查看“RNA-Seq”的源代码

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            <strong>RNA-Seq</strong>（RNA Sequencing，转录组测序）是一种利用 <strong>[[NGS]] (二代测序)</strong> 或 <strong>[[TGS]] (三代测序)</strong> 技术来检测样本中 <strong>RNA</strong> 的存在与数量的方法。与其前身 <strong>[[Microarray]] (基因芯片)</strong> 相比，RNA-Seq 不依赖预先设计的探针，因此能够“无偏倚”地检测整个 <strong>[[Transcriptome]] (转录组)</strong>。它不仅能进行高精度的基因表达定量（Quantitative Analysis），还能发现新的转录本、识别 <strong>[[Alternative Splicing]] (可变剪接)</strong> 事件以及检测 <strong>[[Fusion Gene]] (融合基因)</strong>。如今，RNA-Seq 已成为连接 <strong>[[Genomics]]</strong> 与 <strong>[[Proteomics]]</strong> 的核心技术，是现代生物医学研究中标配的“显微镜”。
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            <div style="font-size: 1.2em; font-weight: bold; letter-spacing: 1.2px;">RNA-Seq</div>
            <div style="font-size: 0.7em; opacity: 0.85; margin-top: 4px; white-space: nowrap;">Whole Transcriptome Sequencing (点击展开)</div>
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                <div style="font-size: 0.8em; color: #64748b; margin-top: 12px; font-weight: 600;">转录组学金标准</div>
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                    <th colspan="2" style="padding: 8px 12px; background-color: #e0f2fe; color: #1e40af; text-align: left; font-size: 0.9em; border-top: 1px solid #bae6fd;">技术参数</th>
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                    <th style="text-align: left; padding: 6px 12px; background-color: #f8fafc; color: #475569; border-bottom: 1px solid #e2e8f0; width: 40%;">核心方法</th>
                    <td style="padding: 6px 12px; border-bottom: 1px solid #e2e8f0; color: #0f172a;">[[NGS]] (Illumina) / Long-read</td>
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                    <th style="text-align: left; padding: 6px 12px; background-color: #f8fafc; color: #475569; border-bottom: 1px solid #e2e8f0;">检测对象</th>
                    <td style="padding: 6px 12px; border-bottom: 1px solid #e2e8f0; color: #0f172a;">mRNA, lncRNA, miRNA</td>
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                    <th style="text-align: left; padding: 6px 12px; background-color: #f8fafc; color: #475569; border-bottom: 1px solid #e2e8f0;">中间产物</th>
                    <td style="padding: 6px 12px; border-bottom: 1px solid #e2e8f0; color: #1e40af;">[[cDNA]] Library</td>
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                    <th style="text-align: left; padding: 6px 12px; background-color: #f8fafc; color: #475569; border-bottom: 1px solid #e2e8f0;">前身技术</th>
                    <td style="padding: 6px 12px; border-bottom: 1px solid #e2e8f0; color: #e11d48;">[[Microarray]] (芯片)</td>
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                    <th style="text-align: left; padding: 6px 12px; background-color: #f8fafc; color: #475569; border-bottom: 1px solid #e2e8f0;">分辨率</th>
                    <td style="padding: 6px 12px; border-bottom: 1px solid #e2e8f0; color: #16a34a;">Single-base (单碱基)</td>
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                    <th colspan="2" style="padding: 8px 12px; background-color: #e0f2fe; color: #1e40af; text-align: left; font-size: 0.9em; border-top: 1px solid #bae6fd;">数据分析</th>
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                    <th style="text-align: left; padding: 6px 12px; background-color: #f8fafc; color: #475569; border-bottom: 1px solid #e2e8f0;">核心指标</th>
                    <td style="padding: 6px 12px; border-bottom: 1px solid #e2e8f0; color: #1e40af;">[[TPM]] / [[FPKM]]</td>
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                    <th style="text-align: left; padding: 6px 12px; background-color: #f8fafc; color: #475569; border-bottom: 1px solid #e2e8f0;">核心算法</th>
                    <td style="padding: 6px 12px; border-bottom: 1px solid #e2e8f0; color: #0f172a;">[[DESeq2]] / [[edgeR]]</td>
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                    <th style="text-align: left; padding: 6px 12px; background-color: #f8fafc; color: #475569; border-bottom: 1px solid #e2e8f0;">原始数据</th>
                    <td style="padding: 6px 12px; border-bottom: 1px solid #e2e8f0; color: #0f172a;">FASTQ</td>
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                    <th style="text-align: left; padding: 6px 12px; background-color: #f8fafc; color: #475569;">比对格式</th>
                    <td style="padding: 6px 12px; color: #0f172a;">BAM / SAM</td>
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    <h2 style="background: #f1f5f9; color: #0f172a; padding: 10px 18px; border-radius: 0 6px 6px 0; font-size: 1.25em; margin-top: 40px; border-left: 6px solid #0f172a; font-weight: bold;">实验流程：从 RNA 到数据</h2>
    
