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Genome Annotation and Assembly03:36

Genome Annotation and Assembly

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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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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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Eukaryotes have large genomes compared to prokaryotes. To fit their genomes into a cell, eukaryotic DNA is packaged extraordinarily tightly inside the nucleus. To achieve this, DNA is tightly wound around proteins called histones, which are packaged into nucleosomes that are joined by linker DNA and coil into chromatin fibers. Additional fibrous proteins further compact the chromatin, which is recognizable as chromosomes during certain phases of cell division.
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Prokaryotic genomes exhibit a streamlined organization of coding and non-coding regions essential for gene expression and protein synthesis. While coding regions contain the genetic instructions for proteins or functional RNAs, non-coding regions regulate the precise transcription and translation of these genes.Coding Regions: Proteins and RNAsThe primary coding regions, known as structural genes, include sequences transcribed into messenger RNA (mRNA) and ultimately translated into...
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Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms
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Combining independent de novo assemblies optimizes the coding transcriptome for nonconventional model eukaryotic

Nicolas Cerveau1, Daniel J Jackson2

  • 1Department of Geobiology, Goldschmidtstr.3, Georg-August University of Göttingen, 37077, Göttingen, Germany.

BMC Bioinformatics
|December 13, 2016
PubMed
Summary

This study presents a novel computational approach for optimizing de novo transcriptome assembly in eukaryotes. Our method consolidates outputs from multiple assemblers, yielding more accurate and complete coding transcriptomes for non-model organisms.

Keywords:
De novo assemblyEukaryoteMergeProtein codingRedundantTranscriptome

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Area of Science:

  • Bioinformatics
  • Genomics
  • Molecular Biology

Background:

  • Next-generation sequencing (NGS) is a revolutionary tool in molecular biology.
  • The dominant Illumina platform fragments nucleic acids, posing bioinformatic challenges for de novo transcriptome assembly, especially for non-model organisms.
  • Existing assembly tools produce varied outputs from the same raw NGS data.

Purpose of the Study:

  • To develop an optimized consensus de novo assembly approach for eukaryotic coding transcriptomes.
  • To address the bioinformatic challenge of assembling novel transcriptome data from fragmented NGS libraries.
  • To provide a reliable method for researchers working with species lacking reference genomes.

Main Methods:

  • A novel pipeline that combines outputs from established assembly packages (CLC, Trinity, IDBA-tran).
  • Redundancy reduction through a series of clustering steps.
  • Validation using Illumina datasets from diverse eukaryotes and simulated datasets.

Main Results:

  • The pipeline generated more concise transcriptomes compared to individual assemblers.
  • The consensus assemblies possessed more unique annotatable protein domains.
  • Assembly completeness, measured by BUSCO databases, confirmed superior information yield.

Conclusions:

  • The developed approach yields coding transcriptome assemblies closer to biological reality.
  • This method is particularly valuable for researchers studying species with limited or no reference genomic data.
  • The approach is available as a free Perl script for broader accessibility.