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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. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Transcription Factors02:16

Transcription Factors

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Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...
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Ribosomal RNA Synthesis02:53

Ribosomal RNA Synthesis

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Ribosome synthesis is a highly complex and coordinated process involving more than 200 assembly factors. The synthesis and processing of ribosomal components occurs not only in the nucleolus but also in the nucleoplasm and the cytoplasm of eukaryotic cells.
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Transcription01:10

Transcription

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Overview
Transcription is the process of synthesizing RNA from a DNA sequence by RNA polymerase. It is the first step in producing a protein from a gene sequence. Additionally, many other proteins and regulatory sequences are involved in the proper synthesis of messenger RNA (mRNA). Regulation of transcription is responsible for the differentiation of all the different types of cells and often for the proper cellular response to environmental signals.
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Transcription Attenuation in Prokaryotes02:42

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Transcriptional attenuation occurs when RNA transcription is prematurely terminated due to the formation of a terminator mRNA hairpin structure.  Bacteria use these hairpins to regulate the transcription process and control the synthesis of several amino acids including histidine, lysine, threonine, and phenylalanine. Transcription attenuation takes place in the non-coding regions of mRNA.
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Transcription Elongation Factors02:35

Transcription Elongation Factors

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Transcription elongation is a dynamic process that alters depending upon the sequence heterogeneity of the DNA being transcribed. Hence, it is not surprising that the elongation complex's composition also varies along the way while transcribing a gene.
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Relative Abundance of Transcripts ( RATs): Identifying differential isoform abundance from RNA-seq.

Kimon Froussios1, Kira Mourão1, Gordon Simpson2,3,4

  • 1Division of Computational Biology, School of Life Sciences, University of Dundee, Dundee, DD1 5EH, UK.

F1000Research
|March 26, 2019
PubMed
Summary

We introduce RATs, an R package for detecting differential transcript isoform usage (DTU) from RNA-seq data without alignment. RATs demonstrates high accuracy and outperforms existing tools, particularly with its bootstrapping reliability estimation.

Keywords:
AlgorithmsDifferential Isoform UsageFeature selectionGene regulationTranscriptional regulationTranscriptomicsVisualization

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Differential transcript isoform usage (DTU) analysis is crucial for understanding gene expression complexity.
  • Existing DTU tools often require aligned or assembled RNA-seq data, limiting their application.
  • There is a need for methods that can detect DTU directly from transcript abundance estimates.

Purpose of the Study:

  • To present RATs, a novel R package for transcriptome-wide DTU detection directly from transcript abundance estimates.
  • To evaluate the performance of RATs against existing DTU tools using simulated and real RNA-seq datasets.
  • To highlight the unique bootstrapping feature of RATs for assessing the reliability of DTU events.

Main Methods:

  • Development of the RATs R package for alignment-free DTU analysis.
  • Application of bootstrapping for estimating the reliability of detected DTU events.
  • Comparative analysis of RATs, DRIM-Seq, and SUPPA2 using simulated and human RNA-seq data.

Main Results:

  • RATs achieved a median false positive fraction below 0.05 across replication levels.
  • On simulated human data, RATs outperformed DRIM-Seq and SUPPA2 in sensitivity, Matthews correlation coefficient, and false discovery rate.
  • The bootstrapping quality filter in RATs effectively removed unreliable DTU calls, improving precision.

Conclusions:

  • RATs provides a robust and accurate method for detecting DTU directly from transcript abundance estimates.
  • The bootstrapping approach in RATs enhances the reliability of DTU event identification.
  • All tested DTU methods are sensitive to RNA-seq annotation differences, emphasizing the importance of consistent annotation.