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Mutations

Mutations

lncRNA - Long Non-coding RNAs

lncRNA - Long Non-coding RNAs

In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...

DNA Packaging

DNA Packaging

Cancers Originate from Somatic Mutations in a Single Cell

Cancers Originate from Somatic Mutations in a Single Cell

Cancer arises from mutations in the critical genes that allow healthy cells to escape cell cycle regulation and acquire the ability to proliferate indefinitely. Though originating from a single mutation event in one of the originator cells, cancer progresses when the mutant cell lines continue to gain more and more mutations, and finally, become malignant. For example, chronic myelogenous leukemia (CML) develops initially as a non-lethal increase in white blood cells, which progressively...

Chromatin Packaging

Chromatin Packaging

Each human somatic cell contains 6 billion base pairs of DNA. Each base pair is 0.34 nm long, meaning each diploid cell contains a staggering 2 meters of DNA. This long DNA strand is packed inside a nucleus measuring only 10-20 microns in diameter with the help of specialized DNA-binding proteins called histones. Together they form a compact DNA-protein complex called chromatin. The chromatin is further compacted into higher-order structures. The highest level of compaction is achieved during...

Chromatin Packaging

Chromatin Packaging

Each human somatic cell contains 6 billion base-pairs of DNA. Each base-pair is 0.34 nm long, which means that each diploid cell contains a staggering 2 meters of DNA. How is such a long DNA strand packed inside a nucleus measuring only 10 - 20 microns in diameter?
The chromatin
In combination with specialized DNA binding protein called Histones, the DNA double helix forms a compact DNA: protein complex called chromatin. The chromatin itself is further compacted into higher-order...

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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.

Frontiers in Genetics

|April 20, 2019

Summary

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.

Keywords:

bioinformatics cancer gene regulation non-coding mutations transcription factors

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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.