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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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Updated: Oct 8, 2025

Quantification and Whole Genome Characterization of SARS-CoV-2 RNA in Wastewater and Air Samples

Quantification and Whole Genome Characterization of SARS-CoV-2 RNA in Wastewater and Air Samples

Published on: June 30, 2023

Semi-Supervised Pipeline for Autonomous Annotation of SARS-CoV-2 Genomes.

Kristen L Beck¹, Edward Seabolt¹, Akshay Agarwal¹

¹AI and Cognitive Software, IBM Almaden Research Center, San Jose, CA 95120, USA.

|December 28, 2021

Summary

This summary is machine-generated.

A new semi-supervised pipeline accurately annotates SARS-CoV-2 genomes, identifying key variants and improving gene and protein sequence accuracy for biomedical interventions.

Keywords:

COVID-19 SARS-CoV-2 bioinformatics computational biology gene prediction genome annotation genomics protein domain protein prediction

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Area of Science:

Genomics
Bioinformatics
Virology

Background:

SARS-CoV-2 genome sequencing is crucial for pandemic response.
Existing annotation methods struggle with accuracy and completeness for SARS-CoV-2.

Purpose of the Study:

Develop a novel semi-supervised pipeline for automated SARS-CoV-2 gene, protein, and domain annotation.
Improve accuracy and completeness compared to existing tools.
Identify key viral variants and molecular targets for interventions.

Main Methods:

A semi-supervised pipeline was developed, not relying on a single reference genome.
Analyzed over 66,000 SARS-CoV-2 genome sequences.
Validated on 4,000 diverse variant genomes.

Main Results:

Achieved 98.5% set membership and 99.1% length prediction accuracy for known proteins.
Outperformed Prokka and VAPiD, yielding 6.4x and 1.8x more protein annotations, respectively.
Identified conserved and emerging variants, including D614G and N501Y, with high accuracy for spike glycoprotein mutations (>99%).

Conclusions:

The novel pipeline offers a scalable, high-accuracy method for SARS-CoV-2 genome annotation.
Accurate annotation aids in refining biomedical interventions.
The method is robust and extensible for analyzing emerging variants.