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ARG profile calling using ‘rgi bwt’ for any downstream metagenomic analysis like MetaPhlAn or HUMAnN (Data_Tam_Metagenomics_2026_Wastewater)

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Yes — exactly. Both scripts call the identical rgi bwt command with the identical flags (-1, -2, -o, -n 16, --clean, --include_wildcard). The only differences are the input reads and the output directory:

rgi_bwt.sh (full) rgi_bwt_on_subsampled_kneaddata.sh (test)
Input Full *_paired_1/2.fastq from KneadData 10% / 200k-read subsample (*_sub_1/2.fastq.gz)
Output dir ARG_results/ ARG_results_test/
rgi bwt command identical identical

So rgi_bwt.sh will produce, per sample, the exact same set and format of files:

${sample}_arg.sorted.length_100.bam (+ .bai)
${sample}_arg.reference_mapping_stats.txt
${sample}_arg.allele_mapping_data.json / .txt
${sample}_arg.gene_mapping_data.txt      ← the one your R script reads
${sample}_arg.overall_mapping_stats.txt
${sample}_arg.artifacts_mapping_stats.txt

What will differ (content, not format)

  • File sizes: BAMs and mapping tables will be larger (~10× more input reads).
  • Richness: gene_mapping_data.txt will contain more ARGs and higher read counts (the test run gave you 360 ARGs; the full run will detect additional low-abundance ARGs that were missed at 10% depth).
  • Runtime: roughly 10× longer per sample — that’s why I suggested the parallel xargs -P 4 version for the full run.

For your R script

Nothing changes except the input path — just switch at the top of analyze_rgi_arg_output.R:

arg_dir <- "~/DATA/Data_Tam_Metagenomics_2026_Wastewater/ARG_results/"

and rerun. The parser (gene_mapping_data\\.txt pattern) will pick up the full-depth files automatically.

NCBI genome submission logs for Data_Tam_Methylation_2026_WT_T_O_T_Trans_O_Trans/trycycler-medaka_polished_genomes_hac_v5.0.0

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while read -r prefix acc; do
  f="${prefix}-trycycler-medaka_polished_genome.fa"

  if [[ ! -f "$f" ]]; then
    echo "Missing file: $f"
    continue
  fi

  echo "Updating $f -> $acc"

  awk -v acc="$acc" '
    BEGIN {
      pfx = acc "_"
    }
    /^>/ {
      hdr = substr($0, 2)
      if (index(hdr, pfx) != 1) {
        $0 = ">" pfx hdr
      }
      gsub(/\[plasmid-name=/, "[plasmid=")
      print
      next
    }
    {
      print
    }
  ' "$f" > "$f.tmp" && mv "$f.tmp" "$f"

done <<'EOF'
O_Trans SAMN62674710
O SAMN62674708
S2_Dark SAMN62674712
S2_Light SAMN62674713
T SAMN62674709
WT_Trans SAMN62674711
WT SAMN62674707
EOF
sed -i 's/\[plasmid=/[plasmid-name=/g' *-medaka_polished_genome.fa

Metadata for submitting the genomes to NCBI of the 7 samples

    #Short-read sequencing was performed using the Illumina platform, and long-read sequencing was performed using Oxford Nanopore Ligation Sequencing Kit V14 on the PromethION platform. The combined use of Illumina and Oxford Nanopore sequencing supports accurate assembly of complete genomes and improves detection of structural variants in repetitive or complex genomic regions.

    NCBI project summary / project description
    This project comprises the complete genome sequences of seven Acinetobacter baumannii AYE subtypes: WT, O, T, O-T, T-T, S2-O, and S2-T. The genomes were generated to characterize genetic differences, with particular emphasis on structural variants, among closely related A. baumannii subtypes. The dataset includes the WT subtype as the baseline/reference lineage, the O and T subtypes, the derivative subtypes O-T and T-T, and the S2 sublines S2-O and S2-T.
    All strains were grown under consistent laboratory conditions in LB medium at 37°C to stationary phase for 16–18 h. Genomic DNA was extracted using the TIANamp Bacteria DNA Kit. Long-read sequencing was performed using Oxford Nanopore Ligation Sequencing Kit V14 on the PromethION platform. The Oxford Nanopore sequencing supports accurate assembly of complete genomes and improves detection of structural variants in repetitive or complex genomic regions.
    The seven genome assemblies were compared to identify structural variants, including insertions, deletions, inversions, duplications, and genomic rearrangements, that differentiate the subtypes. The resulting dataset provides closed genome sequences for comparative genomics and supports future studies of genome stability, subtype-specific variation, and evolutionary relationships among A. baumannii AYE subtypes.

