Examples

Simulations

# Simulate library example

import mpathic as mpa

# reate a library of random mutants from an initial wildtype sequence and mutation rate
sim_library = mpa.SimulateLibrary(wtseq="TAATGTGAGTTAGCTCACTCAT", mutrate=0.24)
sim_library.output_df.head()

# Load dataset and model dataframes
dataset_df = mpa.io.load_dataset('sort_seq_data.txt')
model_df = mpa.io.load_model('crp_model.txt')

# Simulate a Sort-Seq experiment example
sim_sort = mpa.SimulateSort(df=dataset_df,mp=model_df)
sim_sort.output_df.head()

Profiles

import mpathic as mpa


# Load dataset and model dataframes
dataset_df = mpa.io.load_dataset('sort_seq_data.txt')
model_df = mpa.io.load_model('crp_model.txt')

# mut profile example
profile_mut = mpa.ProfileMut(dataset_df = dataset_df)
profile_mut.mut_df.head()

# freq profile example
profile_freq = mpa.ProfileFreq(dataset_df = dataset_df)
profile_freq.freq_df.head()

# info profile example
profile_info = mpa.ProfileInfo(dataset_df = dataset_df)
profile_info.info_df.head()

Models

import mpathic as mpa

# Load dataset and model dataframes
dataset_df = mpa.io.load_dataset('sort_seq_data.txt')
model_df = mpa.io.load_model('crp_model.txt')

# learn models example
learned_model = mpa.LearnModel(df=dataset_df)
learned_model.output_df.head()

# evaluate models example
eval_model = mpa.EvaluateModel(dataset_df = dataset_df, model_df = model_df)
eval_model.out_df.head()

# scan models example
# get contigs, provided with mpathic
fastafile = "./mpathic/examples/genome_ecoli_1000lines.fa"
contig = mpa.io.load_contigs_from_fasta(fastafile, model_df)

scanned_model = mpa.ScanModel(model_df = model_df, contigs_list = contigs_list)
scanned_model.sitelist_df.head()

# predictive info example
predictive_info = mpa.PredictiveInfo(data_df = dataset_df, model_df = model_df,start=52)