Home

Awesome

PRIME: Predicted Regulatory Impact of a Mutation in an Enhancer

Requirements:

  1. Python 2.7
  2. NumPy (>= 1.6.1)
  3. SciPy (>= 0.9)
  4. scikit-learn (>=14.0)
  5. bx.python(>=0.7)
  6. Pandas
  7. Matplotlib
  8. Python 3 with pandas for ./src/make_feature_table.py

Installing from PyPI:

pip install -U numpy scipy scikit-learn bx-python pandas matplotlib

pip3 install pandas

Then you can clone the code:

# Clone primescore repo.
git clone https://github.com/aertslab/primescore

# Enter cloned dir.
cd primescore

# Clone Cluster-Buster repo inside primscore dir.
git clone https://github.com/weng-lab/cluster-buster

cd cluster-buster

# Compile Cluster-Buster.
make

# Go back to primescore cloned dir.
cd ..

Description of the folders:

  1. ./scr: folder with scripts.
  2. ./cluster-buster: folder with Cluster-buster tool.
  3. ./pwms_folder: folder with PWMs (Position Weight Matrix).
  4. ./example: folder with example data.
  5. ./results: folder with results.

Step 1. Feature-vector representation of the positives and negatives

Make table with feature values of the data.

At this step you represent your fasta sequences as feature-vectors. For each sequence in the fasta file will be assigned set of scores and number of the features correspond to the number of used PWMs (PATH_TO_FEATURE_ORDER).

# for positives
# file with names of the PWMs (correspond to the name of the PWM in the folder ./pwms_folder)
PATH_TO_FEATURE_ORDER=./example/feature_order.txt 
#.fasta file
PATH_TO_FASTA_FILE=./example/pos.fa
# path to save results.
PATH_TO_SAVE=./results/pos.FT.tsv
# python script
PATH_TO_SCRIPT=./src/make_feature_table.py
# folder where files with PWMs are
PATH_TO_SINGLETONS=./pwms
# path to compiled Cluster-buster tool
PATH_TO_CBUST=./cluster-buster/cbust
# number of threads to use while scoring PWMs.
NBR_THREADS=8

# command to run
python3 ${PATH_TO_SCRIPT} \
    -f ${PATH_TO_FASTA_FILE} \
    -M ${PATH_TO_SINGLETONS}/ \
    -m ${PATH_TO_FEATURE_ORDER} \
    -o ${PATH_TO_SAVE} \
    -O 'tsv' \
    -c ${PATH_TO_CBUST} \
    -t ${NBR_THREADS}


# for negatives
# file with names of the PWMs (correspond to the name of the PWM in the folder ./pwms_folder)
PATH_TO_FEATURE_ORDER=./example/feature_order.txt
#.fasta file
PATH_TO_FASTA_FILE=./example/neg.fa
# path to save results.
PATH_TO_SAVE=./results/neg.FT.tsv
# python script
PATH_TO_SCRIPT=./src/make_feature_table.py
# folder where files with PWMs are
PATH_TO_SINGLETONS=./pwms
# path to compiled Cluster-buster tool
PATH_TO_CBUST=./cluster-buster/cbust
# number of threads to use while scoring PWMs.
NBR_THREADS=8

# command to run
python3 ${PATH_TO_SCRIPT} \
    -f ${PATH_TO_FASTA_FILE} \
    -M ${PATH_TO_SINGLETONS}/ \
    -m ${PATH_TO_FEATURE_ORDER} \
    -o ${PATH_TO_SAVE} \
    -O 'tsv' \
    -c ${PATH_TO_CBUST} \
    -t ${NBR_THREADS}

Step 2. Train classifier

At this step will be generated .pcl (pickle) file with trained classifier. Models generated with RF classifiers are always different due to internal training procedure and in order to calculate PRIME score the same classifier shoulde be applied to reference and mutated sequences.

# name of the file where model will be saved
PATH_TO_SAVE_MODEL=./results/model.pcl
# python script
PATH_TO_SCRIPT=./src/train_model.py
# file for table with feature values for positives
PATH_TO_POSITIVES=./results/pos.FT.tsv
# file for table with feature values for negatives
PATH_TO_NEGATIVES=./results/neg.FT.tsv

# command to run
python2 ${PATH_TO_SCRIPT} \
    -p ${PATH_TO_POSITIVES} \
    -n ${PATH_TO_NEGATIVES} \
    -s ${PATH_TO_SAVE_MODEL}

Make cross-validation

You can estimate quality of the classifier using cross-validation.

