Skip to content

jisraeli/DeepBind

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

2 Commits
 
 
 
 
 
 

Repository files navigation

DEEPBIND training scripts

This source tree was distributed as part of the Nature Biotechnology supplementary software release for DeepBind. Users of DeepBind are encouraged to instead use the latest source code and binaries for scoring sequences at

http://tools.genes.toronto.edu/deepbind/

The commands below assume starting from the 'code' subdirectory.

REQUIREMENTS

CUDA 7.0+ SDK https://developer.nvidia.com/cuda-downloads

Python 2.7+ 64-bit http://www.python.org/getit/

Numpy 1.8+ linked with MKL Matplotlib 1.2+ Scipy 0.11+ PIL 1.1.7+ http://www.lfd.uci.edu/~gohlke/pythonlibs/ (if on Windows)

Visual C++ 2012 / GCC 4.8+

BUILDING (WINDOWS)

You must have the CUDA 7.0+ runtime DLLs somewhere in your PATH. You need Visual C++ 2012 to compile the internal libraries: libs/smat/vcproj/smat.sln (open and build) libs/deepity/vcproj/deepity.sln (open and build) libs/kangaroo/vcproj/kangaroo.sln (open and build)

BUILDING (LINUX)

You need g++ 4.8+. The library makefiles assume cuda is installed to /usr/local/cuda but this can be overridden with environment variable CUDADIR.

Once you've unzipped the source, compile the sub-projects as follows: cd libs/smat/src make cd ../py python run_tests.py

cd ../../deepity/deepity_smat make

cd ../../kangaroo/kangaroo_smat make

Add absolute paths for the following directories to your LD_LIBRARY_PATH

code/libs/deepity/build/release/bin code/libs/smat/build/release/bin code/libs/kangaroo/build/release/bin

GETTING THE FULL DATA SETS

Due to the 150MB size limit on Supplementary Software, this package contains only small subset of the training and testing data, and not all scripts will run without the missing data. Please visit

http://tools.genes.toronto.edu/deepbind/nbtcode

to download the full data sets and include them in the 'data' subdirectory. The full DREAM5 data is however included in the supplement.

TRAINING RBPs ON RNACOMPETE

Train RBP models on RNAcompete Set A, then test on Set B

python deepbind_train_rnac.py A calib,train,test,report

out/rnac/A/report/index.html (model info) out/rnac/test/pbm/ (model predictions on SetB)

Train RBP models on RNAcompete Set AB, then test on in vivo sequences

python deepbind_train_rnac.py AB calib,train,test,report

out/rnac/AB/report/index.html (model info) out/rnac/test/invivo/deepbind_all.txt (AUCs when applied different ways)

For more information on the RNAcompete training and testing data, please refer to Ray et al., 2013 (Nature, doi:10.1038/nature12311)

TRAINING TFs ON DREAM5

Train TF models on DREAM5 training array (ME or HK depending on the TF), then test on the opposite array:

python deepbind_train_dream5.py calib,train,test,report

out/dream5/report/index.html (model info) out/dream5/report/performance_pbm.txt (performance on held-out array design) out/dream5/report/performance_chipseq.txt (performance on ChIP-seq peaks against various types of background sequence)

For more information on the 2013 DREAM5 Motif Recognition Challenge protocols and evaluation, please refer to Weirauch et al., 2013 (Nature Biotechnology, doi:10.1038/nbt.2486)

TRAINING TFs ON ENCODE CHIP-SEQ

Train TF models on ENCODE ChIP-seq peaks, then test on held-out subset of peaks. See supplementary information if training/testing set is not clear from descriptions below. Use top 500 even to train, top 500 odd to test:

python deepbind_train_encode.py top calib,train,test,report

out/encode/top/report/index.html (model info)

Using all non-test peaks to train (top 500 even + 1000-and-beyond), then test on top 500 odd:

python deepbind_train_encode.py all calib,train,test,report

out/encode/all/report/index.html (model info)

TRAINING TFs ON HT-SELEX

Train TF models on HT-SELEX sequences, then test on held-out subset of sequences. Using the SELEX cycles selected by Jolma et al.:

python deepbind_train_selex.py --jolma calib,train,test,report

out/selex/jolma/report/index.html

Using the SELEX cycles selected by Aipanahi et al.:

python deepbind_train_selex.py calib,train,test,report

out/selex/best/report/index.html

About

Training and testing of DeepBind models.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published