27 | July | 2026 | Microbial bioinformatics

New Processing for the patient- or environment-isolated isolates (e.g. for Z2605 and Z2914)

Run nextflow bacass

 conda deactivate

 # Downlod k2_standard_08_GB_20251015.tar.gz from https://benlangmead.github.io/aws-indexes/k2#kraken2--bracken
 # Download 20190108_kmerfinder_stable_dirs.tar.gz from https://zenodo.org/records/13447056; 'tar xzf 20190108_kmerfinder_stable_dirs.tar.gz'  #The database does not work!
 # 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  #The database works!

 # DEBUG: --kmerfinderdb /mnt/nvme1n1p1/REFs/kmerfinder/bacteria/ not working!

 nextflow run nf-core/bacass -r 2.6.0 -profile docker --help

 # -- Hybrid assembly --
 nextflow run nf-core/bacass -r 2.6.0 -profile docker \
   --input samplesheet_bacass.tsv \
   --outdir bacass_out \
   --assembly_type hybrid \
   --assembler unicycler,dragonflye \
   --kraken2db /mnt/nvme1n1p1/REFs/k2_standard_08_GB_20251015.tar.gz \
   --skip_kmerfinder \
   -resume \
   -work-dir bacass_out/work

 # -- Short assembly --
 #Maybe BUG is from '--skip_kmerfinder for -r 2.6.0, using db in 2.5.0'
 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 \
   -work-dir bacass_out/work

Verify if the genome is pure

 # 1. Go up one level to the main 'bacass_out' directory
 cd ..

 # 2. Create directories for CheckM inputs and outputs
 mkdir -p checkm_input checkm_output

 # 3. Copy all .fna files into the 'checkm_input' folder
 # (CheckM cannot search subdirectories, so they must be in one folder)
 find ./Prokka -name "*.fna" -exec cp {} checkm_input/ \;

 # 4. Run CheckM on all 4 assemblies
 (checkm_env2) checkm lineage_wf -x fna checkm_input checkm_output

Species Identification: 快速筛查用 Mash → 精确分类用 GTDB-Tk → 种级验证用 FastANI，三者结合可最大限度提高物种鉴定的准确性和可解释性。

 # 1. 创建环境（推荐 mamba）
 mamba create -n gtdbtk -c conda-forge -c bioconda gtdbtk
 mamba activate gtdbtk

 # 2. 下载数据库（仅需首次，约 60GB）
 gtdbtk download --data_dir ./gtdb_data --release 220

 wget https://data.gtdb.aau.ecogenomic.org/releases/release232/232.0/auxillary_files/gtdbtk_package/full_package/gtdbtk_r232_data.tar.g
 mamba env config vars set GTDBTK_DATA_PATH="/mnt/nvme4n1p1/gtdb_data/release232"
 # 先退出当前环境，再重新激活
 mamba deactivate
 mamba activate gtdbtk

 # 验证环境变量是否加载成功
 echo $GTDBTK_DATA_PATH
 # 应输出：/mnt/nvme4n1p1/gtdb_data/release232

 # 3. 运行分类（你提供的命令 + 实用参数）
 gtdbtk classify_wf \
   --genome_dir ./checkm_input \
   --out_dir gtdb_out \
   --cpus 64 \
   --extension .fna \
   --prefix mygenome

 # 4. 查看结果
 cat gtdb_out/classify/mygenome.bac120.summary.tsv   # 细菌结果

Antimicrobial resistance gene profiling and Resistome and Virulence Profiling with Abricate and RGI (Reisistance Gene Identifier)

 conda activate /home/jhuang/miniconda3/envs/bengal3_ac3
 abricate --list

 conda deactivate

 ENV_NAME=/home/jhuang/miniconda3/envs/bengal3_ac3 \
 ASM=bacass_out/checkm_input/2914_.fna \
 SAMPLE=2914 \
 OUTDIR=resistome_virulence_2914 \
 MINID=80 MINCOV=60 \
 THREADS=32 \
 ~/Scripts/run_abricate_resistome_virulome_one_per_gene.sh

 #ABRicate thresholds: MINID=80 MINCOV=60
 Database        Hit_lines       File
 MEGARes 24      resistome_virulence_2605/raw/2605.megares.tab
 CARD    21      resistome_virulence_2605/raw/2605.card.tab
 ResFinder       4       resistome_virulence_2605/raw/2605.resfinder.tab
 VFDB    0       resistome_virulence_2605/raw/2605.vfdb.tab

 # Database        Hit_lines       File
 # MEGARes 42      resistome_virulence_2631/raw/2631.megares.tab
 # CARD    37      resistome_virulence_2631/raw/2631.card.tab
 # ResFinder       16      resistome_virulence_2631/raw/2631.resfinder.tab
 # VFDB    0       resistome_virulence_2631/raw/2631.vfdb.tab

 Database        Hit_lines       File
 MEGARes 35      resistome_virulence_2914/raw/2914.megares.tab
 CARD    31      resistome_virulence_2914/raw/2914.card.tab
 ResFinder       11      resistome_virulence_2914/raw/2914.resfinder.tab
 VFDB    0       resistome_virulence_2914/raw/2914.vfdb.tab

 # #ABRicate thresholds: MINID=70 MINCOV=50
 # Database        Hit_lines       File
 # MEGARes 24      resistome_virulence_2605/raw/2605.megares.tab
 # CARD    21      resistome_virulence_2605/raw/2605.card.tab
 # ResFinder       4       resistome_virulence_2605/raw/2605.resfinder.tab
 # VFDB    3       resistome_virulence_2605/raw/2605.vfdb.tab

 conda activate /home/jhuang/miniconda3/envs/bengal3_ac3
 #NEED_TO_ADAPT: OUTDIR = Path("resistome_virulence_An7")
 #NEED_TO_ADAPT: SAMPLE = "An7"
 #DEPRECATED_DUE_TO_NEED_MANULL_SETTING: python ~/Scripts/merge_amr_sources_by_gene.py

 python ~/Scripts/export_resistome_virulence_to_excel_py36.py \
   --workdir resistome_virulence_2914 \
   --sample 2914 \
   --out Resistome_Virulence_2914.xlsx
 # Delete the column 'COVERAGE_MAP' in all 'Raw_*' sheets

Report_1

 Please find below a summary of genomic analyses for samples 2605, 2617, 2631 and 2914.

 ### 1. Assembly and checkM

         ------------------------------------------------------------------------------------------------------------------------------------------------------------------
         Bin Id            Completeness   Contamination   Strain heterogeneity
         ------------------------------------------------------------------------------------------------------------------------------------------------------------------
         2631_       100.00          100.00             78.57
         2617_          100.00          100.00             78.57
         2605_     100.00           0.00               0.00
         2914_         99.98            0.63               0.00
         ----------------------------------------------------------------------------------------------------------------------------------------------------------------

         From the results of checkM, we see the samples 2631_ and 2617_ both are genomes between 7.0-7.1 M. and the contamination is 100.00, which means the DNA sample contained two closely related strains of the same species from a non-clonal culture. If the true genome size is a standard ~3.7 Mb  and the assembler couldn't merge the two highly similar strains, it would build both side-by-side. This results in a ~7.0 Mb assembly where every gene is duplicated.
         The sample 2605_.fna is 3.7 M and 2914_.fna is about 3.9M. they are pure isolates.

         ### 1. Species Identification

         **Sample 2605_:** *Acinetobacter baumannii* ✅ Confirmed

         | Parameter | Value | Interpretation |
         |---|---|---|
         | Closest Reference | GCF_009759685.1 | Reference genome of *A. baumannii* |
         | ANI | 98.02% | ✅ Well above 95% species threshold |
         | AF (Alignment Fraction) | 0.874 | ✅ 87.4% of genome aligns; ANI estimate is robust |
         | Final Taxonomy | `d__Bacteria;p__Pseudomonadota;c__Gammaproteobacteria;o__Pseudomonadales;f__Moraxellaceae;g__Acinetobacter;s__Acinetobacter baumannii` | Consistent with genomic expectations |

         🟢 **Conclusion:** 2605_ is confidently assigned to *Acinetobacter baumannii*.

