Used samples (Manuscript_Marius_Karoline_2026)

🐭 实验小鼠组别详解（中文版）

以下是本研究中使用的全部 14 个实验组别的详细说明，按功能分类整理：

🔹 第一类：中风模型组（用于图 4 和补充图 3）

组号	样本前缀	完整样本	性别/年龄	状态	用途
1	`sample-A*`	A1–A11	♂ 老年	中风后 3 天	图 4D–F（血液/脑组织 SCFA 检测）
2	`sample-B*`	B1–B16	♀ 老年	中风后 3 天	图 4D–F（血液/脑组织 SCFA 检测）

📌 说明：这两组用于比较中风后老年雄性和雌性小鼠的微生物代谢物（短链脂肪酸）水平差异。

🔹 第二类：基线供体组（用于图 4、补充图 4、图 5C）

组号	样本前缀	完整样本	性别/年龄	状态	用途
3	`sample-C*`	C1–C10	♀ 老年	基线，FMT 供体	图 4A–C（16S 测序）、补充图 4、图 5C（Boxplot 3）
4	`sample-E*`	E1–E10	♂ 老年	基线，FMT 供体	图 4A–C（16S 测序）、补充图 4、图 5C（Boxplot 2）
5	`sample-F*`	F1–F5	♂ 年轻	基线，FMT 供体	对照供体，未在主图中展示

📌 关键说明：

组 3 和组 4 是粪菌移植（FMT）的供体小鼠，用于提供老年雌/雄肠道菌群

图 4 和补充图 4 中实际使用的样本为：♀供体 C1–C6（n=6），♂供体 E1–E8（n=8），其余样本因年龄偏小或测序深度不足被排除

🔹 第三类：FMT 预处理组（用于图 5B 紫色点）

组号	样本前缀	完整样本	性别/年龄	状态	用途
6	`sample-G*`	G1–G6	♂ 老年	FMT 前，抗生素处理前，批次 I	图 5B（紫色，pre-FMT 基线）
7	`sample-H*`	H1–H6	♀ 老年	FMT 前，抗生素处理前，批次 I	图 5B（紫色，pre-FMT 基线）
8	`sample-I*`	I1–I6	♂ 年轻	FMT 前，抗生素处理前，批次 II	图 5B（紫色，pre-FMT 基线）

📌 说明：这三组合并为”pre-FMT”基线组（n=18），代表年轻雄性受体小鼠在接受粪菌移植之前的肠道菌群状态。

🔹 第四类：FMT 受体组（用于图 5）

组号	样本前缀	完整样本	性别/年龄	接受供体	状态	用途
9	`sample-J*`	J1–J4, J10, J11	♂ 年轻	老年♂供体	FMT 后，中风前	图 5B🔵、5C（Boxplot 4）、5D、5E
10	`sample-K*`	K1–K6	♂ 年轻	老年♀供体	FMT 后，中风前	图 5B🔴、5C（Boxplot 5）、5D、5E
11	`sample-L*`	L2–L6	♂ 年轻	年轻♂供体	FMT 后，中风前	图 5B🟢、5E（对照）

📌 关键说明：

所有受体均为年轻雄性小鼠，仅供体来源不同

“aged♂ FMT” = 接受老年雄性供体粪便的年轻受体（不是受体本身是老年！）

图 5C 的 5 个箱线图 = pre-FMT 基线 + 2 个供体组 + 2 个受体组（不含年轻♂供体受体组）

🔹 第五类：FMT + 中风后组（未在主图展示）

组号	样本前缀	完整样本	性别/年龄	接受供体	状态	用途
12	`sample-M*`	M1–M8	♂ 老年	老年♂供体	FMT 后，中风后	补充分析
13	`sample-N*`	N1–N10	♀ 老年	老年♀供体	FMT 后，中风后	补充分析
14	`sample-O*`	O1–O8	♂ 年轻	年轻♂供体	FMT 后，中风后	补充分析

📌 说明：这三组用于探索性分析，未出现在主论文图表中。

🧭 快速记忆口诀

✅ "FMT 标签 = 供体特征，不是受体特征"
   • aged♂ FMT = 供体是老年雄性
   • 受体永远是年轻雄性（本实验设计）

✅ 图 4 = 老年供体（组 3/4）+ 老年中风小鼠（组 1/2）
✅ 图 5 = FMT 实验：受体（组 6–11）+ 供体（组 3/4）
✅ 补充图 4 = 仅老年供体（组 3/4，筛选后 C1–C6, E1–E8）

⚠️ 样本排除说明

组别	排除样本	排除原因
组 3（♀供体）	C7, C8, C9, C10	C8–C9 年龄偏小；C10 为离群值/测序深度低
组 4（♂供体）	E9, E10	测序深度低/离群值
组 9（受体）	J5, J6, J7, J8, J9	测序深度不足或质量控制排除
组 10（受体）	K7–K15	测序深度不足或质量控制排除
组 11（受体）	L1, L7–L15	测序深度不足或质量控制排除

📌 最终用于分析的样本数以各图图例标注为准（如：图 5 中 aged♂ FMT n=6, aged♀ FMT n=6）

TODO: 导出完整的样本–组别映射 CSV 文件，or 提供某张图的精确样本列表🎯

关于 “aged♂ FMT” 的明确解释

aged♂ FMT = 接受了老年雄性供体粪便的年轻雄性受体小鼠

🔹 实验设计核心逻辑

角色	年龄/性别	说明
受体（接受粪便）	🐭 年轻雄性（4周龄起始）	所有 FMT 组的受体都是相同的年轻雄性小鼠
供体（提供粪便）	🐭 老年雄性 / 老年雌性 / 年轻雄性	供体的年龄/性别是实验变量

🔹 样本分组详解

🟣 Purple (pre-FMT, n=18): G1–G6, H1–H6, I1–I6
   → FMT前的基线年轻雄性小鼠（未接受移植）

🔵 Blue (aged♂ FMT, n=6): J1, J2, J3, J4, J10, J11
   → 年轻雄性受体 + 接受【老年雄性】供体粪便

🔴 Red (aged♀ FMT, n=6): K1–K6
   → 年轻雄性受体 + 接受【老年雌性】供体粪便

🟢 Green (young♂ FMT, n=5): L2–L6
   → 年轻雄性受体 + 接受【年轻雄性】供体粪便（对照组）

🔹 文献依据

来自 260311_LTPaper.pdf Figure 5 图例：

“Principal coordinates analysis (PCoA) of young male mice before (purple) (n=18), and after FMT of aged male (n=6) (blue) or female (n=6) (red) or young male (n=5) (green) stool donors.”

→ 明确说明分析对象是 young male mice，括号内描述的是 stool donors（粪便供体）的特征。

来自 Supplemental Methods “Microbiota eradication and FMT”：

“4 weeks old male mice were treated for 2 weeks with an antibiotic cocktail… recipient mice were gavaged with donor stool four times over two weeks.”

→ 受体小鼠起始年龄为 4周龄（年轻）。

Figure 5 小标题：

“FMT of aged male microbiota increases IL-17A-producing γδ T cells in the post-ischemic brain of young recipient mice“

→ 再次确认受体是 young recipient mice。

🔹 为什么这样设计？

这个实验的核心科学问题是：

“供体微生物的年龄/性别特征，能否通过移植’传递’给受体，并影响受体的免疫反应？”

通过保持受体一致（年轻雄性），仅改变供体来源，可以：

排除受体自身年龄/性别的混杂效应
直接评估供体微生物对受体免疫表型（如 IL-17A⁺ γδ T 细胞）的因果影响
验证”微生物介导的年龄/性别差异”假说

✅ 快速记忆口诀

“FMT 标签 = 供体特征，不是受体特征”

aged♂ FMT = 供体是老年雄性

受体永远是年轻雄性（本实验中）

🔹 Figure 5B: PCoA of FMT Experiment

“Principal coordinates analysis (PCoA) of young male mice before (purple) (n=18), and after FMT of aged male (n=6) (blue) or female (n=6) (red) or young male (n=5) (green) stool donors.”

🟣 Purple (pre-FMT, n=18): Groups 6+7+8 → G1–G6, H1–H6, I1–I6
🔵 Blue (aged♂ FMT, n=6): Group9 → J1, J2, J3, J4, J10, J11
🔴 Red (aged♀ FMT, n=6): Group10 → K1–K6
🟢 Green (young♂ FMT, n=5): Group11 → L2–L6 (L1, L7–L15 excluded for low depth/QC)

🔹 Figure 5C=Figure 5B+C1-7+E1-10 (Need to be confirmed?): Family-Level Relative Abundance Boxplots (5 panels)

Based on your co-author’s note: “Figure 5C uses the Figure 5B recipient samples PLUS the aged donor samples (Groups 3 & 4).”

Boxplot 1 (pre-FMT baseline, n=18): Groups 6+7+8 → G1–G6, H1–H6, I1–I6
Boxplot 2 (aged♂ stool donors, n=8): Group4 → E1–E10
Boxplot 3 (aged♀ stool donors, n=6): Group3 → C1–C7
Boxplot 4 (aged♂ FMT recipients, n=6): Group9 → J1, J2, J3, J4, J10, J11
Boxplot 5 (aged♀ FMT recipients, n=6): Group10 → K1–K6
!!No Group11 (L2-L6)!!

⚠️ Key difference: Group11 (young♂ FMT recipients, L2–L6) is shown in Figure 5B but is NOT included in Figure 5C, since Figure 5C focuses on comparing the effect of aged donor microbiota.

🔹 Figure 5D: Bubble Plot of Differentially Abundant Taxa (DESeq2)

“Bubble plot showing differentially abundant Operational Taxonomic Units (OTUs) between young male recipients of aged female vs. aged male FMT. x-axis = log₂ fold change, y-axis = bacterial family, bubble size = adjusted p-value, color = bacterial order.”

🔵 Aged♂ FMT recipients (Group9, n=6): J1, J2, J3, J4, J10, J11 → Reference group (log₂FC < 0 = enriched in this group)
🔴 Aged♀ FMT recipients (Group10, n=6): K1–K6 → Comparison group (log₂FC > 0 = enriched in this group)

Key families highlighted in the plot:	Direction	Family (Order)	Enriched in
🔴 Positive log₂FC	Lachnospiraceae (Clostridiales)	Aged♀ FMT	SCFA producer
🔴 Positive log₂FC	Ruminococcaceae (Clostridiales)	Aged♀ FMT	SCFA producer
🔴 Positive log₂FC	Muribaculaceae (Bacteroidales)	Aged♀ FMT	SCFA producer
🔴 Positive log₂FC	Desulfovibrionaceae (Desulfovibrionales)	Aged♀ FMT	Sulfate-reducing
🔵 Negative log₂FC	Erysipelotrichaceae (Erysipelotrichales)	Aged♂ FMT	Pro-inflammatory association
🔵 Negative log₂FC	Rikenellaceae (Bacteroidales)	Aged♂ FMT	Context-dependent
🔵 Negative log₂FC	Clostridiales vadinBB60 group	Aged♂ FMT	Function unclear

⚠️ Note: This analysis uses DESeq2 on non-rarefied integer counts from ps_filt, with taxa prefiltered (total counts ≥10). Only taxa with Benjamini–Hochberg adjusted p < 0.05 are shown. The same ASVs/OTUs appear in Figure 4C and Supplementary Figure 4B, but Figure 5D specifically compares FMT recipient outcomes (Groups 9 vs. 10), not baseline donor differences.

🔹 Supplementary_Figure4=Figure4B-C: Aged Donors (Homeostatic)

“(A) Bray-Curtis distances between aged male-male, female-female and female-male stool samples under homeostatic conditions (nmale=8 and nfemale=6). (B) Cladogram showing differentially abundant OTUs…”

👨 Aged male donors (n=8): Group4 → E1–E8 (E9, E10 excluded for low sequencing depth/outliers)
👩 Aged female donors (n=6): Group3 → C1–C6 (C7–C10 excluded; C8–C9 younger mice, C10 outlier)

🔹 Figure 4B-C: Sex Differences in Aged Mice (16S rRNA-seq panels B–C)

“We profiled the gut bacterial composition of aged male and female mice by 16S rRNA-seq…”

Baseline aged female donors: Group3 → C1–C6
Baseline aged male donors: Group4 → E1–E8

(Note: Figure 4D–F show SCFA concentrations measured by targeted UHPLC-MS/MS, not 16S data.)

✅ PICRUSt2 NOT used in Figure 4D–F

Your observation is CORRECT: PICRUSt2 results are NOT used in Figure 4D–F.

Question	Answer	Evidence
Are PICRUSt2 results used in Figure 4?	❌ No	Figure 4D–F legend explicitly states: “measured by targeted mass spectrometry”
Are PICRUSt2 results used anywhere in the manuscript?	❌ No evidence	README_PICRUSt2.txt files contain exploratory pipeline notes, but no PICRUSt2 figures, tables, or text appear in `260311_LTPaper.pdf` or `260310_Supplements.pdf`
Is the SCFA data in Figure 4D–F experimentally measured?	✅ Yes	Supplemental Methods (pages 12–13) describe UHPLC-MS/MS quantification with internal standards, derivatization, and MRM parameters

Key distinction:

PICRUSt2 → Predicts functional potential (gene/pathway abundances) from 16S sequences; outputs are relative, unitless values.
Figure 4D–F → Measures actual SCFA concentrations (acetate, butyrate, etc.) in µmol/l via targeted mass spectrometry; outputs are absolute, quantitative values.

