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Genome Annotation and Assembly03:36

Genome Annotation and Assembly

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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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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Updated: Oct 21, 2025

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes
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GapPredict - A Language Model for Resolving Gaps in Draft Genome Assemblies.

Eric Chen, Justin Chu, Jessica Zhang

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
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    Summary
    This summary is machine-generated.

    GapPredict, a new deep learning tool, effectively fills gaps in genome assemblies left by other methods. This approach improves genome sequence contiguity using short-read DNA sequencing data.

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    Area of Science:

    • Genomics
    • Bioinformatics
    • Computational Biology

    Background:

    • Short-read DNA sequencing offers high throughput but challenges de novo genome assembly due to repetitive and hard-to-sequence regions.
    • Genome assembly algorithms struggle to reconstruct contiguous sequences from short reads, resulting in gaps within scaffolds.
    • Paired-end reads aid scaffolding but do not fully resolve all sequence gaps.

    Purpose of the Study:

    • To introduce GapPredict, a novel computational tool for predicting nucleotides in scaffold gaps.
    • To evaluate the efficacy of a character-level language model for addressing unresolved sequences in genome assembly.
    • To demonstrate the utility of deep learning in improving genome sequence contiguity.

    Main Methods:

    • Development of GapPredict, a proof-of-concept implementation utilizing a character-level language model.
    • Benchmarking GapPredict against Sealer, a state-of-the-art gap-filling tool.
    • Assessment of gap-filling accuracy by comparing predicted sequences to a reference genome.

    Main Results:

    • GapPredict successfully filled 65.6% of gaps that remained unresolved by Sealer.
    • The nucleotides predicted by GapPredict showed high similarity to the reference genome.
    • This demonstrates the practical application of deep learning in genome assembly gap-filling.

    Conclusions:

    • GapPredict offers a significant advancement in filling gaps within genome scaffolds generated from short-read data.
    • Deep learning-based approaches show considerable promise for improving the contiguity and completeness of genome assemblies.
    • The tool's ability to accurately fill previously unresolvable gaps highlights its potential impact on genomic research.