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Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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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.
GWAS does not require the identification of the target gene involved in...
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High-Performance Liquid Chromatography: Types of Detectors01:15

High-Performance Liquid Chromatography: Types of Detectors

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The role of the detectors in High-Performance Liquid Chromatography (HPLC) is to analyze the solutes as they exit from the chromatographic column. The detector recognizes the solute's property and generates corresponding electrical signals, which are converted into a readable graph of the detector's response versus elution time called a chromatogram at the computer. There are several types of HPLC detectors, each with its own advantages and limitations, depending on the analyte...
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Cell Specific Gene Expression01:58

Cell Specific Gene Expression

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Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
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Cell Specific Gene Expression

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Associative Learning01:27

Associative Learning

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
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Association Areas of the Cortex01:21

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Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...
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Isolation and Transcriptome Analysis of Plant Cell Types
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scPrediXcanフレームワークを使用して,細胞タイプ特異のトランスクリプトーム全体の関連研究を行うためのプロトコル.

Yichao Zhou1, Sarah Sumner2, Temidayo Adeluwa1

  • 1Committee of Genetic, Genomics, and Systems Biology, University of Chicago, Chicago, IL, USA.

STAR protocols
|February 14, 2026
PubMed
まとめ
この要約は機械生成です。

scPrediXcanのフレームワークは,ディープラーニングを使用して,細胞タイプ特異のトランスクリプトーム全体の関連研究 (TWAS) を可能にします. このアプローチは,スケーラブルな分析のために,DNA配列とエピジェネティックデータからの遺伝子発現予測を統合しています.

キーワード:
バイオインフォマティックス遺伝学 遺伝学とは遺伝子発現は遺伝子発現であるゲノミクスゲノミクスとはシーケンス分析 シーケンス分析

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iCLIP - Transcriptome-wide Mapping of Protein-RNA Interactions with Individual Nucleotide Resolution
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関連する実験動画

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Cell-Specific Paired Interrogation of the Mouse Ovarian Epigenome and Transcriptome
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iCLIP - Transcriptome-wide Mapping of Protein-RNA Interactions with Individual Nucleotide Resolution
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科学分野:

  • ゲノミクスゲノミクスとは
  • バイオインフォマティックス
  • コンピュータ生物学 コンピュータ生物学

背景:

  • トランスクリプトーム全体の関連性研究 (TWAS) は,遺伝子特性の関連性を特定します.
  • 既存のTWAS方法は,しばしば細胞型特異性が欠けている.
  • 遺伝子発現予測を遺伝データと統合することは,病気のメカニズムを理解するために極めて重要です.

研究 の 目的:

  • 細胞型特異のTWASのための新しい枠組みであるscPrediXcanのためのプロトコルを提示する.
  • 配列とエピジェネティックデータからのディープラーニングを使用して,遺伝子発現の正確な予測を可能にする.
  • 多様なセルラーコンテキストでスケーラブルで計算効率の良いTWASを容易にするために.

主な方法:

  • 遺伝子発現予測のための細胞タイプ固有のディープラーニングモデルを訓練する.
  • パーソナライズされた遺伝子発現プロフィールを予測する.
  • 予測された発現と全ゲノム関連研究 (GWAS) の概要統計の間の関連性をテストする.

主要な成果:

  • scPrediXcanフレームワークは,特定の細胞タイプに合わせたスケーラブルなTWASモデルを提供します.
  • このプロトコルは,ゲノムと表遺伝子の特徴から遺伝子発現の予測のためのディープラーニングを統合しています.
  • 多様な細胞の文脈を分析するには,最小限の計算負荷が必要である.

結論:

  • scPrediXcanは,細胞型特異のTWASに対する強力なアプローチを提供します.
  • このフレームワークは,細胞の文脈を考慮することによって,TWASの生物学的関連性を高めています.
  • このプロトコルは,異なる細胞タイプにおける複雑な特徴の遺伝的構造に関するより深い洞察を容易にする.