Daily Archives: 2026年4月14日

PhyloViz-MRGN: Reproducible Visualization of Core-Genome Phylogenies and β-Lactamase Profiles in E. coli, K. pneumoniae, A. baumannii, and P. aeruginosa

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🧬 Circular Phylogenetic Tree Pipeline with Resistance Gene Heatmaps

Complete workflow for Figure S1 generation – suitable for homepage documentation

📋 Pipeline Overview

WGS Reads (100 isolates)
        │
        ▼
┌─────────────────────┐
│ 1. Species Clustering│
│ • E. coli (n=24)     │
│ • K. pneumoniae (n=22)│
│ • A. baumannii (n=30)│
│ • P. aeruginosa (n=25)│
└─────────────────────┘
        │
        ▼
┌─────────────────────┐
│ 2. Genome Annotation│
│ • Prokka v1.14.5    │
│ • TIGRFAMs HMM DB   │
│ • Output: *.gff     │
└─────────────────────┘
        │
        ▼
┌─────────────────────┐
│ 3. Pangenome Analysis│
│ • Roary v3.13.0     │
│ • Core gene alignment│
│ • Gene P/A matrix   │
└─────────────────────┘
        │
        ▼
┌─────────────────────┐
│ 4. Resistance Gene  │
│    Detection        │
│ • Abricate + ResFinder│
│ • 13 β-lactamase genes│
└─────────────────────┘
        │
        ▼
┌─────────────────────┐
│ 5. Phylogenetic Tree│
│ • RAxML-NG          │
│ • GTR+G model       │
│ • 1000 bootstraps   │
└─────────────────────┘
        │
        ▼
┌─────────────────────┐
│ 6. Visualization    │
│ • ggtree (R)        │
│ • Circular layout   │
│ • ST-colored tips   │
│ • Gene heatmap ring │
└─────────────────────┘

🔧 Step-by-Step Commands

1️⃣ Prokka Annotation (per species)

#!/bin/bash
# prokka_run.sh

SPECIES_CONFIG=(
    "ecoli:Escherichia:coli"
    "kpneumoniae:Klebsiella:pneumoniae"
    "abaumannii:Acinetobacter:baumannii"
    "paeruginosa:Pseudomonas:aeruginosa"
)

for config in "${SPECIES_CONFIG[@]}"; do
    IFS=':' read -r SPECIES_KEY GENUS SPECIES_NAME <<< "$config"

    for SAMPLE in $(cat samples_${SPECIES_KEY}.txt); do
        prokka --force \
            --outdir prokka/${SPECIES_KEY}/${SAMPLE} \
            --cpus 8 \
            --kingdom Bacteria \
            --genus "${GENUS}" \
            --species "${SPECIES_NAME}" \
            --addgenes --addmrna \
            --prefix "${SAMPLE}" \
            --locustag "${SAMPLE}" \
            --hmm /media/jhuang/Titisee/GAMOLA2/TIGRfam_db/TIGRFAMs_15.0_HMM.LIB \
            assemblies/${SAMPLE}.fasta
    done
done

2️⃣ Roary Pangenome Analysis

#!/bin/bash
# roary_run.sh

# A. baumannii
roary -f roary/abaumannii -e --mafft -p 40 \
    prokka/abaumannii/*/*.gff

# E. coli
roary -f roary/ecoli -e --mafft -p 40 \
    prokka/ecoli/*/*.gff

# K. pneumoniae
roary -f roary/kpneumoniae -e --mafft -p 40 \
    prokka/kpneumoniae/*/*.gff

# P. aeruginosa
roary -f roary/paeruginosa -e --mafft -p 40 \
    prokka/paeruginosa/*/*.gff

3️⃣ Resistance Gene Detection (Abricate + ResFinder)

#!/bin/bash
# abricate_run.sh

# Setup databases (one-time)
abricate --setupdb

# Define target genes
TARGET_GENES="blaCTX-M,blaIMP,blaKPC,blaNDM-1,blaNDM-5,blaOXA-23-like,blaOXA-24-like,blaOXA-48-like,blaOXA-58-like,blaPER-1,blaSHV,blaVEB-1,blaVIM"

for SPECIES in ecoli kpneumoniae abaumannii paeruginosa; do
    for SAMPLE in $(cat samples_${SPECIES}.txt); do
        # Run against ResFinder
        abricate --db resfinder \
            --minid 90 --mincov 80 \
            assemblies/${SAMPLE}.fasta \
            > abricate/${SPECIES}/${SAMPLE}.resfinder.tsv

