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関連する概念動画

Improving Translational Accuracy02:07

Improving Translational Accuracy

11.8K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
11.8K
Genomics02:02

Genomics

37.4K
Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
37.4K
Genetic Lingo01:11

Genetic Lingo

104.5K
Overview
104.5K
Genome Annotation and Assembly03:36

Genome Annotation and Assembly

19.3K
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.
19.3K
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

14.1K
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.
GWAS does not require the identification of the target gene involved in...
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Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

17.9K
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.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
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Updated: Sep 9, 2025

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

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機能的ゲノミクスをより良くモデリングするための変種を持つゲノム言語モデル

Tianyu Liu, Xiangyu Zhang, Jiecong Lin

    bioRxiv : the preprint server for biology
    |September 2, 2025
    PubMed
    まとめ

    UKBioBERTという DNA言語モデルを 開発しました 遺伝データを 配列から機能モデルに統合することで 遺伝子発現の予測を 改善します このアプローチは遺伝子調節と遺伝子変異の効果の理解を高めます.

    科学分野:

    • ゲノミクス
    • コンピュータ生物学
    • バイオ情報学

    背景:

    • ゲノム言語モデル (GLM) は,ゲノム文脈を表すためにDNA配列から学習します.
    • 配列から機能 (S2F) のモデルは,遺伝情報を遺伝子発現とフェノタイプにリンクします.
    • 個別化された遺伝子発現の予測のためのGLMとS2Fモデルのブリッジングは依然として課題です.

    研究 の 目的:

    • UKBioBERTという新しいDNA言語モデルを開発し,UK BioBankの遺伝子データを活用する.
    • UKBioBERTを既存のS2Fモデル (Enformer, Borzoi) と統合し,より優れた予測モデルを作成する.
    • 遺伝子発現レベルを予測し,遺伝子変異の影響を理解する.

    主な方法:

    • 英国バイオバンクからの遺伝子変異に関するDNA言語モデル (UKBioBERT) の予備訓練
    • UKBioBERTから情報的な配列の埋め込みを生成する.
    • UKBioBERTの組み込みとS2Fアーキテクチャ (Enformer,Borzoi) を組み合わせてUKBioFormerとUKBioZoiを形成する.
    • 異なるコホートにおける遺伝子発現予測に関するモデルのパフォーマンスを評価する.

    主要な成果:

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    Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
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    Last Updated: Sep 9, 2025

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    Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

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    Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
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    Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

    Published on: June 6, 2025

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    Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
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    Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

    Published on: April 4, 2018

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    • UKBioBERTの埋め込みは,遺伝子機能を効果的に識別し,細胞系における遺伝子発現の予測を改善します.
    • UKBioFormerとUKBioZoiは,高度に予測可能な遺伝子発現レベルを予測する上で優れたパフォーマンスを示しています.
    • 統合されたモデルは,多様なコホートにわたってよく一般化されます.
    • UKBioFormerは遺伝子型-フェノタイプ関係を正確に把握し,シリコン変異分析を可能にします.

    結論:

    • ゲノム言語モデルと 配列から機能へのアプローチを統合することで 機能的ゲノミクスは大きく進歩します
    • UKBioBERTは遺伝子機能と発現の予測性を理解するための貴重な埋め込み情報を提供します.
    • 開発されたUKBioFormerとUKBioZoiモデルは,遺伝子発現を予測し,遺伝子変異の影響を分析するための改善されたツールを提供します.