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        RNA-Seq 的核心挑战在于 RNA 不稳定且大部分是无用的 rRNA（核糖体RNA）。标准建库流程包含以下关键步骤：
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        <h3 style="margin-top: 0; color: #1e40af; font-size: 1.1em;">1. RNA 提取与富集 (Enrichment)</h3>
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            细胞中 >80% 的 RNA 是 rRNA，必须去除。
            <br><strong>Poly-A Selection：</strong> 利用磁珠捕获带 Poly-A 尾巴的 mRNA（仅适用于真核生物）。
            <br><strong>rRNA Depletion：</strong> 直接移除 rRNA（适用于降解样本或检测 lncRNA）。
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        <h3 style="margin-top: 0; color: #9f1239; font-size: 1.1em;">2. 逆转录与测序 (RT & Sequencing)</h3>
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            RNA 被打断成短片段，通过<strong>逆转录酶</strong>合成 <strong>[[cDNA]]</strong>。接上接头 (Adapters) 后，在测序仪（如 Illumina NovaSeq）上进行扩增和测序，生成数以亿计的 <strong>Reads</strong>。
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                <th style="padding: 12px; border: 1px solid #cbd5e1; color: #0f172a; width: 25%;">步骤</th>
                <th style="padding: 12px; border: 1px solid #cbd5e1; color: #1e40af; width: 30%;">常用软件</th>
                <th style="padding: 12px; border: 1px solid #cbd5e1; color: #475569;">任务描述</th>
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                <td style="padding: 10px; border: 1px solid #cbd5e1; font-weight: 600;">1. QC & Trimming</td>
                <td style="padding: 10px; border: 1px solid #cbd5e1;">FastQC, Trimmomatic</td>
                <td style="padding: 10px; border: 1px solid #cbd5e1;">检查数据质量，切除低质量碱基和接头序列。</td>
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                <td style="padding: 10px; border: 1px solid #cbd5e1; font-weight: 600;">2. Mapping (比对)</td>
                <td style="padding: 10px; border: 1px solid #cbd5e1;"><strong>STAR</strong>, HISAT2</td>
                <td style="padding: 10px; border: 1px solid #cbd5e1;">将 Reads 贴回参考基因组（需处理跨内含子的比对）。</td>
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                <td style="padding: 10px; border: 1px solid #cbd5e1; font-weight: 600;">3. Quantification</td>
                <td style="padding: 10px; border: 1px solid #cbd5e1;">HTSeq, <strong>Salmon</strong></td>
                <td style="padding: 10px; border: 1px solid #cbd5e1;">数一数每个基因上有多少条 Reads (Raw Counts)。</td>
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                <td style="padding: 10px; border: 1px solid #cbd5e1; font-weight: 600;">4. Differential Exp</td>
                <td style="padding: 10px; border: 1px solid #cbd5e1;"><strong>[[DESeq2]]</strong>, edgeR</td>
                <td style="padding: 10px; border: 1px solid #cbd5e1;">寻找样本间表达量有显著差异的基因，并绘制火山图。</td>
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    <h2 style="background: #f1f5f9; color: #0f172a; padding: 10px 18px; border-radius: 0 6px 6px 0; font-size: 1.25em; margin-top: 40px; border-left: 6px solid #0f172a; font-weight: bold;">关键概念：标准化 (Normalization)</h2>
    
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        不能直接比较原始 Reads 数，因为<strong>基因长度</strong>（长的基因落得Reads多）和<strong>测序深度</strong>（测得深的总数多）会造成偏差。主流的标准化方法如下：
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        <h3 style="margin-top: 0; color: #c2410c; font-size: 1.1em;">[[TPM]] (Transcripts Per Million)</h3>
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            当前最推荐的单位。它先校正基因长度，再校正测序深度。
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            $$TPM_i = \frac{X_i / l_i}{\sum_j (X_j / l_j)} \cdot 10^6$$
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            <span style="font-size: 0.85em; opacity: 0.8;">其中 $X_i$ 是基因 $i$ 的 Read Count，$l_i$ 是基因长度。TPM 的优势在于每个样本的总和都是 100万，方便样本间比较。</span>
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        <span style="color: #0f172a; font-weight: bold; font-size: 1.05em; display: inline-block; margin-bottom: 15px;">学术参考文献 [Academic Review]</span>
        
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            [1] <strong>Wang Z, Gerstein M, Snyder M. (2009).</strong> <em>RNA-Seq: a revolutionary tool for transcriptomics.</em> <strong>[[Nature Reviews Genetics]]</strong>. <br>
            <span style="color: #475569;">[点评]：RNA-Seq 领域的“圣经”级综述，详细阐述了技术原理及其取代芯片的必然性。</span>
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            [2] <strong>Stark R, Grzelak M, Hadfield J. (2019).</strong> <em>RNA sequencing: the teenage years.</em> <strong>[[Nature Reviews Genetics]]</strong>. <br>
            <span style="color: #475569;">[点评]：全面回顾了 RNA-Seq 十年来的技术演进，特别是长读长 (Long-read) 测序在异构体识别中的应用。</span>
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            [3] <strong>Mortazavi A, et al. (2008).</strong> <em>Mapping and quantifying mammalian transcriptomes by RNA-Seq.</em> <strong>[[Nature Methods]]</strong>. <br>
            <span style="color: #475569;">[点评]：早期经典文献，提出了 [[RPKM]] 概念（虽然现在多用 TPM，但该文具有里程碑意义）。</span>
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            转录组测序体系 · 知识图谱
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                <td style="width: 85px; background-color: #f8fafc; color: #334155; font-weight: 600; padding: 10px 12px; text-align: right; vertical-align: middle;">上级学科</td>
                <td style="padding: 10px 15px; color: #334155;">[[Transcriptomics]] • [[Genomics]] • [[Omics]]</td>
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                <td style="width: 85px; background-color: #f8fafc; color: #334155; font-weight: 600; padding: 10px 12px; text-align: right; vertical-align: middle;">衍生技术</td>
                <td style="padding: 10px 15px; color: #334155;">[[scRNA-seq]] (单细胞) • [[Total RNA-Seq]] • [[Small RNA-Seq]]</td>
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                <td style="width: 85px; background-color: #f8fafc; color: #334155; font-weight: 600; padding: 10px 12px; text-align: right; vertical-align: middle;">关键分析</td>
                <td style="padding: 10px 15px; color: #334155;">[[Differential Expression]] • [[Alternative Splicing]] • [[Pathway Analysis]]</td>
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