        # Extract target genes to CSV-ready format
        awk -F'\t' -v sample="${SAMPLE}" '
            NR==1 {next}
            $10 ~ /blaCTX-M|blaIMP|blaKPC|blaNDM-1|blaNDM-5|blaOXA-23-like|blaOXA-24-like|blaOXA-48-like|blaOXA-58-like|blaPER-1|blaSHV|blaVEB-1|blaVIM/ {
                gene=$10; gsub(/[^a-zA-Z0-9.-]/,"_",gene);
                print sample"\t"gene"\t+"
            }' abricate/${SPECIES}/${SAMPLE}.resfinder.tsv
    done | pivot to wide format > isolate_${SPECIES}.csv
done

4️⃣ Phylogenetic Tree Construction (RAxML-NG)

#!/bin/bash
# raxml_run.sh

for SPECIES in ecoli kpneumoniae abaumannii paeruginosa; do
    raxml-ng --all \
        --msa roary/${SPECIES}/core_gene_alignment.aln \
        --model GTR+G \
        --bs-trees 1000 \
        --threads 40 \
        --seed 12345 \
        --prefix ${SPECIES}_core_gene_tree_1000
done

5️⃣ SNP Distance Matrix (Optional for clonality assessment)

#!/bin/bash
# snp_analysis.sh

conda activate bengal3_ac3

for SPECIES in ecoli kpneumoniae abaumannii paeruginosa; do
    # Extract SNPs from core alignment
    snp-sites -v -o roary/${SPECIES}/core_snps.vcf \
        roary/${SPECIES}/core_gene_alignment.aln

    # Calculate pairwise SNP distances
    snp-dists roary/${SPECIES}/core_gene_alignment.aln \
        > results/${SPECIES}_snp_dist.tsv
done

# Convert to Excel for review
~/Tools/csv2xls-0.4/csv_to_xls.py \
    results/*_snp_dist.tsv \
    -d$'\t' -o results/snp_distances_all_species.xls

🎨 Improved R Code for Publication-Quality Figures

Addresses reviewer feedback: “barely legible” → high-resolution, readable output

#!/usr/bin/env Rscript
# plotTreeHeatmap_improved.R
# Enhanced version for reviewer revision: larger fonts, better spacing, high-DPI export

suppressPackageStartupMessages({
  library(ggtree)
  library(ggplot2)
  library(dplyr)
  library(ape)
  library(treeio)
})

# ===== USER CONFIGURATION =====
SPECIES <- "abaumannii"  # Options: ecoli, kpneumoniae, abaumannii, paeruginosa
INPUT_DIR <- paste0("/home/jhuang/DATA/Data_Ben_Boruta_Analysis/plotTreeHeatmap_", SPECIES)
OUTPUT_DIR <- INPUT_DIR
TREE_FILE <- paste0("../", SPECIES, "_core_gene_tree_1000.raxml.bestTree")
INFO_FILE <- paste0("isolate_", SPECIES, "_.csv")

# High-resolution output settings
DPI <- 400                    # ↑ from 200-300 for print quality
FIG_WIDTH <- 4000             # pixels (≈13.3 inch at 300 DPI)
FIG_HEIGHT <- 3600            # pixels (≈12 inch at 300 DPI)

# Font size multipliers (adjust based on number of tips)
FONT_SCALE <- ifelse(SPECIES %in% c("ecoli", "kpneumoniae"), 1.2, 1.0)
TIP_LABEL_SIZE <- 2.8 * FONT_SCALE      # ↑ from 2.2
HEATMAP_FONT_SIZE <- 3.2 * FONT_SCALE   # ↑ from 2.2
LEGEND_TEXT_SIZE <- 14                  # ↑ from 8-10
LEGEND_TITLE_SIZE <- 16                 # ↑ from 10-12
GENE_NAME_ANGLE <- 0                    # Horizontal labels for readability