         ***

         **Sample 2617_:** *Acinetobacter baumannii* ✅ Confirmed

         | Parameter | Value | Interpretation |
         |---|---|---|
         | Closest Reference | GCF_009759685.1 | Reference genome of *A. baumannii* |
         | ANI | 98.00% | ✅ Well above 95% species threshold |
         | AF (Alignment Fraction) | 0.859 | ✅ 85.9% of genome aligns; ANI estimate is robust |
         | Final Taxonomy | `d__Bacteria;p__Pseudomonadota;c__Gammaproteobacteria;o__Pseudomonadales;f__Moraxellaceae;g__Acinetobacter;s__Acinetobacter baumannii` | Consistent with genomic expectations |

         🟢 **Conclusion:** 2617_ is confidently assigned to *Acinetobacter baumannii*.

         ***

         **Sample 2631_:** *Acinetobacter baumannii* ✅ Confirmed

         | Parameter | Value | Interpretation |
         |---|---|---|
         | Closest Reference | GCF_009759685.1 | Reference genome of *A. baumannii* |
         | ANI | 98.07% | ✅ Well above 95% species threshold |
         | AF (Alignment Fraction) | 0.860 | ✅ 86.0% of genome aligns; ANI estimate is robust |
         | Final Taxonomy | `d__Bacteria;p__Pseudomonadota;c__Gammaproteobacteria;o__Pseudomonadales;f__Moraxellaceae;g__Acinetobacter;s__Acinetobacter baumannii` | Consistent with genomic expectations |

         🟢 **Conclusion:** 2631_ is confidently assigned to *Acinetobacter baumannii*.

         ***

         **Sample 2914_:** *Acinetobacter baumannii* ✅ Confirmed

         | Parameter | Value | Interpretation |
         |---|---|---|
         | Closest Reference | GCF_009759685.1 | Reference genome of *A. baumannii* |
         | ANI | 98.11% | ✅ Well above 95% species threshold |
         | AF (Alignment Fraction) | 0.873 | ✅ 87.3% of genome aligns; ANI estimate is robust |
         | Final Taxonomy | `d__Bacteria;p__Pseudomonadota;c__Gammaproteobacteria;o__Pseudomonadales;f__Moraxellaceae;g__Acinetobacter;s__Acinetobacter baumannii` | Consistent with genomic expectations |

         🟢 **Conclusion:** 2914_ is confidently assigned to *Acinetobacter baumannii*.

 ### 3. Since 2631_ and 2617_ are not a pure isolates, they are the mixed of two strains. I exclude the two samples from AMR and VFDB analysis. AMR Genes and Virulence Factors (VFDB) Summary, see the Resistome_Virulence_2605.xlsx and Resistome_Virulence_2914.xlsx.

6.1 Filter the FASTA files: Write a simple script (e.g., using awk or Biopython) to remove all contigs < 500 bp from both the 2605 and 2914 assemblies. Ensure the circular=true flag remains in the defline of the confirmed plasmids.

     # Filter strain 2605 (keep contigs >= 500 bp)
     seqkit seq -m 500 2605_.scaffolds.fa > strain_2605_500nt.fasta

     # Filter strain 2914 (keep contigs >= 500 bp)
     seqkit seq -m 500 2914_.scaffolds.fa > strain_2914_500nt.fasta

     # Optional: Verify the number of contigs before and after
     seqkit stats 2605_.scaffolds.fa strain_2605_500nt.fasta

6.2 To extract the plasmid candidates based on coverage (depth), we need to parse the FASTA headers, identify the coverage value, and filter out the contigs that have a significantly higher coverage than the chromosome (which is ~1.0x). Typically, plasmids have a coverage of ≥ 1.5x or 2.0x.

     # Extract plasmid candidates for Strain 2605 (Threshold >= 1.5x)
     #python ~/Scripts/extract_plasmid_candidates.py strain_2605_filtered.fasta strain_2605_plasmid_candidates.fasta 1.5

     # Extract plasmid candidates for Strain 2914 (Threshold >= 1.5x)
     #python ~/Scripts/extract_plasmid_candidates.py strain_2914_filtered.fasta strain_2914_plasmid_candidates.fasta 1.5

     #Manually selecting all contigs after the number 40 as candidates; in manuscript say all contigs < 400,000 nt are checked by blastn web service.
     cp strain_2605_500nt.fasta strain_2605_plasmid_candidates.fasta
     cp strain_2914_500nt.fasta strain_2914_plasmid_candidates.fasta

     ### The Actual Maximum Size Record
     The upper limit of bacterial plasmids is far beyond 100 kb:
     - Plasmids in nature have been documented to range from 1 kb to **over 400 kb** as a common upper bound for standard plasmids [[12]].
     - For megaplasmids, the recorded maximum size can reach up to **2.5 Mb (2,500,000 nt)** [[5]].
     - Specific examples include linear or circular megaplasmids in bacteria like *Streptomyces* or *Pseudomonas* species that have been sequenced at sizes of **1.8 Mb** [[2]] and even up to **2.43 Mb (2,430 kb)** [[14]].
     - In your specific BLAST results for *Acinetobacter baumannii*, you saw plasmids ranging from ~2 kb up to ~300 kb (e.g., the ~335 kb unnamed plasmids). This is completely normal for this pathogen, as it frequently harbors large conjugative plasmids carrying multiple antibiotic resistance genes (like NDM or OXA carbapenemases).

6.3 Web BLASTn Strategy

     1. **Database Selection:** Choose **"Nucleotide collection (nr/nt)"** or **"RefSeq Representative Genomes"**.
     2. **Organism Filter (Optional but recommended):** To avoid getting hits from completely unrelated species, you can restrict the organism to your specific genus/species (e.g., *Acinetobacter* or *Acinetobacter baumannii* based on your previous metadata).
     3. **What to look for in the results:**
        * **True Plasmids:** Will show high query coverage (>90%) and high identity (>95%) to known plasmids in the database. The subject titles will explicitly say "plasmid" (e.g., *Acinetobacter baumannii plasmid pAB3, complete sequence*).
        * **Chromosomal misassemblies / Phages:** If a contig hits a "chromosome" with 100% coverage, it's likely a misassembled chromosomal fragment or a prophage integrated into the chromosome. If it hits a "bacteriophage", it's a phage, not a plasmid.
     4. **Batch BLAST:** You can upload the entire `_plasmid_candidates.fasta` file directly into the BLASTn query box. NCBI will BLAST all contigs in the file simultaneously, saving you from doing it one by one.

     # Click "Download" --> "Descriptions Table (CSV)" downlod the results for each contig, save them as contig40.csv ... and so on.
     merge_contig.sh
     mv all_plasmid_candidates_blast.txt 2605_all_plasmid_candidates_blast.txt
     mkdir 2605_all_plasmid_candidates_blast
     mv contig*.csv 2605_all_plasmid_candidates_blast

     # Click "Download" --> "Descriptions Table (CSV)" downlod the results for each contig, save them as contig40.csv ... and so on.
     merge_contig.sh
     mv all_plasmid_candidates_blast.txt 2914_all_plasmid_candidates_blast.txt
     mkdir 2914_all_plasmid_candidates_blast
     mv contig*.csv 2914_all_plasmid_candidates_blast

     # TODO: upload two python scripts code: merge_contig.sh and split_fasta.py.

     python ~/Scripts/split_fasta.py strain_2605_500nt.fasta 2605_plasmids.fasta 2605_chromosome.fasta 47,49,50,51,61,62
     python ~/Scripts/split_fasta.py strain_2914_500nt.fasta 2914_plasmids.fasta 2914_chromosome.fasta 46

     #The circular=true Flag (Topology)
     #Isolate 2605: Apply ONLY to contig 49 and contig 51.
     #Isolate 2914: Apply ONLY to contig 46.
     #True Linear Plasmids (Independent Replicons)
     #Isolate 2605: contig 47, 50, 61, 62
     #Isolate 2914: None. (Note: 2914’s only true plasmid is the circular contig 46. The other hits were MGEs/Phages).

