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General Transcription Factors01:30

General Transcription Factors

6.1K
Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...
6.1K
Transcription Factors02:16

Transcription Factors

79.8K
Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...
79.8K
RNA Splicing01:32

RNA Splicing

58.1K
Splicing is the process by which eukaryotic RNA is edited before its translation into protein. The RNA strand transcribed from eukaryotic DNA is called the primary transcript. The primary transcripts that become mRNAs are called precursor messenger RNAs (pre-mRNAs). Eukaryotic pre-mRNA contains alternating sequences of exons and introns. Exons are nucleotide sequences that code for proteins, whereas introns are the non-coding regions. In RNA splicing, introns are removed and exons are bonded...
58.1K
Cell Specific Gene Expression01:58

Cell Specific Gene Expression

14.4K
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...
14.4K
Cell Specific Gene Expression01:58

Cell Specific Gene Expression

5.0K
5.0K
Alternative RNA Splicing02:18

Alternative RNA Splicing

22.7K
Alternative RNA splicing is the regulated splicing of exons and introns to produce different mature mRNAs from a single pre-mRNA. Unlike in constitutive splicing where a single gene produces a single type of mRNA, alternative splicing allows an organism to produce multiple proteins from a single gene and plays an important role in protein diversity.
There are five types of alternative RNA splicing that vary in the ways the pre-mRNA segments are removed or retained in the mature mRNA. The first...
22.7K

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Related Experiment Video

Updated: Oct 31, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

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Tissue Specificity Based Isoform Function Prediction.

Guoxian Yu, Qiuyue Huang, Xiangliang Zhang

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |June 29, 2021
    PubMed
    Summary
    This summary is machine-generated.

    Predicting protein isoform functions is challenging due to limited annotations. Our novel TS-Isofun approach effectively predicts isoform functions by integrating tissue-specific networks, improving accuracy.

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    Area of Science:

    • Genomics
    • Bioinformatics
    • Computational Biology

    Background:

    • Alternative splicing generates diverse protein isoforms, contributing to functional complexity.
    • Predicting individual isoform functions is crucial but hindered by scarce functional annotations.
    • Current computational methods often overlook tissue-specific alternative splicing patterns.

    Purpose of the Study:

    • To develop a novel computational approach (TS-Isofun) for predicting protein isoform functions.
    • To address the challenge of limited isoform annotations by leveraging tissue specificity.
    • To improve the accuracy of isoform function prediction through integrated network analysis.

    Main Methods:

    • Constructing tissue-specific isoform functional association networks from RNA-seq data.
    • Integrating multiple networks with adaptive weights to model tissue specificity.
    • Employing a joint matrix factorization model for data fusion and function inference.
    • Co-optimizing network weights and isoform function prediction in a unified objective.

    Main Results:

    • TS-Isofun significantly outperforms existing state-of-the-art methods for isoform function prediction.
    • Incorporating tissue specificity demonstrably enhances prediction accuracy.
    • The joint optimization framework effectively integrates diverse data sources.

    Conclusions:

    • TS-Isofun provides a robust and accurate method for computational isoform function prediction.
    • Tissue specificity is a critical factor for improving isoform function prediction accuracy.
    • The developed approach offers a valuable tool for deciphering protein functional diversity.