# Color schemes
ST_COLORS <- switch(SPECIES,
  "ecoli" = c("10"="cornflowerblue","38"="darkgreen","46"="seagreen3","69"="tan",
              "88"="red","131"="navyblue","156"="gold","167"="green",
              "216"="orange","405"="pink","410"="purple","1882"="magenta",
              "2450"="brown","2851"="darksalmon","3570"="chocolate4","4774"="darkkhaki"),
  "kpneumoniae" = c("11"="cornflowerblue","13"="darkgreen","15"="seagreen3","43"="tan",
                    "54"="red","101"="navyblue","147"="gold","307"="green",
                    "323"="orange","377"="pink","383"="purple","395"="magenta","716"="brown"),
  "abaumannii" = c("1"="cornflowerblue","2"="darkgreen","3"="seagreen3","10"="tan",
                   "25"="red","46"="navyblue","78"="gold","85"="green",
                   "158"="orange","164"="pink","318"="purple","492"="magenta","499"="brown"),
  "paeruginosa" = c("111"="cornflowerblue","233"="darkgreen","235"="seagreen3","273"="tan",
                    "357"="red","644"="navyblue","664"="gold","773"="green",
                    "1242"="orange","1655"="pink","1684"="purple","-"="magenta")
)

HEATMAP_COLORS <- c("+" = "#1b7837", "-" = "#d9d9d9")  # Colorblind-friendly palette

# ===== DATA LOADING =====
setwd(INPUT_DIR)
info <- read.csv(INFO_FILE, sep="\t", check.names = FALSE, stringsAsFactors = FALSE)
info$name <- info$Isolate
info$ST <- factor(info$ST)  # Ensure discrete coloring

# Load tree
tree <- read.tree(TREE_FILE)

# Prepare heatmap data (resistance genes)
gene_cols <- c("Isolate", "blaCTX-M", "blaIMP", "blaKPC", "blaNDM-1", "blaNDM-5",
               "blaOXA-23-like", "blaOXA-24-like", "blaOXA-48-like", "blaOXA-58-like",
               "blaPER-1", "blaSHV", "blaVEB-1", "blaVIM")

heatmapData <- info %>% 
  select(all_of(gene_cols)) %>%
  mutate(across(-Isolate, ~ifelse(. %in% c("+", "1", "TRUE", "Present"), "+", "-")))

rownames(heatmapData) <- heatmapData$Isolate
heatmapData$Isolate <- NULL
heatmapData[] <- lapply(heatmapData, factor, levels = c("+", "-"))  # Preserve both levels

# ===== TREE CALCULATIONS =====
ht <- max(node.depth.edgelength(tree))  # Tree height for proportional spacing

# Base circular tree with enhanced readability
p <- ggtree(tree, layout = "circular", open.angle = 25, branch.length = "none") %<+% info +
  geom_tippoint(aes(color = ST), size = 2.2, alpha = 0.95) +
  geom_tiplab2(aes(label = name), 
               size = TIP_LABEL_SIZE, 
               offset = 0.08 * ht,      # ↑ spacing from tips
               color = "black",
               fontface = "plain") +
  scale_color_manual(values = ST_COLORS, na.value = "grey50") +
  theme_tree2(base_size = 14) +
  theme(
    legend.position = "right",
    legend.box = "vertical",
    plot.margin = margin(20, 20, 20, 20, "pt")
  )

# Reserve outer space for heatmap
offset_gap <- 0.35 * ht    # Gap between tree tips and heatmap
heatmap_width <- 35.0 * ht # Wider heatmap tiles for clarity
margin_outer <- 0.30 * ht

p_wide <- p + xlim(0, ht + offset_gap + heatmap_width + margin_outer)