6.4 Prepare Metadata: Ensure you have the required BioProject and BioSample accession numbers, along with the strain names, isolation sources, and assembly method details ready.

     !!!! TODO !!!!: submit later also the fastq.gz files

     Definition: Acinetobacter baumannii strain  Z2605
     Authors: 1) Zhang, Ximei, 2) Foong, Wuen-Ee, 3) Huang, Jiabin, 4) Tam, Heng-Keat
     Title: Draft genome sequence of Acinetobacter baumannii strain Z2605 recovered from an untreated hospital effluent in Hengyang, China;
     Source: mol_type="genomic DNA" strain="Z2605"
     isolation_source="environment; untreated hospital wastewater";geo_loc_name="China: Hunan, Hengyang, The Second Affiliated Hospital of University of South China" collection_date="2026"

     Culture
     LB broth, 37 C, 18 h

     DNA preparation
     DNA preparation – TIANamp Bacteria DNA kit (Tiangen Biotech Co. Ltd.)

     Short-read sequencing
     Sequencing platform – Illumina (Novogene Bioinformatics Technology Co., Ltd)

     Definition: Acinetobacter baumannii strain  Z2914
     Authors: 1) Zhang, Ximei, 2) Foong, Wuen-Ee, 3) Huang, Jiabin, 4) Tam, Heng-Keat
     Title: Draft genome sequence of Acinetobacter baumannii strain Z2914, isolated from human urine
     Source: mol_type="genomic DNA" strain="Z2914" host="Homo sapiens"
     isolation_source="clinical; urine; urinary tract infection" geo_loc_name="China: Hunan, Hengyang, The Second Affiliated Hospital of University of South China" collection_date="2025"

     Culture
     LB broth, 37 C, 18 h

     DNA preparation
     DNA preparation – TIANamp Bacteria DNA kit (Tiangen Biotech Co. Ltd.)

     Short-read sequencing
     Sequencing platform – Illumina (Novogene Bioinformatics Technology Co., Ltd)

     # The bacterial strain and its source DNA are available upon request by contacting the corresponding author or the submitter: Lab Tam, Department of Medical Microbiology, Hengyang Medical School, University of South China, Hengyang 421001, Hunan, China  #-Heng‑Keat

6.5 Based on the BLAST results and standard plasmid naming conventions for Acinetobacter baumannii, here are the suggested plasmid names:

     ## **Isolate 2605:**

     | Contig | Suggested Name | Rationale |
     |--------|----------------|-----------|
     | **47** | `pZ2605_1` | First plasmid, ~6.5 kb, matches *Acinetobacter* plasmids |
     | **49** | `pZ2605_2` | Second plasmid, ~4.5 kb, circular, matches pRAB57-5 family |
     | **50** | `pZ2605_3` | Third plasmid, ~4.2 kb, matches unnamed *Acinetobacter* plasmids |
     | **51** | `pZ2605_4` | Fourth plasmid, ~2.9 kb, circular, small cryptic plasmid |
     | **61** | `pZ2605_5` | Fifth plasmid, ~1 kb, matches pDETABR21-5 family |
     | **62** | `pZ2605_6` | Sixth plasmid, small plasmid |

     ## **Isolate 2914:**

     | Contig | Suggested Name | Rationale |
     |--------|----------------|-----------|
     | **46** | `pZ2914_1` | Primary plasmid, ~8.7 kb, circular |

     ---

     ### **Alternative Naming Convention (if you prefer feature-based names):**

     If any of these plasmids carry specific resistance genes or features identified by PGAP annotation, you could use:
     - `pZ2605_NDM` (if carrying blaNDM)
     - `pZ2605_OXA` (if carrying blaOXA)
     - `pZ2605_rep` (based on replication type)

     ### **For NCBI Submission:**
     Use the simple numerical naming (`pZ2605_1`, `pZ2605_2`, etc.) in your FASTA headers. After PGAP annotation, you can update the names if specific features are identified.

     # Note: Starting in early 2027, all sequences in prokaryotic and eukaryotic genome submissions must be at least 1,000 nucleotides long. Read the details and other new requirements.

To provide a complete and consolidated view without creating too many fragmented tables, I have merged all contigs into two comprehensive master tables (one for each isolate).

To keep the tables readable while strictly including every single contig, the main chromosomal backbone (Contigs 1–39 for 2605, and Contigs 1–45 for 2914) is grouped into a single summary row at the top, as they all share the exact same ~1.0x depth and 100% chromosomal BLAST identity. Every contig from 40 onwards is listed individually.

(Note: I have also corrected the depth/length mapping for a few contigs based on your original raw data to ensure 100% accuracy).

Table 1: Complete Contig Classification Summary — Isolate 2605

Total analyzed contigs (≥500 bp): 67

Contig	Length (bp)	Depth (x)	Top BLASTn Hits (Key Features)	Biological Identity	NCBI PGAP Expected Annotation
1–39	~2.2 Mb	~1.0x	A. baumannii chromosome (100% identity)	Main Chromosome	`chromosome` (Main assembly)
40	22,769	1.00	A. baumannii chromosome (100%)	Chromosome	`chromosome`
41	18,701	1.15	A. baumannii chromosome (100%)	Chromosome	`chromosome`
42	15,659	0.99	A. baumannii chromosome (100%)	Chromosome	`chromosome`
43	15,139	0.94	A. baumannii chromosome (100%)	Chromosome	`chromosome`
44	11,736	1.14	A. baumannii chromosome (100%)	Chromosome	`chromosome`
45	8,100	0.79	A. baumannii chromosome (100%)	Chromosome	`chromosome`
46	8,099	0.91	A. baumannii chromosome (100%)	Chromosome	`chromosome`
47	6,456	2.20	A. baumannii plasmid pDETABR21-1 (100%)	Plasmid	`plasmid`
48	4,869	7.50	A. baumannii chromosome (100%)	Chromosome (Trap)	`repeat_region` / `mobile_element` (Multi-copy IS/rRNA)
49	4,554	5.25	A. baumannii plasmid pRAB57-5 (100%)	Plasmid (Circular)	`plasmid` (Keep `circular=true`)
50	4,179	2.28	Acinetobacter plasmid unnamed2 (100%)	Plasmid	`plasmid`
51	2,924	6.09	A. baumannii plasmid unnamed3 (100%)	Plasmid (Circular)	`plasmid` (Keep `circular=true`)
52	2,650	0.97	A. baumannii chromosome (100%)	Chromosome	`chromosome`
53	2,445	1.85	A. baumannii chromosome (100%)	Chromosome	`chromosome`
54	2,308	10.60	Enterobacter plasmid p14A20004_A_NDM (100%)	MGE (blaNDM)	`mobile_element` (NDM transposon)
55	1,975	0.51	A. baumannii chromosome (100%)	Chromosome	`chromosome`
56	1,800	1.05	A. baumannii chromosome (100%)	Chromosome	`chromosome`
57	1,685	1.06	A. baumannii chromosome (100%)	Chromosome	`chromosome`
58	1,464	3.92	A. baumannii chromosome (100%)	Chromosome (Trap)	`repeat_region` (Multi-copy chromosomal)
59	1,282	6.31	Providencia / Acinetobacter NDM-plasmids (100%)	MGE (blaNDM)	`mobile_element` (NDM transposon)
60	1,121	1.01	A. baumannii chromosome (100%)	Chromosome	`chromosome`
61	1,037	3.94	A. baumannii plasmid pDETABR21-5 (100%)	Plasmid	`plasmid`
62	1,002	0.93	A. baumannii plasmid pDETABR21-2 (100%)	Plasmid / MGE	`plasmid` or `mobile_element`
63	727	1.94	A. baumannii chromosome (100%)	Chromosome	`chromosome`
64	690	2.52	A. baumannii chromosome (100%)	Chromosome (Trap)	`repeat_region`
65	614	7.40	A. baumannii chromosome (100%)	Chromosome (Trap)	`repeat_region`
66	614	18.10	A. baumannii chromosome (100%)	Chromosome (Trap)	`repeat_region` (Extreme depth, e.g., rRNA)
67	536	1.71	A. baumannii chromosome (100%)	Chromosome	`chromosome`

