Skip to content
Impact of mouse contamination in genomic profiling of patient-derived model and best practice for robust analysis
Shell Python R
Branch: master
Clone or download
Pull request Compare This branch is even with ShockYoung:master.
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
Figure5.png
MouseContamEstimator.py
README.md
pipe_BBsplit.sh
pipe_BBsplit_making_merged_ref.sh
pipe_Bamcmp.sh
pipe_ConcatRef.sh
pipe_Disambiguate.sh
pipe_DualRef-L.sh
pipe_DualRef-S.sh
pipe_XenofilteR.R
pipe_Xenome_classify.sh
pipe_Xenome_index.sh

README.md

Best Practice for Analysis of PDM sequencing

BestPractice

Introduction

A robust workflow to analyze human genome data contaminated by mouse genome. ConcatRef, Disambiguate and XenofilteR are the best suggested filtering method for general purpose. Alternatively, Xenome, XenofilteR and ConcatRef are also recommended for SNV analysis. After applying filtering method, further filtering can be achieved by blacklisting using HAMA list. Estimation of contamination ratio can be used as an indicator of whether strict or lenient blacklisting should be applied.

Description

  • Filtering Methods

    • BBsplit
    • Bamcmp
    • ConcatRef
    • Disambiguate
    • TwinRef-L
    • TwinRef-S
    • XenofilteR
    • Xenome
  • HAMA blacklisting

  • MouseContamEstimator

    • Simple script for calculating estimated contamination level.

Reference

Se-Young Jo, Eunyoung Kim, Sangwoo Kim: Impact of Mouse contamination in genomic profiling of patient-derived model and best practice for robust analysis, under revision

You can’t perform that action at this time.