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Related Concept Videos

Transfer RNA Synthesis02:36

Transfer RNA Synthesis

One of the unique features of tRNA is the presence of modified bases. In some tRNAs, modified bases account for nearly 20% of the total bases in the molecule. Altogether, these unusual bases protect the tRNA from enzymatic degradation by RNases.
Each of these chemical modifications is carried by a specific enzyme, post-transcription. All of these enzymes have unique base and site-specificity. Methylation, the most common chemical modification, is carried by at least nine different enzymes, with...
Master Transcription Regulators02:23

Master Transcription Regulators

Master transcription regulators are regulatory proteins that are predominantly responsible for regulating the expression of multiple genes. Often these genes work in concert to drive a  complex process. Activation of a master transcription regulator can lead to a cascade of transcriptional activation necessary for that outcome. These regulators can directly bind to the regulatory sequences of the various genes involved, or they can indirectly regulate transcription by binding to regulatory...
Transcription01:17

Transcription

Transcription is the synthesis of RNA from a DNA sequence by RNA polymerase. It is the first step in producing a protein from a gene sequence. Additionally, many other proteins and regulatory sequences are involved in correctly synthesizing messenger RNA (mRNA). Transcriptional regulation is responsible for the differentiation of different types of cells and often for the proper cellular response to environmental signals.
Transcription Can Produce Different Kinds of RNA Molecules
In eukaryotes,...
General Transcription Factors01:30

General Transcription Factors

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...
Transfer RNA Synthesis02:36

Transfer RNA Synthesis

One of the unique features of tRNA is the presence of modified bases. In some tRNAs, modified bases account for nearly 20% of the total bases in the molecule. Altogether, these unusual bases protect the tRNA from enzymatic degradation by RNases.
Each of these chemical modifications is carried by a specific enzyme, post-transcription. All of these enzymes have unique base and site-specificity. Methylation, the most common chemical modification, is carried by at least nine different enzymes, with...
Master Transcription Regulators02:23

Master Transcription Regulators

Master transcription regulators are regulatory proteins that are predominantly responsible for regulating the expression of multiple genes. Often these genes work in concert to drive a  complex process. Activation of a master transcription regulator can lead to a cascade of transcriptional activation necessary for that outcome. These regulators can directly bind to the regulatory sequences of the various genes involved, or they can indirectly regulate transcription by binding to regulatory...

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Tisslet tissues-based learning estimation for transcriptomics.

Ahmed Miloudi1, Aisha Al-Qahtani2, Thamanna Hashir3

  • 1Faculty of Medicine and Pharmacy-FUSMBA, Fes, Morocco.

BMC Bioinformatics
|February 25, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new nonlinear model to predict gene expression across multiple tissues by accounting for tissue interactions. This improves multi-omics data analysis and the identification of complex trait-associated regions.

Keywords:
EQTLLikelihood estimatorMachine learningMultiple-tissuesSparse covariance matrixTranscriptomics

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Area of Science:

  • Genomics
  • Bioinformatics
  • Systems Biology

Background:

  • Transcriptome-wide association studies (TWAS) are crucial for linking genetic variations to complex traits.
  • Current multi-tissue gene expression prediction methods lack accuracy due to ignoring tissue-tissue expression correlations.

Purpose of the Study:

  • To develop an advanced method for predicting gene expression across multiple tissues.
  • To improve the accuracy of multi-omics data analytics and TWAS by incorporating tissue-tissue expression interactions.

Main Methods:

  • A nonlinear multivariate model was developed to predict gene expression.
  • The model explicitly incorporates correlations between gene expression in different tissues.

Main Results:

  • The proposed model effectively estimates tissue-tissue expression interactions.
  • Accurate prediction of missing gene expression data across multiple tissues was achieved.
  • The model demonstrated superior performance in capturing inter-tissue relationships.

Conclusions:

  • Incorporating tissue-tissue expression correlations enhances gene expression prediction accuracy.
  • This novel approach offers a significant advancement for multi-omics data analytics and TWAS.
  • The method provides a new avenue for identifying genetic regions associated with complex traits.