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Updated: Oct 10, 2025

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A computational pipeline for functional gene discovery.

Aolani Colon1, Rishabh Hirday1, Ami Patel1

  • 1Department of Biomedical Engineering, Rutgers University, 599 Taylor Road, Piscataway, NJ, 08854, USA.

Scientific Reports
|December 8, 2021
PubMed
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We developed a novel computational pipeline for functional gene identification using transcriptome data. This tool accurately predicts novel genes involved in biological processes, validated in mouse retinal development.

Area of Science:

  • Genomics
  • Bioinformatics
  • Developmental Biology

Background:

  • Existing computational pipelines primarily focus on differential gene expression analysis.
  • There is a significant need for robust computational tools for functional gene identification from transcriptomic data.

Purpose of the Study:

  • To develop and validate a novel computational pipeline for functional gene identification from transcriptome profiling data.
  • To identify novel genes critical for photoreceptor outer segment development in the mouse retina.

Main Methods:

  • The pipeline incorporates batch effect correction, clustering optimization using gap statistics, and gene ontology analysis.
  • Functional gene discovery is further enhanced by literature analysis.
  • RNA-sequencing datasets from mouse retinal development studies were analyzed.

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Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data
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Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

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Related Experiment Videos

Last Updated: Oct 10, 2025

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Published on: October 3, 2025

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A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
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Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

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Main Results:

  • The pipeline successfully identified 7 candidate genes involved in mouse photoreceptor outer segment formation.
  • Experimental validation using immunohistochemistry confirmed the expression of top candidate genes (Pde8b, Laptm4b, Nr1h4) in the outer segment.
  • The computational pipeline demonstrated accuracy in predicting novel functional genes for specific biological processes.

Conclusions:

  • The developed computational pipeline is effective for identifying novel functional genes related to specific biological processes, such as photoreceptor development.
  • This pipeline has broad applicability for functional gene discovery across various biological processes, organs, and tissues.