    sample_name sample_title    bioproject_accession    organism    strain  isolate host    isolation_source    collection_date geo_loc_name    sample_type
    AYE_WT  Acinetobacter baumannii AYE subtype WT      Acinetobacter baumannii AYE subtype WT      laboratory culture  2025    China:Hengyang  isolate
    AYE_O   Acinetobacter baumannii AYE subtype O       Acinetobacter baumannii AYE subtype O       laboratory culture  2025    China:Hengyang  isolate
    AYE_T   Acinetobacter baumannii AYE subtype T       Acinetobacter baumannii AYE subtype T       laboratory culture  2025    China:Hengyang  isolate
    AYE_O-T Acinetobacter baumannii AYE subtype O-T     Acinetobacter baumannii AYE subtype O-T     laboratory culture  2025    China:Hengyang  isolate
    AYE_T-T Acinetobacter baumannii AYE subtype T-T     Acinetobacter baumannii AYE subtype T-T     laboratory culture  2025    China:Hengyang  isolate
    AYE_S2-O    Acinetobacter baumannii AYE subtype S2-O        Acinetobacter baumannii AYE subtype S2-O        laboratory culture  2025    China:Hengyang  isolate
    AYE_S2-T    Acinetobacter baumannii AYE subtype S2-T        Acinetobacter baumannii AYE subtype S2-T        laboratory culture  2025    China:Hengyang  isolate

    FASTA file name Submission name (AYE subtype)
    WT-trycycler-medaka_polished_genome.fa  WT
    O-trycycler-medaka_polished_genome.fa   O
    T-trycycler-medaka_polished_genome.fa   T
    O_Trans-trycycler-medaka_polished_genome.fa O-T
    WT_Trans-trycycler-medaka_polished_genome.fa    T-T
    S2_Dark-trycycler-medaka_polished_genome.fa S2-O
    S2_Light-trycycler-medaka_polished_genome.fa    S2-T

    #Submitted Log
    Sample name Files
    AYE_WT      WT-trycycler-medaka_polished_genome.fa
    AYE_O       O-trycycler-medaka_polished_genome.fa
    AYE_T       T-trycycler-medaka_polished_genome.fa
    AYE_O-T         O_Trans-trycycler-medaka_polished_genome.fa
    AYE_T-T         WT_Trans-trycycler-medaka_polished_genome.fa
    AYE_S2-O        S2_Dark-trycycler-medaka_polished_genome.fa
    AYE_S2-T        S2_Light-trycycler-medaka_polished_genome.fa

    ✅ WT 完成！ 平均深度: 335.6x
    ✅ O 完成！  平均深度: 473.2x
    ✅ T 完成！  平均深度: 535.5x
    ✅ O_Trans 完成:   平均深度: 293.1x
    ✅ WT_Trans 完成！  平均深度: 380.8x
    ✅ S2_Dark 完成!   平均深度: 488.8x
    ✅ S2_Light 完成！ 平均深度: 505.7x

    Accession   Title   BioProject  Sample name     Organism name   Status
    SAMN62674707    Acinetobacter baumannii AYE subtype WT  PRJNA1517976
    SAMN62674708    Acinetobacter baumannii AYE subtype O   PRJNA1517976
    SAMN62674709    Acinetobacter baumannii AYE subtype T   PRJNA1517976
    SAMN62674710    Acinetobacter baumannii AYE subtype O-T PRJNA1517976
    SAMN62674711    Acinetobacter baumannii AYE subtype T-T PRJNA1517976
    SAMN62674712    Acinetobacter baumannii AYE subtype S2-O    PRJNA1517976
    SAMN62674713    Acinetobacter baumannii AYE subtype S2-T    PRJNA1517976

Yes — based on the headers you showed, all headers are correct for NCBI Option 1 complete-genome submission, assuming:

  1. the FASTA sequences are complete and contain no runs of N,
  2. the files have normal Unix/Linux line endings, and
  3. WT_Trans really corresponds to BioSample SAMN62674711 / subtype T-T.

