Copyright (C) 2019, MD Anderson Cancer Center (whe3@mdanderson.org)

ProTiler is a novel computational method for fine-mapping of protein regions that are hyper-sensitive to CRISPR/Cas9 mediated gene knockouts(CKHS region) from high-throughput tiling-sgRNA functional screens.

Also, ProTiler is able to predict CKHS regions for protein encoded by any given gene from other common protein features including conservation, domain annotation, secondary structures and PTMs distribution.

If you use ProTiler please cite the following paper we published on Nature Communications:

He et al. De novo identification of essential protein domains from CRISPR-Cas9 tiling-sgRNA knockout screens. Nat Commun 10, 4547(2019).

1. New version 1.0.2 is now compatible to python3.

2. The package has been uploaded to PyPI. Users now can install directly through:

   pip install protiler

ProTiler is written in Python and R, Python>=2.7 and R>=3.5.0 is needed

Python Packages:

• scikit-learn==0.22.1, matplotlib >=2.2.3, pandas >=1.2.0, numpy >=1.17.5, seaborn >=0.9.0

R packages:

• breakfast(v0.1.0), stringr

wget https://repo.continuum.io/archive/Anaconda2-2018.12-Linux-x86_64.sh 
bash Anaconda2-2018.12-Linux-x86_64.sh 

Install Python Packages with pip:

pip install matplotlib pandas sklearn numpy seaborn

Install R packages in R IDE:

install.packages('stringr')

require(devtools)
install_version("breakfast", version = "0.1.0", repos = "http://cran.us.r-project.org")

git clone https://github.com/MDhewei/ProTiler-1.0.0.git
cd ProTiler-1.0.0
python setup.py install

• Symbol: This column record the symbol of target gene, for example: 'CREBBP','ACTL6A'

• AA: The cutting loci of the corresponding sgRNA at amino acid level

• CRISPR score: the signals for each sgRNA, in the example file, z-scores in three different cell lines are used. User should select at least one column.

• -i/--inputfile:

  the file path to the input table recording tiling CRISPR sgRNA annotations and signals. .csv,.txt,.xlsx format are supported

• -g/--gene_id:

  the official symbol of target gene, for example: 'CREBBP','ACTL6A'

• -s/--score_columns:

  the column number(s) of input table that recording CRISPR knowckout scores

• -o/--outputdir:

  the directory name created in the current working directory to save output files, default='ProTilerOutput'

• -f/--half_size:

  The number of neiboring signals from each side selected to filter inefficient sgRNAs',default='5'

• -t1/--threshold:

  Threshold to supress the outliers among the signals',default='2'

• -t2/--threshold2:

  Threshold to detect changing points using TGUH method',default='1.5'

protiler call -i sample.txt -g CREBBP -s 9,10,11 -o ProtilerOutput

• AA.start: the start residue position of the segments called with TGUH
• AA.end: the end residue position of the segments called with TGUH
• n: the number of sgRNAs targeting the region
• m: the mean score of sgRNAs targeting the region
• is.HS.site: to judge whether the segment is a hyper-sensitive region
• length: the length of the segment
• Gene: the symbol of the target gene

• -l/--gene_list:

  A list of candidate genes for which you want to predict HS regions. eg: CREBBP,FAM122A,AURKB

• -b1/--bandwidth1:

  Bandwidth for PTMs kernel density estimation training

• -b2/--bandwidth2:

  Bandwidth for SIFT score kernel density estimation training

• -o/--outputdir:

  the directory name created to save output files

• -m/--gamma:

  The gamma parameter for SVM model,default='10'

• -c/--penalty:

  The penalty parameter for SVM model,default='0.01'

protiler predict -l CREBBP,FAM122A,SAMRCB1,AURKB -o ProtilerOutput