Allelic imbalance identification software tools | Whole-exome sequencing data analysis
Whole-exome sequencing (WES) has extensively been used in cancer genome studies; however, the use of WES data in the study of loss of heterozygosity or more generally allelic imbalance (AI) has so far been very limited, which highlights the need for user-friendly and flexible software that can handle low-quality datasets.
A statistical approach for the detection of recurrent allelic imbalance (AI) events using WES datasets, specifically where matched normal samples are not available. Using a control database of non-cancer samples, ExomeAI overcomes the limitation of the ‘obligatory’ usage of matched normal samples and efficiently reduces the rate of false positive calls.
A software tool for the detection of subtle allelic imbalance (AI) events from next-generation sequencing data. hapLOHseq identified events of 10 megabases or greater occurring in as little as 16% of the sample in exome sequencing data (at 80x) and 4% in whole genome sequencing data (at 30x), far exceeding the capabilities of existing software. We also found hapLOHseq to be superior at detecting large chromosomal changes across a series of pancreatic samples from TCGA.
Allows simultaneous testing of allelic imbalance (AI) and differences in AI across environments. This Bayesian model allows formal testing of AI between environments, while accounting for potential bias, and models the total number of reads as a random sample from the library. The model also accounts for differences in magnitude of gene expression counts and unassigned reads. It protects against increases in type I errors, including when the number of sequenced reads in the two environments differ by 10-fold.