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Jianhua Zhang

Publications and source records attributed to Jianhua Zhang.

2 recordsLinked to original sources

Accounting for tumor heterogeneity using a sample-specific error model improves sensitivity and specificity in mutation calling for sequencing data

Subclonal mutations reveal important features of the genetic architecture of tumors. However, accurate detection of mutations in genetically heterogeneous tumor cell populations using NGS remains challenging. We developed MuSE (http://bioinformatics.mdanderson.org/main/MuSE), mutation calling using a Markov substitution model for evolution, a novel approach modeling the evolution of the allelic composition of the tumor and normal tissue at each reference base. MuSE adopts a sample-specific error model that reflects the underlying tumor heterogeneity to greatly improve overall accuracy. We demonstrate the accuracy of MuSE in calling subclonal mutations in the context of large-scale tumor sequencing projects using whole exome and whole genome sequence.

Bioinformatics

Flowr: Robust and efficient pipelines using a simple language-agnostic approach

MotivationBioinformatics analyses have become increasingly intensive computing processes, with lowering costs and increasing numbers of samples. Each laboratory spends time creating and maintaining a set of pipelines, which may not be robust, scalable, or efficient. Further, the existence of different computing environments across institutions hinders both collabo-ration and the portability of analysis pipelines.\n\nResultsFlowr is a robust and scalable framework for designing and deploying computing pipelines in an easy-to-use fashion. It implements a scatter-gather approach using computing clusters, simplifying the concept to the use of five simple terms (in submission and dependency types). Most importantly, it is flexible, such that customizing existing pipelines is easy, and since it works across several computing environments (LSF, SGE, Torque, and SLURM), it is portable.\n\nAvailabilityhttp://docs.flowr.space

Bioinformatics