Overall summary

Item Status
BioSample accession included in sequence ID ✅ Correct
[isolate=...] matches the BioSample accession ✅ Correct
[organism=Acinetobacter baumannii] present ✅ Correct
[strain=AYE] present ✅ Correct
Chromosome identified by [location=chromosome] ✅ Correct
Plasmids identified by [plasmid-name=...] ✅ Correct
[topology=circular] present ✅ Correct
Sequence IDs are unique across files ✅ Correct
Expected count: 7 chromosomes + 28 plasmids ✅ Correct

File-level confirmation table

File BioSample Chromosome Plasmids Header status
O_Trans-trycycler-medaka_polished_genome.fa SAMN62674710 1 4: pOT_1 to pOT_4 ✅ Correct
O-trycycler-medaka_polished_genome.fa SAMN62674708 1 4: pO_1 to pO_4 ✅ Correct
S2_Dark-trycycler-medaka_polished_genome.fa SAMN62674712 1 4: pS2O_1 to pS2O_4 ✅ Correct
S2_Light-trycycler-medaka_polished_genome.fa SAMN62674713 1 4: pS2T_1 to pS2T_4 ✅ Correct
T-trycycler-medaka_polished_genome.fa SAMN62674709 1 4: pT_1 to pT_4 ✅ Correct
WT_Trans-trycycler-medaka_polished_genome.fa SAMN62674711 1 4: pTT_1 to pTT_4 ✅ Correct, but confirm WT_Trans = T-T
WT-trycycler-medaka_polished_genome.fa SAMN62674707 1 4: pWT_1 to pWT_4 ✅ Correct

Detailed header-by-header confirmation

All headers also contain:

[organism=Acinetobacter baumannii]
[strain=AYE]
[isolate=SAMN...]
[topology=circular]

The table below summarizes the variable and biologically important parts.

# File BioSample Sequence header ID Replicon type NCBI definition used Correct?
1 O_Trans-...fa SAMN62674710 SAMN62674710_cluster_001_consensus Chromosome [location=chromosome] ✅
2 O_Trans-...fa SAMN62674710 SAMN62674710_cluster_002_consensus Plasmid [plasmid-name=pOT_1] ✅
3 O_Trans-...fa SAMN62674710 SAMN62674710_E_tig00000049 Plasmid [plasmid-name=pOT_2] ✅
4 O_Trans-...fa SAMN62674710 SAMN62674710_cluster_003_consensus Plasmid [plasmid-name=pOT_3] ✅
5 O_Trans-...fa SAMN62674710 SAMN62674710_cluster_005_consensus Plasmid [plasmid-name=pOT_4] ✅
6 O-...fa SAMN62674708 SAMN62674708_cluster_001_consensus Chromosome [location=chromosome] ✅
7 O-...fa SAMN62674708 SAMN62674708_cluster_002_consensus Plasmid [plasmid-name=pO_1] ✅
8 O-...fa SAMN62674708 SAMN62674708_cluster_003_consensus Plasmid [plasmid-name=pO_2] ✅
9 O-...fa SAMN62674708 SAMN62674708_cluster_004_consensus Plasmid [plasmid-name=pO_3] ✅
10 O-...fa SAMN62674708 SAMN62674708_cluster_006_consensus Plasmid [plasmid-name=pO_4] ✅
11 S2_Dark-...fa SAMN62674712 SAMN62674712_cluster_001_consensus Chromosome [location=chromosome] ✅
12 S2_Dark-...fa SAMN62674712 SAMN62674712_cluster_002_consensus Plasmid [plasmid-name=pS2O_1] ✅
13 S2_Dark-...fa SAMN62674712 SAMN62674712_cluster_005_consensus Plasmid [plasmid-name=pS2O_2] ✅
14 S2_Dark-...fa SAMN62674712 SAMN62674712_cluster_004_consensus Plasmid [plasmid-name=pS2O_3] ✅
15 S2_Dark-...fa SAMN62674712 SAMN62674712_cluster_008_consensus Plasmid [plasmid-name=pS2O_4] ✅
16 S2_Light-...fa SAMN62674713 SAMN62674713_cluster_001_consensus Chromosome [location=chromosome] ✅
17 S2_Light-...fa SAMN62674713 SAMN62674713_cluster_002_consensus Plasmid [plasmid-name=pS2T_1] ✅
18 S2_Light-...fa SAMN62674713 SAMN62674713_D_tig00000003 Plasmid [plasmid-name=pS2T_2] ✅
19 S2_Light-...fa SAMN62674713 SAMN62674713_cluster_003_consensus Plasmid [plasmid-name=pS2T_3] ✅
20 S2_Light-...fa SAMN62674713 SAMN62674713_cluster_005_consensus Plasmid [plasmid-name=pS2T_4] ✅
21 T-...fa SAMN62674709 SAMN62674709_cluster_001_consensus Chromosome [location=chromosome] ✅
22 T-...fa SAMN62674709 SAMN62674709_cluster_002_consensus Plasmid [plasmid-name=pT_1] ✅
23 T-...fa SAMN62674709 SAMN62674709_cluster_003_consensus Plasmid [plasmid-name=pT_2] ✅
24 T-...fa SAMN62674709 SAMN62674709_A_tig00000003 Plasmid [plasmid-name=pT_3] ✅
25 T-...fa SAMN62674709 SAMN62674709_cluster_006_consensus Plasmid [plasmid-name=pT_4] ✅
26 WT_Trans-...fa SAMN62674711 SAMN62674711_cluster_001_consensus Chromosome [location=chromosome] ✅
27 WT_Trans-...fa SAMN62674711 SAMN62674711_cluster_002_consensus Plasmid [plasmid-name=pTT_1] ✅
28 WT_Trans-...fa SAMN62674711 SAMN62674711_cluster_004_consensus Plasmid [plasmid-name=pTT_2] ✅
29 WT_Trans-...fa SAMN62674711 SAMN62674711_cluster_005_consensus Plasmid [plasmid-name=pTT_3] ✅
30 WT_Trans-...fa SAMN62674711 SAMN62674711_cluster_007_consensus Plasmid [plasmid-name=pTT_4] ✅
31 WT-...fa SAMN62674707 SAMN62674707_cluster_001_consensus Chromosome [location=chromosome] ✅
32 WT-...fa SAMN62674707 SAMN62674707_cluster_002_consensus Plasmid [plasmid-name=pWT_1] ✅
33 WT-...fa SAMN62674707 SAMN62674707_cluster_003_consensus Plasmid [plasmid-name=pWT_2] ✅
34 WT-...fa SAMN62674707 SAMN62674707_cluster_005_consensus Plasmid [plasmid-name=pWT_3] ✅
35 WT-...fa SAMN62674707 SAMN62674707_cluster_008_consensus Plasmid [plasmid-name=pWT_4] ✅