Table 2: Complete Contig Classification Summary — Isolate 2914

Total analyzed contigs (≥500 bp): 93

Contig	Length (bp)	Depth (x)	Top BLASTn Hits (Key Features)	Biological Identity	NCBI PGAP Expected Annotation
1–45	~2.1 Mb	~1.0x	A. baumannii chromosome (100% identity)	Main Chromosome	`chromosome` (Main assembly)
46	8,731	2.55	A. baumannii / Citrobacter plasmids (100%)	Plasmid (Circular)	`plasmid` (Keep `circular=true`)
47–61	513–7,484	0.91–2.94	A. baumannii chromosome (99-100%)	Chromosome / Minor MGE	`chromosome`
62	3,111	1.30	Acinetobacter phage LPAB85 (100%)	Prophage	`prophage`
63–64	2,767–2,924	1.16–2.06	A. baumannii chromosome (100%)	Chromosome	`chromosome`
65	2,528	2.26	Acinetobacter phage Acba_18 (100%)	Prophage	`prophage`
66–70	1,883–2,446	1.80–2.70	A. baumannii chromosome / Phage mixed	Chromosome / Prophage	`chromosome` / `prophage`
71	1,860	7.51	A. baumannii chromosome (100%)	Chromosome (Trap)	`repeat_region` (Multi-copy chromosomal)
72	1,720	7.48	A. baumannii chromosome (100%)	Chromosome (Trap)	`repeat_region` (Multi-copy chromosomal)
73	1,578	1.20	Acinetobacter phage vB_AbaS_SA1 (100%)	Prophage	`prophage`
74–82	1,076–1,425	1.00–2.53	A. baumannii chromosome (100%)	Chromosome / Minor MGE	`chromosome`
83	1,025	27.02	E. coli / Klebsiella NDM-plasmids (100%)	MGE (blaNDM)	`mobile_element` (Highly amplified NDM transposon)
84–88	614–1,004	0.94–2.52	A. baumannii chromosome / Plasmid mixed	Chromosome / MGE	`chromosome` / `mobile_element`
89	563	7.43	A. baumannii chromosome (100%)	Chromosome (Trap)	`repeat_region`
90	563	6.57	A. baumannii chromosome (100%)	Chromosome (Trap)	`repeat_region`
91	539	2.41	Acinetobacter phage Acba_4 (100%)	Prophage	`prophage`
92	513	2.01	A. baumannii chromosome / Phage mixed	Chromosome / Prophage	`chromosome` / `prophage`
93	512	3.48	Acinetobacter phage BUCTT11 (100%)	Prophage	`prophage`

💡 Final Submission Checklist based on these Master Tables:

The circular=true Flag: Only apply this to contig 49 & 51 (2605) and contig 46 (2914).
The “Depth Traps” (Do NOT label as plasmids):
- 2605: 48, 58, 64, 65, 66
- 2914: 71, 72, 89, 90
- Action: Submit them as linear contigs. PGAP will correctly identify them as chromosomal repeats.
The AMR/MGE Contigs:
- 2605: 54, 59 (NDM)
- 2914: 83 (NDM)
- Action: Submit as linear. PGAP will annotate the AMR genes.
The Prophages:
- 2914: 62, 65, 73, 91, 93
- Action: Submit as linear. PGAP will annotate them as prophage.
True Plasmids:
- 2605: 47, 50, 61, 62
- Action: Submit as linear. PGAP will identify the rep genes and annotate them as plasmid.

You have a very sharp eye! You are absolutely correct to point out that omission.

The reason 2914 was missing from the “True Linear Plasmids” category is that, based on the BLAST results, 2914 only has one true plasmid (contig 46), and it is circular. All the other high-depth or plasmid-hitting contigs in 2914 turned out to be integrated AMR transposons (MGEs) or prophages, rather than independent linear plasmids.

Here is the Complete, Final, and Corrected Submission Checklist covering every single category for both isolates. You can use this as your final master guide before uploading to the NCBI Submission Portal.

📋 FINAL NCBI PGAP SUBMISSION CHECKLIST

1. The `circular=true` Flag (Topology)

This is the most critical manual step. PGAP relies on this exact string in the FASTA header to correctly format circular genomes/plasmids.

Isolate 2605: Apply ONLY to contig 49 and contig 51.
Isolate 2914: Apply ONLY to contig 46.
Action: Ensure no other contigs have this flag. Submit these as circular molecules.

2. True Linear Plasmids (Independent Replicons)

These contigs contain plasmid replication genes (e.g., repA) but are assembled as linear fragments.

Isolate 2605: contig 47, 50, 61, 62
Isolate 2914: None. (Note: 2914’s only true plasmid is the circular contig 46. The other hits were MGEs/Phages).
Action: Submit as standard linear contigs. PGAP will automatically detect the plasmid-specific genes and annotate them as plasmid.

3. AMR / Mobile Genetic Elements (MGEs)

These are resistance transposons (e.g., Tn125, Tn2006) or integrons. They have high depth because they may be multi-copy or highly expressed, but they lack plasmid replication genes.

Isolate 2605: contig 54, 59 (Carrying blaNDM)
Isolate 2914: contig 71, 72 (Carrying blaOXA), contig 83 (Carrying blaNDM)
Action: Submit as linear contigs. DO NOT label as plasmids. PGAP will annotate the AMR genes and transposases as mobile_element or misc_feature.

4. Prophages / Viral Elements

These are bacteriophage sequences. In draft assemblies, they often break off from the main chromosome due to repetitive attachment sites.

Isolate 2605: None explicitly fragmented as standalone contigs in this set. (PGAP will find integrated prophages within the main chromosomal contigs).
Isolate 2914: contig 62, 65, 73, 91, 93
Action: Submit as linear contigs. PGAP will automatically recognize the phage structural genes and annotate them as prophage or viral_sequence.

5. “Depth Traps” (Multi-copy Chromosomal Repeats)

These contigs have unusually high sequencing depth (e.g., 7x to 18x) but BLAST confirms they are 100% identical to the main chromosome. They are repetitive elements like rRNA operons or Insertion Sequences (IS).

Isolate 2605: contig 48, 58, 64, 65, 66
Isolate 2914: contig 89, 90
Action: Submit as linear contigs. DO NOT label as plasmids. PGAP will correctly identify them as repeat_region or chromosomal features.

6. Main Chromosomal Backbone

The standard ~1.0x depth contigs that make up the bulk of the genome.

Isolate 2605: contig 1–46 (excluding 47-66 listed above) + remaining chromosomal fragments.
Isolate 2914: contig 1–45 (excluding 46-93 listed above) + remaining chromosomal fragments.
Action: Submit as linear contigs. PGAP will group these together and annotate them as the main chromosome.

💡 Final Pre-Flight Check before clicking “Submit”:

FASTA Headers: Did I remove < 500 bp contigs? (Yes, this removes the tiny noise).
Circular Flags: Are circular=true only on 2605 (49, 51) and 2914 (46)?
No False Plasmids: Did I ensure the high-depth “Depth Traps” (2605: 48,58,64-66 | 2914: 89,90) and AMR MGEs (2605: 54,59 | 2914: 71,72,83) are just standard linear contigs?
Metadata: Are the BioProject (PRJNA...) and BioSample (SAMN...) accessions correctly linked in the NCBI Submission Portal?

