Article

Estimating tumor mutational burden from RNA-sequencing without a matched-normal sample

2021-08-15

Abstract excerpt

Detection of somatic point mutations using patients sequencing data has many clinical applications, including the identification of cancer driver genes, detection of mutational signatures, and estimation of tumor mutational burden (TMB). In a recent work we developed a tool for detection of somatic mutations using tumor RNA and matched-normal DNA. Here, we further extend it to detect somatic mutations from RNA seq...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 7c547c58-9741-5e8d-b6f2-bb1a3546abb7
DOI: 10.1101/2021.08.15.456379

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Estimating tumor mutational burden from RNA-sequencing without a matched-normal sampleDOI 10.1101/2021.08.15.456379Read

Pan-cancer discovery of somatic mutations from RNA sequencing data.Semantic proximity Read

FFixR: A Machine Learning Framework for Accurate Somatic Mutation Calling from FFPE RNA-Seq Data in CancerSemantic proximity Read

FFixR: a machine learning framework for accurate somatic mutation calling from FFPE RNA-seq data in cancer.Semantic proximity Read

A machine learning approach for somatic mutation discovery.Semantic proximity Read

Evaluating somatic tumor mutation detection without matched normal samples.Semantic proximity Read

A somatic reference standard for cancer genome sequencing.Semantic proximity Read

Determining mutational burden and signature using RNA-seq from tumor-only samples.Semantic proximity Read

RNA Sequencing Data for FFPE Tumor Blocks Can Be Used for Robust Estimation of Tumor Mutation Burden in Individual BiosamplesSemantic proximity Read

Improving Accuracy of Somatic Mutation Profiling in Large Epidemiologic Studies: Addressing Cases without Matched Normal SamplesSemantic proximity Read

ISOWN: accurate somatic mutation identification in the absence of normal tissue controls.Semantic proximity Read

Clinical Validation and Implementation of a Targeted Next-Generation Sequencing Assay to Detect Somatic Variants in Non-Small Cell Lung, Melanoma, and Gastrointestinal Malignancies.Semantic proximity Read

A deep learning solution to predict tumor mutational status : an evaluation as a first-line diagnostic toolSemantic proximity Read