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Related Concept Videos

Transcription01:10

Transcription

157.9K
Overview
Transcription is the process of synthesizing 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 the proper synthesis of messenger RNA (mRNA). Regulation of transcription is responsible for the differentiation of all the different types of cells and often for the proper cellular response to environmental signals.
Transcription Can Produce Different Kinds...
157.9K
Constitutive and Regulated Gene Expression01:27

Constitutive and Regulated Gene Expression

1.5K
Gene expression in prokaryotes is governed by constitutive and regulated systems, allowing cells to balance the production of essential proteins with adaptive responses to environmental changes.Constitutive Gene ExpressionConstitutive, or housekeeping, genes are continuously expressed as they encode proteins vital for fundamental cellular processes. These include enzymes for glycolysis, ribosomal components for protein synthesis, and proteins involved in DNA replication. Their constant...
1.5K

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FIT: statistical modeling tool for transcriptome dynamics under fluctuating field conditions.

Koji Iwayama1, Yuri Aisaka2, Natsumaro Kutsuna2,3

  • 1Research Institute for Food and Agriculture, Ryukoku University, Otsu, Shiga, Japan.

Bioinformatics (Oxford, England)
|February 4, 2017
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Summary

This study introduces an efficient R package, FIT, for predicting transcriptome variation in response to environmental changes. The new method offers comparable or improved accuracy with reduced computational time, aiding environmental transcriptomics research.

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Area of Science:

  • Environmental science
  • Genomics
  • Bioinformatics

Background:

  • Environmental factors continuously change, influencing organisms in complex ways.
  • Understanding these effects requires analyzing transcriptome data from field environments.
  • Previous models for predicting transcriptomic variation faced computational limitations.

Purpose of the Study:

  • To develop a more computationally efficient statistical model for predicting transcriptomic variation.
  • To create an R package (FIT) for parameter optimization and transcriptome prediction.
  • To facilitate research on environmental impacts on gene expression.

Main Methods:

  • Developed a new statistical model and efficient parameter optimization techniques.
  • Implemented the model in an R package named FIT.
  • Utilized existing transcriptome data and environmental condition datasets for validation.

Main Results:

  • The FIT package provides functions for parameter optimization and transcriptome prediction.
  • The proposed method achieves comparable or superior prediction performance to previous approaches.
  • The computational time for parameter optimization is significantly reduced.

Conclusions:

  • The FIT R package offers an efficient solution for analyzing environmental effects on transcriptomic variation.
  • This tool will accelerate the study of how environmental changes impact gene expression in natural settings.
  • The findings enable broader application of predictive modeling in environmental transcriptomics.