# file to save average across folds are under precision-recall and roc curve values
PATH_TO_SAVE_SCORES_PER_SAMPLE=./results/aupr_auroc.txt
# file to save RF score for each region in the test set
PATH_TO_SAVE_AVERAGE_SCORES=./results/rf_score_per_region.txt
# python script
PATH_TO_SCRIPT=./src/make_cross_validation.py
# # file for table with feature values for positives
PATH_TO_POSITIVES=./results/pos.FT.tsv
# file for table with feature values for negatives
PATH_TO_NEGATIVES=./results/neg.FT.tsv

# command to run
python2 ${PATH_TO_SCRIPT} \
    -p ${PATH_TO_POSITIVES} \
    -n ${PATH_TO_NEGATIVES} \
    -c ${PATH_TO_SAVE_AVERAGE_SCORES} \
    -s ${PATH_TO_SAVE_SCORES_PER_SAMPLE}

Step 3: Score regions with classifier

To score new sequences with trained classifier use classify_sequences.py script

PATH_TO_TRAINED_CLASSIFIER=./results/model.pcl
# Path to feature-table file
PATH_TO_DATA=./results/pos.FT.tsv
# path to save Rf prediction results
PATH_TO_SAVE=./results/pos.scored_with_model.txt
# path to script
PATH_TO_SCRIPT=./src/classify_sequences.py

# command to run
python2 ${PATH_TO_SCRIPT} \
    -d ${PATH_TO_DATA} \
    -m ${PATH_TO_TRAINED_CLASSIFIER} \
    -s ${PATH_TO_SAVE}

Step 4: Calculate PRIME score

To calculate PRIME scores the following data should be provided:

  1. .2bit file (here we assume it is in the folder ./example/).
  2. File with SNVs.

For each mutation (SNV, insertion or deletion) script checks 10 regions (windows) around mutation.

Output:

chr start end ref mut	ID	ref_score	mut_score

Difference between classification score in mutant versus reference sequence:

PRIME=mut_score - ref_score

SNVs

Structure of the file:

chr	start	end	ref_nucleotide	mut_nucleotide	ID

# python script to calculate PRIME score for each SNV
PATH_TO_SCRIPT=./src/calculate_PRIME_snvs.py
# file with named of the PWMs (correspond to the name of the PWM in the folder ./pwms_folder)
# use the same file (with the same PWMs and order of the PWMs inside the file) as for making of feature-tables
PATH_TO_FEATURE_ORDER=./example/feature_order.txt
# path to .2bit file 
PATH_TO_2BIT_FILE=./example/hg19.2bit
# path to file with SNVs
PATH_TO_SNVs=./example/snvs.txt
# path to file where you want to save results
PATH_TO_SAVE=./results/snvs_prime_test_2.txt
# path to trained classifier file
PATH_TO_MODEL=./results/model.pcl
# path to folder with PWMs
PATH_TO_SINGLETONS=./pwms
# path to compiled Cluster-buster tool
PATH_TO_CBUST=./cluster-buster/cbust

# command to run
python2 ${PATH_TO_SCRIPT} \
    -f ${PATH_TO_2BIT_FILE} \
    -o ${PATH_TO_FEATURE_ORDER} \
    -b ${PATH_TO_SNVs} \
    -s ${PATH_TO_SAVE} \
    -m ${PATH_TO_MODEL} \
    -w 800 \
    -p ${PATH_TO_SINGLETONS}/ \
    -c ${PATH_TO_CBUST}

Insertions

Structure of the file for insertions:

chr	start	ref_nucleotide ref_nucleotide_insertion	ID

chr6	161094665	C	CA	LPA
161094665: zero-based coordinate of the nucleotids before the insertion

# python script to calculate PRIME score for each insertion
PATH_TO_SCRIPT=./src/calculate_PRIME_insertions.py
# file with named of the PWMs (correspond to the name of the PWM in the folder ./pwms_folder)
# use the same file (with the same PWMs and order of the PWMs inside the file) as for making of feature-tables
PATH_TO_FEATURE_ORDER=./example/feature_order.txt
# path to .2bit file 
PATH_TO_2BIT_FILE=./example/hg19.2bit
# path to file with insertions
PATH_TO_INSERTIONS=./example/insertions.txt
# path to file where you want to save results
PATH_TO_SAVE=./results/insertions_prime.txt
# path to trained classifier file
PATH_TO_MODEL=./results/model.pcl
# path to folder with PWMs
PATH_TO_SINGLETONS=./pwms
# path to compiled Cluster-buster tool
PATH_TO_CBUST=./cluster-buster/cbust

# command to run
python2 ${PATH_TO_SCRIPT} \
    -f ${PATH_TO_2BIT_FILE} \
    -o ${PATH_TO_FEATURE_ORDER} \
    -b ${PATH_TO_INSERTIONS} \
    -s ${PATH_TO_SAVE} \
    -m ${PATH_TO_MODEL} \
    -w 800 \
    -p ${PATH_TO_SINGLETONS}/ \
    -c ${PATH_TO_CBUST}

Deletions

Structure of the file for deletions:

chr	start	ref_nucleotide_deleted_sequence	ref_nucleotide	ID

chr10	100164477	GA	G ID
100164477: zero-based coordinate of the nucleotids before the deletion