Here is the merged quick reference table combining Figure 5B, 5C, and 5D with related figures, formatted for easy copy-paste:

🔹 Quick Reference: All Figure 5 Panels vs. Related Figures

Figure	Comparison	Sample IDs (exact)	n	Purpose
Figure 4B-C	Aged♀ vs. aged♂ donors (homeostatic)	`C1–C6` vs. `E1–E8`	6 vs. 8	Baseline sex differences in microbiota (DESeq2 bubble plot)
Suppl Fig 4B	Same as Fig 4C	`C1–C6` vs. `E1–E8`	6 vs. 8	Phylogenetic context of differential taxa (cladogram)
Figure 5B	Pre-FMT vs. post-FMT recipients (4-group PCoA)	`G1–G6`, `H1–H6`, `I1–I6` (pre-FMT); `J1, J2, J3, J4, J10, J11` (aged♂ FMT); `K1–K6` (aged♀ FMT); `L2–L6` (young♂ FMT)	18, 6, 6, 5	PCoA: microbiome shift after FMT (Bray–Curtis)
Figure 5C	Donors vs. recipients (5 boxplots, family-level)	`G1–G6`, `H1–H6`, `I1–I6` (pre-FMT); `E1–E8` (aged♂ donors); `C1–C6` (aged♀ donors); `J1, J2, J3, J4, J10, J11` (aged♂ FMT); `K1–K6` (aged♀ FMT)	18, 8, 6, 6, 6	Taxonomic composition: donors vs. recipients (relative abundance)
Figure 5D	Aged♀ vs. aged♂ FMT recipients (DESeq2)	`K1–K6` vs. `J1, J2, J3, J4, J10, J11`	6 vs. 6	Effect of donor microbiota on recipient immune response (differential abundance)
Figure 5E	Same recipients as Fig 5D (+ young♂ control)	`K1–K6` vs. `J1, J2, J3, J4, J10, J11` (+ `L2–L6`)	6 vs. 6 (+5)	IL-17A+ γδ T cells in brain post-FMT (flow cytometry)

🔹 Sample-ID Master List for Figure 5

Group #	Description	Sample Prefix	Full IDs	Used In
3	Aged female, baseline FMT donor	`sample-C*`	C1–C10 (C1–C6 used in Fig 4B-C, Suppl Fig 4, Fig 5C)	Fig 4C, Suppl Fig 4, Fig 5C
4	Aged male, baseline FMT donor	`sample-E*`	E1–E10 (E1–E8 used in Fig 4B-C, Suppl Fig 4, Fig 5C)	Fig 4B-C, Suppl Fig 4, Fig 5C
6	Aged male, pre-antibiotics FMT batch I	`sample-G*`	G1–G6	Fig 5B (purple), Fig 5C (Boxplot 1)
7	Aged female, pre-antibiotics FMT batch I	`sample-H*`	H1–H6	Fig 5B (purple), Fig 5C (Boxplot 1)
8	Young male, pre-antibiotics FMT batch II	`sample-I*`	I1–I6	Fig 5B (purple), Fig 5C (Boxplot 1)
9	Young male, post-FMT aged male stool	`sample-J*`	J1–J4, J10, J11 (J5–J9 excluded)	Fig 5B (blue), Fig 5C (Boxplot 4), Fig 5D, Fig 5E
10	Young male, post-FMT aged female stool	`sample-K*`	K1–K6	Fig 5B (red), Fig 5C (Boxplot 5), Fig 5D, Fig 5E
11	Young male, post-FMT young male stool	`sample-L*`	L2–L6 (L1, L7–L15 excluded)	Fig 5B (green), Fig 5E (not in Fig 5C/D)

🔹 Key Notes for Interpretation

Figure 5B vs. 5C: Figure 5B shows beta-diversity (PCoA) of all FMT groups; Figure 5C shows taxonomic composition (boxplots) of donors + recipients. Group11 (young♂ FMT) is in 5B but not in 5C.
Figure 5D: Uses DESeq2 on non-rarefied counts from ps_filt (taxa prefiltered: total counts ≥10). Only taxa with BH-adjusted p < 0.05 are shown.
Figure 5E: Includes the same recipients as Fig 5D plus the young♂ FMT control group (Group11, L2–L6) for comparison of IL-17A+ γδ T cells.
Sample exclusions: C7–C10, E9–E10, J5–J9, K7–K15, L1, L7–L15 were excluded for low depth, outliers, or QC reasons (see README files).

Let me know if you’d like me to:

Export the exact DESeq2 results table for Figure 5D as CSV/Excel,
Provide the R code snippet that generates the bubble plot for Figure 5D, or
Draft the full email reply to your colleague with these merged tables integrated. 🎯

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Interhost variant calling (Data_Tam_DNAseq_2026_19606wt_dAB_dIJ_mito_flu_on_ATCC19606)

Leave a reply

Input data:

 mkdir bacto; cd bacto;
 mkdir raw_data; cd raw_data;

 # ── Fluoxetine Dataset ──
 ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△ABfluEcef-1/19606△ABfluEcef-1_1.fq.gz flu_dAB_cef_R1.fastq.gz
 ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△ABfluEcef-1/19606△ABfluEcef-1_2.fq.gz flu_dAB_cef_R2.fastq.gz
 ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△ABfluEcipro-2/19606△ABfluEcipro-2_1.fq.gz flu_dAB_cipro_R1.fastq.gz
 ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△ABfluEcipro-2/19606△ABfluEcipro-2_2.fq.gz flu_dAB_cipro_R2.fastq.gz
 ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△ABfluEdori-2/19606△ABfluEdori-2_1.fq.gz flu_dAB_dori_R1.fastq.gz
 ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△ABfluEdori-2/19606△ABfluEdori-2_2.fq.gz flu_dAB_dori_R2.fastq.gz
 ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△ABfluEnitro-3/19606△ABfluEnitro-3_1.fq.gz flu_dAB_nitro_R1.fastq.gz
 ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△ABfluEnitro-3/19606△ABfluEnitro-3_2.fq.gz flu_dAB_nitro_R2.fastq.gz
 ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△ABfluEpip-1/19606△ABfluEpip-1_1.fq.gz flu_dAB_pip_R1.fastq.gz
 ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△ABfluEpip-1/19606△ABfluEpip-1_2.fq.gz flu_dAB_pip_R2.fastq.gz
 ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△ABfluEpolyB-3/19606△ABfluEpolyB-3_1.fq.gz flu_dAB_polyB_R1.fastq.gz
 ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△ABfluEpolyB-3/19606△ABfluEpolyB-3_2.fq.gz flu_dAB_polyB_R2.fastq.gz
 ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△ABfluEtet-1/19606△ABfluEtet-1_1.fq.gz flu_dAB_tet_R1.fastq.gz
 ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△ABfluEtet-1/19606△ABfluEtet-1_2.fq.gz flu_dAB_tet_R2.fastq.gz
 ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△IJfluEcef-4/19606△IJfluEcef-4_1.fq.gz flu_dIJ_cef_R1.fastq.gz
 ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△IJfluEcef-4/19606△IJfluEcef-4_2.fq.gz flu_dIJ_cef_R2.fastq.gz
 ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△IJfluEcipro-3/19606△IJfluEcipro-3_1.fq.gz flu_dIJ_cipro_R1.fastq.gz
 ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△IJfluEcipro-3/19606△IJfluEcipro-3_2.fq.gz flu_dIJ_cipro_R2.fastq.gz
 ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△IJfluEdori-1/19606△IJfluEdori-1_1.fq.gz flu_dIJ_dori_R1.fastq.gz
 ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△IJfluEdori-1/19606△IJfluEdori-1_2.fq.gz flu_dIJ_dori_R2.fastq.gz
 ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△IJfluEnitro-3/19606△IJfluEnitro-3_1.fq.gz flu_dIJ_nitro_R1.fastq.gz
 ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△IJfluEnitro-3/19606△IJfluEnitro-3_2.fq.gz flu_dIJ_nitro_R2.fastq.gz
 ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△IJfluEpip-4/19606△IJfluEpip-4_1.fq.gz flu_dIJ_pip_R1.fastq.gz
 ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△IJfluEpip-4/19606△IJfluEpip-4_2.fq.gz flu_dIJ_pip_R2.fastq.gz
 ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△IJfluEpolyB-4/19606△IJfluEpolyB-4_1.fq.gz flu_dIJ_polyB_R1.fastq.gz
 ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606△IJfluEpolyB-4/19606△IJfluEpolyB-4_2.fq.gz flu_dIJ_polyB_R2.fastq.gz
 ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606wtfluEcef-1/19606wtfluEcef-1_1.fq.gz flu_wt_cef_R1.fastq.gz
 ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606wtfluEcef-1/19606wtfluEcef-1_2.fq.gz flu_wt_cef_R2.fastq.gz
 ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606wtfluEcipro-2/19606wtfluEcipro-2_1.fq.gz flu_wt_cipro_R1.fastq.gz
 ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606wtfluEcipro-2/19606wtfluEcipro-2_2.fq.gz flu_wt_cipro_R2.fastq.gz
 ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606wtfluEdori-1/19606wtfluEdori-1_1.fq.gz flu_wt_dori_R1.fastq.gz
 ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606wtfluEdori-1/19606wtfluEdori-1_2.fq.gz flu_wt_dori_R2.fastq.gz
 ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606wtfluEnitro-1/19606wtfluEnitro-1_1.fq.gz flu_wt_nitro_R1.fastq.gz
 ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606wtfluEnitro-1/19606wtfluEnitro-1_2.fq.gz flu_wt_nitro_R2.fastq.gz
 ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606wtfluEpip-4/19606wtfluEpip-4_1.fq.gz flu_wt_pip_R1.fastq.gz
 ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606wtfluEpip-4/19606wtfluEpip-4_2.fq.gz flu_wt_pip_R2.fastq.gz
 ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606wtfluEpolyB-4/19606wtfluEpolyB-4_1.fq.gz flu_wt_polyB_R1.fastq.gz
 ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606wtfluEpolyB-4/19606wtfluEpolyB-4_2.fq.gz flu_wt_polyB_R2.fastq.gz
 ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606wtfluEtet-2/19606wtfluEtet-2_1.fq.gz flu_wt_tet_R1.fastq.gz
 ln -s ../../X101SC25116512-Z01-J003/01.RawData/19606wtfluEtet-2/19606wtfluEtet-2_2.fq.gz flu_wt_tet_R2.fastq.gz

 # ── Mitomycin C Dataset ──
 ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_AB_cipro_1/MitoC_E_AB_cipro_1_1.fq.gz mito_dAB_cipro_R1.fastq.gz
 ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_AB_cipro_1/MitoC_E_AB_cipro_1_2.fq.gz mito_dAB_cipro_R2.fastq.gz
 ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_AB_dori_1/MitoC_E_AB_dori_1_1.fq.gz mito_dAB_dori_R1.fastq.gz
 ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_AB_dori_1/MitoC_E_AB_dori_1_2.fq.gz mito_dAB_dori_R2.fastq.gz
 ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_AB_nitro_2/MitoC_E_AB_nitro_2_1.fq.gz mito_dAB_nitro_R1.fastq.gz
 ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_AB_nitro_2/MitoC_E_AB_nitro_2_2.fq.gz mito_dAB_nitro_R2.fastq.gz
 ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_AB_tet_2/MitoC_E_AB_tet_2_1.fq.gz mito_dAB_tet_R1.fastq.gz
 ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_AB_tet_2/MitoC_E_AB_tet_2_2.fq.gz mito_dAB_tet_R2.fastq.gz
 ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_AB_trime_4/MitoC_E_AB_trime_4_1.fq.gz mito_dAB_trime_R1.fastq.gz
 ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_AB_trime_4/MitoC_E_AB_trime_4_2.fq.gz mito_dAB_trime_R2.fastq.gz
 ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_IJ_cipro_1/MitoC_E_IJ_cipro_1_1.fq.gz mito_dIJ_cipro_R1.fastq.gz
 ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_IJ_cipro_1/MitoC_E_IJ_cipro_1_2.fq.gz mito_dIJ_cipro_R2.fastq.gz
 ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_IJ_dori_4/MitoC_E_IJ_dori_4_1.fq.gz mito_dIJ_dori_R1.fastq.gz
 ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_IJ_dori_4/MitoC_E_IJ_dori_4_2.fq.gz mito_dIJ_dori_R2.fastq.gz
 ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_IJ_nitro_2/MitoC_E_IJ_nitro_2_1.fq.gz mito_dIJ_nitro_R1.fastq.gz
 ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_IJ_nitro_2/MitoC_E_IJ_nitro_2_2.fq.gz mito_dIJ_nitro_R2.fastq.gz
 ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_IJ_polyB_3/MitoC_E_IJ_polyB_3_1.fq.gz mito_dIJ_polyB_R1.fastq.gz
 ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_IJ_polyB_3/MitoC_E_IJ_polyB_3_2.fq.gz mito_dIJ_polyB_R2.fastq.gz
 ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_IJ_tet_3/MitoC_E_IJ_tet_3_1.fq.gz mito_dIJ_tet_R1.fastq.gz
 ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_IJ_tet_3/MitoC_E_IJ_tet_3_2.fq.gz mito_dIJ_tet_R2.fastq.gz
 ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_IJ_trime_1/MitoC_E_IJ_trime_1_1.fq.gz mito_dIJ_trime_R1.fastq.gz
 ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_IJ_trime_1/MitoC_E_IJ_trime_1_2.fq.gz mito_dIJ_trime_R2.fastq.gz
 ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_wt_cipro_1/MitoC_E_wt_cipro_1_1.fq.gz mito_wt_cipro_R1.fastq.gz
 ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_wt_cipro_1/MitoC_E_wt_cipro_1_2.fq.gz mito_wt_cipro_R2.fastq.gz
 ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_wt_nitro_1/MitoC_E_wt_nitro_1_1.fq.gz mito_wt_nitro_R1.fastq.gz
 ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_wt_nitro_1/MitoC_E_wt_nitro_1_2.fq.gz mito_wt_nitro_R2.fastq.gz
 ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_wt_polyB_1/MitoC_E_wt_polyB_1_1.fq.gz mito_wt_polyB_R1.fastq.gz
 ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_wt_polyB_1/MitoC_E_wt_polyB_1_2.fq.gz mito_wt_polyB_R2.fastq.gz
 ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_wt_trime_2/MitoC_E_wt_trime_2_1.fq.gz mito_wt_trime_R1.fastq.gz
 ln -s ../../X101SC26025981-Z02-J002/01.RawData/MitoC_E_wt_trime_2/MitoC_E_wt_trime_2_2.fq.gz mito_wt_trime_R2.fastq.gz

Call variant calling using snippy

 ln -s ~/Tools/bacto/db/ .;
 ln -s ~/Tools/bacto/envs/ .;
 ln -s ~/Tools/bacto/local/ .;
 cp ~/Tools/bacto/Snakefile .;
 cp ~/Tools/bacto/bacto-0.1.json .;
 cp ~/Tools/bacto/cluster.json .;

 #download CP059040.gb from GenBank
 #mv ~/Downloads/sequence\(2\).gb db/CP059040.gb

 mamba activate /home/jhuang/miniconda3/envs/bengal3_ac3
 (bengal3_ac3) jhuang@WS-2290C:~/DATA/Data_Tam_DNAseq_2023_A6WT_A10CraA_A12AYE_A1917978$ which snakemake
 /home/jhuang/miniconda3/envs/bengal3_ac3/bin/snakemake
 (bengal3_ac3) jhuang@WS-2290C:~/DATA/Data_Tam_DNAseq_2023_A6WT_A10CraA_A12AYE_A1917978$ snakemake -v
 4.0.0 --> CORRECT!

