.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Transcriptome sequencing (RNA-seq) is standard for gene expression quantification.
  • Short read mapping in RNA-seq is time-consuming and a bottleneck.
  • Increasing Next Generation Sequencing (NGS) data volume challenges storage and processing.

Purpose of the Study:

  • Introduce HSRA, a Big Data tool for efficient RNA-seq read alignment.
  • Extend multithreading capabilities of HISAT2 to distributed memory systems.
  • Address challenges of large genomic data volume in RNA read alignment.

Main Methods:

  • Utilizes the Hadoop MapReduce programming model.
  • Extends the HISAT2 spliced read aligner to distributed systems.
  • Supports single- and paired-end reads from FASTQ/FASTA, outputting SAM format.

Main Results:

  • HSRA is a Big Data tool built on the Hadoop MapReduce framework.
  • Achieves an average speedup of 2.3 times compared to previous Hadoop-based tools on a 16-node cluster.
  • Optimized design overcomes limitations of prior Big Data mapping tools.

Conclusions:

  • HSRA offers a significant performance improvement for RNA-seq read alignment.
  • Enables efficient processing of large genomic datasets on clusters and cloud platforms.
  • Provides a publicly available Java-based tool for the research community.