app.py

import gradio as gr
import numpy as np
import random
import pandas as pd
import matplotlib.pyplot as plt
from io import BytesIO
import base64

# 模拟数据 - 实际使用时需要替换为真实数据
species_data = {
    "human": {"codon_table": {}, "trna": {}, "codon_usage": {}},
    "mouse": {"codon_table": {}, "trna": {}, "codon_usage": {}},
    "virus": {"codon_table": {}, "trna": {}, "codon_usage": {}},
    "Escherichia coli": {"codon_table": {}, "trna": {}, "codon_usage": {}},
    "酿酒酵母": {"codon_table": {}, "trna": {}, "codon_usage": {}},
    "Pichia": {"codon_table": {}, "trna": {}, "codon_usage": {}},
}

# 模拟函数 - 实际需要生物信息学算法实现
def find_longest_cds(seq):
    # 简化的ORF查找 - 实际应使用生物信息学库
    start = seq.find("ATG")
    stops = [seq.find("TAA", start), seq.find("TAG", start), seq.find("TGA", start)]
    stops = [s for s in stops if s > start]
    end = min(stops) + 3 if stops else len(seq)
    return start, end

def calculate_cds_variants(protein_seq):
    # 简化的计算 - 实际应根据密码子表计算
    aa_count = len(protein_seq)
    return 2 ** aa_count  # 示例值

def optimize_cds(protein_seq, species, method):
    # 生成20个优化序列示例
    results = []
    for i in range(20):
        # 实际应根据优化方法生成序列
        seq = ''.join(random.choices("ACGT", k=len(protein_seq)*3))
        gc = random.uniform(0.3, 0.7)
        trna = random.uniform(0.5, 1.0)
        usage = random.uniform(0.6, 0.95)
        mfe = random.uniform(-30, -10)
        score = gc*0.25 + trna*0.25 + usage*0.25 + (-mfe/40)*0.25
        
        results.append({
            "Sequence": seq,
            "GC%": f"{gc*100:.1f}%",
            "tRNA": f"{trna:.3f}",
            "Usage": f"{usage:.3f}",
            "MFE": f"{mfe:.1f}",
            "Score": f"{score:.3f}"
        })
    return pd.DataFrame(results)

def design_mrna(utr5_candidates, utr3_candidates, cds_seq):
    # 生成20个设计结果示例
    designs = []
    for i in range(20):
        utr5 = random.choice(utr5_candidates)
        utr3 = random.choice(utr3_candidates)
        full_seq = utr5 + cds_seq + utr3
        mfe = random.uniform(-50, -20)
        designs.append({
            "Design": f"Design_{i+1}",
            "5'UTR": utr5[:10] + "..." if len(utr5) > 13 else utr5,
            "3'UTR": utr3[:10] + "..." if len(utr3) > 13 else utr3,
            "MFE": f"{mfe:.1f}",
            "Sequence": full_seq
        })
    return pd.DataFrame(designs)

# 标注可视化函数
def visualize_annotation(seq):
    start, end = find_longest_cds(seq)
    html = f"""
    <div style="font-family: monospace; font-size: 14px; line-height: 1.8;">
        <div style="background-color: #ffcccc; display: inline-block; padding: 2px;">
            5'UTR: {seq[:start] if start > 0 else 'N/A'}
        </div>
        <div style="background-color: #ccffcc; display: inline-block; padding: 2px;">
            CDS: {seq[start:end] if start >=0 else 'N/A'}
        </div>
        <div style="background-color: #ccccff; display: inline-block; padding: 2px;">
            3'UTR: {seq[end:] if end < len(seq) else 'N/A'}
        </div>
    </div>
    <p>Annotation Legend:</p>
    <div style="display: flex; gap: 10px;">
        <div style="background-color: #ffcccc; padding: 5px;">5'UTR</div>
        <div style="background-color: #ccffcc; padding: 5px;">CDS</div>
        <div style="background-color: #ccccff; padding: 5px;">3'UTR</div>
    </div>
    """
    return html

