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Genome Annotation and Assembly

Genome Annotation and Assembly

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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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

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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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Updated: Oct 16, 2025

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

Published on: May 9, 2017

A consensus-based ensemble approach to improve transcriptome assembly.

Adam Voshall^1,2,3, Sairam Behera^2,4, Xiangjun Li^5,6

¹School of Biological Sciences, University of Nebraska-Lincoln, Lincoln, NE, 68588, USA.

BMC Bioinformatics

|October 22, 2021

Summary

This summary is machine-generated.

Accurate transcriptome assembly is crucial for biological analyses. A new consensus approach, ConSemble, significantly improves transcriptome assembly accuracy, even for complex datasets lacking reference genomes.

Keywords:

Benchmarking De novo assembly Ensemble assembly Genome-guided assembly Illumina RNAseq Simulation Transcriptome assembly

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Accurate transcriptome assembly is essential for systems-level biological analyses like gene expression studies.
Existing transcriptome assembly tools struggle with non-model organisms and alternative splicing, lacking robust benchmarking methods.
The absence of reference genomes and true reference transcriptomes complicates quality assessment.

Purpose of the Study:

To develop a robust pipeline for simulating benchmark transcriptome and RNA-seq data.
To evaluate the performance of various de novo and genome-guided transcriptome assembly methods.
To introduce a novel consensus-based ensemble approach, ConSemble, for improved transcriptome assembly.

Main Methods:

Generated simulated benchmark transcriptomes and RNA-seq data for performance evaluation.
Compared multiple de novo and genome-guided transcriptome assembly algorithms.
Developed and applied the ConSemble consensus strategy for ensemble transcriptome assembly.

Main Results:

Transcriptome assembly accuracy significantly decreases with alternative splicing and absent reference genomes.
ConSemble demonstrated up to twofold higher accuracy than individual de novo assemblers without a reference genome.
ConSemble improved precision and accuracy for genome-guided assemblies and matched or exceeded best genome-guided methods with de novo assemblers.

Conclusions:

The ConSemble consensus strategy enhances transcriptome assembly for both de novo and genome-guided approaches.
ConSemble effectively handles complex transcriptomes, including those with alternative splicing and limited genomic references.
Freely available resources include a simulation pipeline, benchmark datasets, and the ConSemble assembly script.