Final verdict

Verdict Explanation
✅ Headers are correct They now satisfy NCBI Option 1 requirements: each sequence is defined as chromosome or plasmid, each file has one chromosome, plasmids use [plasmid-name=...], and sequence IDs are unique.
⚠️ One biological check Confirm that WT_Trans should really map to SAMN62674711 / subtype T-T. The plasmid names pTT_1 to pTT_4 support this, but the filename WT_Trans could be confusing.
⚠️ Final sequence-content check Make sure there are no N gaps if you are submitting as complete genomes.

You can check for Ns with:

grep -v '^>' *.fa | grep -i 'n'

Expected output: nothing.

Yes, these headers are now CORRECT. ✅

They fully comply with the NCBI Option 1 (non-WGS / complete genome) requirements. You have successfully addressed the specific errors from the validator.

Here is the breakdown of why these will pass validation:

1. ✅ Plasmid Identification is Fixed

The previous error was:

Sequence ‘SAMN62674708_cluster_002_consensus’ is not defined as a chromosome or plasmid or organelle… For plasmids, this is provided via the plasmid-name, eg [plasmid-name=pBR322]

You have correctly changed the modifier to [plasmid-name=...]:

[plasmid-name=pOT_1]

This tells the validator exactly what the sequence is.

2. ✅ Chromosome Identification is Present

The validator requires at least one chromosome per file. You have correctly identified the first sequence in every file as the chromosome:

[location=chromosome]

3. ✅ Sequence IDs are Unique

Because you prefixed every sequence ID with the BioSample accession (e.g., SAMN62674708_cluster_001_consensus), there will be no “duplicate sequence ID” errors, even if you were to upload all files together.

4. ✅ Topology is Specified

[topology=circular]

This is correct for complete bacterial genomes assembled with Trycycler.

🚀 Final Pre-Flight Checks

Before you upload, run these two quick checks in your terminal to ensure there are no hidden formatting issues that could cause a silent failure.

Check 1: Ensure there are NO gaps (Ns)

Since you are submitting a complete genome, there should be zero N bases in your sequences. Run this:

grep -v '^>' *.fa | grep -i 'n'

Expected output: (nothing).
If this returns lines, your genome has gaps and you should not select “Option 1” / “Complete”.

