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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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In 1928, a German botanist Emil Heitz observed the moss nuclei with a DNA binding dye. He observed that while some chromatin regions decondense and spread out in the interphase nucleus, others do not. He termed them euchromatin and heterochromatin, respectively. He proposed that the heterochromatin regions reflect a functionally inactive state of the genome. It was later confirmed that heterochromatin is transcriptionally repressed, and euchromatin is transcriptionally active chromatin.
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Related Experiment Video

Updated: Jan 15, 2026

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
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Germline Variation Calling from Long Reads' Alignment Data through Spatiotemporal Attention.

Ying Shi, Shifu Luo, Yi Pan

    IEEE Transactions on Computational Biology and Bioinformatics
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    Summary

    Attdeepcaller, a novel deep learning model, significantly reduces false variant calls in Oxford Nanopore long-read sequencing data. This advancement improves variant calling accuracy in complex genomic regions, enhancing genomic analysis reliability.

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    Detection of Rare Mutations in CtDNA Using Next Generation Sequencing
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    Area of Science:

    • Genomics
    • Bioinformatics
    • Computational Biology

    Background:

    • Oxford Nanopore (ONT) long-read sequencing offers advantages but retains a higher error rate (1%) compared to short-read sequencing (0.1%).
    • This error rate poses challenges for accurate variant calling, especially in complex genomic regions, leading to thousands of false variant calls per chromosome.
    • Existing deep learning methods struggle with high error rates in ONT data, particularly with newer basecalling versions like Guppy v5.0.14.

    Purpose of the Study:

    • To introduce Attdeepcaller, a spatiotemporal attention-based deep learning model designed to differentiate sequencing errors from true germline variants.
    • To enhance the robustness and accuracy of variant calling in challenging genomic regions using long-read sequencing data.
    • To evaluate Attdeepcaller's performance across different ONT data versions and basecalling software.

    Main Methods:

    • Development of Attdeepcaller, a deep learning model incorporating spatiotemporal attention mechanisms.
    • Application of Attdeepcaller to analyze Q20-calibrated ONT long-read whole-genome sequencing data (HG002, HG003, HG004).
    • Benchmarking Attdeepcaller against existing methods on datasets processed with different Guppy versions (v5.0.14 and v3.4.5).

    Main Results:

    • Attdeepcaller reduced false variant calls by 12.69% on HG002 chr1 Q20 data.
    • Significant reductions in misidentifications were observed on HG003 (16.49%) and HG004 (23.58%) datasets.
    • Performance improvements were noted across different Guppy versions, with accuracy increasing by 3% and recall by 1% on v5.0.14 data, and accuracy by 16% and recall by 10% on v3.4.5 data.

    Conclusions:

    • Attdeepcaller effectively disentangles sequencing errors from true variants, improving prediction robustness in complex genomic regions.
    • The model demonstrates strong adaptability and improved performance on low-quality sequencing data and across different software versions.
    • Attdeepcaller represents a significant advancement for accurate variant calling in long-read sequencing data, particularly for challenging genomic loci.