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Related Concept Videos

Genomics02:02

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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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Cancer-Associated Fibroblasts from Mouse Mammary Tumors as Tools for Molecular and Computational Studies
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Comprehensive Analysis of Large-Scale Transcriptomes from Multiple Cancer Types.

Baoting Nong1, Mengbiao Guo1, Weiwen Wang2

  • 1Key Laboratory of Gene Engineering of the Ministry of Education, Institute of Healthy Aging Research, School of Life Sciences, Sun Yat-sen University, Guangzhou 510006, China.

Genes
|December 24, 2021
PubMed
Summary

PipeOne integrates multi-modal RNA sequencing data for cancer research. This workflow identifies key cancer features and subtypes, revealing shared and specific disease mechanisms across multiple cancer types.

Keywords:
RNA-seq workflowTCGAalternative splicingcancer subtypingfeature prioritizationmitochondriaribosomesomatic mutation

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Transcriptional regulation abnormalities are common in cancers.
  • Integrating multi-modal RNA sequencing (RNA-seq) data remains a challenge for uncovering cancer mechanisms.

Purpose of the Study:

  • To present PipeOne, a comprehensive workflow for large-scale transcriptome data analysis.
  • To integrate multi-modal RNA-seq data for improved disease mechanism discovery and patient stratification.

Main Methods:

  • Developed PipeOne, a cross-platform workflow using Nextflow for reproducible analysis.
  • Integrated eight tools for RNA-seq data processing and feature extraction.
  • Employed a random forest algorithm for patient stratification based on multi-modal information.

Main Results:

  • Applied PipeOne to five cancer types (colon, liver, kidney, stomach, thyroid; n=2024), identifying key dysregulated features (e.g., PVT1 expression, ABI3BP splicing) and pathways (e.g., liver/kidney dysfunction).
  • Discovered clinically relevant patient subtypes in four cancers, often linked to specific driver mutations (TP53, BRAF, etc.) and biological processes (ribosome biogenesis, mitochondrial function).

Conclusions:

  • PipeOne efficiently analyzes diverse cancer types, revealing both shared and cancer-specific contributing factors.
  • The workflow is adaptable for other diseases and available for public use.