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BayesPI-BAR2: A New Python Package for Predicting Functional Non-coding Mutations in Cancer Patient Cohorts
Kirill Batmanov1, Jan Delabie2, Junbai Wang1
1Department of Pathology, Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway.
Abstract:
Most of somatic mutations in cancer occur outside of gene coding regions. These mutations may disrupt the gene regulation by affecting protein-DNA interaction. A study of these disruptions is important in understanding tumorigenesis. However, current computational tools process DNA sequence variants individually, when predicting the effect on protein-DNA binding. Thus, it is a daunting task to identify functional regulatory disturbances among thousands of mutations in a patient. Previously, we have reported and validated a pipeline for identifying functional non-coding somatic mutations in cancer patient cohorts, by integrating diverse information such as gene expression, spatial distribution of the mutations, and a biophysical model for estimating protein binding affinity. Here, we present a new user-friendly Python package BayesPI-BAR2 based on the proposed pipeline for integrative whole-genome sequence analysis. This may be the first prediction package that considers information from both multiple mutations and multiple patients. It is evaluated in follicular lymphoma and skin cancer patients, by focusing on sequence variants in gene promoter regions. BayesPI-BAR2 is a useful tool for predicting functional non-coding mutations in whole genome sequencing data: it allows identification of novel transcription factors (TFs) whose binding is altered by non-coding mutations in cancer. BayesPI-BAR2 program can analyze multiple datasets of genome-wide mutations at once and generate concise, easily interpretable reports for potentially affected gene regulatory sites. The package is freely available at http://folk.uio.no/junbaiw/BayesPI-BAR2/.
Insights
BayesPI-BAR2 identifies functional non-coding cancer mutations by analyzing multiple mutations and patients. This tool aids in understanding gene regulation disruptions and discovering new transcription factors affected by these mutations.
Area of Science:
- Genomics
- Bioinformatics
- Cancer Research
Background:
- Most cancer-associated somatic mutations occur outside gene coding regions, potentially disrupting gene regulation via altered protein-DNA interactions.
- Current computational tools analyze DNA sequence variants individually, complicating the identification of functional regulatory disruptions among numerous mutations.
- Understanding these non-coding mutations is crucial for comprehending tumorigenesis.
Purpose of the Study:
- To introduce BayesPI-BAR2, a user-friendly Python package for integrative whole-genome sequence analysis of non-coding somatic mutations.
- To provide a novel computational tool that considers information from multiple mutations and multiple patients for predicting functional regulatory disturbances.
- To facilitate the identification of novel transcription factors (TFs) affected by non-coding mutations in cancer.
Main Methods:
- Development of the BayesPI-BAR2 Python package integrating gene expression, mutation spatial distribution, and a biophysical model for protein binding affinity.
- Application of the pipeline to analyze whole-genome sequencing data from follicular lymphoma and skin cancer patients, focusing on promoter regions.
- Evaluation of the package's ability to predict functional non-coding mutations and identify affected TFs.
Main Results:
- BayesPI-BAR2 successfully analyzes multiple datasets of genome-wide mutations, providing interpretable reports on affected gene regulatory sites.
- The package enables the identification of novel transcription factors whose binding is altered by non-coding mutations in cancer.
- Demonstrated utility in follicular lymphoma and skin cancer patient cohorts.
Conclusions:
- BayesPI-BAR2 is a valuable tool for predicting functional non-coding mutations in whole genome sequencing data.
- The package advances the analysis of cancer genomics by considering multi-mutation and multi-patient data.
- Facilitates discovery of new regulatory mechanisms disrupted in cancer development.
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