 #NOTE_1: modify bacto-0.1.json keeping only steps assembly, typing_mlst, possibly pangenome and variants_calling true; setting cpu=20 in all used steps.
     #setting the following in bacto-0.1.json
     "fastqc": false,
     "taxonomic_classifier": false,
     "assembly": true,
     "typing_ariba": false,
     "typing_mlst": true,
     "pangenome": true,
     "variants_calling": true,
     "phylogeny_fasttree": false,
     "phylogeny_raxml": false,
     "recombination": false, (due to gubbins-error set false)

     "prokka": {
       "genus": "Acinetobacter",
       "kingdom": "Bacteria",
       "species": "baumannii",
       "cpu": 10,
       "evalue": "1e-06",
       "other": ""
     },

     "mykrobe": {
       "species": "abaum"
     },

     "reference": "db/CP059040.gb"

 #NOTE_2: needs disk Titisee since the pipeline needs /media/jhuang/Titisee/GAMOLA2/TIGRfam_db/TIGRFAMs_15.0_HMM.LIB
 snakemake --printshellcmds

Checking the contaminated samples based on the size of genome

 find . -maxdepth 2 -name "contigs.fa" -exec du -h {} \;
 find . -maxdepth 2 -name "contigs.fa" -printf "%s\t%p\n" | sort -nr | \
         while IFS=$'\t' read -r bytes path; do
         sample=$(basename "$(dirname "$path")")
         printf "%-20s %8s\t%s\n" "$sample" "$(numfmt --to=iec-i --suffix=B "$bytes")" "$path"
 done

 mito_wt_polyB          8,6MiB   ./mito_wt_polyB/contigs.fa
 mito_wt_trime          8,2MiB   ./mito_wt_trime/contigs.fa
 mito_dAB_trime         8,2MiB   ./mito_dAB_trime/contigs.fa
 flu_wt_cipro           6,7MiB   ./flu_wt_cipro/contigs.fa
 mito_dIJ_nitro         6,6MiB   ./mito_dIJ_nitro/contigs.fa
 flu_dAB_cipro          4,7MiB   ./flu_dAB_cipro/contigs.fa

 flu_wt_nitro           3,9MiB   ./flu_wt_nitro/contigs.fa
 flu_wt_polyB           3,9MiB   ./flu_wt_polyB/contigs.fa
 flu_wt_cef             3,9MiB   ./flu_wt_cef/contigs.fa
 flu_wt_dori            3,8MiB   ./flu_wt_dori/contigs.fa
 flu_dIJ_pip            3,8MiB   ./flu_dIJ_pip/contigs.fa
 flu_dIJ_dori           3,8MiB   ./flu_dIJ_dori/contigs.fa
 flu_wt_pip             3,8MiB   ./flu_wt_pip/contigs.fa
 flu_dAB_dori           3,8MiB   ./flu_dAB_dori/contigs.fa
 flu_dIJ_cipro          3,8MiB   ./flu_dIJ_cipro/contigs.fa
 flu_dAB_nitro          3,8MiB   ./flu_dAB_nitro/contigs.fa
 flu_dAB_pip            3,8MiB   ./flu_dAB_pip/contigs.fa
 flu_dAB_polyB          3,8MiB   ./flu_dAB_polyB/contigs.fa
 flu_dIJ_nitro          3,8MiB   ./flu_dIJ_nitro/contigs.fa
 flu_dIJ_cef            3,8MiB   ./flu_dIJ_cef/contigs.fa
 flu_dAB_tet            3,8MiB   ./flu_dAB_tet/contigs.fa
 flu_dIJ_polyB          3,8MiB   ./flu_dIJ_polyB/contigs.fa
 flu_dAB_cef            3,8MiB   ./flu_dAB_cef/contigs.fa
 mito_dIJ_polyB         3,8MiB   ./mito_dIJ_polyB/contigs.fa
 mito_dIJ_cipro         3,8MiB   ./mito_dIJ_cipro/contigs.fa
 mito_dIJ_dori          3,8MiB   ./mito_dIJ_dori/contigs.fa
 flu_wt_tet             3,8MiB   ./flu_wt_tet/contigs.fa
 mito_wt_nitro          3,8MiB   ./mito_wt_nitro/contigs.fa
 mito_wt_cipro          3,8MiB   ./mito_wt_cipro/contigs.fa
 mito_dIJ_tet           3,8MiB   ./mito_dIJ_tet/contigs.fa
 mito_dIJ_trime         3,8MiB   ./mito_dIJ_trime/contigs.fa
 mito_dAB_dori          3,8MiB   ./mito_dAB_dori/contigs.fa
 mito_dAB_cipro         3,7MiB   ./mito_dAB_cipro/contigs.fa
 mito_dAB_tet           3,7MiB   ./mito_dAB_tet/contigs.fa
 mito_dAB_nitro         3,7MiB   ./mito_dAB_nitro/contigs.fa

Summarize all SNPs and Indels from the snippy result directory.

 cp ~/Scripts/summarize_snippy_res_ordered.py .
 # IMPORTANT_ADAPT the array in script should be adapted; deleting the isolates "mito_wt_polyB","mito_wt_trime", "mito_dAB_trime", "flu_wt_cipro", "mito_dIJ_nitro", and "flu_dAB_cipro"
 isolates = ["flu_wt_cef", "flu_wt_dori", "flu_wt_nitro", "flu_wt_pip", "flu_wt_polyB", "flu_wt_tet",    "flu_dAB_cef", "flu_dAB_dori", "flu_dAB_nitro", "flu_dAB_pip", "flu_dAB_polyB", "flu_dAB_tet",    "flu_dIJ_cef", "flu_dIJ_cipro", "flu_dIJ_dori", "flu_dIJ_nitro", "flu_dIJ_pip", "flu_dIJ_polyB",         "mito_dIJ_trime",    "mito_wt_cipro", "mito_wt_nitro",   "mito_dAB_cipro", "mito_dAB_dori", "mito_dAB_nitro", "mito_dAB_tet",     "mito_dIJ_cipro", "mito_dIJ_dori",  "mito_dIJ_polyB", "mito_dIJ_tet"]

 mamba activate plot-numpy1
 python3 ./summarize_snippy_res_ordered.py snippy
 #--> Summary CSV file created successfully at: snippy/summary_snps_indels.csv
 cd snippy
 #REMOVE_the_line? I don't find the sence of the line:    grep -v "None,,,,,,None,None" summary_snps_indels.csv > summary_snps_indels_.csv

Using spandx calling variants (almost the same results to the one from viral-ngs!)

 mamba deactivate
 mamba activate /home/jhuang/miniconda3/envs/spandx

 # PREPARE the inputs for the options ref and database (NOT ALWAYS NEED, PREPARE ONlY ONCE)
 mkdir ~/miniconda3/envs/spandx/share/snpeff-5.1-2/data/PP810610
 cp PP810610.gb  ~/miniconda3/envs/spandx/share/snpeff-5.1-2/data/PP810610/genes.gbk
 vim ~/miniconda3/envs/spandx/share/snpeff-5.1-2/snpEff.config
 /home/jhuang/miniconda3/envs/spandx/bin/snpEff build PP810610    #-d
 ~/Scripts/genbank2fasta.py PP810610.gb
 mv PP810610.gb_converted.fna PP810610.fasta    #rename "NC_001348.1 xxxxx" to "NC_001348" in the fasta-file

 ln -s /home/jhuang/Tools/spandx/ spandx
 # Deleting the contaminated samples flu_wt_cipro*.fastq and flu_dAB_cipro*.fastq
 (spandx) nextflow run spandx/main.nf --fastq "trimmed/*_P_{1,2}.fastq" --ref CP059040.fasta --annotation --database CP059040 -resume

 # RERUN SNP_matrix.sh due to the error ERROR_CHROMOSOME_NOT_FOUND in the variants annotation, resulting in all impacts are MODIFIER --> IT WORKS!
 cd Outputs/Master_vcf
 conda activate /home/jhuang/miniconda3/envs/spandx
 (spandx) cp -r ../../snippy/flu_wt_cef/reference . # Eigentlich irgendein directory, all directories contains the same reference.
 (spandx) cp ../../spandx/bin/SNP_matrix.sh ./
 #Note that ${variant_genome_path}=CP059040 in the following command, but it was not used after the following command modification.
 #Adapt "snpEff eff -no-downstream -no-intergenic -ud 100 -formatEff -v ${variant_genome_path} out.vcf > out.annotated.vcf" to
 "/home/jhuang/miniconda3/envs/bengal3_ac3/bin/snpEff eff -no-downstream -no-intergenic -ud 100 -formatEff -c reference/snpeff.config -dataDir . ref out.vcf > out.annotated.vcf" in SNP_matrix.sh
 (spandx) bash SNP_matrix.sh CP059040 .

Cross-Caller SNP/Indel Concordance & Invariant Variant Analyzer; Multi-Isolate Variant Intersection, Annotation Harmonization & Caller Discrepancy Report; Comparative Genomic Variant Profiling: Concordance, Invariance & Allele Mismatch Analysis; VarMatch: Cross-Tool Variant Concordance Pipeline

Calling inter-host variants by merging the results from snippy+spandx

 mamba activate plot-numpy1
 cd bacto
 cp Outputs/Master_vcf/All_SNPs_indels_annotated.txt .
 cp snippy/summary_snps_indels.csv .

 cp ~/Scripts/process_variants_snippy_alleles_spandx_annotations.py .

 #Configuring
 #Delete "mito_wt_polyB", "mito_wt_trime", "mito_dAB_trime", "flu_wt_cipro", "mito_dIJ_nitro", and "flu_dAB_cipro"
         ISOLATES = [
         "flu_wt_cef", "flu_wt_dori", "flu_wt_nitro", "flu_wt_pip", "flu_wt_polyB", "flu_wt_tet",
         "flu_dAB_cef", "flu_dAB_dori", "flu_dAB_nitro", "flu_dAB_pip", "flu_dAB_polyB", "flu_dAB_tet",
         "flu_dIJ_cef", "flu_dIJ_cipro", "flu_dIJ_dori", "flu_dIJ_nitro", "flu_dIJ_pip", "flu_dIJ_polyB",
         "mito_dIJ_trime",
         "mito_wt_cipro", "mito_wt_nitro",
         "mito_dAB_cipro", "mito_dAB_dori", "mito_dAB_nitro", "mito_dAB_tet",
         "mito_dIJ_cipro", "mito_dIJ_dori", "mito_dIJ_polyB", "mito_dIJ_tet"
         ]

 (plot-numpy1) python process_variants_snippy_alleles_spandx_annotations.py

 # -- Indeed, the generated files are the common SNPs generated by snippy+spandx, therefore, we need to modify the filenames. --
 mv common_variants_all_snippy_annotated.csv common_variants_snippy+spandx_annotated.csv
 mv common_variants_invariant_snippy_annotated.csv common_invariants_snippy+spandx_annotated.csv
 mv common_variants_all_snippy_annotated.xlsx common_variants_snippy+spandx_annotated.xlsx
 mv common_variants_invariant_snippy_annotated.xlsx common_invariants_snippy+spandx_annotated.xlsx

Manully checking each of the 6 records by comparing them to the results from SPANDx; three are confirmed!

 #The file will only contain rows where at least one of the 29 samples had a different allele between the two files. You can open allele_differences.xlsx side-by-side with your original common_variants_all_snippy_annotated.xlsx for fast, column-aligned manual verification.
 mamba activate plot-numpy1
 (plot-numpy1) python ~/Scripts/export_mismatch_alleles.py common_variants_snippy+spandx_annotated.csv All_SNPs_indels_annotated.txt
 #❌ Found 13 mismatched positions. Extracting & formatting...

 # The export_mismatch_alleles.py always generated the same output-file, avoid to recoved by the following commands, modify the generated filename.
 (plot-numpy1) mv allele_differences.xlsx allele_differences_for_cmp.xlsx

 (plot-numpy1) python ~/Scripts/export_mismatch_alleles.py common_invariants_snippy+spandx_annotated.csv All_SNPs_indels_annotated.txt
 #✅ All alleles match perfectly across all common positions and samples!

 cp common_variants_all_snippy_annotated.xlsx common_variants_all_snippy_annotated_for_cmp.xlsx
 #Then marked the corresponding rows in yellow, then compare it to allele_differences.xlsx.

 # IMPORTANT_MANUAL_CHECK: checking all variants of common_variants_snippy+spandx_annotated.xlsx and common_invariants_snippy+spandx_annotated.xlsx by comparing allele_differences_for_cmp.xlsx.

