Related Experiment Video
Updated: Feb 19, 2026
Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
Published on: October 18, 2013
MOAT: efficient detection of highly mutated regions with the Mutations Overburdening Annotations Tool
Lucas Lochovsky1,2, Jing Zhang1,2, Mark Gerstein1,2,3
1Program in Computational Biology and Bioinformatics.
Summary:
Identifying genomic regions with higher than expected mutation count is useful for cancer driver detection. Previous parametric approaches require numerous cell-type-matched covariates for accurate background mutation rate (BMR) estimation, which is not practical for many situations. Non-parametric, permutation-based approaches avoid this issue but usually suffer from considerable compute-time cost. Hence, we introduce Mutations Overburdening Annotations Tool (MOAT), a non-parametric scheme that makes no assumptions about mutation process except requiring that the BMR changes smoothly with genomic features. MOAT randomly permutes single-nucleotide variants, or target regions, on a relatively large scale to provide robust burden analysis. Furthermore, we show how we can do permutations in an efficient manner using graphics processing unit acceleration, speeding up the calculation by a factor of ∼250.
Availability And Implementation:
MOAT is available at moat.gersteinlab.org.
Contact:
mark@gersteinlab.org.
Supplementary Information:
Supplementary data are available at Bioinformatics online.
Related Concept Videos
01:20Mismatch Repair
The Mutator Protein Family Plays a Key Role in DNA Mismatch Repair
The human genome has more than 3 billion base pairs of DNA per cell. Prior to cell division, that vast amount of genetic...
02:26Comparing Copy Number Variations and SNPs
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...