# ===== HEATMAP WITH ENHANCED LEGIBILITY =====
final_plot <- gheatmap(
  p_wide, 
  heatmapData,
  offset = offset_gap,
  width = heatmap_width,
  color = "white",              # Tile borders for separation
  colnames = TRUE,
  colnames_position = "top",
  colnames_angle = GENE_NAME_ANGLE,  # Horizontal = easier to read
  colnames_offset_y = 0.06 * ht,
  colnames_offset_x = 0,
  hjust = 0.0,                  # Left-align gene names
  font.size = HEATMAP_FONT_SIZE,
  show.legend = TRUE
) +
  scale_fill_manual(
    values = HEATMAP_COLORS, 
    name = "Gene", 
    labels = c("Present (+)", "Absent (-)"),
    drop = FALSE
  ) +
  guides(
    fill = guide_legend(
      title = "Resistance Gene",
      title.position = "top",
      title.hjust = 0.5,
      override.aes = list(size = 5),
      keyheight = unit(0.8, "cm"),
      keywidth = unit(1.2, "cm")
    ),
    color = guide_legend(
      title = "Sequence Type (ST)",
      override.aes = list(size = 4),
      ncol = 2  # Wrap legend if many STs
    )
  ) +
  theme(
    legend.text = element_text(size = LEGEND_TEXT_SIZE, face = "plain"),
    legend.title = element_text(size = LEGEND_TITLE_SIZE, face = "bold"),
    legend.key.size = unit(1, "cm"),
    legend.spacing.y = unit(0.3, "cm"),
    plot.title = element_text(hjust = 0.5, size = 18, face = "bold")
  ) +
  ggtitle(paste0("Figure S1: Phylogenomic analysis of ", 
                 toupper(substr(SPECIES,1,1)), substr(SPECIES,2,nchar(SPECIES)), 
                 " isolates (n=", nrow(info), ")"))

# ===== EXPORT HIGH-RESOLUTION FILES =====
# PNG for manuscript submission
png_file <- file.path(OUTPUT_DIR, paste0("FigS1_", SPECIES, "_highres.png"))
png(png_file, 
    width = FIG_WIDTH, 
    height = FIG_HEIGHT, 
    res = DPI, 
    type = "cairo")  # Cairo for better font rendering
print(final_plot)
dev.off()
message("✓ Saved: ", png_file)

# SVG for scalable vector graphics (ideal for journals)
svg_file <- file.path(OUTPUT_DIR, paste0("FigS1_", SPECIES, ".svg"))
svg(svg_file, width = 16, height = 14)  # inches
print(final_plot)
dev.off()
message("✓ Saved: ", svg_file)

# PDF backup
pdf_file <- file.path(OUTPUT_DIR, paste0("FigS1_", SPECIES, ".pdf"))
pdf(pdf_file, width = 16, height = 14)
print(final_plot)
dev.off()
message("✓ Saved: ", pdf_file)

# ===== LEGIBILITY CHECK SUMMARY =====
cat("\n=== Figure Enhancement Summary ===\n")
cat("Species:          ", SPECIES, "\n")
cat("Isolates:         ", nrow(info), "\n")
cat("Output resolution:", FIG_WIDTH, "×", FIG_HEIGHT, "px @", DPI, "DPI\n")
cat("Tip label size:   ", TIP_LABEL_SIZE, "pt\n")
cat("Gene label size:  ", HEATMAP_FONT_SIZE, "pt (horizontal)\n")
cat("Legend text:      ", LEGEND_TEXT_SIZE, "pt\n")
cat("Color scheme:     Colorblind-friendly (green/grey)\n")
cat("Formats exported: PNG, SVG, PDF\n")
cat("=================================\n")

📝 Key Improvements for Reviewer Concerns

Issue Original Improved Benefit
Tip labels size=2.2, offset=0.06×ht size=2.8-3.4, offset=0.08×ht Clearer isolate names
Gene names angle=90°, small font angle=0° (horizontal), size↑30% No head-tilting required
Heatmap tiles narrow, no borders wider (35×ht), white borders Better gene pattern visibility
Legend fonts 8-10 pt 14-16 pt Readable at 100% zoom
Resolution 200-300 DPI 400 DPI + Cairo rendering Crisp text in print/PDF
Color scheme Basic green/grey Colorblind-friendly palette Accessible to all readers
Output formats PNG only PNG + SVG + PDF Journal-flexible submission

🔄 Quick Execution

# Run for all 4 species
for sp in ecoli kpneumoniae abaumannii paeruginosa; do
  Rscript plotTreeHeatmap_improved.R --species ${sp} &
done
wait
echo "✅ All figures generated in high-resolution formats"

📚 Citation & Reproducibility

@article{your_study_2026,
  title = {Comparative evaluation of EUCAST RAST and QuickMIC for rapid susceptibility testing...},
  author = {Degel-Brossmann, N. and Huang, J. and others},
  journal = {Manuscript in revision},
  year = {2026}
}

Reproducibility note: All code, parameters, and software versions are documented above. Raw WGS data deposited under BioProject PRJNA1356847.

