To provide a more detailed explanation of how to define promoter types based on the frequency and distribution of the ‘GRGGC’ motif on both + and – strands within the promoter region, I will outline the steps using Python and the Biopython library.
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Load your genome and annotation file (e.g., in FASTA and GFF3 formats, respectively):
import re from Bio import SeqIO from Bio.Seq import Seq genome_file = "your_genome.fasta" annotation_file = "your_annotation.gff3" genome_records = SeqIO.to_dict(SeqIO.parse(genome_file, "fasta")) -
Extract promoter sequences: Define a function to extract promoter sequences based on the annotation file.
def extract_promoter_sequences(annotation_file, genome_records, promoter_length=1000): promoters = [] with open(annotation_file, "r") as gff: for line in gff: if line.startswith("#"): continue fields = line.strip().split("\t") if fields[2] == "gene": start, end, strand = int(fields[3]), int(fields[4]), fields[6] seq_id = fields[0] if strand == "+": promoter_start = max(start - promoter_length, 1) promoter_end = start - 1 elif strand == "-": promoter_start = end + 1 promoter_end = min(end + promoter_length, len(genome_records[seq_id])) promoter_seq = genome_records[seq_id].seq[promoter_start-1:promoter_end] if strand == "-": promoter_seq = promoter_seq.reverse_complement() promoters.append(promoter_seq) return promoters promoter_sequences = extract_promoter_sequences(annotation_file, genome_records) -
Search for the motif and calculate motif frequency and distribution:
def find_motif_frequency_and_distribution(promoter_sequences, motif_1="GRGGC", motif_2="GCCYR"): motif_1 = motif_1.replace("R", "[AG]").replace("Y", "[CT]") motif_2 = motif_2.replace("R", "[AG]").replace("Y", "[CT]") motif_data = [] for promoter in promoter_sequences: motif_positions = [] for match in re.finditer(motif_1, str(promoter)): motif_positions.append(match.start()) for match in re.finditer(motif_2, str(promoter)): motif_positions.append(match.start()) motif_positions.sort() motif_data.append({"count": len(motif_positions), "positions": motif_positions}) return motif_data motif_data = find_motif_frequency_and_distribution(promoter_sequences) -
Define promoter types: Based on the frequency and distribution of the motif within the promoter regions, you can categorize promoters into different types. For example:
def classify_promoter_types(motif_data, low_count=0, high_count=3): promoter_types = [] for data in motif_data: if data["count"] <= low_count: promoter_types.append("low") elif data["count"] >= high_count: promoter_types.append("high") else: promoter_types.append("medium") return promoter_typespromoter_types = classify_promoter_types(motif_data)
5.1. Perform statistical analyses and visualizations: With the promoter types defined, you can now perform various statistical analyses and create visualizations to explore the relationships between the types and other genomic features or expression levels. Here’s an example of how to create a bar plot of promoter types using the matplotlib library:
#pip install matplotlib #Install matplotlib
import matplotlib.pyplot as plt
def plot_promoter_types(promoter_types):
type_counts = {}
for promoter_type in promoter_types:
if promoter_type not in type_counts:
type_counts[promoter_type] = 1
else:
type_counts[promoter_type] += 1
types = list(type_counts.keys())
counts = list(type_counts.values())
plt.bar(types, counts)
plt.xlabel("Promoter Types")
plt.ylabel("Frequency")
plt.title("Frequency of Promoter Types Based on 'GRGGC' Motif")
plt.show()
plot_promoter_types(promoter_types)
#This code will produce a bar plot that shows the frequency of the different promoter types based on the 'GRGGC' motif in the promoter regions. You can further analyze the relationship between the promoter types and gene expression levels or other genomic features, depending on your research question.5.2. In order to define promoter types based on the distance of the ‘GRGGC’ motif to the transcription start site (TSS), we can modify the previous code to include the distance information.
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Define a function to find the distance of the motif to the TSS for each promoter:
def find_motif_distances_to_tss(promoters, motif): distances = [] for promoter in promoters: for strand, sequence in promoter.items(): motif_positions = [i for i in range(len(sequence)) if sequence.startswith(motif, i)] if strand == '+': tss_distance = [abs(pos - len(sequence)) for pos in motif_positions] else: tss_distance = [abs(pos) for pos in motif_positions] distances.extend(tss_distance) return distances motif_distances_to_tss = find_motif_distances_to_tss(promoters, 'GRGGC') -
Define promoter types based on the distance to the TSS: We can define the promoter types by categorizing the distances into different groups.
Very close: < 50 bp; Close: 50 - 200 bp; Moderate: 200 - 500 bp; Far: > 500 bp
def categorize_distances(distances): promoter_types = [] for distance in distances: if distance < 50: promoter_types.append("Very close") elif 50 <= distance < 200: promoter_types.append("Close") elif 200 <= distance < 500: promoter_types.append("Moderate") else: promoter_types.append("Far") return promoter_types promoter_types_distance = categorize_distances(motif_distances_to_tss) -
Visualize the promoter types based on distance: Use the plot_promoter_types function we defined earlier to create a bar plot of promoter types based on the distance to the TSS:
plot_promoter_types(promoter_types_distance)This plot will show the frequency of promoter types based on the distance of the ‘GRGGC’ motif to the TSS. You can further analyze the relationship between promoter types and gene expression levels or other genomic features, depending on your research question.