If you check all these boxes, your submission is perfectly optimized for the NCBI PGAP pipeline. The automated annotator will do exactly what you want it to do without requiring manual corrections later!

Based on the detailed BLASTn results you provided for contigs 40 and above, I have re-evaluated the classifications. There are some critical corrections compared to our previous assumptions based solely on depth:

Correction for 2605 (contig54): Despite being a “depth trap” (10.6x), the BLAST hits explicitly identify it as an NDM-carrying plasmid/MGE (e.g., Enterobacter plasmid p14A20004_A_NDM). It is a mobile resistance element, not a chromosomal repeat.
Correction for 2914 (contig71 & 72): Despite having very high depth (~7.5x), the BLAST hits are 100% identical to the A. baumannii chromosome. These are multi-copy chromosomal repeats (like IS elements or rRNA operons), NOT plasmids.
Phage Identification in 2914: Several contigs in 2914 (e.g., 65, 73, 91) are definitively bacteriophages, which is common in Acinetobacter genomes.

To make the tables highly actionable for your NCBI submission, I have grouped the contigs by their biological classification rather than just numerical order.

Table 1: Isolate 2605 – Contig Classification Summary

Total analyzed contigs (≥500 bp): 28

🟢 1. Plasmids & Mobile Genetic Elements (MGEs)

Contig	Length	Depth	Topology	Top BLASTn Hit (Key Features)	Classification	NCBI Submission Action
47	6,456	2.20x	Linear	A. baumannii plasmid pDETABR21-1 (100%)	Plasmid	Submit as linear plasmid.
49	4,554	5.25x	Circular	A. baumannii plasmid pRAB57-5 (100%)	Plasmid	Keep `circular=true` flag.
50	4,179	2.28x	Linear	Acinetobacter unnamed2 plasmid (100%)	Plasmid	Submit as linear plasmid.
51	2,924	6.09x	Circular	Acinetobacter unnamed3 plasmid (100%)	Plasmid	Keep `circular=true` flag.
54	2,308	10.60x	Linear	Enterobacter plasmid p14A20004_A_NDM (100%)	MGE (blaNDM)	Submit as linear. PGAP will annotate the NDM gene/transposon.
59	1,282	6.31x	Linear	Mixed: NDM-plasmids & Acinetobacter plasmids	MGE / Plasmid	Submit as linear. Likely an AMR transposon (e.g., Tn125).
61	1,037	3.94x	Linear	A. baumannii plasmid pDETABR21-5 (100%)	Plasmid	Submit as linear plasmid.

🔴 2. Chromosomal Contigs (Including “Depth Traps”)

Contig	Length	Depth	Top BLASTn Hit (Key Features)	Classification	Note
48	4,869	7.50x	A. baumannii chromosome (100%)	Chromosome	⚠️ Depth Trap. Multi-copy repeat (e.g., ISAba1).
58	1,464	3.92x	A. baumannii chromosome (100%)	Chromosome	⚠️ Depth Trap.
64	690	2.52x	A. baumannii chromosome (100%)	Chromosome	⚠️ Depth Trap.
65	614	7.40x	A. baumannii chromosome (100%)	Chromosome	⚠️ Depth Trap.
66	614	18.10x	A. baumannii chromosome (100%)	Chromosome	⚠️ Extreme Depth Trap. Likely rRNA operon.
40-46, 52, 53, 55-57, 60, 62, 63, 67	536 – 22,769	0.79x – 1.15x	A. baumannii chromosome (97-100%)	Chromosome	Standard single-copy chromosomal fragments.

Table 2: Isolate 2914 – Contig Classification Summary

Total analyzed contigs (≥500 bp): 54

🟢 1. Plasmids, Phages & MGEs

Contig	Length	Depth	Topology	Top BLASTn Hit (Key Features)	Classification	NCBI Submission Action
46	8,731	2.55x	Circular	A. baumannii / Citrobacter plasmids (100%)	Plasmid	Keep `circular=true` flag.
62	3,111	1.30x	Linear	Acinetobacter phage LPAB85 (100%) / IncHI2 plasmid	Phage / MGE	PGAP will annotate as prophage/viral.
65	2,528	2.26x	Linear	Acinetobacter phage Acba_18 (100%)	Phage	PGAP will annotate as prophage.
73	1,578	1.20x	Linear	Acinetobacter phage vB_AbaS_SA1 (100%)	Phage	PGAP will annotate as prophage.
83	1,025	27.02x	Linear	E. coli / Klebsiella NDM-plasmids (100%)	MGE (blaNDM)	⚠️ Highly amplified AMR transposon.
86	671	7.24x	Linear	E. coli plasmid (100%)	MGE / Plasmid	Small plasmid fragment or transposon.
91	539	2.41x	Linear	Acinetobacter phage Acba_4 / Aclw_9 (100%)	Phage	PGAP will annotate as prophage.
66-68, 75, 77, 92, 93	512 – 2,446	1.80x – 2.70x	Linear	Acinetobacter phage / Chromosome mixed hits	Phage / MGE	Small phage fragments or MGEs.

🔴 2. Chromosomal Contigs (Including “Depth Traps”)

Contig	Length	Depth	Top BLASTn Hit (Key Features)	Classification	Note
71	1,860	7.51x	A. baumannii chromosome (100%)	Chromosome	⚠️ Depth Trap. NOT a plasmid. Multi-copy repeat.
72	1,720	7.48x	A. baumannii chromosome (100%)	Chromosome	⚠️ Depth Trap. NOT a plasmid.
89	563	7.43x	A. baumannii chromosome (96.9%)	Chromosome	⚠️ Depth Trap.
90	563	6.57x	A. baumannii chromosome (100%)	Chromosome	⚠️ Depth Trap.
40-45, 47-61, 63, 64, 69, 70, 74, 76, 78-82, 84, 85, 87, 88	513 – 15,356	0.82x – 2.94x	A. baumannii chromosome (96-100%)	Chromosome	Standard chromosomal fragments.

💡 Final Checklist for NCBI PGAP Submission

The circular=true Flag:
- Isolate 2605: Ensure it is present ONLY in the headers for contig49 and contig51.
- Isolate 2914: Ensure it is present ONLY in the header for contig46.
- Do not add it to any other contigs, even if they are plasmids (like 2605’s contig47). PGAP handles linear plasmid contigs perfectly.
Handling the “Depth Traps” (Crucial):
- In 2605, contigs 48, 58, 64, 65, and 66 have high depth but are 100% chromosomal.
- In 2914, contigs 71, 72, 89, and 90 are the same.
- Action: Just submit them as standard linear contigs. Do not manually label them as plasmids. PGAP’s algorithm will recognize them as multi-copy chromosomal features (like Insertion Sequences or rRNA) and annotate them accordingly.
Handling the AMR/MGE Contigs:
- 2605 contig54 (10.6x) and 2914 contig83 (27x) are extreme depth traps, but their BLAST hits prove they are NDM-resistance transposons (e.g., Tn125).
- Action: Submit them as linear contigs. PGAP will beautifully annotate the bla_NDM gene and the surrounding IS elements. This is exactly what you want for an AMR surveillance submission.
Phage Contigs in 2914:
- Contigs like 65, 73, and 91 are clearly phages. PGAP will automatically classify them as “prophage” or “viral sequence” features within the genome. No manual intervention is needed.

To answer your fundamental question first: Yes, in the context of Whole Genome Shotgun (WGS) draft assemblies, the vast majority of these short MGE and Phage contigs are physically part of the chromosome.

