Genome Research: "Probing the effect of promoters on noise in gene expression using thousands of designed sequences".
Nature Biotechology: "Inferring gene regulatory logic from high-throughput measurements of thousands of systematically designed promoters"
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GSE55346_TableS1_PromotersExpressionValues.txt: Promoter expression mean and noise from paper "Probing the effect of promoters on noise in gene expression using thousands of designed sequences". -
GSM929011_promoter_sequence_and_experiment_replicate_1.txt: Promoter sequence and expression mean from paper "Inferring gene regulatory logic from high-throughput measurements of thousands of systematically designed promoters" -
GSM929012_promoter_sequence_and_experiment_replicate_2.txt: Another replicate experiment from "Inferring gene regulatory logic from high-throughput measurements of thousands of systematically designed promoters" -
data_deal.py: Get the final sequence and expression data.
all_data.h5: Include three parts as below:- sequence: 6500 promoter sequences (150bps)
- GR_exp: The expression mean and noise in the Genome Research paper
- NBT_exp: The expression mean in the Nature biotechology paper
GR_exp.csv: Sequence and expression mean and noise from "Probing the effect of promoters on noise in gene expression using thousands of designed sequences".NBT_exp.csv: Sequence and expression mean from "Inferring gene regulatory logic from high-throughput measurements of thousands of systematically designed promoters"
- Load the data by below python script
import pandas as pd
store = pd.HDFStore('all_data.h5')
seq = store['sequence']
NBT_exp = store['NBT_exp']
GR_exp = store['GR_exp']