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Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

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Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
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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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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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The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...
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Related Experiment Video

Updated: Apr 8, 2026

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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Better prediction of functional effects for sequence variants.

Maximilian Hecht, Yana Bromberg, Burkhard Rost

    BMC Genomics
    |June 26, 2015
    PubMed
    Summary

    SNAP2, a new neural network tool, accurately distinguishes harmful genetic variants from neutral ones. This advance aids personalized medicine by improving variant classification and experimental selection.

    Area of Science:

    • Genomics
    • Bioinformatics
    • Computational Biology

    Background:

    • Understanding genetic variation is crucial for personalized medicine.
    • Distinguishing between functional (effect) and non-functional (neutral) genetic variants remains a challenge.

    Purpose of the Study:

    • Introduce SNAP2, a novel neural network classifier to improve the prediction of variant effects.
    • Enhance the accuracy of variant effect prediction over existing state-of-the-art methods.

    Main Methods:

    • Developed SNAP2, a neural network classifier utilizing protein features and refined datasets.
    • Cross-validated SNAP2 on over 100,000 experimentally annotated variants.
    • Evaluated SNAP2's performance with and without multiple sequence alignments.

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    Main Results:

    • SNAP2 achieved 83% two-state accuracy (effect/neutral), outperforming other methods and combinations.
    • Performance improved significantly for non-human variants.
    • A reliability index aids in selecting variants for experimental validation, with high accuracy for top predictions.

    Conclusions:

    • SNAP2 offers a significant improvement in distinguishing effect from neutral variants.
    • The method's ability to perform well without sequence alignments speeds up predictions and aids in analyzing sequence orphans.
    • SNAP2 provides a valuable tool for personalized medicine and genomic research.