SortSeqTools is a software package for use in analyzing Sort-Seq, Massively Parallel Reporter Assays, Deep Protein Mutagenesis Assays, and Selex-Seq experiments.
For help using and installing this package please refer to our paper, the github examples, and the sortseq_tools documentation
Code for Analysis:
The commands for producing the models analysed in our paper are below. In order to run these commands download the code from github, open a terminal, and navigate to the data/formatted/ folder. Copy one of the following commands into the terminal and it will produce one model. To run all the commands will take approximately 30 hours on a laptop.
Commands for Figure 4:
CRP matrix models:
sortseq_tools learn_model -lm IM -i sortseq/crp-wt-formatted.txt -o crp-wt_MCMC_crp.txt -s 3 -e 25
sortseq_tools learn_model -lm LS -i sortseq/crp-wt-formatted.txt -o crp-wt_LS_crp.txt -s 3 -e 25
sortseq_tools learn_model -lm ER -i sortseq/crp-wt-formatted.txt -o crp-wt_ER_crp.txt -s 3 -e 25
sortseq_tools learn_model -lm IM -i sortseq/full-wt-formatted.txt -o full-wt_MCMC_crp.txt -s 3 -e 25
sortseq_tools learn_model -lm LS -i sortseq/full-wt-formatted.txt -o full-wt_LS_crp.txt -s 3 -e 25
sortseq_tools learn_model -lm ER -i sortseq/full-wt-formatted.txt -o full-wt_ER_crp.txt -s 3 -e 25
sortseq_tools learn_model -lm IM -i sortseq/full-500-formatted.txt -o full-500_MCMC_crp.txt -s 3 -e 25
sortseq_tools learn_model -lm LS -i sortseq/full-500-formatted.txt -o full-500_LS_crp.txt -s 3 -e 25
sortseq_tools learn_model -lm ER -i sortseq/full-500-formatted.txt -o full-500_ER_crp.txt -s 3 -e 25
sortseq_tools learn_model -lm IM -i sortseq/full-150-formatted.txt -o full-150_MCMC_crp.txt -s 3 -e 25
sortseq_tools learn_model -lm LS -i sortseq/full-150-formatted.txt -o full-150_LS_crp.txt -s 3 -e 25
sortseq_tools learn_model -lm ER -i sortseq/full-150-formatted.txt -o full-150_ER_crp.txt -s 3 -e 25
sortseq_tools learn_model -lm IM -i sortseq/full-0-formatted.txt -o full-0_MCMC_crp.txt -s 3 -e 25
sortseq_tools learn_model -lm LS -i sortseq/full-0-formatted.txt -o full-0_LS_crp.txt -s 3 -e 25
sortseq_tools learn_model -lm ER -i sortseq/full-0-formatted.txt -o full-0_ER_crp.txt -s 3 -e 25
CRP neighbor models: sortseq_tools learn_model -lm IM -i sortseq/crp-wt-formatted.txt -o crp-wt_MCMC_crp_NBR.txt -s 3 -e 25 -mt NBR
sortseq_tools learn_model -lm IM -i sortseq/full-wt-formatted.txt -o full-wt_MCMC_crp_NBR.txt -s 3 -e 25 -mt NBR
sortseq_tools learn_model -lm IM -i sortseq/full-500-formatted.txt -o full-500_MCMC_crp_NBR.txt -s 3 -e 25 -mt NBR
sortseq_tools learn_model -lm IM -i sortseq/full-150-formatted.txt -o full-150_MCMC_crp_NBR.txt -s 3 -e 25 -mt NBR
sortseq_tools learn_model -lm IM -i sortseq/full-0-formatted.txt -o full-0_MCMC_crp_NBR.txt -s 3 -e 25 -mt NBR
RNAP matrix models:
sortseq_tools learn_model -lm IM -i sortseq/rnap-wt-formatted.txt -o rnap-wt_MCMC_rnap.txt -s 37 -e 71
sortseq_tools learn_model -lm LS -i sortseq/rnap-wt-formatted.txt -o rnap-wt_LS_rnap.txt -s 37 -e 71
sortseq_tools learn_model -lm ER -i sortseq/rnap-wt-formatted.txt -o rnap-wt_ER_rnap.txt -s 37 -e 71
