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Related Concept Videos

What is Gene Expression?01:42

What is Gene Expression?

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Overview
Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
Genetic Information Flows from DNA to RNA to Protein
A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is made up of nucleotides and proteins consist of amino...
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RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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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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Model Approaches for Pharmacokinetic Data: Physiological Models01:15

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Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
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Chromatin Position Affects Gene Expression02:35

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Chromatin is the massive complex of DNA and proteins packaged inside the nucleus. The complexity of chromatin folding and how it is packaged inside the nucleus greatly influences  access to genetic information. Generally, the nucleus' periphery is considered transcriptionally repressive, while the cell's interior is considered a transcriptionally active area. 
Topologically Associated Domains (TADs)
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Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
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Related Experiment Video

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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
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BALLI: Bartlett-adjusted likelihood-based linear model approach for identifying differentially expressed genes with

Kyungtaek Park1, Jaehoon An2, Jungsoo Gim3

  • 1Interdisciplinary Program of Bioinformatics, Seoul National University, Seoul, 08826, South Korea.

BMC Genomics
|July 4, 2019
PubMed
Summary

A new method called Bartlett-Adjusted Likelihood-based LInear mixed model approach (BALLI) improves RNA-sequencing analysis by accurately identifying differentially expressed genes (DEGs) even with small sample sizes.

Keywords:
Bartlett’s correctionDifferentially expressed genesLinear mixed modelRNA sequencing

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Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical genetics

Background:

  • RNA sequencing (RNA-seq) data analysis is crucial for understanding gene expression.
  • Existing methods for identifying differentially expressed genes (DEGs) often struggle with small sample sizes.
  • Global variance estimates in RNA-seq analysis limit generalizability to complex scenarios.

Purpose of the Study:

  • To introduce a novel statistical approach for RNA-seq data analysis.
  • To address limitations of existing methods in handling small sample sizes and complex data.
  • To improve the identification of differentially expressed genes (DEGs).

Main Methods:

  • Developed a Bartlett-Adjusted Likelihood-based LInear mixed model approach (BALLI).
  • Employed a linear mixed-effects model to estimate technical and biological variances.
  • Incorporated Bartlett's corrections to adjust for small sample bias.

Main Results:

  • BALLI demonstrated robust control of type-1 error rates across various significance levels.
  • Simulations showed BALLI offers superior statistical power and precision compared to edgeR, DESeq2, and voom.
  • The method proved robust to library size variations and was successfully applied to milk yield data.

Conclusions:

  • BALLI is a statistically efficient and valid method for RNA-seq analysis.
  • The approach enhances the identification of differentially expressed genes (DEGs).
  • BALLI offers practical value in biological research and complex data analysis.