Related Experiment Video
Updated: May 9, 2026
08:16
Comparative Lesions Analysis Through a Targeted Sequencing Approach
Published on: November 5, 2019
A genomic random interval model for statistical analysis of genomic lesion data
Stan Pounds1, Cheng Cheng, Shaoyu Li
1Department of Biostatistics, Department of Computational Biology and Department of Pathology, St. Jude Children's Research Hospital, Memphis, TN 38135, USA. stanley.pounds@stjude.org
Bioinformatics (Oxford, England)
|July 12, 2013
Summary
We developed the genomic random interval (GRIN) model to identify significant genomic lesions in tumors. GRIN effectively detects biologically relevant pathways and genes, outperforming existing methods in analyses.
Area of Science:
- Genomics
- Cancer Research
- Statistical Modeling
Background:
- Tumors display diverse genomic alterations, including copy number, structural, and sequence variations.
- Identifying specific genes or pathways targeted by these lesions across multiple tumors is challenging.
- Genomic lesions can span different chromosomes while participating in a single biological process.
Purpose of the Study:
- To introduce a novel statistical model and analysis method for evaluating the significance of genomic lesions.
- To assess the abundance of genomic lesions overlapping specific or related gene sets.
- To provide a robust tool for cancer genomic data analysis.
Main Methods:
- Developed the genomic random interval (GRIN) statistical model and analysis method.
- Evaluated the statistical significance of genomic lesion abundance at specific or pre-defined loci.
- Compared GRIN with permutation-of-markers models using simulations and leukemia data.
Main Results:
- GRIN effectively identifies significant loci and biologically relevant pathways with abundant lesions.
- The model retains important biological properties of genomic lesions often overlooked by other methods.
- GRIN demonstrated superior performance compared to three permutation-based methods in simulation and leukemia data analyses.
Conclusions:
- The GRIN model offers a statistically rigorous and biologically informed approach to analyzing cancer genomic data.
- GRIN enhances the identification of key genes and pathways implicated in tumorigenesis.
- An R package for GRIN is available, facilitating its application in cancer research.
More Related Videos
Related Concept Videos
01:11Genome-wide Association Studies-GWAS
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
GWAS does not require the identification of the target gene involved in...
03:18Non-LTR Retrotransposons
As the name suggests, non-LTR retrotransposons lack the long terminal repeats characteristic of the LTR retrotransposons. Additionally, both LTR and non-LTR retrotransposons use distinct mechanisms of mobilization. Non-LTR retrotransposons are further divided into two classes - Long interspersed nuclear elements (LINEs) and short interspersed nuclear elements (SINEs), both of which occur abundantly in most mammals, including humans. Some of the active non-LTR retrotransposons in humans are L1...