sortseq_tools learn_model -lm IM -i sortseq/full-wt-formatted.txt -o full-wt_MCMC_rnap.txt -s 37 -e 71
sortseq_tools learn_model -lm LS -i sortseq/full-wt-formatted.txt -o full-wt_LS_rnap.txt -s 37 -e 71
sortseq_tools learn_model -lm ER -i sortseq/full-wt-formatted.txt -o full-wt_ER_rnap.txt -s 37 -e 71
sortseq_tools learn_model -lm IM -i sortseq/full-500-formatted.txt -o full-500_MCMC_rnap.txt -s 37 -e 71
sortseq_tools learn_model -lm LS -i sortseq/full-500-formatted.txt -o full-500_LS_rnap.txt -s 37 -e 71
sortseq_tools learn_model -lm ER -i sortseq/full-500-formatted.txt -o full-500_ER_rnap.txt -s 37 -e 71
sortseq_tools learn_model -lm IM -i sortseq/full-150-formatted.txt -o full-150_MCMC_rnap.txt -s 37 -e 71
sortseq_tools learn_model -lm LS -i sortseq/full-150-formatted.txt -o full-150_LS_rnap.txt -s 37 -e 71
sortseq_tools learn_model -lm ER -i sortseq/full-150-formatted.txt -o full-150_ER_rnap.txt -s 37 -e 71
sortseq_tools learn_model -lm IM -i sortseq/full-0-formatted.txt -o full-0_MCMC_rnap.txt -s 37 -e 71
sortseq_tools learn_model -lm LS -i sortseq/full-0-formatted.txt -o full-0_LS_rnap.txt -s 37 -e 71
sortseq_tools learn_model -lm ER -i sortseq/full-0-formatted.txt -o full-0_ER_rnap.txt -s 37 -e 71
RNAP neighbor models:
sortseq_tools learn_model -lm IM -i sortseq/crp-wt-formatted.txt -o crp-wt_MCMC_crp_NBR.txt -s 37 -e 71 -mt NBR
sortseq_tools learn_model -lm IM -i sortseq/full-wt-formatted.txt -o full-wt_MCMC_crp_NBR.txt -s 37 -e 71 -mt NBR
sortseq_tools learn_model -lm IM -i sortseq/full-500-formatted.txt -o full-500_MCMC_crp_NBR.txt -s 37 -e 71 -mt NBR
sortseq_tools learn_model -lm IM -i sortseq/full-150-formatted.txt -o full-150_MCMC_crp_NBR.txt -s 37 -e 71 -mt NBR
sortseq_tools learn_model -lm IM -i sortseq/full-0-formatted.txt -o full-0_MCMC_crp_NBR.txt -s 37 -e 71 -mt NBR
Commands for Figure 5:
Simulation of Sort Seq Data:
CRP:
Simulate Data Set:
sortseq_tools simulate_library -w ACAGGTATACAGTACCATACCT -n 1000000 -o Simulated/CRP/simulated_library_train.txt
sortseq_tools simulate_sort -m Simulated/CRP/true_model.txt -n_bins 2 -i simulated_library_train.txt -o Simulated/CRP/2_bin_sorted-formatted.txt
sortseq_tools simulate_sort -m Simulated/CRP/true_model.txt -n_bins 10 -i simulated_library_train.txt -o Simulated/CRP/10_bin_sorted-formatted.txt
sortseq_tools simulate_library -w ACAGGTATACAGTACCATACCT -n 1000000 -o Simulated/CRP/simulated_library_test.txt
sortseq_tools simulate_sort -m Simulated/CRP/true_model.txt -n_bins 2 -i simulated_library_test.txt -o Simulated/CRP/2_bin_sorted_test-formatted.txt
sortseq_tools simulate_sort -m Simulated/CRP/true_model.txt -n_bins 10 -i simulated_library_test.txt -o Simulated/CRP/10_bin_sorted_test-formatted.txt
Learn Models:
sortseq_tools learn_model -lm IM -i Simulated/CRP/2_bin_sorted-formatted.txt -o sim_2_bin_IM_crp.txt
sortseq_tools learn_model -lm LS -i Simulated/CRP/2_bin_sorted-formatted.txt -o sim_2_bin_LS_crp.txt
sortseq_tools learn_model -lm ER -i Simulated/CRP/2_bin_sorted-formatted.txt -o sim_2_bin_ER_crp.txt
