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Related Experiment Video

Updated: May 29, 2025

Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project
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RNA sequence analysis landscape: A comprehensive review of task types, databases, datasets, word embedding methods,

Muhammad Nabeel Asim1, Muhammad Ali Ibrahim1,2, Tayyaba Asif2

  • 1German Research Center for Artificial Intelligence GmbH, Kaiserslautern, 67663, Germany.

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|February 3, 2025
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Summary

This study bridges the gap between AI and RNA sequence analysis, providing AI researchers with biological foundations for 47 tasks. It facilitates benchmark datasets and surveys AI models for RNA sequence analysis applications.

Keywords:
AI applications in genomicsArtificial intelligenceDeep learningDisease analysisGene analysisGene expression regulationGene network analysisMachine learningMulti-omicsRNA functional analysisRNA modificationsRNA sequence analysisRNA-sequencing

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • RNA sequences are crucial for gene regulation and protein synthesis, with aberrations linked to diseases like cancer.
  • RNA's therapeutic potential is being explored for personalized medicine through RNA-based drugs and gene therapies.
  • Accurate RNA sequence analysis is vital for understanding biological functions and early disease detection.

Purpose of the Study:

  • To bridge the knowledge gap between AI researchers and molecular biologists in RNA sequence analysis.
  • To provide a comprehensive resource for developing AI-driven RNA sequence analysis applications.
  • To facilitate the creation of benchmark datasets and evaluate AI models for RNA analysis.

Main Methods:

  • Equipping AI researchers with biological foundations for 47 distinct RNA sequence analysis tasks.
  • Facilitating benchmark dataset development by integrating data from 64 biological databases.
  • Presenting applications of word embeddings and language models across 47 RNA sequence analysis tasks.

Main Results:

  • A comprehensive survey of 58 word embeddings and 70 language models for predictive pipelines.
  • Performance values for AI-based and traditional sequence encoding predictors across 47 tasks.
  • Identification of top-performing predictive pipelines for RNA sequence analysis.

Conclusions:

  • This manuscript serves as a foundational resource for AI researchers entering RNA sequence analysis.
  • It promotes the development of AI-driven tools for diverse RNA sequence analysis applications.
  • The provided benchmarks and performance evaluations streamline the creation of novel RNA analysis predictors.