TNER

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############################################################################### README

TNER: Tri-Nucleotide Error Reducer

TNER is a novel background error suppression tool that provides a robust estimation of background noise to reduce sequencing errors using tri-nucleotide context data for better identification of low frequency somatic mutations in ctDNA (Circulating tumor DNA).

Version: 1.1 Last updated: Oct/04/2018 Contact: shibing.deng {at} pfizer.com or tao.xie {at} pfizer.com Pfizer Early Clinical Development Biostatistics Pfizer Oncology Research & Development ###############################################################################

Pre-requisites/Installation:

Download and install R (version 3 or later) Download and install PERL (version 5.8 or later) Download and install the TNER package and unzip the files

Test the TNER main function using the demo dataset

"Rscript TNER_main.R TNER_example_input_file.txt hs_ave_bg_error.csv hs_depth.csv"

Note: “TNER_example_input_file.txt” is the example input data to be analyzed; the 2nd argument (default: “hs_ave_bg_error.csv“) is a csv file with the average background error rate from healthy subjects; the last argument (default: “hs_depth.csv”) is a csv file with the base coverage in healthy subjects. TNER report the nosiy bases detected in the input data which can be used as a filter to apply to the variant calls from any existing pipeline.

Build background error profile using "Create_background_error_rate.R”

"Rscript Create_background_error_rate.R"

Note: "Create_background_error_rate.R” creates an average background error rate file from individual healthy subject data (should be stored in the subfolder named as "healthy_subjects", file format is as same as "TNER_example_input_file.txt"). The function outputs “hs_ave_bg_error.csv” and “hs_depth.csv” for the main function “TNER_main.R”

Process pileup files for TNER using "pileup2actg.pl"

"perl pileup2actg.pl

Note: this perl script processes a pileup file to generate a tab-delimited txt file for running “TNER_main.R”.

Citation

TNER: A Novel Bayesian Background Error Suppression Method for Mutation Detection in Circulating Tumor DNA Shibing Deng, Maruja Lira, Donghui Huang, Kai Wang, Crystal Valdez, Jennifer Kinong, Paul A. Rejto, Jadwiga Bienkowska, James Hardwick, Tao Xie doi: https://doi.org/10.1101/214379 ###############################################################################