# python script to calculate PRIME score for each deletions
PATH_TO_SCRIPT=./src/calculate_PRIME_deletions.py
# file with named of the PWMs (correspond to the name of the PWM in the folder ./pwms_folder)
# use the same file (with the same PWMs and order of the PWMs inside the file) as for making of feature-tables
PATH_TO_FEATURE_ORDER=./example/feature_order.txt
# path to .2bit file 
PATH_TO_2BIT_FILE=./example/hg19.2bit
# path to file with insertions
PATH_TO_INSERTIONS=./example/deletions.txt
# path to file where you want to save results
PATH_TO_SAVE=./results/deletions_prime.txt
# path to trained classifier file
PATH_TO_MODEL=./results/model.pcl
# path to folder with PWMs
PATH_TO_SINGLETONS=./pwms
# path to compiled Cluster-buster tool
PATH_TO_CBUST=./cluster-buster/cbust

# command to run
python2 ${PATH_TO_SCRIPT} \
    -f ${PATH_TO_2BIT_FILE} \
    -o ${PATH_TO_FEATURE_ORDER} \
    -b ${PATH_TO_INSERTIONS} \
    -s ${PATH_TO_SAVE} \
    -m ${PATH_TO_MODEL} \
    -w 800 \
    -p ${PATH_TO_SINGLETONS}/ \
    -c ${PATH_TO_CBUST}

Additional scripts:

Select features (PWMs) for model

If you are using i-cis-Target tool for motif enrichment anaysis we provide additional script to select PWMs (features) for your RF model.

To select motifs the following data are necessary:

  1. File with motif enrichment results (from i-cis-Target tool): statistics.tbl
  2. File with list of all singletones (PWMs) you have (all .cb files in the ./pwms_folder). This files can be found in the folder with examples.
# python script making selection of PWMs based on i-cis-Target enrichment analysis
PATH_TO_SCRIPT=./src/select_tf_model_features.py
# i-cis-Target enrichment analysis results
PATH_TO_ENRICHMENT_RESULTS=./example/ctx_rcc_enrichment_results.txt
# path to save results: selected PWMs for TF specific RF model
PATH_TO_SAVE=./example/my_selected_pwms.txt
# file with annotation of the motifs (PWM->TF)
PWM2TF_ANNOTATION=./example/pwm2tf.tbl
# text file with list of all avialable PWMs for further scoring
PATH_TO_SINGLETONSLIST=./example/pwm_list.txt
# path to save selected features
PATH_TO_SAVE=./example/selected_model_features.txt

# command to run
python2 ${PATH_TO_SCRIPT} \
    -s ${PATH_TO_ENRICHMENT_RESULTS} \
    -t ATF2 \
    -a ${PWM2TF_ANNOTATION} \
    -m ${PATH_TO_SINGLETONSLIST} \
  > ${PATH_TO_SAVE}

Scripts to make .fasta files with mutations

# Path to script
PATH_TO_SCRIPT=./src/mut2fasta.py
# Path to save fasta with mutations
PATH_TO_SAVE_REF_FASTA=./results/ref.fa
# Path to save refeence fasta sequence
PATH_TO_SAVE_MUT_FASTA=./results/mut.fa

PATH_TO_2BIT_FILE=./example/hg19.2bit
# file with mutations (can me mixed insertions, deletions and SNVs in the same file)
PATH_TO_MUTATIONS=./example/mutations.txt

# command to run
python2 ${PATH_TO_SCRIPT} \
    -r ${PATH_TO_SAVE_REF_FASTA} \
    -m ${PATH_TO_SAVE_MUT_FASTA} \
    -f ${PATH_TO_2BIT_FILE} \
    -v ${PATH_TO_MUTATIONS}

This fasta files could be used to make feature-tables (apply make_feature_table.py ) and then be scored with classify_sequences.py. To calculate PRIME score you can substract mutant and reference classifier scores. This approach is faster comparing to usage of calculate_PRIME<>.py scripts because only one region is scored (mutation will be in the center of the window). But when using calculate_PRIME<>.py, 10 windows around each mutation are checked.

Depends on the following tools

References

  1. Imrichová,H., Hulselmans,G., Kalender Atak,Z., Potier,D. and Aerts,S. (2015) i-cisTarget 2015 update: generalized cis-regulatory enrichment analysis in human, mouse and fly. Nucleic Acids Res. doi: 10.1093/nar/gkv395

  2. Herrmann,C., Van de Sande,B., Potier,D. and Aerts,S. (2012) i-cisTarget: an integrative genomics method for the prediction of regulatory features and cis-regulatory modules. Nucleic Acids Res. doi: 10.1093/nar/gks543

  3. Frith MC1, Li MC, Weng Z. Cluster-Buster: Finding dense clusters of motifs in DNA sequences. Nucleic Acids Res. 2003 Jul 1;31(13):3666-8.