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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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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

Sma3s: a three-step modular annotator for large sequence datasets.

Antonio Muñoz-Mérida¹, Enrique Viguera², M Gonzalo Claros³

¹Integrated Bioinformatics, National Institute for Bioinformatics, University of Málaga, Campus de Teatinos, Spain.

DNA Research : an International Journal for Rapid Publication of Reports on Genes and Genomes

|February 7, 2014

Summary

This summary is machine-generated.

Sma3s is a new tool for automatic sequence annotation in

Keywords:

bioinformatic tool functional annotation genome annotation transcriptome annotation

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Published on: October 10, 2017

Area of Science:

Bioinformatics
Genomics
Computational Biology

Background:

Automatic sequence annotation is crucial for 'omics' studies.
Existing homology-based tools face challenges with similarity thresholds, impacting annotation quality and coverage.
Non-specialist users often find configuring annotation tools difficult.

Purpose of the Study:

To develop a robust automatic annotation technique for large biological sequence datasets.
To provide high-quality annotations without requiring expert knowledge.
To introduce Sma3s, a versatile tool for gene library and genome annotation.

Main Methods:

Sma3s utilizes a three-module system for progressive sequence annotation.
Annotation strategies include using highly similar homologues, orthologous sequences, and enriched terms in homologous groups.
The system was trained and validated on diverse random sets of known sequences.

Main Results:

Sma3s demonstrates average sensitivity and specificity values of approximately 85%.
The tool outperforms the accuracy of established annotation algorithms.
Sma3s effectively enriches existing database annotations and reveals novel sequence features.

Conclusions:

Sma3s is a versatile, high-throughput tool for annotating various sequence datasets.
It offers improved accuracy and can uncover hidden features in biological sequences.
The tool has practical applications, as evidenced by its use in annotating two published transcriptomes.