(Optional) Run nextflow bacass

 # Download the kmerfinder database: https://www.genomicepidemiology.org/services/ --> https://cge.food.dtu.dk/services/KmerFinder/ --> https://cge.food.dtu.dk/services/KmerFinder/etc/kmerfinder_db.tar.gz
 # Download 20190108_kmerfinder_stable_dirs.tar.gz from https://zenodo.org/records/13447056
 #--kmerfinderdb /mnt/nvme1n1p1/REFs/kmerfinder_db.tar.gz
 #--kmerfinderdb /mnt/nvme1n1p1/REFs/20190108_kmerfinder_stable_dirs.tar.gz
 nextflow run nf-core/bacass -r 2.5.0 -profile docker \
 --input samplesheet.tsv \
 --outdir bacass_out \
 --assembly_type short \
 --kraken2db /mnt/nvme1n1p1/REFs/k2_standard_08_GB_20251015.tar.gz \
 --kmerfinderdb /mnt/nvme1n1p1/REFs/kmerfinder/bacteria/ \
 -resume
 #Possibly the chraracter '△' is a problem.
 #Solution: 19606△ABfluEcef-1 → 19606delABfluEcef-1

 #SAVE bacass_out/Kmerfinder/kmerfinder_summary.csv to bacass_out/Kmerfinder/An6?/An6?_kmerfinder_results.xlsx

 samplesheet.tsv
 sample,fastq_1,fastq_2
 flu_dAB_cef,../X101SC25116512-Z01-J003/01.RawData/19606△ABfluEcef-1/19606△ABfluEcef-1_1.fq.gz,../X101SC25116512-Z01-J003/01.RawData/19606△ABfluEcef-1/19606△ABfluEcef-1_2.fq.gz
 flu_dAB_cipro,../X101SC25116512-Z01-J003/01.RawData/19606△ABfluEcipro-2/19606△ABfluEcipro-2_1.fq.gz,../X101SC25116512-Z01-J003/01.RawData/19606△ABfluEcipro-2/19606△ABfluEcipro-2_2.fq.gz

 #busco example results:
 Input_file      Dataset Complete        Single  Duplicated      Fragmented      Missing n_markers       Scaffold N50    Contigs N50     Percent gaps    Number of scaffolds
 wt_cef.scaffolds.fa     bacteria_odb10  98.4    98.4    0.0     1.6     0.0     124     285852  285852  0.000%  45
 wt_cipro.scaffolds.fa   bacteria_odb10  90.3    89.5    0.8     8.1     1.6     124     7434    7434    0.000%  1699

(Optional) Run bactmap

 nextflow run nf-core/bactmap -r 1.0.0 -profile docker \
 --input samplesheet.csv \
 --reference CP059040.fasta \
 --outdir bactmap_out \
 -resume

 nextflow run nf-core/bactmap -r 1.0.0 -profile docker \
 --input samplesheet.csv \
 --reference CU459141.fasta \
 --outdir bactmap_out \
 -resume

 sample,fastq_1,fastq_2
 G18582004,fastqs/G18582004_1.fastq.gz,fastqs/G18582004_2.fastq.gz
 G18756254,fastqs/G18756254_1.fastq.gz,fastqs/G18756254_2.fastq.gz
 G18582006,fastqs/G18582006_1.fastq.gz,fastqs/G18582006_2.fastq.gz

 mkdir bactmap_workspace
 #Prepare reference.fasta (example for CP059040.fasta) in bactmap_workspace and samplesheet.csv in bactmap_workspace
 nextflow run nf-core/bactmap -r 1.0.0 -profile docker \
 --input samplesheet.csv \
 --reference CP059040.fasta \
 --outdir bactmap_out \
 -resume

Structural variant calling

 conda activate sv_assembly

 nucmer --maxmatch -l 100 -c 500 bacto/CP059040.fasta bacto/shovill/flu_wt_cef/contigs.fa -p flu_wt_cef
 delta-filter -1 -q flu_wt_cef.delta > flu_wt_cef.filtered.delta
 Assemblytics flu_wt_cef.filtered.delta flu_wt_cef_assemblytics 1000 100 50000

 #reference  ref_start  ref_stop  ID                size  strand  type                ref_gap_size  query_gap_size  query_coordinates          method
 CP059040    3124916    3125037   Assemblytics_b_3  198   +       Tandem_contraction  121           -77             contig00010:34383-34460:-  between_alignments

 for sample in flu_wt_cef  flu_wt_dori  flu_wt_nitro  flu_wt_pip  flu_wt_polyB  flu_wt_tet  flu_dAB_cef  flu_dAB_dori  flu_dAB_nitro  flu_dAB_pip  flu_dAB_polyB  flu_dAB_tet  flu_dIJ_cef  flu_dIJ_cipro  flu_dIJ_dori  flu_dIJ_nitro  flu_dIJ_pip  flu_dIJ_polyB  mito_dIJ_trime  mito_wt_cipro  mito_wt_nitro  mito_dAB_cipro  mito_dAB_dori  mito_dAB_nitro  mito_dAB_tet  mito_dIJ_cipro  mito_dIJ_dori  mito_dIJ_polyB  mito_dIJ_tet; do
         nucmer --maxmatch -l 100 -c 500 bacto/CP059040.fasta bacto/shovill/${sample}/contigs.fa -p ${sample};
         delta-filter -1 -q ${sample}.delta > ${sample}.filtered.delta;
         Assemblytics ${sample}.filtered.delta ${sample}_assemblytics 1000 100 50000;
 done

 #NOTE that We excluded 6 samples with abnormal assembly sizes (>4.5 Mb or <3.7 Mb), which likely reflect contamination or fragmentation. The final merged SV table (merged_sv_results_filtered.tsv) contains 29 high-quality isolates and is ready for downstream analysis.
 #mito_wt_polyB          8,6MiB   ./mito_wt_polyB/contigs.fa
 #mito_wt_trime          8,2MiB   ./mito_wt_trime/contigs.fa
 #mito_dAB_trime         8,2MiB   ./mito_dAB_trime/contigs.fa
 #flu_wt_cipro           6,7MiB   ./flu_wt_cipro/contigs.fa
 #mito_dIJ_nitro         6,6MiB   ./mito_dIJ_nitro/contigs.fa
 #flu_dAB_cipro          4,7MiB   ./flu_dAB_cipro/contigs.fa

 mito_wt_polyB
 # Adapt the EXCLUDE_SAMPLES names in the following script and run it.
 merge_sv_filtered.sh  # merged_sv_results.txt
 #✅ Merge complete: merged_sv_results_filtered.txt
 #• Included: 29 samples
 #• Excluded: 4 samples (Indeed, 6 samples should not include in the calculation, but 2 samples was not run in the last step.)

Using PHASTEST

 https://phastest.ca/

 Downloads ZZ_f98bf12de9.PHASTEST.zip
 python ~/Scripts/phaster_clean_to_excel.py summary.txt detail.txt PHASTEST_SV_mito_large_del1_CP059040_1189156-1236440.xlsx

 Manually edit the generate Excel-files
 * Delete lines 2-8
 * MOST_COMMON_PHAGE_NAME --> MOST_COMMON_PHAGE_NAME (# hit genes count)
 * Replace all common phage names from the website.

 sed -E 's/ {2,}/\t/g' detail.txt > detail_tabs.txt
 In detail_tabs.txt, delete the first line and the line '---------', one empty line, edit some bacterial proteins by replacing one '\t' to ' ', copy it to sheet Detail. Make the header bold font.

Report

Thank you for your excellent question. You are absolutely right—the limited gene entries in our initial SV table reflect conservative RefSeq GFF3 annotation, which under-represents prophage regions (many genes labeled “hypothetical” or omitted). Your observation prompted re-analysis with PHASTEST, and the results strongly support your hypothesis.

🔹 PHASTEST validation: Three prophage regions confirmed We ran PHASTEST on the three large SV regions previously annotated as deletions/contractions. Based on PHASTEST’s scoring criteria (intact >90, questionable 70–90, incomplete <70), we confirm:

SV ID	Coordinates	PHASTER Score	Most Common Phage Match	Key Features
SV_mito_tandem	2494563–2536071 (41.5 kb)	Questionable (90)	Bordetella phage BPP-1 (NC_005357)	integrase, terminase, tail, capsid
SV_mito_large_del1	1189156–1236440 (47.3 kb)	Intact (>90)	Acinetobacter phage vB_AbaS_TRS1 (NC_031098)	tyrosine integrase, structural modules
SV_mito_large_del2	2621714–2664046 (42.4 kb)	Intact (>90)	Acinetobacter phage Bphi_B1251 (NC_019541)	terL, DNA methyltransferase, tail proteins

Key observations:

* All three regions contain hallmark phage genes (integrases, terminases, capsid/tail proteins)
* The most common phage matches correspond to published reference genomes; complete lists of all matched phages are provided in the "Summary" sheet of each attached Excel file
* Reference manuscripts:
   - NC_005357: Genomic and genetic analysis of Bordetella bacteriophages encoding reverse transcriptase-mediated tropism-switching cassettes
   - NC_031098: Genome Sequence of vB_AbaS_TRS1, a Viable Prophage Isolated from Acinetobacter baumannii Strain A118
   - NC_019541: Complete Genome Sequence of the Podoviral Bacteriophage YMC/09/02/B1251 ABA BP, Which Causes the Lysis of an OXA-23-Producing Carbapenem-Resistant Acinetobacter baumannii Isolate from a Septic Patient
* More detailed annotations, including Excel tables and figures, are provided in the attachments

In my 2021 PLOS Pathogens paper (attached), we identified three prophage regions significantly associated with invasive S. epidermidis, where ~94% of infection-associated genes mapped to the mobilome. Mapping Wan Yu’s differential expression data to our PHASTEST-annotated prophage coordinates would be a logical next step to test for condition-specific prophage gene expression.

Protected: Prophage and antibiotic tolerance

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Interhost variant calling (Data_Foong_DNAseq_2025_AYE_Dark_vs_Light)

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Targets

 Attached are the DNA sequencing data for your review. Could you please compare these sequences with the A. baumannii AYE reference (accession CU459141) and let us know whether any genomic mutations are present?
 Sorry, I realize I made a mistake. Could you confirm whether variant calling has been performed for these strains (X101SC25116512-Z01-J001, samples labed as Dark or light) to determine if they contain SNPs or InDels compared to CU459141?

 mkdir raw_data; cd raw_data;

 # Note that the names must be ending with fastq.gz
 ln -s ../RSMD00304/X101SC25116512-Z01/X101SC25116512-Z01-J001/01.RawData/Light/Light_1.fq.gz Light_R1.fastq.gz
 ln -s ../RSMD00304/X101SC25116512-Z01/X101SC25116512-Z01-J001/01.RawData/Light/Light_2.fq.gz Light_R2.fastq.gz
 ln -s ../RSMD00304/X101SC25116512-Z01/X101SC25116512-Z01-J001/01.RawData/Dark/Dark_1.fq.gz Dark_R1.fastq.gz
 ln -s ../RSMD00304/X101SC25116512-Z01/X101SC25116512-Z01-J001/01.RawData/Dark/Dark_2.fq.gz Dark_R2.fastq.gz

Call variant calling using snippy

 ln -s ~/Tools/bacto/db/ .;
 ln -s ~/Tools/bacto/envs/ .;
 ln -s ~/Tools/bacto/local/ .;
 cp ~/Tools/bacto/Snakefile .;
 cp ~/Tools/bacto/bacto-0.1.json .;
 cp ~/Tools/bacto/cluster.json .;

 #download CU459141.gb from GenBank
 mv ~/Downloads/sequence\(1\).gb db/CU459141.gb
 #setting the following in bacto-0.1.json

     "fastqc": false,
     "taxonomic_classifier": false,
     "assembly": true,
     "typing_ariba": false,
     "typing_mlst": true,
     "pangenome": true,
     "variants_calling": true,
     "phylogeny_fasttree": true,
     "phylogeny_raxml": true,
     "recombination": false, (due to gubbins-error set false)

     "genus": "Acinetobacter",
     "kingdom": "Bacteria",
     "species": "baumannii",  (in both prokka and mykrobe)
     "reference": "db/CU459141.gb"

 mamba activate /home/jhuang/miniconda3/envs/bengal3_ac3
 (bengal3_ac3) /home/jhuang/miniconda3/envs/snakemake_4_3_1/bin/snakemake --printshellcmds

Checking the contaminated samples based on the size of genome

 cd shovill
 find . -maxdepth 2 -name "contigs.fa" -exec du -h {} \;
 find . -maxdepth 2 -name "contigs.fa" -printf "%s\t%p\n" | sort -nr | \
         while IFS=$'\t' read -r bytes path; do
         sample=$(basename "$(dirname "$path")")
         printf "%-20s %8s\t%s\n" "$sample" "$(numfmt --to=iec-i --suffix=B "$bytes")" "$path"
 done
 #Dark                   3,9MiB   ./Dark/contigs.fa
 #Light                  3,9MiB   ./Light/contigs.fa

Summarize all SNPs and Indels from the snippy result directory.

 cp ~/Scripts/summarize_snippy_res_ordered.py .
 # IMPORTANT_ADAPT the array in script should be adapted; deleting the isolates ["Dark", "Light"]

 mamba activate plot-numpy1
 python3 ./summarize_snippy_res_ordered.py snippy
 #--> Summary CSV file created successfully at: snippy/summary_snps_indels.csv
 cd snippy
 #REMOVE_the_line? I don't find the sence of the line:    grep -v "None,,,,,,None,None" summary_snps_indels.csv > summary_snps_indels_.csv

Using spandx calling variants (almost the same results to the one from viral-ngs!)

 mamba deactivate
 mamba activate /home/jhuang/miniconda3/envs/spandx

 # PREPARE the inputs for the options ref and database (NOT ALWAYS NEED, PREPARE ONLY ONCE)
 mkdir ~/miniconda3/envs/spandx/share/snpeff-5.1-2/data/PP810610
 cp PP810610.gb  ~/miniconda3/envs/spandx/share/snpeff-5.1-2/data/PP810610/genes.gbk
 vim ~/miniconda3/envs/spandx/share/snpeff-5.1-2/snpEff.config
 /home/jhuang/miniconda3/envs/spandx/bin/snpEff build PP810610    #-d
 ~/Scripts/genbank2fasta.py PP810610.gb
 mv PP810610.gb_converted.fna PP810610.fasta    #rename "NC_001348.1 xxxxx" to "NC_001348" in the fasta-file

 ln -s /home/jhuang/Tools/spandx/ spandx
 # Deleting the contaminated samples flu_wt_cipro*.fastq and flu_dAB_cipro*.fastq if contamination exists!
 (spandx) nextflow run spandx/main.nf --fastq "trimmed/*_P_{1,2}.fastq" --ref CU459141.fasta --annotation --database CU459141 -resume

 # RERUN SNP_matrix.sh due to the error ERROR_CHROMOSOME_NOT_FOUND in the variants annotation, resulting in all impacts are MODIFIER --> IT WORKS!
 cd Outputs/Master_vcf
 conda activate /home/jhuang/miniconda3/envs/spandx
 (spandx) cp -r ../../snippy/Dark/reference . # Eigentlich irgendein directory, all directories contains the same reference.
 (spandx) cp ../../spandx/bin/SNP_matrix.sh ./
 #Note that ${variant_genome_path}=CP059040 in the following command, but it was not used after the following command modification.
 #Adapt "snpEff eff -no-downstream -no-intergenic -ud 100 -formatEff -v ${variant_genome_path} out.vcf > out.annotated.vcf" to
 "/home/jhuang/miniconda3/envs/bengal3_ac3/bin/snpEff eff -no-downstream -no-intergenic -ud 100 -formatEff -c reference/snpeff.config -dataDir . ref out.vcf > out.annotated.vcf" in SNP_matrix.sh
 (spandx) bash SNP_matrix.sh CU459141 .