Check 2: Check for Windows Line Endings (CRLF)

Sometimes editing files on Windows adds hidden carriage return characters (\r) that can confuse the NCBI validator. Run this:

file *.fa

Expected output: Should say ASCII text or UTF-8 text.
Bad output: If it says with CRLF line terminators, run this to fix it:

sed -i 's/\r$//' *.fa

📝 Submission Summary

  • Headers: ✅ Correct.
  • Linkage Evidence: Select paired-ends (or align-genus) if forced, as discussed previously.
  • Genome Info Tab: Ensure you select “Option 1” (or the option that says “Complete Genome” / “Non-WGS”).
  • File Upload: Upload the .fa files directly.

You are good to submit! 🎉

Yes — the FASTA sequence names/headers now look correct and much better for automatic NCBI recognition.

You have fixed the main issues:

  1. BioSample accession is now part of the sequence ID
    Example:

    >SAMN62674710_cluster_001_consensus ...

    This makes the sequence IDs unique across files and helps NCBI link each sequence to the correct BioSample.

  2. [isolate=SAMN...] matches the accession in the sequence ID
    Example:

    [isolate=SAMN62674710]

  3. [strain=AYE] is present, which is appropriate given the sample titles.

  4. Plasmid modifier is now standard
    You changed:

    [plasmid-name=pOT_1]

    to:

    [plasmid=pOT_1]

    which is the preferred NCBI/GenBank format.

  5. Chromosome/plasmid/topology information looks consistent
    For example:

    [location=chromosome] [topology=circular]

    and

    [plasmid=pOT_1] [topology=circular]

Mapping check

Your headers map as follows:

File BioSample in headers Correct?
O_Trans-trycycler-medaka_polished_genome.fa SAMN62674710 ✅
O-trycycler-medaka_polished_genome.fa SAMN62674708 ✅
S2_Dark-trycycler-medaka_polished_genome.fa SAMN62674712 ✅
S2_Light-trycycler-medaka_polished_genome.fa SAMN62674713 ✅
T-trycycler-medaka_polished_genome.fa SAMN62674709 ✅
WT_Trans-trycycler-medaka_polished_genome.fa SAMN62674711 ✅, but double-check that WT_Trans should correspond to title T-T
WT-trycycler-medaka_polished_genome.fa SAMN62674707 ✅

The only one I would manually confirm is:

WT_Trans -> SAMN62674711

because your accession table lists that sample as:

Acinetobacter baumannii AYE subtype T-T

but the filename is WT_Trans. Based on the plasmid names pTT_1, pTT_2, etc., it looks logical, but just make sure that WT_Trans is indeed the sample registered as T-T.

Optional but recommended: also add accession to filenames

Your headers are now good. However, some NCBI submission/upload systems also try to match files based on the filename. Your filenames still look like this:

O_Trans-trycycler-medaka_polished_genome.fa
O-trycycler-medaka_polished_genome.fa
S2_Dark-trycycler-medaka_polished_genome.fa
...

If the submission portal still does not automatically recognize the samples, rename the files so that the BioSample accession is also in the filename.

For example:

SAMN62674710_O_Trans-trycycler-medaka_polished_genome.fa
SAMN62674708_O-trycycler-medaka_polished_genome.fa
SAMN62674712_S2_Dark-trycycler-medaka_polished_genome.fa
SAMN62674713_S2_Light-trycycler-medaka_polished_genome.fa
SAMN62674709_T-trycycler-medaka_polished_genome.fa
SAMN62674711_WT_Trans-trycycler-medaka_polished_genome.fa
SAMN62674707_WT-trycycler-medaka_polished_genome.fa

You can do that with:

while read -r prefix acc; do
  f="${prefix}-trycycler-medaka_polished_genome.fa"
  if [[ -f "$f" ]]; then
    mv -n "$f" "${acc}_${prefix}-trycycler-medaka_polished_genome.fa"
  fi
done <<'EOF'
O_Trans SAMN62674710
O SAMN62674708
S2_Dark SAMN62674712
S2_Light SAMN62674713
T SAMN62674709
WT_Trans SAMN62674711
WT SAMN62674707
EOF

The -n option prevents overwriting an existing file.

Final quality checks

You can run these to confirm everything is clean.

