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Regulation of Expression at Multiple Steps

Regulation of Expression at Multiple Steps

The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...

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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
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Chromatin Immunoprecipitation- ChIP

Chromatin Immunoprecipitation- ChIP

Chromatin immunoprecipitation, or ChIP, is an antibody-based technique used to identify sites on DNA that bind to transcription factors of interest or histone proteins. It also helps determine the type of histone modifications such as acetylation, phosphorylation, or methylation.
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lncRNA - Long Non-coding RNAs

lncRNA - Long Non-coding RNAs

In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...

Regulation of Expression Occurs at Multiple Steps

Regulation of Expression Occurs at Multiple Steps

Regulation of Expression Occurs at Multiple Steps

Regulation of Expression Occurs at Multiple Steps

Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
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Related Experiment Video

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Inferring gene regulatory networks by integrating ChIP-seq/chip and transcriptome data via LASSO-type regularization

Jing Qin¹, Yaohua Hu², Feng Xu¹

¹Department of Biochemistry, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China; Shenzhen Institute of Research & Innovation, The University of Hong Kong, Shenzhen, China.

Methods (San Diego, Calif.)

|March 22, 2014

Summary

New L0 and L1/2 regularization models significantly improve gene regulatory network inference in large genomes. These methods outperform standard LASSO, enhancing our understanding of gene regulation in complex organisms.

Keywords:

ChIP-seq/chip Gene regulatory networks Integrative omics data LASSO-type regularization methods Transcriptome

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Area of Science:

Computational Biology
Systems Biology
Genomics

Background:

Inferring gene regulatory networks (GRNs) from gene expression data is challenging in higher organisms due to large genomes and limited samples.
Current methods, including Least Absolute Shrinkage and Selection Operator (LASSO), show reduced accuracy at genome scale for complex organisms like humans and mice.

Purpose of the Study:

To evaluate the efficacy of extended LASSO models (L0 and L1/2 regularization) for GRN inference in large genomes.
To assess the impact of integrating transcription factor binding data with gene expression data.

Main Methods:

Application of L0 and L1/2 regularization models to infer GRNs from gene expression and transcription factor binding data in mouse embryonic stem cells (mESCs).
Comparative analysis of L0, L1/2, and standard LASSO model performance.

Main Results:

Both L0 and L1/2 regularization models demonstrated significantly superior performance compared to LASSO in GRN inference.
Integrating transcription factor-target interactions markedly enhanced prediction accuracy.

Conclusions:

L0 and L1/2 regularization models offer efficient and applicable solutions for genome-wide GRN inference in large genomes.
These advanced models facilitate the study of gene regulation in higher model organisms using integrative omics data.