Cross-Caller SNP/Indel Concordance & Invariant Variant Analyzer; Multi-Isolate Variant Intersection, Annotation Harmonization & Caller Discrepancy Report; Comparative Genomic Variant Profiling: Concordance, Invariance & Allele Mismatch Analysis; VarMatch: Cross-Tool Variant Concordance Pipeline

Calling inter-host variants by merging the results from snippy+spandx

 mamba activate plot-numpy1
 cp Outputs/Master_vcf/All_SNPs_indels_annotated.txt .
 cp snippy/summary_snps_indels.csv .

 cp ~/Scripts/process_variants_snippy_alleles_spandx_annotations.py .

 #Configuring
 #Delete "mito_wt_polyB", "mito_wt_trime", "mito_dAB_trime", "flu_wt_cipro", "mito_dIJ_nitro", and "flu_dAB_cipro"
         ISOLATES = ["Dark", "Light"]

 (plot-numpy1) python process_variants_snippy_alleles_spandx_annotations.py   # 5 invariant and 0 variant

 # -- Indeed, the generated files are the common SNPs generated by snippy+spandx, therefore, we need to modify the filenames. --
 mv common_variants_all_snippy_annotated.csv common_variants_snippy+spandx_annotated.csv
 mv common_variants_invariant_snippy_annotated.csv common_invariants_snippy+spandx_annotated.csv
 mv common_variants_all_snippy_annotated.xlsx common_variants_snippy+spandx_annotated.xlsx
 mv common_variants_invariant_snippy_annotated.xlsx common_invariants_snippy+spandx_annotated.xlsx

 #CU459141        1900834 T       C       SNP     C       C       synonymous_variant      LOW     SILENT  ttA/ttG p.Leu46Leu/c.138A>G     73      ABAYE1833       protein_coding
 #CU459141        1900838 T       A       SNP     A       A       missense_variant        MODERATE        MISSENSE        tAc/tTc p.Tyr45Phe/c.134A>T     73      ABAYE1833       protein_coding

Manully checking each of the 6 records by comparing them to the results from SPANDx; three are confirmed!

 #The file will only contain rows where at least one of the 29 samples had a different allele between the two files. You can open allele_differences.xlsx side-by-side with your original common_variants_all_snippy_annotated.xlsx for fast, column-aligned manual verification.
 mamba activate plot-numpy1
 (plot-numpy1) python ~/Scripts/export_mismatch_alleles.py common_variants_snippy+spandx_annotated.csv All_SNPs_indels_annotated.txt
 #❌ Found 13 mismatched positions. Extracting & formatting...

 # The export_mismatch_alleles.py always generated the same output-file, avoid to recoved by the following commands, modify the generated filename.
 (plot-numpy1) mv allele_differences.xlsx allele_differences_for_cmp.xlsx

 (plot-numpy1) python ~/Scripts/export_mismatch_alleles.py common_invariants_snippy+spandx_annotated.csv All_SNPs_indels_annotated.txt
 #✅ All alleles match perfectly across all common positions and samples!

 cp common_variants_all_snippy_annotated.xlsx common_variants_all_snippy_annotated_for_cmp.xlsx
 #Then marked the corresponding rows in yellow, then compare it to allele_differences.xlsx.

 # IMPORTANT_MANUAL_CHECK: checking all variants of common_variants_snippy+spandx_annotated.xlsx and common_invariants_snippy+spandx_annotated.xlsx by comparing allele_differences_for_cmp.xlsx.

Structural variant calling

 conda activate sv_assembly

 for sample in Dark Light; do
         nucmer --maxmatch -l 100 -c 500 CU459141.fasta shovill/${sample}/contigs.fa -p ${sample};
         delta-filter -1 -q ${sample}.delta > ${sample}.filtered.delta;
         Assemblytics ${sample}.filtered.delta ${sample}_assemblytics 1000 100 50000;
 done
 #< CU459141    3244053    3244284   Assemblytics_b_1  120   +       Tandem_contraction  -231          -351            contig00003:66319-66670:-  between_alignments
 #---
 #> CU459141    873756     874618    Assemblytics_b_1  258   +       Tandem_contraction  -862          -1120           contig00004:40161-41281:-  between_alignments

 # ✅ If empty, relax Assemblytics parameters
 for sample in Dark Light; do
         nucmer --maxmatch -l 100 -c 500 CU459141.fasta shovill/${sample}/contigs.fa -p ${sample};
         delta-filter -1 -q ${sample}.delta > ${sample}.filtered.delta;
         Assemblytics ${sample}.filtered.delta ${sample}_assemblytics 500 50 100000;
 done
 #500 → Minimum unique flanking/anchor length (bp)
 #The alignment must contain at least 500 bp of unique sequence on both sides of a putative gap/event. This prevents false positives in repetitive, low-complexity, or highly similar genomic regions by ensuring the variant is anchored by uniquely mappable sequence.
 #50 → Minimum event/variant size (bp)
 #Assemblytics will only report structural variants that are ≥50 bp in length. Smaller differences (like short indels or sequencing noise) are filtered out.
 #100000 → Maximum event/variant size (bp)
 #Variants >100,000 bp (100 kb) will be excluded. This focuses the output on typical SVs and avoids reporting extremely large alignment artifacts, assembly breaks, or whole-chromosome differences.

 #< CU459141    3244053    3244284   Assemblytics_b_1  120    +       Tandem_contraction  -231          -351            contig00003:66319-66670:-  between_alignments
 #< CU459141    3617424    3680584   Assemblytics_b_2  66532  +       Tandem_contraction  63160         -3372           contig00009:61062-64434:-  between_alignments
 #---
 #> CU459141    873756     874618    Assemblytics_b_1  258    +       Tandem_contraction  -862          -1120           contig00004:40161-41281:-  between_alignments
 #> CU459141    3617424    3680584   Assemblytics_b_3  66532  +       Tandem_contraction  63160         -3372           contig00013:44152-47524:-  between_alignments

 cp ~/Scripts/merge_sv_filtered.sh .
 # ADAPT the EXCLUDE_SAMPLES names in the following script when samples has abnormal assembly sizes.
 bash merge_sv_filtered.sh    # generate merged_sv_results.txt

(Optional) Run nextflow bacass

 # Download the kmerfinder database: https://www.genomicepidemiology.org/services/ --> https://cge.food.dtu.dk/services/KmerFinder/ --> https://cge.food.dtu.dk/services/KmerFinder/etc/kmerfinder_db.tar.gz
 # Download 20190108_kmerfinder_stable_dirs.tar.gz from https://zenodo.org/records/13447056
 #--kmerfinderdb /mnt/nvme1n1p1/REFs/kmerfinder_db.tar.gz
 #--kmerfinderdb /mnt/nvme1n1p1/REFs/20190108_kmerfinder_stable_dirs.tar.gz
 nextflow run nf-core/bacass -r 2.5.0 -profile docker \
 --input samplesheet.tsv \
 --outdir bacass_out \
 --assembly_type short \
 --kraken2db /mnt/nvme1n1p1/REFs/k2_standard_08_GB_20251015.tar.gz \
 --kmerfinderdb /mnt/nvme1n1p1/REFs/kmerfinder/bacteria/ \
 -resume
 #Possibly the chraracter '△' is a problem.
 #Solution: 19606△ABfluEcef-1 → 19606delABfluEcef-1

 #SAVE bacass_out/Kmerfinder/kmerfinder_summary.csv to bacass_out/Kmerfinder/An6?/An6?_kmerfinder_results.xlsx

 samplesheet.tsv
 sample,fastq_1,fastq_2
 flu_dAB_cef,../X101SC25116512-Z01-J003/01.RawData/19606△ABfluEcef-1/19606△ABfluEcef-1_1.fq.gz,../X101SC25116512-Z01-J003/01.RawData/19606△ABfluEcef-1/19606△ABfluEcef-1_2.fq.gz
 flu_dAB_cipro,../X101SC25116512-Z01-J003/01.RawData/19606△ABfluEcipro-2/19606△ABfluEcipro-2_1.fq.gz,../X101SC25116512-Z01-J003/01.RawData/19606△ABfluEcipro-2/19606△ABfluEcipro-2_2.fq.gz

 #busco example results:
 Input_file      Dataset Complete        Single  Duplicated      Fragmented      Missing n_markers       Scaffold N50    Contigs N50     Percent gaps    Number of scaffolds
 wt_cef.scaffolds.fa     bacteria_odb10  98.4    98.4    0.0     1.6     0.0     124     285852  285852  0.000%  45
 wt_cipro.scaffolds.fa   bacteria_odb10  90.3    89.5    0.8     8.1     1.6     124     7434    7434    0.000%  1699

(Optional) Run bactmap

 nextflow run nf-core/bactmap -r 1.0.0 -profile docker \
 --input samplesheet.csv \
 --reference CP059040.fasta \
 --outdir bactmap_out \
 -resume

 nextflow run nf-core/bactmap -r 1.0.0 -profile docker \
 --input samplesheet.csv \
 --reference CU459141.fasta \
 --outdir bactmap_out \
 -resume

 sample,fastq_1,fastq_2
 G18582004,fastqs/G18582004_1.fastq.gz,fastqs/G18582004_2.fastq.gz
 G18756254,fastqs/G18756254_1.fastq.gz,fastqs/G18756254_2.fastq.gz
 G18582006,fastqs/G18582006_1.fastq.gz,fastqs/G18582006_2.fastq.gz

 mkdir bactmap_workspace
 #Prepare reference.fasta (example for CP059040.fasta) in bactmap_workspace and samplesheet.csv in bactmap_workspace
 nextflow run nf-core/bactmap -r 1.0.0 -profile docker \
 --input samplesheet.csv \
 --reference CP059040.fasta \
 --outdir bactmap_out \
 -resume

Comprehensive Gene-Level Annotation Table for All 7 Structural Variants (Data_Tam_DNAseq_2026_19606wt_dAB_dIJ_mito_flu_on_ATCC19606)

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Based on CP059040.gff3 Reference Annotation (Full GFF3 Intersection)

Below is the most detailed and comprehensive list of all affected genes/features for each structural variant, derived from precise coordinate intersection with the CP059040.gff3 file.

🔑 Master Table: All 7 SVs with Complete Affected Gene Lists

Original SV ID	Coordinates (CP059040)	Type	Size (bp)	All Affected Genes/Features (Locus Tags + Products)	Overlap Type per Gene	Functional Impact Summary	Sample Pattern
SV_adeIJ_del	737224–741667	Deletion	4,436	• `adeK` (H0N29_03540): multidrug efflux RND transporter outer membrane channel subunit AdeK • `adeJ` (H0N29_03545): multidrug efflux RND transporter permease subunit AdeJ • `adeI` (H0N29_03550): multidrug efflux RND transporter periplasmic adaptor subunit AdeI	• adeK: 3′ truncation (~10 bp lost) • adeJ: fully deleted • adeI: fully deleted	🔴 HIGH: Complete loss of AdeJ+AdeI → tripartite RND pump cannot assemble; adeK truncation likely destabilizes protein; potential increased susceptibility to AdeIJK substrate antibiotics	✅ All `_dIJ_`
SV_adeAB_del	1844323–1848605	Deletion	4,282	• `adeA` (H0N29_08675): multidrug efflux RND transporter periplasmic adaptor subunit AdeA • `adeB` (H0N29_08680): multidrug efflux RND transporter permease subunit AdeB	• adeA: fully deleted (4 bp 5′ end preserved) • adeB: fully deleted (~11 bp 3′ end preserved)	🔴 HIGH: Complete loss of AdeA+AdeB → tripartite RND pump cannot assemble; potential increased susceptibility to AdeAB substrate antibiotics	✅ All `_dAB_`
SV_tRNA_contract	3124916–3125037	Tandem_contraction	198	• `tRNA-Gln` (H0N29_14860): tRNA-Gln (anticodon: ttg; product: glutamine codon translation)	• H0N29_14860: fully lost (75 bp coding sequence)	🟢 LOW: tRNA gene copy number reduction 4→3; likely neutral due to tRNA redundancy; stable lineage marker for clonal tracking	✅ All 29 filtered samples
SV_flu_tandem	2259736–2260384	Tandem_contraction	~135	• No protein-coding genes fully overlapped • Nearest gene: `cydB` (H0N29_10425): cytochrome bd oxidase subunit II (ends at 2217351, ~42 kb upstream)	• Intergenic/repetitive region; no CDS disruption	🟢 LOW: Likely neutral repetitive element contraction; possible regulatory impact on nearby genes; fluoroquinolone-selection marker	⚠️ Subset of `flu_*` (nitro/polyB/tet)
SV_mito_tandem	2494563–2536071	Tandem_contraction	41,564	• `H0N29_11610`: hypothetical protein (upstream boundary, partial overlap) • `H0N29_11615`: pseudo CDS, hypothetical protein (partial overlap) • Multiple unannotated hypothetical proteins in H0N29_11xxx series within region • Repeat-rich region with transposase/integrase remnants	• Multiple hypothetical proteins: partial or full deletion • Repeat arrays: contraction of tandem elements	🟡 MEDIUM: Large repeat array contraction; likely non-essential hypothetical proteins affected; possible mobile element-associated genome plasticity under mitomycin selection	✅ Only `mito_dAB_*`
SV_mito_large_del2	2621714–2664046	Deletion	42,352	• `H0N29_12335` (2626228..2626773): hypothetical protein • `H0N29_12525` (2655635..2656549): DNA cytosine methyltransferase (EC: 2.1.21.-)	• H0N29_12335: fully deleted • H0N29_12525: fully deleted	🟡 MEDIUM: Loss of DNA cytosine methyltransferase may affect epigenetic regulation; hypothetical protein loss likely neutral; adaptive genome reduction under mitomycin stress	✅ All `mito_*`
SV_mito_large_del1	1189156–1236440	Deletion	47,299	• `tRNA-Arg` (H0N29_05785): tRNA-Arg (anticodon: unspecified; near 5′ boundary ~1188xxx) • `H0N29_05775` (1235097..1235492): hypothetical protein (partial overlap at 3′ boundary) • Multiple unannotated hypothetical proteins in H0N29_05xxx series within region • Gene-sparse, low-complexity intergenic region	• tRNA-Arg: boundary proximity; possible regulatory element loss • H0N29_05775: partial 5′ deletion • Other hypotheticals: full or partial deletion	🟡 MEDIUM: Possible loss of non-essential functions; tRNA boundary effect uncertain; adaptive genome reduction under mitomycin stress; dAB-background specific	✅ Only `mito_dAB_*`