1. Check for duplicate sequence IDs

grep -h '^>' *.fa | awk '{print substr($1,2)}' | sort | uniq -d

Expected output: nothing.

2. Check that no old plasmid modifier remains

grep -h 'plasmid-name=' *.fa

Expected output: nothing.

3. Check that every sequence ID accession matches its [isolate=...]

grep -h '^>' *.fa | awk '
{
  id = substr($1, 2)
  split(id, a, "_")
  acc = a[1]
  if ($0 !~ ("\\[isolate=" acc "\\]")) {
    print "Mismatch:", $0
  }
}'

Expected output: nothing.

Bottom line

Yes, the sequence header names are now correct for NCBI submission/auto-linking purposes.
The only optional additional step is to rename the actual .fa files with the BioSample accession prefix if the submission portal does not automatically map the files from the headers alone.

To ensure your FASTA files are automatically recognized and correctly linked to your BioSamples by the NCBI Submission Portal (or GenBank), you need to address three main issues in your current headers:

  1. Missing BioSample Linker: NCBI requires the BioSample accession to be explicitly stated in the header to auto-link sequences. This is best done using the [isolate=SAMN...] modifier and prefixing the Sequence ID.
  2. Non-unique Sequence IDs: IDs like cluster_001_consensus are repeated across files. If NCBI processes them together, it will throw “duplicate sequence ID” errors. Prefixing them with the BioSample accession guarantees global uniqueness.
  3. Non-standard Modifiers: [plasmid-name=...] is not a standard GenBank modifier. It should be corrected to [plasmid=...] so the parser correctly registers the plasmid names. Additionally, adding [strain=AYE] (from your titles) fulfills NCBI’s metadata requirements.

The Mapping

Based on your file prefixes, plasmid names (e.g., pOT, pTT), and the accession table, here is the correct mapping:

File Prefix Subtype (Title) BioSample Accession
WT WT SAMN62674707
O O SAMN62674708
T T SAMN62674709
O_Trans O-T SAMN62674710
WT_Trans T-T* SAMN62674711
S2_Dark S2-O SAMN62674712
S2_Light S2-T SAMN62674713

(Note: Please double-check WT_Trans -> T-T. The accession title says “T-T”, which matches your pTT plasmids, but the file is named WT_Trans. Ensure this is intentional!)

Example of the Improved Header

Before: >cluster_002_consensus [organism=Acinetobacter baumannii] [plasmid-name=pOT_1] [topology=circular]

After: >SAMN62674710_cluster_002_consensus [organism=Acinetobacter baumannii] [strain=AYE] [isolate=SAMN62674710] [plasmid=pOT_1] [topology=circular]

Automated Bash Script to Fix All Files

You can run the following script directly in your terminal. It will safely update all your .fa files in place using sed.

#!/bin/bash

# Loop through the mapped prefixes and accessions
while read -r prefix acc; do
    file="${prefix}-trycycler-medaka_polished_genome.fa"

    if [[ -f "$file" ]]; then
        echo "Updating $file with BioSample $acc..."

        # 1. Prefix the sequence ID with the BioSample accession (ensures uniqueness)
        # 2. Add [strain=AYE] and [isolate=BioSample] (triggers NCBI auto-linking)
        # 3. Correct [plasmid-name=...] to the standard GenBank [plasmid=...]
        sed -i -e "s/^>/>${acc}_/" \
               -e "s/\[organism=Acinetobacter baumannii\]/[organism=Acinetobacter baumannii] [strain=AYE] [isolate=${acc}]/" \
               -e "s/\[plasmid-name=/[plasmid=/g" \
               "$file"
    else
        echo "Warning: $file not found."
    fi
done <<EOF
WT SAMN62674707
O SAMN62674708
T SAMN62674709
O_Trans SAMN62674710
WT_Trans SAMN62674711
S2_Dark SAMN62674712
S2_Light SAMN62674713
EOF

echo "All headers successfully updated!"

How to use:

  1. Copy the code block above.
  2. Paste it directly into your terminal in the ~/DATA/Data_Tam_Methylation_2026_WT_T_O_T_Trans_O_Trans/trycycler-medaka_polished_genomes_hac_v5.0.0/ directory and press Enter.
  3. Run grep ">" *.fa again to verify that the headers have been correctly transformed.

When you upload these to the NCBI Submission Portal, the system will read the >SAMN... prefix and the [isolate=SAMN...] tag, automatically matching each contig/plasmid to the correct BioSample without requiring manual mapping.