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f8f245b 1a53a69 f334a00 f8f245b a9090c5 f8f245b 7e893cc f8f245b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 | import numpy as np
import pandas as pd
import plotly.express as px
import streamlit as st
def saturated_mutations(dna_seq):
dna_seq = dna_seq.upper().replace("U", "T")
dna_seq = "".join(c for c in dna_seq if c in "ATCG")
n = len(dna_seq)
bases = np.array(list("ATCG"))
seq_array = np.array(list(dna_seq))
replace_matrix = np.array([bases[bases != base] for base in seq_array])
result = np.tile(seq_array, (3 * n, 1))
rows = np.repeat(np.arange(n), 3)
result[np.arange(3 * n), rows] = replace_matrix.flatten()
result = np.vstack([seq_array, result])
return ["".join(row) for row in result]
if __name__ == "__main__":
model = st.session_state["model"]
input_format = st.session_state["input_format"]
predict = st.session_state["predict"]
mrna_env = st.session_state["mrna_env"]
UTR3_GLUC = st.session_state["UTR3_GLUC"]
CDS_GLUC = st.session_state["CDS_GLUC"]
st.set_page_config(layout="wide")
st.title("UTR-CODE")
with st.form("single_input_form"):
utr5_input = st.text_area(
"5' UTR:",
key="utr5_input",
max_chars=1000,
help="Only 'A', 'C', 'G', 'T' and 'U' is allowed, other characters will be ignored",
)
with st.expander("Optional Input"):
optional_input_col1, optional_input_col2 = st.columns(2)
with optional_input_col1:
cds_input = st.text_area(
"CDS",
value=CDS_GLUC,
key="cds_input",
max_chars=4500,
help="GLUC's CDS is used by default.",
)
with optional_input_col2:
utr3_input = st.text_area(
"3' UTR:",
value=UTR3_GLUC,
key="utr3_input",
max_chars=4000,
help="GLUC's UTR3 is used by default.",
)
mutation_rounds = st.slider(
"ISM Rounds",
min_value=1,
max_value=10,
value=5,
step=1,
label_visibility="visible",
)
submitted = st.form_submit_button("Submit", use_container_width=True)
if submitted:
if not "".join([c for c in utr5_input.upper() if c in "ATCGU"]):
st.warning("Please input 5' UTR")
elif any(c not in "ATCGU\n\t, " for c in f"{utr5_input.upper()}{cds_input.upper()}{utr3_input.upper()}"):
st.warning("Only 'A', 'C', 'G', 'T' and 'U' is allowed.")
else:
utr5_optimize_use = "".join(
[c for c in utr5_input.upper().replace("U", "T") if c in "ATCG"]
)
results = dict()
utr, cds = input_format(utr5_optimize_use, cds_input, utr3_input)
results[0] = {
"rpf": (np.expm1(predict(model, utr, cds, mrna_env)[0]) / 5) / 108.7144 ,
"seq": utr5_optimize_use,
"base_change": "Origin",
}
cds_seq = [
cds_input,
] * (3 * len(utr5_input) + 1)
utr3_seq = [
utr3_input,
] * (3 * len(utr5_input) + 1)
progress_bar = st.empty()
progress_bar = st.progress(0, f"Progress: 0%")
progress_text = st.empty()
chart_placeholder = st.empty()
for i in range(1, mutation_rounds + 1):
used_seq = saturated_mutations(utr5_optimize_use)
utr, cds = input_format(used_seq, cds_seq, utr3_seq)
result = predict(model, utr, cds, mrna_env)
best_idx = np.argmax(result)
max_val = result[best_idx]
base_change = next(
(
f"{i+1}:{c1}->{c2}"
for i, (c1, c2) in enumerate(
zip(utr5_optimize_use, used_seq[best_idx])
)
if c1 != c2
),
"No change",
)
utr5_optimize_use = used_seq[best_idx]
results[i] = {
"rpf": (np.expm1(max_val) / 5) / 108.7144,
"seq": utr5_optimize_use,
"base_change": base_change,
}
progress_bar.progress(
i / mutation_rounds,
f"Progress: {i / mutation_rounds * 100:.0f}%",
)
df = pd.DataFrame(
[
{
"Round": k,
"Predicted Value": float(v["rpf"]),
"UTR5 Sequence": v["seq"],
"Base Change": v["base_change"],
}
for k, v in results.items()
]
)
fig = px.line(
df,
x="Round",
y="Predicted Value",
text="Base Change",
markers=True,
)
fig.update_layout(
template="plotly_white",
title="UTR5 Optimization Progress",
title_font_family="Arial, sans-serif",
title_font_color="black",
xaxis_title="Mutation Round",
yaxis_title="Predicted Value (RPF)",
xaxis_title_font=dict(color="black"),
yaxis_title_font=dict(color="black"),
font_family="Arial, sans-serif",
font_color="black",
margin=dict(l=50, r=50, t=80, b=50),
)
fig.update_xaxes(
showgrid=False,
showline=True,
linecolor="black",
tickcolor="black",
ticks="outside",
tickfont_color="black",
)
fig.update_yaxes(
showgrid=False,
showline=True,
linecolor="black",
tickcolor="black",
ticks="outside",
tickfont_color="black",
tickformat=".2f",
)
fig.update_traces(
textposition="top center",
marker=dict(color="#cf3326", line=dict(color="#cf3326")),
line=dict(color="#cf3326"),
textfont_color="black",
)
fig.update_xaxes(dtick=1)
chart_placeholder.plotly_chart(fig, use_container_width=True)
progress_bar.empty()
st.download_button(
"Download Result",
data=df.to_csv(index=False),
file_name="results.csv",
mime="text/csv",
use_container_width=True,
icon="📥",
)
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