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Related Concept Videos

RNA-seq03:21

RNA-seq

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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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RNA interference (RNAi) is a process in which a small non-coding RNA molecule blocks the post-transcriptional expression of a gene by binding to its messenger RNA (mRNA) and preventing the protein from being translated.
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RNA Structure01:23

RNA Structure

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Overview
The basic structure of RNA consists of a five-carbon sugar and one of four nitrogenous bases. Although most RNA is single-stranded, it can form complex secondary and tertiary structures. Such structures play essential roles in the regulation of transcription and translation.
Different Types of RNA Have the Same Basic Structure
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RNA Stability01:53

RNA Stability

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Intact DNA strands can be found in fossils, while scientists sometimes struggle to keep RNA intact under laboratory conditions. The structural variations between RNA and DNA underlie the differences in their stability and longevity. Because DNA is double-stranded, it is inherently more stable. The single-stranded structure of RNA is less stable but also more flexible and can form weak internal bonds. Additionally, most RNAs in the cell are relatively short, while DNA can be up to 250 million...
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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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RNA Editing02:23

RNA Editing

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RNA editing is a post-transcriptional modification where a precursor mRNA (pre-mRNA) nucleotide sequence is changed by base insertion, deletion, or modification. The extent of RNA editing varies from a few hundred bases, in mitochondrial DNA of trypanosomes, to a just single base, in nuclear genes of mammals. Even a single base change in the pre-mRNA can convert a codon for one amino acid into the codon for another amino acid or a stop codon. This type of re-coding can significantly affect the...
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Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project
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CAFU: a Galaxy framework for exploring unmapped RNA-Seq data.

Siyuan Chen1, Chengzhi Ren1, Jingjing Zhai1

  • 1State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences, Northwest Agriculture and Forestry University.

Briefings in Bioinformatics
|March 1, 2019
PubMed
Summary
This summary is machine-generated.

Unmapped RNA sequencing (RNA-Seq) reads often contain vital biological information. The Comprehensive Assembly and Functional annotation of Unmapped RNA-Seq data (CAFU) framework analyzes these reads, recovering lost data for deeper biological insights.

Keywords:
GalaxyRNA-Seqmachine learningpipelineunmapped readsworkflow

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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
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Area of Science:

  • Bioinformatics
  • Genomics
  • Transcriptomics

Background:

  • Short reads are typically aligned to reference genomes in transcriptome analysis.
  • Unmapped reads are often discarded, leading to loss of biological information.

Purpose of the Study:

  • To introduce the Comprehensive Assembly and Functional annotation of Unmapped RNA-Seq data (CAFU) framework.
  • To enable large-scale analysis of unmapped RNA sequencing (RNA-Seq) reads from single- and mixed-species samples.
  • To recover and analyze biological information from unmapped RNA-Seq reads.

Main Methods:

  • Development of a Galaxy-based framework (CAFU).
  • Integration of machine learning techniques for species identification in mixed-species samples.
  • Inclusion of functions for transcript confidence evaluation, coding potential calculation, sequence and expression characterization, and function annotation.

Main Results:

  • CAFU facilitates the analysis of unmapped RNA-Seq reads.
  • Machine learning accurately identifies species origin in mixed-species samples.
  • The framework provides comprehensive transcript analysis and annotation capabilities.

Conclusions:

  • CAFU simplifies the exploration of unmapped RNA-Seq reads.
  • The framework recovers significant biological information typically lost in standard analysis.
  • CAFU enhances transcriptome analysis by utilizing previously discarded data.