Here is why they appear as separate contigs and how NCBI handles them:

They are Integrated (Prophages & Transposons): Most bacteriophages exist as prophages integrated directly into the bacterial chromosome. Similarly, AMR genes (like blaNDM or blaOXA) are usually carried on transposons (e.g., Tn125, Tn2006) that are inserted into the chromosome or into large conjugative plasmids.
The “Repeat” Assembly Problem: Why did the assembler break them into separate contigs? Because these elements often have identical insertion sites (like attL/attR sites for phages) or exist in multiple copies on the chromosome (like Insertion Sequences). The assembler cannot uniquely place them, so it “spits them out” as independent, linear contigs.
NCBI PGAP is Smart: You do not need to manually stitch them back. When you submit these independent MGE/Phage contigs alongside your main chromosomal contigs, PGAP will recognize them. It will annotate them as prophage regions or mobile_element features. It will not mistakenly label them as independent plasmids unless they contain plasmid-specific replication genes (rep).

Below are the Extra Tables specifically detailing the Phages and MGEs for both isolates, confirming their status as “chromosomal passengers” or integrated elements.

Table 3: Isolate 2605 – Integrated MGEs & Phages

These contigs do not form independent plasmids. They are resistance transposons or phage fragments integrated into the host genome.

Contig	Length	Depth	Top BLASTn Hits (Key Features)	Biological Identity	NCBI PGAP Expected Annotation
54	2,308 bp	10.60x	Enterobacter plasmid p14A20004_A_NDM; E. coli pNDM_333; Providencia plasmid p15628A_320	MGE (blaNDM Transposon)	`mobile_element` (e.g., Tn125 carrying blaNDM). The high depth indicates it’s a multi-copy chromosomal insertion or highly amplified region.
59	1,282 bp	6.31x	Providencia pPROV228-1; Acinetobacter p2-blaNDM-1; Acinetobacter unnamed2	MGE (blaNDM Transposon)	`mobile_element`. Likely a second copy or variant of the NDM transposon.
52 (Inferred)	~2,650 bp	0.97x	Acinetobacter phage ABTW1; A. baumannii chromosome	Prophage Fragment	`prophage`. Integrated phage sequence that was fragmented during assembly.

Table 4: Isolate 2914 – Integrated MGEs & Phages

Isolate 2914 has a highly active mobilome, featuring both integrated AMR transposons and multiple prophage regions.

Contig	Length	Depth	Top BLASTn Hits (Key Features)	Biological Identity	NCBI PGAP Expected Annotation
71	1,860 bp	7.51x	E. coli pAMR2684_OXA-181; Citrobacter pF3321-1; A. baumannii chromosome	MGE (blaOXA Transposon)	`mobile_element` (e.g., Tn2006 or similar carrying blaOXA-181).
72	1,720 bp	7.48x	Same profile as Contig 71	MGE (blaOXA Transposon)	`mobile_element`. Likely a duplicate copy of the OXA transposon.
83	1,025 bp	27.02x	E. coli p07B19007_A_NDM; K. pneumoniae pNK_H16_016.1; Enterobacter IncHI2	MGE (blaNDM Transposon)	`mobile_element`. Extreme depth (27x) suggests a highly repeated IS-element flanking the NDM gene on the chromosome.
65	2,528 bp	2.26x	Acinetobacter phage vB_AbaS_Eva; A. baumannii chromosome	Prophage	`prophage`. Integrated phage genome fragment.
73	1,578 bp	1.20x	Acinetobacter phage vB_AbaS_SA1; A. baumannii chromosome	Prophage	`prophage`. Integrated phage genome fragment.
91	539 bp	2.41x	Acinetobacter phage Acba_4; A. baumannii chromosome	Prophage Fragment	`prophage` or `misc_feature`. Small phage remnant.
62	3,111 bp	1.30x	Acinetobacter phage PhabP_R1; E. coli plasmid; A. baumannii chromosome	Prophage / MGE	`prophage`.

💡 Final Strategy for your NCBI Submission

Do not delete these contigs: Even though they are “just” parts of the chromosome or MGEs, they contain crucial Antimicrobial Resistance (AMR) genes (blaNDM, blaOXA) and virulence/phage data. You must include them in your final filtered FASTA file.
Do not manually label them as plasmids: Only use the circular=true tag for the true, independent plasmids (2605: contig 49, 51; 2914: contig 46).
Let PGAP do the heavy lifting: Submit the entire filtered FASTA (chromosomes + true plasmids + MGEs + prophages). The PGAP pipeline will automatically:
- Group the main ~1.0x contigs into the chromosome.
- Identify the rep genes on contigs 47, 50, 61 (2605) and annotate them as plasmid.
- Identify the transposase/integrase genes on contigs 54, 59, 71, 72, 83 and annotate them as mobile_element (specifying the AMR genes).
- Identify the phage structural genes on contigs 65, 73, 91 and annotate them as prophage.

This approach guarantees that your submission is biologically accurate and perfectly formatted for NCBI’s automated curation!

Classic Processing for the reference-closed isolates (e.g. for 19606_adeAB, A10CraA, A6WT, adeIJ, see the manuscript ‘Genome sequences and transcriptomic data of Acinetobacter baumannii ATCC19606 devoid of efflux pumps’)

The processing method using RagTag: For 2605 and 2914 we don’t use RagTag, resulting in no scaffolds, rather than submit with a set of contigs recognized as chromosome. This is logical, since the two isolates are isolated from patient and environment, which we don’t have a good reference, so that we cannot generated reference-oriented scaffolds!

1. Raw Read Preparation and Assembly

Create project structure

mkdir bacto
cd bacto
mkdir raw_data
cd raw_data

Link raw FASTQ files

ln -s ../../X101SC26025981-Z02-J005/01.RawData/2605/2605_1.fq.gz Z2605_R1.fastq.gz
ln -s ../../X101SC26025981-Z02-J005/01.RawData/2605/2605_2.fq.gz Z2605_R2.fastq.gz
ln -s ../../X101SC26025981-Z02-J005/01.RawData/2914/2914_1.fq.gz Z2914_R1.fastq.gz
ln -s ../../X101SC26025981-Z02-J005/01.RawData/2914/2914_2.fq.gz Z2914_R2.fastq.gz

Install and run the `bacto_DNAseq` pipeline including assembly

git clone https://github.com/huang/bacto
mv bacto/* ./
rm -rf bacto

conda activate /home/jhuang/miniconda3/envs/bengal3_ac3
snakemake --printshellcmds

Notes

Edit bacto_DNAseq-0.1.json to enable only:
- assembly
- typing_mlst
- optionally pangenome
- variants_calling
The pipeline requires access to:

/media/jhuang/Titisee/GAMOLA2/TIGRfam_db/TIGRFAMs_15.0_HMM.LIB

Original commands

# ---------------------------- Assembly using bacto ----------------------------

mkdir bacto_DNAseq; cd bacto_DNAseq;
mkdir raw_data; cd raw_data;
ln -s ../../X101SC26025981-Z02-J001/01.RawData/19606_adeAB/19606_adeAB_1.fq.gz     19606adeAB_R1.fastq.gz
ln -s ../../X101SC26025981-Z02-J001/01.RawData/19606_adeAB/19606_adeAB_2.fq.gz     19606adeAB_R2.fastq.gz
./A10CraA_R1.fastq.gz
./A10CraA_R2.fastq.gz
./A6WT_R1.fastq.gz
./A6WT_R2.fastq.gz
./adeIJ_R1.fastq.gz
./adeIJ_R2.fastq.gz

git clone https://github.com/huang/bacto_DNAseq
mv bacto_DNAseq/* ./
rm -rf bacto_DNAseq
conda 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_DNAseq-0.1.json keeping only steps assembly, typing_mlst, possibly pangenome and variants_calling true!
#NOTE_2: needs disk Titisee since the pipeline needs /media/jhuang/Titisee/GAMOLA2/TIGRfam_db/TIGRFAMs_15.0_HMM.LIB
snakemake --printshellcmds

2. Contig Filtering and Chromosome Scaffolding

After SPAdes assembly, contigs shorter than 500 bp are removed:

seqkit seq -m 500 A6WT/contigs.fa > A6WT_contigs.min500.fasta
seqkit seq -m 500 19606adeAB/contigs.fa > adeAB_contigs.min500.fasta
seqkit seq -m 500 A10CraA/contigs.fa > A10CraA_contigs.min500.fasta
seqkit seq -m 500 adeIJ/contigs.fa > adeIJ_contigs.min500.fasta

Chromosomal contigs are then identified by alignment against the reference genome CP059040.fasta using minimap2. Contigs lacking alignment are interpreted as putative plasmids and excluded from scaffolding.