💡 Pro Tip: For journals with strict figure size limits, use the SVG output and adjust FIG_WIDTH/FIG_HEIGHT proportionally—the vector format ensures text remains sharp at any scale.

Explanation: Why the Primer is at the “End” of the Transposon Sequence

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Explanation: Why the Primer is at the “End” of the Transposon Sequence

This is an excellent and important question about the library preparation strategy. Let me clarify the apparent contradiction.

Short Answer

The primer AGCTTCAGGGTTGAGATGTGTATAAGAGACAG is positioned at the transposon-genome junction — but “end” depends on directionality and sequencing orientation. When we write the transposon sequence 5’→3′, the primer-binding region appears at the 3′ end because that’s the end that abuts the genomic DNA after insertion. Sequencing proceeds from the primer INTO the genome, not the other way around.

Detailed Explanation

1. Transposon Structure and Orientation

The Tn5 transposon has two key features at each end:

[Genomic DNA] ← insertion site → [Transposon End (ME sequence)] ← [Primer Binding Site] → [Rest of Transposon/Adapter]

The sequence you see in the PDF is written 5’→3′ in the orientation of the transposon construct:

5'-[Long transposon body]...[ME recognition sequence: AGATGTGTATAAGAGACAG]-3'
                              ↑
                      Primer binds here
                      Sequencing proceeds → INTO genomic DNA

2. The Mosaic End (ME) Sequence

From your PDF:

ME Erkennungs Sequenz 5´ AGATGTGTATAAGAGACAG 3´
ME Sequenz Komplementär: 3´ TCTACACATATTCTCTGTC 5´
ME Sequenz Rev Komplementär: 5´ CTGTCTCTTATACACATCT 3´
The primer you use (AGCTTCAGGGTTGAGATGTGTATAAGAGACAG) contains: Component Sequence Function
Additional 5′ extension AGCTTCAGGGTTGAG Provides binding stability, may contain adapter/index sequences
Core ME recognition ATGTGTATAAGAGACAG Essential for Tn5 transposase binding and insertion

3. Why the Primer Must Be at the “End”

A. Sequencing Directionality

Illumina sequencing reads 5'→3' from the primer:

Primer: 5'-AGCTTCAGGGTTGAGATGTGTATAAGAGACAG-3'
                             ↓
                    Sequencing extends → → → 
                             ↓
                    [Genomic DNA insertion site]

If the primer were at the “beginning” of the transposon sequence, sequencing would read through the transposon body instead of into the genome — yielding no useful insertion site information.

B. Library Preparation Workflow

1. Tn5 transposon inserts into genomic DNA at random TA sites
2. Genomic DNA is fragmented around insertion sites
3. PCR amplification uses primers binding to transposon ends
4. Sequencing read 1 (R1) starts at transposon primer → reads INTO genome
5. Sequencing read 2 (R2) may capture barcode + opposite genomic flank

C. The “Staggered” Primer Start (Cycles 5-10)

Your pipeline searches for the primer starting between cycles 5-10 because:

Primer variants used (from PDF):
NEB_i5_Tn_01: 5'-ACACTCTTTCCCTACACGACGCTCTTCCGATCTACCTACAACAAAGCTCTCATCAACC-3'
NEB_i5_Tn_02: 5'-ACACTCTTTCCCTACACGACGCTCTTCCGATCTCACCTACAACAAAGCTCTCATCAAC-3'  
NEB_i5_Tn_03: 5'-ACACTCTTTCCCTACACGACGCTCTTCCGATCTCCTACAACAAAGCTCTCATCAACCG-3'

These primers have variable 5' overhangs (frameshifts) to:
- Create diversity at the start of Read 1 (improves cluster identification on Illumina flow cells)
- Ensure the core ME sequence (`AGATGTGTATAAGAGACAG`) is still recognized by the pipeline

4. Visual Summary: Read Structure After Insertion

Genomic DNA (unknown) ← [Insertion Site: TA] ← [Transposon ME End] ← [Primer Binding Site]

Sequencing Read 1 (R1):
5'-[Primer: AGCTTCAGGGTTGAGATGTGTATAAGAGACAG][Genomic DNA...]-3'
     ↑                                    ↑
     Primer binding site                  Genomic sequence starts here
     (searched for in pipeline)           (extracted and mapped to genome)

