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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.

RNA-seq

RNA-seq

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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Related Experiment Video

Updated: Jun 30, 2025

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Tradeoffs in alignment and assembly-based methods for structural variant detection with long-read sequencing data.

Yichen Henry Liu¹, Can Luo², Staunton G Golding²

¹Department of Computer Science, Vanderbilt University, 37235, Nashville, TN, USA.

Nature Communications

|March 20, 2024

Summary

Long-read sequencing enables better genome assembly and structural variant (SV) detection. This study benchmarks alignment-based and assembly-based SV callers, finding neither universally superior but offering guidance for tool selection.

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Long-read sequencing advances diploid genome assembly and structural variant (SV) detection.
Efficient and robust SV identification algorithms are critical due to increasing data availability.
Current benchmarking is insufficient, hindering algorithm development and comprehension.

Purpose of the Study:

To systematically compare alignment-based and assembly-based SV calling methods.
To evaluate the performance of various aligners and assemblers.
To provide comprehensive guidelines for selecting SV detection tools.

Main Methods:

Systematic benchmarking of 14 alignment-based SV callers (including deep learning and hybrid methods).
Comparison of 4 assembly-based SV callers, 4 aligners, and 7 assemblers.
Evaluation across diverse criteria, including performance at varying sequencing coverage.

Main Results:

Assembly-based tools excel at detecting large SVs (e.g., insertions) and are robust to parameter/coverage changes.
Alignment-based tools show superior genotyping accuracy at low coverage (5-10×) and detect complex SVs (translocations, inversions, duplications).
No single SV calling tool demonstrated universal superiority.

Conclusions:

The choice between alignment-based and assembly-based SV callers depends on specific research needs and data characteristics.
Guidelines are provided for tool selection across 31 criteria combinations.
This evaluation offers directions for future SV detection algorithm development.