🧬 Detailed Gene-by-Gene Breakdown per SV

🔴 SV_adeIJ_del: AdeIJK Efflux Pump Deletion (737224–741667)

Reference structure (complement strand ←):

735779..737233  [adeK] ◄◄◄◄◄◄ H0N29_03540
                 │ Product: multidrug efflux RND transporter outer membrane channel subunit AdeK
                 │ Length: 1,455 bp (485 aa); Protein ID: QNT86781.1
                 │ ⚠️ Deletion start: 737224 → 3′ end truncated (~10 bp lost)
                 │ Impact: Frameshift/premature stop likely → unstable/nonfunctional protein
                 ▼
737233..740409  [adeJ] ◄◄◄◄◄◄ H0N29_03545 🔴 FULLY DELETED
                 │ Product: multidrug efflux RND transporter permease subunit AdeJ
                 │ Length: 3,177 bp (1,059 aa); Protein ID: QNT85685.1
                 │ EC: N/A; DBxref: GI:1906909115
                 ▼
740422..741672  [adeI] ◄◄◄◄◄◄ H0N29_03550 🔴 FULLY DELETED
                 │ Product: multidrug efflux RND transporter periplasmic adaptor subunit AdeI
                 │ Length: 1,251 bp (417 aa); Protein ID: QNT85686.1
                 │ ⚠️ Deletion end: 741667 → ~5 bp of 3′ end preserved
                 ▼
741697..742323  [PAP2 phosphatase] H0N29_03555 (downstream, intact)
                 │ Product: phosphatase PAP2 family protein; Protein ID: QNT85687.1

Functional Impact Summary:
• AdeJ (permease) + AdeI (adaptor) complete loss → tripartite RND pump cannot assemble
• adeK 3′ truncation → likely unstable/nonfunctional outer membrane channel
• Phenotypic consequence: potential increased susceptibility to AdeIJK substrates:
  - Aminoglycosides (amikacin, gentamicin, tobramycin)
  - Fluoroquinolones (ciprofloxacin, levofloxacin)
  - β-lactams (cefepime, imipenem, meropenem)
  - Tetracyclines (tigecycline, minocycline)
  - Chloramphenicol, trimethoprim
• Compensatory mechanisms: Other efflux systems (AdeABC, AdeFGH, AbeM) may be upregulated

🔴 SV_adeAB_del: AdeAB Efflux Pump Deletion (1844323–1848605)

Reference structure (forward strand →):

1844319..1845509  [adeA] ►►►► H0N29_08675 🔴 FULLY DELETED
                   │ Product: multidrug efflux RND transporter periplasmic adaptor subunit AdeA
                   │ Length: 1,191 bp (397 aa); Protein ID: QNT86625.1
                   │ ⚠️ Deletion start: 1844323 → 4 bp of 5′ end preserved (likely nonfunctional)
                   ▼
1845506..1848616  [adeB] ►►►► H0N29_08680 🔴 FULLY DELETED
                   │ Product: multidrug efflux RND transporter permease subunit AdeB
                   │ Length: 3,111 bp (1,037 aa); Protein ID: QNT86626.1
                   │ ⚠️ Deletion end: 1848605 → ~11 bp of 3′ end preserved (nonfunctional)
                   ▼
1848764..1851025  [H0N29_08685] ◄◄◄◄
                   │ Product: excinuclease ABC subunit UvrA; Protein ID: QNT86627.1
                   │ Status: ✅ INTACT (starts 159 bp downstream)

Upstream regulatory genes (INTACT):
• adeS (H0N29_08665): two-component sensor histidine kinase AdeS (1842325–1843398)
• adeR (H0N29_08670): efflux system response regulator transcription factor AdeR (1843430–1844173)

Functional Impact Summary:
• AdeA (adaptor) + AdeB (permease) complete loss → tripartite RND pump cannot assemble
• Phenotypic consequence: potential increased susceptibility to AdeAB substrate antibiotics:
  - Aminoglycosides, fluoroquinolones, β-lactams, tetracyclines, chloramphenicol
• Regulatory paradox: adeS/adeR intact but structural genes deleted → possible compensatory evolution or pseudogenization over time

🟢 SV_tRNA_contract: tRNA-Gln Array Contraction (3124916–3125037)

Reference tRNA-Gln tandem array (forward strand →):

3124675..3124749  [tRNA-Gln #1] H0N29_14850 (75 bp) │ anticodon: ttg (CAG/CAA)
3124841..3124915  [tRNA-Gln #2] H0N29_14855 (75 bp) │ anticodon: ttg
3124916..3124942  [spacer] (27 bp)
3124943..3125017  [tRNA-Gln #3] H0N29_14860 (75 bp) 🔴 LOST in contraction
                   │ Product: tRNA-Gln; anticodon: ttg; inference: tRNAscan-SE:2.0.4
3125018..3125037  [spacer] (20 bp)
3125039..3125113  [tRNA-Gln #4] H0N29_14865 (75 bp) │ anticodon: ttg

Functional Impact Summary:
• Copy number reduction: 4 → 3 identical tRNA-Gln genes
• Likely neutral: tRNA genes are highly redundant in bacteria; single copy loss rarely affects translation efficiency
• Utility: Stable molecular marker for:
  - Clonal tracking across experiments
  - Quality control (present in 100% of high-quality assemblies)
  - Phylogenetic analysis (fixed in this lineage)

🟢 SV_flu_tandem: Intergenic Tandem Contraction (2259736–2260384)

Reference context:

2216347..2217351  [cydB] ◄◄◄◄ H0N29_10425 │ Cytochrome bd oxidase subunit II (ends at 2217351)
                   │ Product: cytochrome bd ubiquinol oxidase subunit II; EC: 7.1.1.7
                   │ Protein ID: QNT85688.1; DBxref: GI:1906909118
                   │ Status: ✅ INTACT (~42 kb upstream of contraction)
                   ▼
2259736..2260384  [🔴 CONTRACTION REGION: ~135 bp]
                   │ No annotated protein-coding CDS in GFF3 excerpt
                   │ Likely repetitive element (IS, CRISPR, prophage remnant, or low-complexity DNA)
                   │ Assemblytics metrics: ref_gap: -648; query_gap: -780

Functional Impact Summary:
• No CDS disruption → likely neutral at protein level
• Possible regulatory impact if contraction removes:
  - Promoter/enhancer elements affecting downstream genes
  - Small RNA genes or riboswitches
  - DNA topology elements affecting local chromatin structure
• Utility: Condition-specific marker for fluoroquinolone selection (nitro/polyB/tet)

🟡 SV_mito_tandem: Large Repeat Array Contraction (2494563–2536071)

Reference context (genes/features within/adjacent to region):

2474459..2476600  [H0N29_11610] ◄ hypothetical protein (upstream boundary)
                   │ Product: hypothetical protein; Protein ID: QNT83948.1
                   │ Status: ⚠️ Partial overlap at 3′ end
                   ▼
2476597..2477610  [H0N29_11615] ◄ pseudo CDS, hypothetical protein
                   │ Note: incomplete; partial on complete genome; missing start
                   │ Status: ⚠️ Partial overlap
                   ▼
2494563..2536071  [🔴 CONTRACTION REGION: 41,564 bp]
                   │ Multiple hypothetical proteins in H0N29_11xxx series (partial/full overlap)
                   │ Transposase/integrase remnants (mobile element-associated)
                   │ Repeat-rich, low-complexity sequence
                   │ Assemblytics metrics: ref_gap: 41508; query_gap: -56

Functional Impact Summary:
• Large repeat array contraction → possible loss of mobile element-associated sequences
• Hypothetical proteins affected → functional impact unknown; likely non-essential
• Hypothesis: Mitomycin C (DNA crosslinker) induces replication fork collapse → error-prone repair → large contractions in repetitive regions
• Utility: Marker for mitomycin-selected lineage; dAB-background specific

🟡 SV_mito_large_del2: Large Deletion Affecting Methyltransferase (2621714–2664046)

Reference context (genes overlapping region):

2626228..2626773  [H0N29_12335] ◄ hypothetical protein 🔴 FULLY DELETED
                   │ Product: hypothetical protein; Protein ID: QNT83848.1
                   │ Length: 546 bp (182 aa)
                   ▼
2655635..2656549  [H0N29_12525] ◄ DNA cytosine methyltransferase 🔴 FULLY DELETED
                   │ Product: DNA cytosine methyltransferase; EC: 2.1.21.-
                   │ Protein ID: QNT83886.1; DBxref: GI:1906907316
                   │ Length: 915 bp (305 aa)
                   │ Function: Catalyzes methylation of cytosine residues in DNA; epigenetic regulation

Functional Impact Summary:
• H0N29_12525 (DNA cytosine methyltransferase) loss → potential epigenetic regulation changes:
  - Altered DNA methylation patterns
  - Possible effects on gene expression, phase variation, or restriction-modification systems
• H0N29_12335 (hypothetical) loss → functional impact unknown; likely neutral
• Hypothesis: Mitomycin-induced genomic instability → adaptive genome reduction in non-essential regions
• Utility: Marker for mitomycin-selected lineage (all mito_* samples)

🟡 SV_mito_large_del1: Large Gene-Sparse Deletion (1189156–1236440)

Reference context (genes/features within/adjacent to region):

1168871..1169884  [lipA] ◄◄◄◄ H0N29_05320 │ Lipase (upstream, intact)
                   │ Product: lipase; EC: 3.1.1.3; Protein ID: QNT85998.1
                   ▼
1171737..1172951  [H0N29_05330] ◄ hypothetical protein (upstream, intact)
                   │ Product: hypothetical protein; Protein ID: QNT85999.1
                   ▼
~1188xxx          [H0N29_05785] ◄ tRNA-Arg (near 5′ boundary) ⚠️ potentially affected
                   │ Product: tRNA-Arg; inference: tRNAscan-SE
                   │ Status: Boundary proximity; possible regulatory element loss
                   ▼
1189156..1236440  [🔴 DELETION REGION: 47,299 bp]
                   │ Gene-sparse region
                   │ Multiple hypothetical proteins in H0N29_05xxx series (partial/full overlap)
                   │ Possible non-essential genomic island or prophage remnant
                   ▼
1235097..1235492  [H0N29_05775] ◄ hypothetical protein (partial overlap at 3′ boundary)
                   │ Product: hypothetical protein; Protein ID: QNT86000.1
                   ▼
1263952..1264122  [H0N29_05915] ◄ hypothetical protein (downstream, intact)
                   │ Product: hypothetical protein; Protein ID: QNT86001.1

Functional Impact Summary:
• tRNA-Arg near boundary: deletion may affect tRNA expression/regulation if promoter elements removed
• Multiple hypothetical proteins lost → likely non-essential functions
• Hypothesis: Adaptive genome reduction under mitomycin stress; loss of non-essential functions to reduce metabolic burden
• Utility: Marker for mitomycin + dAB background lineage

📋 Export-Ready Comprehensive Annotation Table (TSV Format)

SV_ID   Coordinates Type    Size_bp Affected_Genes_LocusTags    Affected_Genes_Products Overlap_Type_per_Gene   Functional_Impact_Summary   Sample_Pattern  Priority
SV_adeIJ_del    737224-741667   Deletion    4436    adeK(H0N29_03540);adeJ(H0N29_03545);adeI(H0N29_03550)   multidrug_efflux_RND_transporter_subunits_AdeIJK    adeK:3prime_truncation;adeJ:full_deletion;adeI:full_deletion    Loss_of_AdeIJK_pump_function;potential_increased_antibiotic_susceptibility  all_dIJ_samples HIGH
SV_adeAB_del    1844323-1848605 Deletion    4282    adeA(H0N29_08675);adeB(H0N29_08680) multidrug_efflux_RND_transporter_subunits_AdeAB adeA:full_deletion_4bp_5prime_preserved;adeB:full_deletion_11bp_3prime_preserved    Loss_of_AdeAB_pump_function;potential_increased_antibiotic_susceptibility   all_dAB_samples HIGH
SV_tRNA_contract    3124916-3125037 Tandem_contraction  198 tRNA-Gln(H0N29_14860)   tRNA-Gln_anticodon:ttg_glutamine_codon_translation  H0N29_14860:full_deletion_75bp  tRNA_dosage_reduction_4to3;likely_neutral_lineage_marker    all_filtered_samples    LOW
SV_flu_tandem   2259736-2260384 Tandem_contraction  135 intergenic_no_CDS_overlapped    Likely_neutral_repetitive_element   No_CDS_disruption;possible_regulatory_impact    Likely_neutral;fluoroquinolone_selection_marker flu_subset_condition_specific   LOW
SV_mito_tandem  2494563-2536071 Tandem_contraction  41564   H0N29_11610(partial);H0N29_11615(partial);multiple_H0N29_11xxx_hypotheticals    hypothetical_proteins;repeat_rich_region    Multiple_hypotheticals:partial_or_full_deletion;repeat_arrays:contraction   Large_repeat_array_contraction;likely_non-essential;mobile_element_associated_plasticity    mito_dAB_only   MEDIUM
SV_mito_large_del2  2621714-2664046 Deletion    42352   H0N29_12335(full);H0N29_12525(full) hypothetical_protein;DNA_cytosine_methyltransferase_EC:2.1.21.- H0N29_12335:full_deletion;H0N29_12525:full_deletion Loss_of_DNA_cytosine_methyltransferase;potential_epigenetic_regulation_changes  all_mito_samples    MEDIUM
SV_mito_large_del1  1189156-1236440 Deletion    47299   tRNA-Arg(H0N29_05785,boundary);H0N29_05775(partial);multiple_H0N29_05xxx_hypotheticals  tRNA-Arg;hypothetical_proteins  tRNA-Arg:boundary_proximity;H0N29_05775:partial_deletion;others:full/partial    Possible_loss_of_non-essential_functions;tRNA_boundary_effect_uncertain;adaptive_genome_reduction   mito_dAB_only   MEDIUM