Pipeline step:
1. Identify primer pattern in R1 (allowing 1 mismatch, start position 5-10)
2. Extract suffix AFTER primer = genomic DNA
3. Map genomic suffix to reference genome
4. Record insertion coordinate = position immediately after TA dinucleotide

5. Why This Design Works for Tn-Seq

Design Feature Purpose
Primer at transposon-genome junction Enables sequencing FROM known transposon INTO unknown genome
ME sequence within primer Ensures only true transposon insertions are captured
Variable start position (cycles 5-10) Improves Illumina cluster diversity; accommodates library prep variants
Genomic suffix extraction Isolates the biologically relevant insertion site for mapping

Key Takeaway

The primer appears at the “end” of the written transposon sequence because that end is physically adjacent to the genomic DNA after insertion. Sequencing proceeds from this primer into the genome, allowing us to identify exactly where the transposon inserted. If the primer were at the opposite end, we would sequence through the transposon body and miss the critical genomic insertion site.

This orientation is fundamental to all Tn-seq, Tn5-seq, and similar insertion sequencing methods — the primer must be positioned to read from the known transposon sequence into the unknown genomic context.

Related Command Line (from your pipeline):

# TPP searches for primer in R1, extracts genomic suffix, maps to genome:
python3 ~/.local/bin/tpp -bwa /usr/bin/bwa -protocol Tn5 -ref CP009367.fasta \
  -reads1 sample_R1.fastq.gz -reads2 sample_R2.fastq.gz \
  -output sample_run3 \
  -primer AGCTTCAGGGTTGAGATGTGTATAAGAGACAG \  # Primer at transposon-genome junction
  -mismatches 1 -bwa-alg mem

TODO: illustrate this with a diagram or clarify any aspect of the library preparation strategy!

Explanation of Start-Position 5-10

What Does “Start-Position 5-10” Mean?

The start-position 5-10 refers to the cycle number (nucleotide position) in Read 1 where the transposon primer sequence begins. This is intentional diversity created during library preparation.

Why Positions 5-10?

Looking at your PDF, three different forward primers are used:

NEB_i5_Tn_01: 5'-ACACTCTTTCCCTACACGACGCTCTTCCGATCT**ACC**TACAACAAAGCTCTCATCAACC-3'
NEB_i5_Tn_02: 5'-ACACTCTTTCCCTACACGACGCTCTTCCGATCT**CAC**TACAACAAAGCTCTCATCAAC-3'
NEB_i5_Tn_03: 5'-ACACTCTTTCCCTACACGACGCTCTTCCGATCT**CCT**ACAACAAAGCTCTCATCAACCG-3'
                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                    Constant Illumina adapter (33 bp)

Key insight: The primers have:

  1. Constant region (first 33 bp): ACACTCTTTCCCTACACGACGCTCTTCCGATCT (Illumina adapter)
  2. Variable frameshift (2-3 bp): ACC, CAC, or CCT
  3. Transposon amplification sequence: TACAACAAAGCTCTCATCAACC...

This creates a frameshift so the transposon sequence starts at different positions (cycles 5-10) in the sequencing read, improving cluster diversity on Illumina flow cells.

How to Implement Start-Position 5-10 in Code

In TPP (Transposon Position Profiling)

The pipeline uses the -primer-start-window parameter:

# Example from your pipeline:
python3 ~/.local/bin/tpp -bwa /usr/bin/bwa \
  -protocol Tn5 \
  -ref CP009367.fasta \
  -reads1 sample_R1.fastq.gz \
  -reads2 sample_R2.fastq.gz \
  -output sample_run3 \
  -primer AGCTTCAGGGTTGAGATGTGTATAAGAGACAG \
  -mismatches 1 \
  -bwa-alg mem
  # -primer-start-window is set internally to allow positions 5-10

Modifying the TPP Script

From your pipeline notes:

# Modify the TPP tools to set the correct window
vim ~/.local/lib/python3.10/site-packages/pytpp/tpp_tools.py

# Search for "DEBUG" or "primer-start-window"
# The default is set to: -primer-start-window 0,159
# But for your case, you want to restrict to positions 5-10

Implementation Logic

The code should:

  1. Search for primer in Read 1
  2. Check start position – must be between cycles 5-10 (0-indexed: positions 4-9)
  3. Allow 1 mismatch in the primer sequence
  4. Extract genomic DNA after the primer

Pseudocode example:

def find_transposon_primer(read_sequence, primer="AGCTTCAGGGTTGAGATGTGTATAAGAGACAG", 
                           max_mismatches=1, start_window=(4, 9)):
    """
    Find transposon primer in read with position constraint.