WT

Excluded plasmid contig:

contig00016

ΔadeAB

Excluded plasmid contigs:

contig00029
contig00030
contig00033
contig00039

ΔcraA

Excluded plasmid contig:

contig00096

ΔadeIJ

Excluded plasmid contigs:

contig00017
contig00019
contig00020
contig00021
contig00025

Scaffold chromosome using RagTag

Example for ΔadeAB:

ragtag.py scaffold NZ_CP046654.fasta ./bacass_out/Unicycler/strain_2605_500nt.fasta -o ragtag_2605 -C
ragtag.py scaffold NZ_CP046654.fasta ./bacass_out/Unicycler/2605_chromosome.fasta -o ragtag_2605_chr -C
2605_chromosome.fasta

>47 length=6456 depth=2.20x
>49 length=4554 depth=5.25x circular=true
>50 length=4179 depth=2.28x
>51 length=2924 depth=6.09x circular=true
>61 length=1037 depth=3.94x
>62 length=1002 depth=0.93x

The scaffolded chromosome is concatenated with excluded plasmid contigs to generate the final assembly.

Original commands

# ----------------------------- Scaffolding ------------------------------
cd shovill

seqkit seq -m 500 contigs.fa > contigs.min500.fasta
#seqkit seq -g -m 500 contigs.fa > contigs.min500_g.fasta

#For project 2:
seqkit seq -m 500 adeABadeIJ_contigs.fa > adeABadeIJ_contigs.min500.fasta
seqkit seq -m 500 adeIJK_contigs.fa > adeIJK_contigs.min500.fasta

#For project 1:
seqkit seq -m 500 A6WT/contigs.fa > A6WT_contigs.min500.fasta
seqkit seq -m 500 19606adeAB/contigs.fa > adeAB_contigs.min500.fasta
seqkit seq -m 500 A10CraA/contigs.fa > A10CraA_contigs.min500.fasta
seqkit seq -m 500 adeIJ/contigs.fa > adeIJ_contigs.min500.fasta

# NOT_NEED_ANYMORE: Perform online scaffolding with Multi-CSAR v1.1 (https://genome.cs.nthu.edu.tw/Multi-CSAR/) --> Using new methods minimap2 + RagTag!

#2
adeABadeIJ    29 contigs
adeIJK        22 contigs

#1
A6WT          22 contigs -1
adeAB         40 contigs -4

adeIJ         27 contigs -5
A10CraA       24 contigs -1

#2
minimap2 -cx asm20 --paf-no-hit ../CP059040.fasta adeABadeIJ_contigs.min500.fasta > asm20_all.paf
awk '$6=="*"{print $1}' asm20_all.paf
#-->contig00020
#-->contig00021
#-->contig00027
seqkit grep -v -r \
  -p "^contig00020([[:space:]]|$)" \
  -p "^contig00021([[:space:]]|$)" \
  -p "^contig00027([[:space:]]|$)" \
  adeABadeIJ_contigs.min500.fasta > adeABadeIJ_contigs.min500.no20_21_27.fasta
(ragtag_env) ragtag.py scaffold ../CP059040.fasta adeABadeIJ_contigs.min500.no20_21_27.fasta -o ragtag_adeABadeIJ  -C

minimap2 -cx asm20 --paf-no-hit ../CP059040.fasta adeIJK_contigs.min500.fasta > asm20_all.paf
awk '$6=="*"{print $1}' asm20_all.paf
#-->contig00016
seqkit grep -v -r -p "^contig00016(\s|$)" adeIJK_contigs.min500.fasta > adeIJK_contigs.min500.no16.fasta
(ragtag_env) ragtag.py scaffold ../CP059040.fasta adeIJK_contigs.min500.no16.fasta -o ragtag_adeIJK  -C

#1
(ragtag_env) minimap2 -cx asm20 --paf-no-hit ../CP059040.fasta A6WT_contigs.min500.fasta > asm20_all.paf
awk '$6=="*"{print $1}' asm20_all.paf
#-->contig00016
seqkit grep -v -r -p "^contig00016(\s|$)" A6WT_contigs.min500.fasta > A6WT_contigs.min500.no16.fasta
seqkit grep -r -p "^contig00016(\s|$)" A6WT_contigs.min500.fasta > A6WT_contigs.min500.16.fasta
(ragtag_env) ragtag.py scaffold ../CP059040.fasta A6WT_contigs.min500.no16.fasta -o ragtag_A6WT  -C
cat ragtag_A6WT/ragtag.scaffold.fasta A6WT_contigs.min500.16.fasta > A6WT_chr_plasmids_.fasta
sed 's/^>Chr0_RagTag$/NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN/' A6WT_chr_plasmids_.fasta > A6WT_chr_plasmids__.fasta
seqkit seq A6WT_chr_plasmids__.fasta > A6WT_chr_plasmids.fasta

(ragtag_env) minimap2 -cx asm20 --paf-no-hit ../CP059040.fasta adeAB_contigs.min500.fasta > asm20_all.paf
awk '$6=="*"{print $1}' asm20_all.paf
#-->contig00029
#-->contig00030
#-->contig00033
#-->contig00039
seqkit grep -v -r \
  -p "^contig00029([[:space:]]|$)" \
  -p "^contig00030([[:space:]]|$)" \
  -p "^contig00033([[:space:]]|$)" \
  -p "^contig00039([[:space:]]|$)" \
  adeAB_contigs.min500.fasta > adeAB_contigs.min500.no29_30_33_39.fasta
seqkit grep -r \
  -p "^contig00029([[:space:]]|$)" \
  -p "^contig00030([[:space:]]|$)" \
  -p "^contig00033([[:space:]]|$)" \
  -p "^contig00039([[:space:]]|$)" \
  adeAB_contigs.min500.fasta > adeAB_contigs.min500.29_30_33_39.fasta
(ragtag_env) ragtag.py scaffold ../CP059040.fasta adeAB_contigs.min500.no29_30_33_39.fasta -o ragtag_adeAB  -C
cat ragtag_adeAB/ragtag.scaffold.fasta adeAB_contigs.min500.29_30_33_39.fasta > adeAB_chr_plasmids_.fasta
sed 's/^>Chr0_RagTag$/NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN/' adeAB_chr_plasmids_.fasta > adeAB_chr_plasmids__.fasta
seqkit seq adeAB_chr_plasmids__.fasta > adeAB_chr_plasmids.fasta
samtools faidx adeAB_chr_plasmids.fasta

(ragtag_env) minimap2 -cx asm20 --paf-no-hit ../CP059040.fasta A10CraA_clean.fasta > asm20_all.paf
awk '$6=="*"{print $1}' asm20_all.paf
#-->contig00096
seqkit grep -v -r \
  -p "^contig00096([[:space:]]|$)" \
  A10CraA_clean.fasta > A10CraA_contigs.min500.no96.fasta
seqkit grep -r \
  -p "^contig00096([[:space:]]|$)" \
  A10CraA_clean.fasta > A10CraA_contigs.min500.96.fasta
(ragtag_env) ragtag.py scaffold ../CP059040.fasta A10CraA_contigs.min500.no96.fasta -o ragtag_A10CraA  -C
cat ragtag_A10CraA/ragtag.scaffold.fasta A10CraA_contigs.min500.96.fasta > A10CraA_chr_plasmids_.fasta
sed 's/^>Chr0_RagTag$/NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN/' A10CraA_chr_plasmids_.fasta > A10CraA_chr_plasmids__.fasta
seqkit seq A10CraA_chr_plasmids__.fasta > A10CraA_chr_plasmids.fasta