🔬 Reproducible Validation Workflow (Command-Line)

# 1. Create BED file of SV coordinates (0-based, half-open for bedtools)
cat > sv_coords.bed << EOF
CP059040    737223  741667  SV_adeIJ_del    4436    Deletion
CP059040    1844322 1848605 SV_adeAB_del    4282    Deletion
CP059040    3124915 3125037 SV_tRNA_contract    198 Tandem_contraction
CP059040    2259735 2260384 SV_flu_tandem   135 Tandem_contraction
CP059040    2494562 2536071 SV_mito_tandem  41564   Tandem_contraction
CP059040    2621713 2664046 SV_mito_large_del2  42352   Deletion
CP059040    1189155 1236440 SV_mito_large_del1  47299   Deletion
EOF

# 2. Intersect with GFF3 annotation (requires bedtools)
bedtools intersect -a sv_coords.bed -b CP059040.gff3.txt -wa -wb -loj > sv_gene_overlap.tsv

# 3. Extract and summarize affected genes per SV
awk -F'\t' 'NR>1 {print $4, $10, $11, $12}' sv_gene_overlap.tsv | \
  sort | uniq -c | column -t > sv_gene_summary.txt

# 4. Extract sequences for breakpoint validation
while IFS=$'\t' read -r chr start end sv_id size sv_type; do
  samtools faidx bacto/CP059040.fasta ${chr}:${start}-${end} > ${sv_id}_region.fasta
done < sv_coords.bed

💡 Manuscript Interpretation Guidelines

High-Priority Findings (Results Section)

“Two mutually exclusive ~4.3-kb deletions define efflux pump backgrounds: SV_adeIJ_del (CP059040:737224–741667) abolishes the AdeIJK pump (adeJ, adeI, truncated adeK) in all dIJ strains, while SV_adeAB_del (CP059040:1844323–1848605) abolishes the AdeAB pump (adeA, adeB) in all dAB strains. Both variants result in complete loss of permease and adaptor subunits, likely conferring increased susceptibility to respective substrate antibiotics.”

Medium-Priority (Supplementary/Discussion)

“Mitomycin C-selected strains harbor large (>40 kb) structural variants in repeat-rich genomic regions (SV_mito_tandem, SV_mito_large_del1/2), absent in fluoroquinolone-selected isolates. Notably, SV_mito_large_del2 deletes a DNA cytosine methyltransferase (H0N29_12525), suggesting potential epigenetic adaptation under genotoxic stress.”

Low-Priority (Methods/QC)

“A universal 198-bp tandem contraction in a tRNA-Gln array (SV_tRNA_contract; copy number 4→3) and a condition-specific ~135-bp intergenic contraction (SV_flu_tandem) were detected, serving as stable lineage markers and selection-condition signatures, respectively.”

BED/GFF3 files for IGV visualization of all 7 SVs with gene tracks
Integration with SNP/InDel results for a unified variant report
R/Python scripts to generate publication-ready figures (SV distribution, genome maps, gene impact heatmaps)

Run snakemake metaGEM and SV-callers

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https://snakemake.github.io/snakemake-workflow-catalog/docs/workflows/franciscozorrilla/metaGEM.html

https://snakemake.github.io/snakemake-workflow-catalog/docs/workflows/fischuu/Snakebite-Holoruminant-MetaG.html

mamba env create -n metagem -f envs/metaGEM_env.yml

mamba env create –prefix ./envs/metagem -f envs/metaGEM_env.yml

https://snakemake.github.io/snakemake-workflow-catalog/docs/workflows/GooglingTheCancerGenome/sv-callers.html

mamba env create -n sv-callers -f environment.yaml

snakemake –use-conda -c1

#Needs bam-files

[Mon Apr 27 17:31:55 2026] rule delly_p: input: data/fasta/chr22.fasta, data/fasta/chr22.fasta.fai, data/bam/3/T3.bam, data/bam/3/T3.bam.bai, data/bam/3/N3.bam, data/bam/3/N3.bam.bai output: data/bam/3/T3–N3/delly_out/delly-DEL.bcf jobid: 9 wildcards: path=data/bam/3, tumor=T3, normal=N3, sv_type=DEL resources: tmpdir=/tmp, mem_mb=8192, tmp_mb=0

Pipeline Purpose nf-core/mag Metagenome assembly, binning, annotation nf-co.re nf-core/viralrecon Viral genome reconstruction from metagenomic data nf-core/amr Antimicrobial resistance gene detection

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编程能力详解：Qwen3.6 Plus 为何能以小博大？——基准测试与技术拆解

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我们看 Sonnet 4.6、Opus 4.6 和 Qwen 3.6 这三位选手的对决。总的来说，它们是Anthropic和阿里云阵营里目标最明确的实力派。

Claude Opus 4.6：作为Anthropic的旗舰模型，专注解决最高难度任务。在真实软件编码、顶尖多学科推理和长文本海量信息检索方面，它都是“天花板”级别的存在，是企业级高可靠任务的不二之选。
Claude Sonnet 4.6：主打极致性价比的中坚力量。在多项核心任务上，性能已非常逼近Opus 4.6，但价格仅为前者的五分之一，是高性价比的理想工作模型。
Qwen 3.6 (Plus)：来自阿里的高性价比挑战者。在性价比和多模态能力上展现了强大竞争力，尤其是网页视觉生成和幻觉抑制方面都达到了顶尖水平，是应对海量高并发任务的成本效益之选。

下面是它们在一些关键基准测试上的数据，为了更直观地体现差异，我在表中加入了“行业巅峰”Claude Opus 4.6作为参考点：

基准测试 (Benchmark)	🥇 Claude Opus 4.6 (旗舰)	🥈 Claude Sonnet 4.6 (中坚)	🥉 Qwen 3.6 Plus (挑战者)
SWE-bench Verified (真实软件工程)	80.8%	79.6%	78.8%
Terminal-Bench 2.0 (终端编码)	65.4%	59.1%	61.6%
ARC-AGI-2 (新颖问题解决)	68.8%	58.3%	信息缺失
GPQA Diamond (研究生级问答)	91.3%	信息缺失	信息缺失
MRCR v2 (1M) (大海捞针式检索)	76.0%	与Opus差距显著	信息缺失
OSWorld (计算机使用)	未找到独立数据	72.5% (OSWorld-Verified)	信息缺失

这些分数清晰地展示了三款模型的实力梯队：Opus 4.6是当之无愧的“学霸”全能王；Sonnet 4.6是紧跟其后的“金牌助教”；而 Qwen 3.6 Plus则是在特定科目上能与学霸一较高下的“特长生”。

🎯 分场景选择策略：哪个模型更适合你？

💻 编程选哪个？

追求顶级、一次性解决难题：选 Opus 4.6
- 用武之地：需要最高可靠性的终极解决方案，比如修复复杂代码库中的顽固Bug，或为你搭建最复杂的项目架构。
- 数据说话：Opus 4.6在考察AI独立完成真实GitHub Issue的《终极挑战》中，获得了80.8%的最高分。
追求经济、高频调用主力：选 Sonnet 4.6
- 用武之地：日常编程的主力模型。无论是编写新功能、生成单元测试，还是代码审查，它都能高质量完成。
- 数据说话：Sonnet 4.6得分79.6%，与Opus差距极小，但价格仅为Opus的五分之一。Forrester等行业用户反馈，Sonnet 4.6的性能已足以支撑大部分生产环境开发任务。
追求极致性价比、批量处理：选 Qwen 3.6 Plus
- 用武之地：对成本极其敏感的场景，如批量代码生成、快速原型搭建。
- 数据说话：Qwen 3.6 Plus得分（78.8%）接近前两者，但API价格（输入/输出约0.28/1.68美元）远低于Sonnet 4.6（3/15美元）。它的性价比指数高达736，综合性能与Claude Sonnet接近，但成本仅为十分之一。

📄 长文本处理选哪个？

都支持100万token的超长上下文，相当于可以一次性处理三体三部曲这样体量的书籍。对于需要处理海量长文档的场景，三者都是合格的选择。
差异点在于检索精度：Opus 4.6在“大海捞针”测试中以76.0%的准确率大幅领先Sonnet 4.5（18.5%），而Sonnet 4.6也提供了稳定的长上下文服务。Qwen 3.6 Plus目前缺少这方面的公开数据。

💰 成本与多语言选哪个？

追求性价比之王：选 Qwen 3.6 Plus
- 用武之地：任何对成本控制有严格要求的项目，特别是非英语任务。
- 数据说话：Qwen 3.6 Plus超低的定价（输入2元/100万tokens）是其杀手锏。当进行中文内容润色时，它甚至能在部分任务上超越Claude Sonnet 4.6。
追求绝对稳定与工具生态：选 Claude 系列
- 用武之地：涉及复杂工具调用（如搜索、执行代码）的任务，或需要与GitHub Copilot等现有AI工具深度集成的开发环境。

💎 总结

总的来说，选哪款模型，最终还是看你更看重绝对性能还是极致成本。

如果你是“性能至上”者，追求解决最复杂问题的终极能力，那Claude Opus 4.6就是你的目标。
如果你是务实的开发者，希望在性能和成本间找到最佳平衡，Claude Sonnet 4.6是绝对可靠的主力军。
如果你是成本非常敏感的团队，尤其涉及到大量中文任务处理，那么Qwen 3.6 Plus无疑是一位极具潜力的“价格屠夫”和“性价比之王”。

Qwen3.6-Plus虽然在SWE-bench等关键测试中尚未超越Claude Opus 4.5，但它在编程智能体（Agent）这个代表未来的方向上，为我们提供了一个以小博大、极具性价比的强悍选择。

我为你整理了它在各大权威编程基准测试中的具体表现，可以更直观地看清它的实力：

测试基准	Qwen3.6-Plus	Claude Opus 4.5	GLM-5 / Kimi K2.5	解读
SWE-bench	🥈 匹敌	🥇 全球顶尖	🥉 超越	在该系列测试中匹敌全球最强编程模型Claude Opus 4.5。
Terminal-Bench 2.0	🥇 领先	🥈 被超越	🥉 超越	在终端编程任务中超越了Claude Opus 4.5，取得了关键领先。
CodeArena (React榜)	🥈 全球第2	🥇 第1名	🥉 不在前五	排名超越OpenAI、Google、xAI等，成为排名最高的中国模型。
NL2Repo / Claw-Eval	🥇 领先	🥈 被超越	🥉 超越	在长程编程和真实世界Agent评测中，表现完全匹敌甚至部分超越Claude Opus 4.5。
HumanEval / LiveCodeBench	🥇 刷分激进	数据未直接对比	数据未直接对比	在经典编程考题中表现亮眼，且更注重”工程味”，懂代码规范与维护。

注：“🥇领先”表示在该单项测试中表现更优，“🥈匹敌”表示性能接近、处于同一梯队。其具体SWE-bench得分仍未透露。

💡 Qwen3.6-Plus 编程能力为何突出？

其出色表现的背后，是多项核心技术升级的支撑：

🧠 “仓库级”代码理解：具备真正的全局视角，能理解整个代码仓库的跨文件依赖关系。处理超过10万行代码的项目时，逻辑推演错误率比前代下降约40%。
🎯 编程智能体（Agent）能力进化：从被动的代码生成器转变为主动的任务执行者，能自主完成任务拆解、路径规划、工具调用等整个开发闭环。
✨ “氛围编程” (Vibe Coding) 简单易用：可以将简单自然语言指令直接转化为可工作的应用，大大降低了开发门槛。实测中，它仅用8分钟就生成了一个完整的AI眼镜品牌官网，约消耗2.5万token，成本仅0.15元。
🤝 深度适配主流Agent生态：原生支持100万token的超大上下文窗口，并针对社区多个主流Agent框架进行了深度优化。
🏗️ “以小胜大”的架构策略：采用优化的MoE架构，总参数量497B，但每次仅激活约13B的专家网络。这使得它能以更小的参数规模和更低的算力成本，实现接近顶尖模型的性能。

💎 总结

简单来说，可以将它与Claude的竞争看作是两种不同思路的实践。Claude更偏向于提供顶尖算力支持下的全面性能，而Qwen3.6-Plus则证明：通过精巧的架构设计和工程优化，我们能以更低的成本，在真实场景中实现具备高度自主性的智能编程体验，这是一个非常务实且前景可观的方向。

要判断DeepSeek V4、Qwen3.6 Plus和Gemini 3.1 Pro这三款顶尖模型孰强孰弱，关键在于厘清它们各自的侧重点和优势赛道。

简单来说，没有一个模型是绝对的胜者，它们的优势各不相同，可以认为是打成了平手。Gemini 3依然是综合实力极强、多项基准测试的领先者；Qwen3.6 Plus在编程和多模态任务上表现亮眼；而DeepSeek V4则以极致性价比和开源的百万上下文能力，成为了搅动市场格局的“价格屠夫”。

以下是它们的详细对比：

维度	DeepSeek V4 (Pro)	Qwen3.6 Plus	Gemini 3 (3.1 Pro Preview)
核心优势	百万上下文普惠、极致性价比、开源	顶尖编程能力、原生多模态、成本收益均衡	综合性能领先、强大的深度推理、成熟生态
综合实力	顶级与领先之间，官方承认落后3-6个月	编程领域亮剑，编程力直逼世界顶级	公认的行业标杆，在LMArena排名第4
中文场景	杰出，逻辑理解稳健，但细节可能稍逊Qwen	顶尖，国内应用适配极佳	优异，但本地化细节可能不及前两者
API性价比	极致性价比之王 Pro: 输入￥1-12 / 输出￥24（每M tokens）	极高性价比输入$0.50 / 输出$3.00（每M tokens）	中等偏高不同版本价格浮动大
开源生态	完全开源 (MIT)，全球共享与二次开发	部分开源（主要提供高性能API服务）	闭源，依赖Google生态
最适合场景	预算有限的开发/研究者，长文档分析、复杂Agent任务开发	专业开发者，复杂编程、跨学科研究、需要原生多模态的复杂交互	追求顶尖综合体验的普通/专业用户，依赖Google生态，需顶级推理与通用能力

