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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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Related Experiment Video

Updated: Jul 3, 2025

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Published on: June 28, 2018

nail: software for high-speed, high-sensitivity protein sequence annotation.

Jack W Roddy¹, David H Rich², Travis J Wheeler^1,2

¹R. Ken Coit College of Pharmacy, University of Arizona, Tucson, Arizona, USA.

Biorxiv : the Preprint Server for Biology

|February 14, 2024

Summary

This summary is machine-generated.

Introducing nail, a new bioinformatics tool for rapid and accurate sequence annotation. It balances speed with the sensitivity of profile hidden Markov models (pHMMs), improving upon existing methods.

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Last Updated: Jul 3, 2025

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Modern sequence databases present challenges in balancing annotation speed and sensitivity.
Profile hidden Markov models (pHMMs) offer high sensitivity, while other tools prioritize speed.
Existing tools often compromise between speed and accuracy in sequence annotation.

Approach:

Developed 'nail', a novel tool approximating the pHMM Forward/Backward (FB) algorithm.
Identifies key cells in the FB dynamic programming matrix for efficient computation.
Achieves high speed and low memory usage while maintaining accuracy.

Key Points:

nail offers speeds comparable to fast tools like MMseqs2.
It retains significant sensitivity, approaching that of pHMMs.
Demonstrates superior performance over existing methods in speed-accuracy trade-offs.

Conclusions:

nail effectively bridges the gap between speed and sensitivity in sequence annotation.
Provides accurate pHMM scores and E-values efficiently.
Represents a significant advancement for large-scale genomic and proteomic analyses.