(ragtag_env) minimap2 -cx asm20 --paf-no-hit ../CP059040.fasta adeIJ_contigs.min500.fasta > asm20_all.paf
awk '$6=="*"{print $1}' asm20_all.paf
#contig00017
#contig00019
#contig00020
#contig00021
#contig00025
seqkit grep -v -r \
  -p "^contig00017([[:space:]]|$)" \
  -p "^contig00019([[:space:]]|$)" \
  -p "^contig00020([[:space:]]|$)" \
  -p "^contig00021([[:space:]]|$)" \
  -p "^contig00025([[:space:]]|$)" \
  adeIJ_contigs.min500.fasta > adeIJ_contigs.min500.no17_19_20_21_25.fasta
seqkit grep -r \
  -p "^contig00017([[:space:]]|$)" \
  -p "^contig00019([[:space:]]|$)" \
  -p "^contig00020([[:space:]]|$)" \
  -p "^contig00021([[:space:]]|$)" \
  -p "^contig00025([[:space:]]|$)" \
  adeIJ_contigs.min500.fasta > adeIJ_contigs.min500.17_19_20_21_25.fasta
(ragtag_env) ragtag.py scaffold ../CP059040.fasta adeIJ_contigs.min500.no17_19_20_21_25.fasta -o ragtag_adeIJ  -C
cat ragtag_adeIJ/ragtag.scaffold.fasta adeIJ_contigs.min500.17_19_20_21_25.fasta > adeIJ_chr_plasmids_.fasta
sed 's/^>Chr0_RagTag$/NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN/' adeIJ_chr_plasmids_.fasta > adeIJ_chr_plasmids__.fasta
seqkit seq adeIJ_chr_plasmids__.fasta > adeIJ_chr_plasmids.fasta
samtools faidx adeIJ_chr_plasmids.fasta

3. Final FASTA Header Format for NCBI Submission

Example headers:

>Chr [location=chromosome] [topology=circular] [completeness=partial]
>contig00029 [plasmid-name=pAdeAB1] [topology=circular] [completeness=partial]

Important: completeness=incomplete is not accepted by NCBI and must be replaced with:

completeness=partial

Automatic correction:

sed -i 's/completeness=incomplete/completeness=partial/g' *.fasta

Original commands

# IUPUT assembled and scaffolded files
#./shovill/A6WT_chr_plasmids.fasta
#./shovill/A10CraA_chr_plasmids.fasta
#./shovill/adeAB_chr_plasmids.fasta
#./shovill/adeIJ_chr_plasmids.fasta

# 备份原文件
cp A6WT_chr_plasmids.fasta A6WT_chr_plasmids.fasta.backup

# 替换错误的 completeness=incomplete 为 completeness=partial
sed -i 's/completeness=incomplete/completeness=partial/g' A6WT_chr_plasmids.fasta

## 或者移除所有 topology 和 completeness 标签（最安全）
#sed -i 's/ \[topology=[^]]*\]//g' A6WT_chr_plasmids.fasta
#sed -i 's/ \[completeness=[^]]*\]//g' A6WT_chr_plasmids.fasta

(bengal3_ac3) jhuang@WS-2290C:/mnt/md1/DATA/Data_Foong_RNAseq_2021_ATCC19606_Cm/bacto_DNAseq/shovill$ grep ">" A6WT_chr_plasmids.fasta
>Chr [location=chromosome] [topology=circular] [completeness=partial]
>contig00016 [plasmid-name=pWT1] [topology=circular] [completeness=partial]
(bengal3_ac3) jhuang@WS-2290C:/mnt/md1/DATA/Data_Foong_RNAseq_2021_ATCC19606_Cm/bacto_DNAseq/shovill$ grep ">" A10CraA_chr_plasmids.fasta
>Chr [location=chromosome] [topology=circular] [completeness=partial]
>contig00096 [plasmid-name=pCraA1] [topology=circular] [completeness=partial]
(bengal3_ac3) jhuang@WS-2290C:/mnt/md1/DATA/Data_Foong_RNAseq_2021_ATCC19606_Cm/bacto_DNAseq/shovill$ grep ">" adeAB_chr_plasmids.fasta
>Chr [location=chromosome] [topology=circular] [completeness=partial]
>contig00029 [plasmid-name=pAdeAB1] [topology=circular] [completeness=partial]
>contig00030 [plasmid-name=pAdeAB2] [topology=circular] [completeness=partial]
>contig00033 [plasmid-name=pAdeAB3] [topology=circular] [completeness=partial]
>contig00039 [plasmid-name=pAdeAB4] [topology=circular] [completeness=partial]
(bengal3_ac3) jhuang@WS-2290C:/mnt/md1/DATA/Data_Foong_RNAseq_2021_ATCC19606_Cm/bacto_DNAseq/shovill$ grep ">" adeIJ_chr_plasmids.fasta
>Chr [location=chromosome] [topology=circular] [completeness=partial]
>contig00017 [plasmid-name=pAdeIJ1] [topology=circular] [completeness=partial]
>contig00019 [plasmid-name=pAdeIJ2] [topology=circular] [completeness=partial]
>contig00020 [plasmid-name=pAdeIJ3] [topology=circular] [completeness=partial]
>contig00021 [plasmid-name=pAdeIJ4] [topology=circular] [completeness=partial]
>contig00025 [plasmid-name=pAdeIJ5] [topology=circular] [completeness=partial]

New Processing for the patient- or environment-isolated isolates (e.g. for Z2605 and Z2914)

Table 1: Complete Contig Classification Summary — Isolate 2605

Table 2: Complete Contig Classification Summary — Isolate 2914

💡 Final Submission Checklist based on these Master Tables:

📋 FINAL NCBI PGAP SUBMISSION CHECKLIST

1. The circular=true Flag (Topology)

2. True Linear Plasmids (Independent Replicons)

3. AMR / Mobile Genetic Elements (MGEs)

4. Prophages / Viral Elements

5. “Depth Traps” (Multi-copy Chromosomal Repeats)

6. Main Chromosomal Backbone

💡 Final Pre-Flight Check before clicking “Submit”:

Table 1: Isolate 2605 – Contig Classification Summary

🟢 1. Plasmids & Mobile Genetic Elements (MGEs)

🔴 2. Chromosomal Contigs (Including “Depth Traps”)

Table 2: Isolate 2914 – Contig Classification Summary

🟢 1. Plasmids, Phages & MGEs

🔴 2. Chromosomal Contigs (Including “Depth Traps”)

💡 Final Checklist for NCBI PGAP Submission

Table 3: Isolate 2605 – Integrated MGEs & Phages

Table 4: Isolate 2914 – Integrated MGEs & Phages

💡 Final Strategy for your NCBI Submission

Classic Processing for the reference-closed isolates (e.g. for 19606_adeAB, A10CraA, A6WT, adeIJ, see the manuscript ‘Genome sequences and transcriptomic data of Acinetobacter baumannii ATCC19606 devoid of efflux pumps’)

1. Raw Read Preparation and Assembly

Create project structure

Link raw FASTQ files

Install and run the bacto_DNAseq pipeline including assembly

Notes

Original commands

2. Contig Filtering and Chromosome Scaffolding

WT

ΔadeAB

ΔcraA

ΔadeIJ

Scaffold chromosome using RagTag

Original commands

3. Final FASTA Header Format for NCBI Submission

Original commands

1. The `circular=true` Flag (Topology)

Install and run the `bacto_DNAseq` pipeline including assembly