Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

NIMBus: a negative binomial regression based Integrative Method for mutation Burden Analysis.

BMC bioinformatics - 22 Oct 2020

Zhang Jing, Liu Jason, McGillivray Patrick, Yi Caroline, Lochovsky Lucas, Lee Donghoon, Gerstein Mark

Abstract excerpt

BACKGROUND: Identifying frequently mutated regions is a key approach to discover DNA elements influencing cancer progression. However, it is challenging to identify these burdened regions due to mutation rate heterogeneity across the genome and across different individuals. Moreover, it is known that this heterogeneity partially stems from genomic confounding factors, such as replication timing and chromatin...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Calibration
Share to Disease
Share to Molecular Sequence Annotation

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Mutation Rate

Share to Open Reading Frames

Share to Regression Analysis

Share to Software

Share to Whole Genome Sequencing

Source

Open in PubMed Open full text

Identifiers

PMID: 33092526
DOI: 10.1186/s12859-020-03758-1
PMCID: PMC7580035

Genes

TERT
TP53

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 5 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

NIMBus: a negative binomial regression based Integrative Method for mutation Burden Analysis.PMID 33092526Read ↗

WITER: A powerful method for the estimation of cancer-driver genes using a weighted iterative regression accurately modelling background mutation rateSemantic proximity Read ↗

LARVA: an integrative framework for large-scale analysis of recurrent variants in noncoding annotations.Semantic proximity Read ↗

MOAT: efficient detection of highly mutated regions with the Mutations Overburdening Annotations Tool.Semantic proximity Read ↗

A comprehensive analysis of clinical and polygenic germline influences on somatic mutational burden.Semantic proximity Read ↗

A site specific model and analysis of the neutral somatic mutation rate in whole-genome cancer dataSemantic proximity Read ↗

A site specific model and analysis of the neutral somatic mutation rate in whole-genome cancer data.Semantic proximity Read ↗

Hotspot propensity across mutational processesSemantic proximity Read ↗

Model-based analysis of positive selection significantly expands the list of cancer driver genes, including RNA methyltransferasesSemantic proximity Read ↗

ecTMB: a robust method to estimate and classify tumor mutational burden.Semantic proximity Read ↗

mirDNMR: a gene-centered database of background de novo mutation rates in human.Semantic proximity Read ↗

A Dirichlet-multinomial mixed model for detecting differential abundance of mutational signaturesSemantic proximity Read ↗

A Dirichlet-multinomial mixed model for determining differential abundance of mutational signaturesSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.