CirQuestDSV

ptdolan@stanford.edu

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Description:

This is a repository for code related to analysis of Circular Resequencing data. Code is written in python and R. Original CirSeq paper is here.

All code in this repository is intended for use downstream of Q-threshold count table generation by the CirSeq software package.

This package currently contains two main scripts: Q20Analysis.py: an annotation program for analyzing the output of CirSeq ('"Q" files') and annotating coding regions, translating ORFs, and annotating biophysical characteristics of the substitutions. AnnotAnalysis.R: An R script for generating a collection of summary plots and data tables based on the naming scheme of the Input Directory. It generates the following:

• In CirQuestDSV_tables directory:

  • A table of high frequency mutations.
  • An agglomerated q20annot file in an .RData format. Useful for further analysis in R, without reloading the dataset.

• In Entropy directory:

  • Plots of the Shannon entropy (scaled 0-1) for each nucleotide position in the genome.

• In CirQuestDSV_figures directory:

  • Frequency plots of the frequency of individual alleles cross the genome.
  • Trajectories for labeled passages (see below.)
  • MDS and PCA dimension reduction of all samples.

Please fork this project to improve annotation in the python portion ("Q20Analysis.py") or to develop analyses based on this data structure in R.

Requirements:

Python (3.X compliant) R

Usage:

Notes on file names:

1. All input file names must end in "Q20.txt" or "Q20threshold.txt"! This makes sure that overwriting does not occur when output files and input files are in the same directory.

2. Each input file requires specific naming for grouping and plotting features. these will also be parsed for data table annotation. e.g.: "~/pathto/myData/MouseAplusTreatment_1-Q20.txt"

3. For grouped passages: In order for files to be grouped correctly in trajectory plots, files in directory must be labeled "MYHEADERTEXT_1-Q20.txt","HEADERTEXT_2-Q20.txt","HEADERTEXT_3-Q20.txt",..., where HEADERTEXT denotes any sample information (can include special characters (except "_" and "-"). **Those files that do not match criteria (passage number at a minimum) are excluded from trajectory plots. Including MYHEADERTEXT is optional but useful for labeling if no header needed, "1-Q20.txt","2-Q20.txt","3-Q20.txt" is sufficient for grouping.

Usage instructions:

1. Open a terminal in the same directory as the scripts and run:

   python Q20Analysis.py <directory with q20 files> <translation start> <translation end> <next ORF start> <next ORF stop> and so on...

   This generates an annotated q20 ("-q20annot.txt") file. You can stop here and use this annotated file for interpretation, or you can use this enhanced q20 to generate plots with the associated R script, "AnnotAnalysis.R".

2. "AnnotAnalysis.R". This program reads a directory of annotated files and combines them to generate frequency plots, trajectory plots, it also performs dimension reduction analysis and outputs a number of useful output files.

with R installed, in command line:

Rscript ./path/to/AnnotAnalysis.R ./pathto/q20annots/

or:

R

then, in R:

source("./path/to/AnnotAnalysis.R","./pathto/q20annots/")

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MIT License

Copyright (c) [2016-2017] [Patrick T. Dolan]

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

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