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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. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Genome Size and the Evolution of New Genes03:21

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While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.
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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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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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Viruses with RNA Genomes01:29

Viruses with RNA Genomes

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RNA viruses are categorized into positive-strand, negative-strand, or double-stranded groups based on their genomic structure and replication mechanisms. This classification dictates how they exploit host cellular machinery for protein synthesis and replication. Some RNA viruses also utilize reverse transcription as part of their life cycle, further diversifying their replication strategies.Positive-Strand RNA VirusesPositive-strand RNA viruses have genomes that function directly as messenger...
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Prokaryotic Transcriptional Activators and Repressors01:58

Prokaryotic Transcriptional Activators and Repressors

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The organization of prokaryotic genes in their genome is notably different from that of eukaryotes. Prokaryotic genes are organized, such that the genes for proteins involved in the same biochemical process or function are located together in groups. This group of genes, along with their regulatory elements, are collectively known as an operon. The functional genes in an operon are transcribed together to give a single strand of mRNA known as polycistronic mRNA.
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RNA Secondary Structure Prediction Using High-throughput SHAPE
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Evaluating Programs for Predicting Genes and Transcripts with RNA-Seq Support in Fungal Genomes.

Ian Reid1

  • 1Centre for Structural and Functional Genomics, Concordia University, Montreal, QC, Canada. ian.reid@concordia.ca.

Methods in Molecular Biology (Clifton, N.J.)
|June 8, 2018
PubMed
Summary

Three gene prediction programs were evaluated for fungal genomes using RNA-Seq data. Harfang achieved the highest gene prediction accuracy at 92%, though coverage impacted success.

Keywords:
BioinformaticsCleaning short sequence readsGene predictionRNA-SeqTranscript prediction

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Accurate gene and transcript prediction is crucial for understanding fungal genome function.
  • RNA-sequencing (RNA-Seq) data provides valuable experimental evidence for gene prediction.
  • Several computational tools exist, but their performance can vary across different fungal species and datasets.

Purpose of the Study:

  • To detail the computational steps for predicting genes and transcripts in fungal genomes using RNA-Seq data.
  • To evaluate and compare the performance of three gene prediction programs: CodingQuarry, BRAKER1, and Harfang.
  • To assess the impact of RNA-Seq read coverage on prediction accuracy.

Main Methods:

  • Detailed description of the workflow for gene and transcript prediction using CodingQuarry, BRAKER1, and Harfang.
  • Application of these programs to the fungal genome of Aspergillus niger strain NRRL3.
  • Utilizing a manually curated reference set for performance evaluation.
  • Analysis of prediction accuracy in relation to RNA-Seq read coverage.

Main Results:

  • The three programs predicted a high percentage of genes, ranging from 86% to 92% for Harfang, against a manually curated reference set.
  • Harfang demonstrated the highest prediction accuracy among the evaluated tools.
  • Genes with limited or no RNA-Seq read coverage were predicted with lower success rates compared to those with sufficient coverage.

Conclusions:

  • CodingQuarry, BRAKER1, and Harfang are effective tools for fungal gene and transcript prediction when supported by adequate RNA-Seq data.
  • RNA-Seq read coverage is a critical factor influencing the accuracy of computational gene prediction in fungal genomes.
  • Further optimization of prediction strategies may be needed for genes with sparse expression data.