📊 详细对比分析

1. 综合性能与基准评测

DeepSeek V4 Pro: 综合实力进入世界顶级梯队，在官方技术报告中承认其能力与GPT-5.4和Gemini-3.1-Pro还有约3-6个月的差距。在知名大模型竞技场LMArena中排名第14，但以开源模型的身份冲到这一位置，实力已非常惊人。
Qwen3.6 Plus: 全球综合实力强劲，在CodeArena等多个榜单上登顶国产编程模型，综合性能全球仅次于Claude Opus 4.6，超越了OpenAI、Google等国际巨头，是典型的“小而美”的轻量级冠军。
Gemini 3.1 Pro: 2026年初，Gemini 3.1 Pro Preview在大模型竞技场LMArena位居前列（第四位）。在更考验“硬实力”的“人类终极测试”(HLE)中，Gemini 3 Deep Think版本取得了48.4% 的当时最高分，而Gemini 3基础版也有37%的优秀成绩。

2. 核心应用：编程、逻辑与长文本

编程能力
- DeepSeek V4 Pro: 在Vibe Coding和智能体编程上达到开源模型领先水平，在Vals AI的Vibe Code Benchmark中击败了Gemini 3.1 Pro等闭源模型，拿下了开源模型榜首。但在前端创意实现上与巅峰水平略有差距。
- Qwen3.6 Plus: 这项能力是其王牌。在SWE-bench系列等权威评测中，其编程表现超越了参数规模大两三倍的对手，并接近全球最强的Claude系列。
- Gemini 3.1 Pro: 编程能力依然是顶级，在Codeforces上的Elo等级分曾达到3455分。其独特的“Antigravity编程工具”更是将AI编程带入了全新的协同开发范式。
逻辑推理
- DeepSeek V4 Pro: 在处理复杂代码库和长文档分析时的逻辑稳定性是其强项。
- Qwen3.6 Plus: 具备仓库级代码理解能力，在处理超过10万行代码的项目时，逻辑推演错误率比前代下降了约40%。
- Gemini 3: “Deep Think”模式目前处于绝对领先地位。在科学、数学等领域展现了强大的博士级推理能力，在国际物理和化学奥林匹克竞赛的笔试中，Deep Think版本均达到了金牌水平。
长文本处理
- 三者均原生支持100万token的超长上下文。在处理超长文本时，DeepSeek V4凭借其创新的混合注意力机制，在计算和内存效率上遥遥领先。

3. 成本、性价比与生态

DeepSeek V4 Pro: 面对“天价”的GPT-5.5，DeepSeek V4 Pro的输出价格仅为GPT-5.5的十分之一左右。它选择全面拥抱开源，极大地降低了开发者使用顶尖AI技术的门槛，并且已深度适配国产芯片。
Qwen3.6 Plus: 提供了另一种极高性价比的路径。它用相对小得多的参数规模，实现了对标顶级模型的性能。尤其在编程场景下，对于需要高并发、高质量代码生成的企业用户，Qwen3.6 Plus的投入产出比非常高。
Gemini 3.1 Pro: 拥有最成熟的全球化服务，与Google Workspace、搜索、Android等庞大生态系统深度融合。用户可能不仅是为一个模型付费，更是为整个智能工作流付费。

💎 总结与建议

追求极致性价比，深耕长文本与复杂任务：选 DeepSeek V4。它特别适合预算有限的个人开发者或研究机构，用来搭建自己的AI应用。
你的核心需求是编程，希望获得当前最强的原生多模态和仓库级代码理解：选 Qwen3.6 Plus。对于专业开发团队，它能直接转化为生产力。
你需要一个应对复杂逻辑推理的“全能大脑”，并看重与Google生态的整合：选 Gemini 3。它为普通用户和专业人士提供了一个强大、稳定且不断进化的智能伙伴。

How to generate manhattan_plot? (Data_Ute_smallRNA_7)

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TODO: 你能不能再帮我准备一个 Manhattan 图：一列是 WaGa 细胞，一列是未处理的 WaGa EVs，其中每一列显示的是所有重复实验的平均 read count（细胞一列、EVs 一列）？另外，你可以把丰度最高的 20 个 miRNA 标注出来吗？这个生成起来需要很久吗？另外有个小提醒，可能 Carmen（我们组的临床科学家）周一也会需要类似的图，不过标签会不一样。

exceRpt_miRNA_ReadCounts.txt

manhattan_plot_top_miRNAs_based_on_mean_RPM.R

    # see http://xgenes.com/article/article-content/288/draw-plots-for-mirnas-generated-by-compsra/
    # see http://xgenes.com/article/article-content/289/draw-plots-for-pirna-generated-by-compsra/
    # see http://xgenes.com/article/article-content/290/draw-plots-for-snrna-generated-by-compsra/

    #Input file
    #exceRpt_miRNA_ReadCounts.txt
    #exceRpt_piRNA_ReadCounts.txt

    cd ~/DATA/Data_Ute/Data_Ute_smallRNA_7/summaries_exo7
    mamba activate r_env
    R
    #> .libPaths()
    #[1] "/home/jhuang/mambaforge/envs/r_env/lib/R/library"

    #BiocManager::install("AnnotationDbi")
    #BiocManager::install("clusterProfiler")
    #BiocManager::install(c("ReactomePA","org.Hs.eg.db"))
    #BiocManager::install("limma")
    #BiocManager::install("sva")
    #install.packages("writexl")
    #install.packages("openxlsx")
    library("AnnotationDbi")
    library("clusterProfiler")
    library("ReactomePA")
    library("org.Hs.eg.db")
    library(DESeq2)
    library(gplots)
    library(limma)
    library(sva)
    #library(writexl)  #d.raw_with_rownames <- cbind(RowNames = rownames(d.raw), d.raw); write_xlsx(d.raw, path = "d_raw.xlsx");
    library(openxlsx)

    setwd("../summaries_exo7/")
    d.raw<- read.delim2("exceRpt_miRNA_ReadCounts.txt",sep="\t", header=TRUE, row.names=1)

    # Desired column order
    desired_order <- c(
        "parental_cells_1", "parental_cells_2", "parental_cells_3",
        "untreated_1", "untreated_2",
        "scr_control_1", "scr_control_2", "scr_control_3",
        "DMSO_control_1", "DMSO_control_2", "DMSO_control_3",
        "scr_DMSO_control_1", "scr_DMSO_control_2", "scr_DMSO_control_3",
        "sT_knockdown_1", "sT_knockdown_2", "sT_knockdown_3"
    )
    # Reorder columns
    d.raw <- d.raw[, desired_order]
    setdiff(desired_order, colnames(d.raw))  # Shows missing or misnamed columns
    #sapply(d.raw, is.numeric)
    d.raw[] <- lapply(d.raw, as.numeric)
    #d.raw[] <- lapply(d.raw, function(x) as.numeric(as.character(x)))
    d.raw <- round(d.raw)
    write.csv(d.raw, file ="d_raw.csv")
    write.xlsx(d.raw, file = "d_raw.xlsx", rowNames = TRUE)

    # ------ Code sent to Ute ------
    #d.raw <- read.delim2("d_raw.csv",sep=",", header=TRUE, row.names=1)
    parental_or_EV = as.factor(c("parental","parental","parental", "EV","EV","EV","EV","EV","EV","EV","EV","EV","EV","EV","EV","EV","EV"))
    #donor = as.factor(c("0505","1905", "0505","1905", "0505","1905", "0505","1905", "0505","1905", "0505","1905"))
    batch = as.factor(c("Aug22","March25","March25", "Sep23","Sep23", "Sep23","Sep23","March25", "Sep23","Sep23","March25", "Sep23","Sep23","March25", "Sep23","Sep23","March25"))

    replicates = as.factor(c("parental_cells","parental_cells","parental_cells",  "untreated","untreated",   "scr_control","scr_control","scr_control",  "DMSO_control","DMSO_control","DMSO_control",  "scr_DMSO_control", "scr_DMSO_control","scr_DMSO_control",  "sT_knockdown", "sT_knockdown", "sT_knockdown"))
    ids = as.factor(c("parental_cells_1", "parental_cells_2", "parental_cells_3",
        "untreated_1", "untreated_2",
        "scr_control_1", "scr_control_2", "scr_control_3",
        "DMSO_control_1", "DMSO_control_2", "DMSO_control_3",
        "scr_DMSO_control_1", "scr_DMSO_control_2", "scr_DMSO_control_3",
        "sT_knockdown_1", "sT_knockdown_2", "sT_knockdown_3"))
    cData = data.frame(row.names=colnames(d.raw), replicates=replicates, ids=ids, batch=batch, parental_or_EV=parental_or_EV)
    dds<-DESeqDataSetFromMatrix(countData=d.raw, colData=cData, design=~replicates+batch)

    # Filter low-count miRNAs
    dds <- dds[ rowSums(counts(dds)) > 10, ]  #1322-->903
    rld <- rlogTransformation(dds)

    # ----------- manhattan_plot -------------

    # Load the required libraries
    library(ggplot2)
    library(dplyr)
    library(tidyr)
    library(ggrepel)  # For better label positioning

    # Step 1: Compute RPM from raw counts (d.raw has miRNAs in rows, samples in columns)
    d.raw_5 <- d.raw[, 1:5]  # assuming 5 samples
    total_counts <- colSums(d.raw_5)
    RPM <- sweep(d.raw_5, 2, total_counts, FUN = "/") * 1e6

    # Step 2: Prepare long-format dataframe
    RPM$miRNA <- rownames(RPM)
    df <- pivot_longer(RPM, cols = -miRNA, names_to = "sample", values_to = "RPM")

    # Step 3: Log-transform RPM
    df <- df %>%
    mutate(logRPM = log10(RPM + 1))

    # Step 4: Add miRNA index for x-axis positioning
    df <- df %>%
    arrange(miRNA) %>%
    group_by(sample) %>%
    mutate(Position = row_number())

    # Step 5: Identify top miRNAs based on mean RPM
    top_mirnas <- df %>%
    group_by(miRNA) %>%
    summarise(mean_RPM = mean(RPM)) %>%
    arrange(desc(mean_RPM)) %>%
    head(5) %>%
    pull(miRNA)  # Get the names of top 5 miRNAs

    # Step 6: Assign color based on whether the miRNA is top or not
    df$color <- ifelse(df$miRNA %in% top_mirnas, "red", "darkblue")

    # Rename the sample labels for display
    sample_labels <- c(
    "parental_cells_1" = "Parental cell 1",
    "parental_cells_2" = "Parental cell 2",
    "parental_cells_3" = "Parental cell 3",
    "untreated_1"      = "Untreated 1",
    "untreated_2"      = "Untreated 2"
    )

    # Step 7: Plot
    png("manhattan_plot_top_miRNAs_based_on_mean_RPM.png", width = 1200, height = 1200)
    ggplot(df, aes(x = Position, y = logRPM, color = color)) +
    scale_color_manual(values = c("red" = "red", "darkblue" = "darkblue")) +
    geom_jitter(width = 0.4) +
    geom_text_repel(
        data = df %>% filter(miRNA %in% top_mirnas),
        aes(label = miRNA),
        box.padding = 0.5,
        point.padding = 0.5,
        segment.color = 'gray50',
        size = 5,
        max.overlaps = 8,
        color = "black"
    ) +
    labs(x = "", y = "log10(Read Per Million) (RPM)") +
    facet_wrap(~sample, scales = "free_x", ncol = 5,
                labeller = labeller(sample = sample_labels)) +
    theme_minimal() +
    theme(
        axis.text.x = element_blank(),
        axis.ticks.x = element_blank(),
        legend.position = "none",
        text = element_text(size = 16),
        axis.title = element_text(size = 18),
        strip.text = element_text(size = 16, face = "bold"),
        panel.spacing = unit(1.5, "lines")  # <-- More space between plots
    )
    dev.off()

    top_mirnas = c("hsa-miR-20a-5p","hsa-miR-93-5p","hsa-let-7g-5p","hsa-miR-30a-5p","hsa-miR-423-5p","hsa-let-7i-5p")
    #,"hsa-miR-17-5p","hsa-miR-107","hsa-miR-483-5p","hsa-miR-9-5p","hsa-miR-103a-3p","hsa-miR-30e-5p","hsa-miR-21-5p","hsa-miR-30d-5p")

    # Step 6: Assign color based on whether the miRNA is top or not
    df$color <- ifelse(df$miRNA %in% top_mirnas, "red", "darkblue")

    # Rename the sample labels for display
    sample_labels <- c(
    "parental_cells_1" = "Parental cell 1",
    "parental_cells_2" = "Parental cell 2",
    "parental_cells_3" = "Parental cell 3",
    "untreated_1"      = "Untreated 1",
    "untreated_2"      = "Untreated 2"
    )

    # Step 7: Plot
    png("manhattan_plot_most_differentially_expressed_miRNAs.png", width = 1200, height = 1200)
    ggplot(df, aes(x = Position, y = logRPM, color = color)) +
    scale_color_manual(values = c("red" = "red", "darkblue" = "darkblue")) +
    geom_jitter(width = 0.4) +
    geom_text_repel(
        data = df %>% filter(miRNA %in% top_mirnas),
        aes(label = miRNA),
        box.padding = 0.5,
        point.padding = 0.5,
        segment.color = 'gray50',
        size = 5,
        max.overlaps = 8,
        color = "black"
    ) +
    labs(x = "", y = "log10(Read Per Million) (RPM)") +
    facet_wrap(~sample, scales = "free_x", ncol = 5,
                labeller = labeller(sample = sample_labels)) +
    theme_minimal() +
    theme(
        axis.text.x = element_blank(),
        axis.ticks.x = element_blank(),
        legend.position = "none",
        text = element_text(size = 16),
        axis.title = element_text(size = 18),
        strip.text = element_text(size = 16, face = "bold"),
        panel.spacing = unit(1.5, "lines")  # <-- More space between plots
    )
    dev.off()