Host depletion strategy

Balancing experimental and computational approaches in Nanopore sequencing

Note

Private This page documents internal strategies and considerations for host depletion in sequencing workflows.

Overview

在多數 biological samples（特別是動物組織）中：

host DNA 通常佔 >90%

因此：

  • 會嚴重影響 pathogen detection sensitivity
  • 增加 sequencing cost
  • 干擾 downstream analysis

Two major strategies

1️⃣ Experimental depletion（wet lab）

方法：

  • host DNA depletion kits
  • selective lysis
  • nuclease treatment
  • enrichment protocols

優點：

  • 從源頭降低 host DNA
  • 提高 pathogen signal

缺點：

  • 可能造成 bias
  • 可能損失 target DNA
  • 成本增加

2️⃣ Computational depletion（bioinformatics）

方法：

  • Kraken2 host database filtering
  • minimap2 alignment to host genome
  • read removal

優點：

  • 靈活
  • 可重複分析
  • 不影響原始樣本

缺點：

  • 無法回收 sequencing capacity
  • 依賴 reference genome
  • 可能誤刪 reads

Strategy comparison

Aspect Experimental Computational
Timing pre-sequencing post-sequencing
Cost impact ↑ →
Flexibility 低 高
Bias risk 有 較低
Sensitivity ↑（若成功） 中等

Practical decision framework

flowchart TD
    A[Sample type] --> B{High host content?}
    B -->|Yes| C[Consider experimental depletion]
    B -->|No| D[Direct sequencing]

    C --> E[Sequencing]
    D --> E

    E --> F[Computational host removal]

Internal considerations（PRIVATE）

When to prioritize experimental depletion

  • host DNA >> microbial DNA
  • 需要高 sensitivity detection
  • sequencing budget limited

When to rely on computational filtering

  • exploratory analysis
  • reference genome available
  • 不確定 target

Combined strategy（推薦）

experimental + computational

  • wet lab 降低背景
  • bioinformatics refine results

Risks and caveats

  • 過度 depletion → loss of pathogen signal
  • reference genome 不完整 → filtering 失效
  • conserved regions → 誤判

Key principle

Tip

Host depletion 是 sensitivity optimization，而不是必要條件。

Internal notes（可自行填）

  • 使用的 kit：
  • bead ratio / protocol：
  • host genome version：
  • filtering criteria：