sortseq_tools learn_model -lm IM -i Simulated/CRP/2_bin_sorted-formatted.txt -o sim_2_bin_IM_crp_NBR.txt -mt NBR
sortseq_tools learn_model -lm LS -i Simulated/CRP/2_bin_sorted-formatted.txt -o sim_2_bin_LS_crp_NBR.txt -mt NBR
RNAP:
Simulate Data Set:
sortseq_tools simulate_library -w GCTTTACACTTTATGCTTCCGGCTCGTATGTTGT -n 1000000 -o Simulated/RNAP/simulated_library_train.txt
sortseq_tools simulate_sort -m Simulated/RNAP/true_model.txt -n_bins 2 -i simulated_library_train.txt -o Simulated/RNAP/RNAP_2_bin_sorted-formatted.txt
sortseq_tools simulate_sort -m Simulated/RNAP/true_model.txt -n_bins 10 -i simulated_library_train.txt -o Simulated/RNAP/RNAP_10_bin_sorted-formatted.txt
sortseq_tools simulate_library -w GCTTTACACTTTATGCTTCCGGCTCGTATGTTGT -n 1000000 -o Simulated/RNAP/simulated_library_test.txt
sortseq_tools simulate_sort -m Simulated/RNAP/true_model.txt -n_bins 2 -i simulated_library_test.txt -o Simulated/RNAP/RNAP_2_bin_sorted_test-formatted.txt
sortseq_tools simulate_sort -m Simulated/RNAP/true_model.txt -n_bins 10 -i simulated_library_test.txt -o Simulated/RNAP/RNAP_10_bin_sorted_test-formatted.txt
Learn Models:
sortseq_tools learn_model -lm IM -i Simulated/RNAP/RNAP_2_bin_sorted-formatted.txt -o sim_2_bin_IM_RNAP.txt
sortseq_tools learn_model -lm LS -i Simulated/RNAP/RNAP_2_bin_sorted-formatted.txt -o sim_2_bin_LS_RNAP.txt
sortseq_tools learn_model -lm ER -i Simulated/RNAP/RNAP_2_bin_sorted-formatted.txt -o sim_2_bin_ER_RNAP.txt
sortseq_tools learn_model -lm IM -i Simulated/RNAP/RNAP_2_bin_sorted-formatted.txt -o sim_2_bin_IM_RNAP_NBR.txt -mt NBR
sortseq_tools learn_model -lm LS -i Simulated/RNAP/RNAP_2_bin_sorted-formatted.txt -o sim_2_bin_LS_RNAP_NBR.txt -mt NBR
Commands for Figure 6:
DMS matrix models:
sortseq_tools learn_model -lm IM -i dms/dms_1_formatted.txt -o dms_1_MCMC.txt
sortseq_tools learn_model -lm LS -i dms/dms_1_formatted.txt -o dms_1_LS.txt
sortseq_tools learn_model -lm ER -i dms/dms_1_formatted.txt -o dms_1_ER.txt
sortseq_tools learn_model -lm IM -i dms/dms_2_formatted.txt -o dms_2_MCMC.txt
sortseq_tools learn_model -lm LS -i dms/dms_2_formatted.txt -o dms_2_LS.txt
sortseq_tools learn_model -lm ER -i dms/dms_2_formatted.txt -o dms_2_ER.txt
MPRA matrix models:
sortseq_tools learn_model -lm IM -i mpra/CRE_100uM_ds_formatted.txt -o CRE_100uM_ds_MCMC.txt
sortseq_tools learn_model -lm LS -i mpra/CRE_100uM_ds_formatted.txt -o CRE_100uM_ds_LS.txt
sortseq_tools learn_model -lm ER -i mpra/CRE_100uM_ds_formatted.txt -o CRE_100uM_ds_ER.txt
sortseq_tools learn_model -lm IM -i mpra/CRE_100uM_test_formatted.txt -o CRE_100uM_test_MCMC.txt
sortseq_tools learn_model -lm LS -i mpra/CRE_100uM_test_formatted.txt -o CRE_100uM_test_LS.txt
sortseq_tools learn_model -lm ER -i mpra/CRE_100uM_test_formatted.txt -o CRE_100uM_test_ER.txt
We also perform predictive information calculations to produce the figues in the paper. The commands have the form
sortseq_tools predictiveinfo -ds dataset.txt -m model.txt -o outfile.txt
Some executable commands which calculate information values seen in the paper are:
sortseq_tools predictiveinfo -ds sortseq/crp-wt-formatted.txt -m crp-wt_LS_crp.txt -s 3 -e 25