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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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Allele-specific expression analysis: pipelines, applications, challenges, and unmet needs.

Stefania Mattevi1, Francesco Mazzarotto2, Paolo Martini1

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Computers in Biology and Medicine
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Summary

Allele-specific expression (ASE) analysis reveals gene regulation insights but current tools lack automation and multi-omics integration. Future pipelines need to enhance single-cell support and data integration for broader biological and clinical applications.

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Diploid organisms normally show balanced maternal and paternal gene expression.
  • Allele-specific expression (ASE) occurs due to genetic/epigenetic variations, leading to biased allele output.
  • ASE analysis is crucial for understanding gene regulation with functional and clinical relevance.

Purpose of the Study:

  • To critically assess 26 current pipelines for allele-specific expression analysis.
  • To identify limitations in existing ASE analysis tools regarding automation, multi-omics, and single-cell data.
  • To guide future development of advanced ASE analysis pipelines.

Main Methods:

  • Review and critical assessment of 26 allele-specific expression analysis pipelines.
  • Categorization based on data type handling, haplotype phasing, statistical methods, and output visualization.
  • Evaluation of input requirements, capabilities, and application scope for each pipeline.

Main Results:

  • Most current ASE analysis pipelines lack end-to-end automation and robust multi-omics integration.
  • Limited support exists for high-throughput single-cell sequencing data in existing pipelines.
  • Significant variations in pipeline capabilities for data handling, phasing, and statistical approaches were observed.

Conclusions:

  • There is a critical need for automated, multi-omic ASE analysis workflows.
  • Future pipelines must enhance compatibility with single-cell technologies for comprehensive gene regulation studies.
  • Advancements in ASE pipeline development will deepen understanding of gene regulation mechanisms and their clinical significance.