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Related Experiment Video

Updated: Sep 3, 2025

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iCOMIC: a graphical interface-driven bioinformatics pipeline for analyzing cancer omics data.

Anjana Anilkumar Sithara1, Devi Priyanka Maripuri1, Keerthika Moorthy1

  • 1Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai 600036, India.

NAR Genomics and Bioinformatics
|July 28, 2022
PubMed
Summary
This summary is machine-generated.

We developed iCOMIC, a user-friendly pipeline for analyzing cancer genomic data. This toolkit simplifies complex omics data analysis, providing insightful statistics from raw sequencing data.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Omics data generation is rapidly increasing.
  • Analyzing genomic and transcriptomic data requires significant bioinformatics expertise.
  • Existing analysis pipelines can be complex and difficult to use.

Purpose of the Study:

  • To develop a user-friendly pipeline for analyzing cancer genomic data.
  • To simplify the process of extracting insightful statistics from raw sequencing data.
  • To integrate novel algorithms for predicting mutation pathogenicity and classifying cancer genes.

Main Methods:

  • Developed the iCOMIC toolkit, a Snakemake-embedded pipeline with a graphical user interface (GUI).
  • Integrated in-house algorithms for pathogenicity prediction and oncogene/tumor suppressor gene classification.
  • Benchmarked DNA-Seq and RNA-Seq pipelines using standard datasets (NA12878, SRP082682).

Main Results:

  • Achieved high F1 scores (0.971 for indels, 0.988 for SNPs) in DNA-Seq analysis.
  • Obtained a high correlation coefficient (r=0.85) in RNA-Seq analysis.
  • Demonstrated ease of use with minimal execution steps and no complex command-line arguments via GUI.

Conclusions:

  • The iCOMIC toolkit significantly simplifies complex omics data analysis.
  • It provides an accessible solution for researchers without extensive bioinformatics expertise.
  • Enables efficient extraction of insightful statistics from whole-genome and transcriptome data.