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protein-qc-strict

Strictest protein sequence analysis quality control workflow (3365→456 sequences). Includes literature validation, CD-HIT redundancy removal, complexity check, motif verification, MSA quality assessment, and conservation/coevolution analysis. Based on real research experience with IRED enzyme family.

skill-install — Terminal

Install via CLI (Recommended)

clawhub install openclaw/skills/skills/billwanttobetop/protein-qc-strict
Or

Protein Sequence Analysis Quality Control Skill

Version: 4.0.0
Created: 2026-04-25
Updated: 2026-04-25 22:16
Purpose: Strictest protein sequence analysis quality control - complete workflow (3365 → 456 sequences)

Quick Start

This skill provides a battle-tested quality control workflow for protein sequence analysis, developed through real research on IRED enzyme family (3,365 → 456 sequences).

Key Features:

  • Literature validation
  • CD-HIT redundancy removal (90%)
  • Complexity check (Shannon entropy)
  • Motif verification (Rossmann fold)
  • MSA quality assessment
  • Conservation & coevolution analysis

Use this skill when:

  • Analyzing protein families
  • Preparing sequences for phylogenetic analysis
  • Ensuring publication-quality data
  • Need to meet strict literature standards

🎯 核心原则(血的教训)

1. 十分严谨,不能猜想 ⭐⭐⭐⭐⭐

用户原话: "我们一定要严谨,十分的严谨,做科研的每一步都不能猜想,每一步都应该做好质检"

2. 必须使用原版工具 ⭐⭐⭐⭐⭐

  • ✅ MAFFT, trimAl, IQ-TREE, CD-HIT, MEME Suite, WebLogo
  • ❌ 不能用 Python 简化实现

3. 每一步都要质检 ⭐⭐⭐⭐⭐

  • 数据准备质检
  • 比对质量质检
  • 分析结果质检
  • 位点位置质检

4. Gap 会严重误导分析 ⭐⭐⭐⭐⭐

  • 必须过滤 gap > 50% 的位点
  • 必须检查每个结果的 gap 比例

📊 完整质检流程(3365 → 456)

阶段 1: 文献追溯(3365 → 840)

目的: 确保所有序列都有实验验证

方法:

  1. 检查每条序列的文献来源
  2. 确认是否有实验验证
  3. 移除无实验验证的序列

标准:

  • ✅ 必须有实验验证
  • ✅ 必须有文献支持

结果:

  • 输入: 3,365 条
  • 输出: 840 条
  • 移除: 2,525 条(75.0%)

质检: ✅ 所有序列都有实验验证


阶段 2: 长度过滤(840 → 793)

目的: 移除异常长度的序列

标准:

  • 最小长度: 200 aa
  • 最大长度: 500 aa
  • 原因: 太短可能是片段,太长可能是融合蛋白

代码:

from Bio import SeqIO

sequences = list(SeqIO.parse("input.fasta", "fasta"))

# 长度过滤
filtered = []
for seq in sequences:
    length = len(seq.seq)
    if 200 <= length <= 500:
        # 检查非标准字符
        seq_str = str(seq.seq)
        bad_chars = set(seq_str) - set('ACDEFGHIKLMNPQRSTVWY')
        if not bad_chars:
            filtered.append(seq)

SeqIO.write(filtered, "filtered.fasta", "fasta")

结果:

  • 输入: 840 条
  • 输出: 793 条
  • 移除: 47 条(5.6%)
    • < 200 aa: 43 条
    • 非标准字符: 4 条

质检: ✅ 所有序列 200-500 aa,无非标准字符


阶段 3: CD-HIT 去冗余(793 → 456)⭐⭐⭐⭐⭐

目的: 移除高度相似的序列,避免偏倚

工具: CD-HIT v4.8.1(原版,必须!)

阈值: 90%(文献推荐)

命令:

cd-hit -i input.fasta \
       -o output.fasta \
       -c 0.90 \
       -n 5 \
       -M 0 \
       -T 0

参数说明:

  • -c 0.90: 90% 序列同一性阈值
  • -n 5: word length(5 for thresholds 0.7-1.0)
  • -M 0: 无内存限制
  • -T 0: 使用所有 CPU 线程

结果:

  • 输入: 793 条
  • 输出: 456 条
  • 聚类: 337 个冗余序列(42.5%)

质检:

# 检查聚类文件
grep "^>" output.fasta.clstr | wc -l  # 应该等于输出序列数

# 检查聚类大小分布
grep "^>" output.fasta.clstr -A 1 | grep "at" | \
  awk '{print $NF}' | sort | uniq -c

质检标准:

  • ✅ 去冗余率 30-50% 合理
  • ✅ 大部分聚类大小 1-3

质检结果: ✅ 通过


阶段 4: 复杂度检查(456 → 456)

目的: 移除低复杂度序列(如重复序列)

方法: Shannon 熵

阈值: >= 2.0

公式:

H = -Σ(p_i * log2(p_i))

代码:

from Bio import SeqIO
from collections import Counter
import numpy as np

Metadata

Stars4473
Views0
Updated2026-05-01
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Add to Configuration

Paste this into your clawhub.json to enable this plugin.

{
  "plugins": {
    "official-billwanttobetop-protein-qc-strict": {
      "enabled": true,
      "auto_update": true
    }
  }
}
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