# 创建Gradio界面
with gr.Blocks(title="Vaccine Designer", theme=gr.themes.Soft()) as app:
    gr.Markdown("# 🧬 Vaccine Design Platform - Academic Collaboration")
    
    with gr.Tab("mRNA Annotation"):
        gr.Markdown("## mRNA Sequence Annotation")
        mrna_input = gr.Textbox(label="mRNA Sequence", placeholder="Enter mRNA sequence here...")
        annotate_btn = gr.Button("Annotate Regions")
        annotation_output = gr.HTML(label="Sequence Annotation")
        annotate_btn.click(visualize_annotation, inputs=mrna_input, outputs=annotation_output)
    
    with gr.Tab("CDS Variants"):
        gr.Markdown("## Calculate Potential CDS Variants")
        protein_input = gr.Textbox(label="Protein Sequence", placeholder="Enter protein sequence here...")
        calc_btn = gr.Button("Calculate Variants")
        variants_output = gr.Number(label="Potential CDS Variants")
        calc_btn.click(calculate_cds_variants, inputs=protein_input, outputs=variants_output)
    
    with gr.Tab("CDS Optimization"):
        gr.Markdown("## Optimize CDS Sequence")
        with gr.Row():
            protein_seq = gr.Textbox(label="Protein Sequence")
            species = gr.Dropdown(list(species_data.keys()), label="Species", value="human")
        
        method = gr.Radio(["Max GC Content", "tRNA Abundance", "Codon Usage", "MFE Optimization"], 
                          label="Optimization Method", value="Max GC Content")
        
        optimize_btn = gr.Button("Generate Optimized Sequences")
        results_table = gr.Dataframe(label="Top 20 Optimized Sequences", headers=["Sequence", "GC%", "tRNA", "Usage", "MFE", "Score"])
        optimize_btn.click(optimize_cds, inputs=[protein_seq, species, method], outputs=results_table)
        
        # 评分可视化
        plot = gr.Plot(label="Optimization Scores")
        def update_plot(df):
            if df is None or len(df) == 0:
                return None
            fig, ax = plt.subplots()
            scores = [float(x) for x in df["Score"]]
            ax.bar(range(len(scores)), scores, color='skyblue')
            ax.set_xlabel("Sequence Rank")
            ax.set_ylabel("Composite Score")
            ax.set_title("Optimization Scores of Top Sequences")
            return fig
        results_table.change(update_plot, inputs=results_table, outputs=plot)
    
    with gr.Tab("Full mRNA Design"):
        gr.Markdown("## Design Full mRNA Sequence")
        with gr.Row():
            utr5_upload = gr.File(label="Upload 5'UTR Candidates (txt)")
            utr3_upload = gr.File(label="Upload 3'UTR Candidates (txt)")
        cds_input = gr.Textbox(label="CDS Sequence")
        design_btn = gr.Button("Design mRNA Sequences")
        design_results = gr.Dataframe(label="Top 20 Designs", headers=["Design", "5'UTR", "3'UTR", "MFE", "Sequence"])
        design_btn.click(design_mrna, inputs=[utr5_upload, utr3_upload, cds_input], outputs=design_results)
    
    with gr.Tab("Resources & Links"):
        gr.Markdown("## Helpful Resources")
        gr.Markdown("""
        - [mRNA Designer Platform](https://www.biosino.org/mRNAdesigner/main)
        - [Nucleic Acid Database](https://ngdc.cncb.ac.cn/ncov/)
        - [NCBI GenBank](https://www.ncbi.nlm.nih.gov/genbank/)
        - [ViralZone](https://viralzone.expasy.org/)
        - [Codon Usage Database](https://www.kazusa.or.jp/codon/)
        """)
        
        gr.Markdown("## Download All Results")
        download_btn = gr.Button("Download Results Package")
        download_btn.click(lambda: "results.zip", outputs=gr.File(label="Download Results"))
    
    gr.Markdown("---")
    gr.HTML("""
    <div style="text-align: center; padding: 20px; background-color: #f0f0f0; border-radius: 10px;">
        <p>Academic Collaboration Platform for Vaccine Design | Contact: [email protected]</p>
    </div>
    """)

# 运行应用
if __name__ == "__main__":
    app.launch(server_name="0.0.0.0", server_port=7860)