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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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Protein Complex Assembly

Proteins can form homomeric complexes with another unit of the same protein or heteromeric complexes with different types. Most protein complexes self-assemble spontaneously via ordered pathways, while some proteins need assembly factors that guide their proper assembly. Despite the crowded intracellular environment, proteins usually interact with their correct partners and form functional complexes.
Many viruses self-assemble into a fully functional unit using the infected host cell to...

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Genome misassembly detection using Stash: A data structure based on stochastic tile hashing.

Armaghan Sarvar^1,2, Lauren Coombe², Inanc Birol^1,2

¹University of British Columbia, Vancouver, Canada.

|July 13, 2026

Summary

Stash, a new hash-based data structure, efficiently detects and corrects genome misassemblies in large sequencing data. This method improves the accuracy of de novo genome assembly, crucial for genomics research.

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Area of Science:

Bioinformatics
Genomics
Computational Biology

Background:

High-throughput sequencing generates large datasets, posing memory and computational challenges.
De novo genome assembly is fundamental to genomics but can suffer from misassemblies due to read errors and algorithmic limitations.
Accurate genome representation is vital for downstream analyses, necessitating improved assembly methods.

Purpose of the Study:

To introduce Stash, a novel hash-based data structure for efficient storage and querying of large sequencing data.
To leverage Stash for detecting and correcting misassemblies in de novo genome assemblies.
To evaluate Stash's performance in improving human genome assembly quality.

Main Methods:

Stash utilizes sliding windows and spaced seed patterns to extract and hash k-mers from input sequences.
Hash values and sequence IDs are stored in Stash to enable querying of read coverage across genomic regions.
The method was applied to detect misassemblies in human genome assemblies (Flye and Shasta) using Pacbio HiFi reads.

Main Results:

Stash effectively detected misassemblies in human genome assemblies.
Scaffolding with Stash reduced misassemblies by 7.6% in Flye assemblies and 3.4% in Shasta assemblies.
The process was computationally efficient, requiring 8 GB of memory and 310 minutes.

Conclusions:

Stash is a viable and efficient data structure for handling large sequencing data in bioinformatics.
Stash-based misassembly detection and correction significantly improves the quality of de novo genome assemblies.
Stash offers a competitive alternative to existing long-read misassembly correction methods, potentially yielding superior genomic data.