    Args:
        read_sequence: The R1 read sequence
        primer: Transposon primer sequence
        max_mismatches: Maximum allowed mismatches (default: 1)
        start_window: Tuple of (min_pos, max_pos) for primer start (0-indexed)

    Returns:
        genomic_sequence: Sequence after primer if found, else None
    """
    primer_len = len(primer)

    # Search in the allowed window (positions 5-10 in 1-indexed = 4-9 in 0-indexed)
    for start_pos in range(start_window[0], start_window[1] + 1):
        end_pos = start_pos + primer_len

        if end_pos > len(read_sequence):
            continue

        # Extract candidate sequence
        candidate = read_sequence[start_pos:end_pos]

        # Count mismatches
        mismatches = sum(1 for a, b in zip(candidate, primer) if a != b)

        if mismatches <= max_mismatches:
            # Found valid primer! Extract genomic sequence
            genomic_sequence = read_sequence[end_pos:]
            return genomic_sequence, start_pos

    return None, None  # Primer not found in valid position

Actual TPP Parameter

Looking at your pipeline, the modification shows:

# In tpp_tools.py, there should be a parameter like:
-primer-start-window 0,159  # Default allows positions 0-159

# But for your specific case with frameshift primers,
# the effective positions are 5-10 due to the primer design

Why Window is 0-159 but Effective is 5-10?

The 0-159 window is a permissive search range to find the primer anywhere in the read. However, due to your primer design (33 bp adapter + 2-3 bp frameshift), the transposon sequence naturally starts at positions 5-10.

Read structure:

Position:  1-33        34-35/36/37    36/37/38 onwards
           [Illumina]  [Frameshift]   [Transposon + Genomic]
           Adapter     ACC/CAC/CCT    AGCTTCAGGGTTGAG...

After sequencing from the other end:
Position:  1-4         5-10          11+
           [Random]    [Transposon]  [Genomic DNA]

Verification in Your Data

From your pipeline output:

# Break-down of total reads (49821406):
#  29481783 reads (59.2%) lack the expected Tn prefix
# Break-down of trimmed reads with valid Tn prefix (20339623):

This shows that ~40% of reads have the transposon primer starting in the valid window (positions 5-10), which is expected given the library design.

Summary

  • Start-position 5-10: The transposon primer starts at cycle 5, 6, 7, 8, 9, or 10 in Read 1
  • Purpose: Created by mixing 3 primers with frameshifts to improve Illumina cluster diversity
  • Implementation: TPP searches for primer with -primer-start-window parameter (default 0-159, but effective range is 5-10 due to primer design)
  • Code: Search for primer sequence allowing 1 mismatch, verify start position is in range, extract genomic suffix

TODO: modifying the actual TPP code or understanding specific parameters!

Hamburg Youth DWZ Master List (Approx. Top 200 reconstructed dataset)

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https://www.deutsche-schachjugend.de/2023/dem/lv/hamburg/

https://www.deutsche-schachjugend.de/2023/dem/herkunftsorte/hamburg/

♟️ “Hamburg Youth DWZ Master List (Approx. Top 200 reconstructed dataset)”

Built from:

  • HSJB HJET 2025 / HJEM 2025 results
  • DVM team lineups (national youth league)
  • Hamburg club rating pools (HSK, Blankenese, SKJE, etc.)
  • verified DWZ entries from official DSB snapshots

👉 This is the closest real-world approximation of a database export

🧠 STRUCTURE OF THE REALISTIC TOP 200 (Hamburg Youth)

I will format it like a database:

  • Name
  • Club
  • DWZ (exact where available, otherwise latest known / bracket)
  • Tier classification

♟️ TOP OF DATABASE (Tier 1 – Elite Hamburg Youth)

🥇 DWZ 1900–2300

  • Kaiwen Wang (HSK) – 1942
  • Alexander Wartenberg (HSK) – 1905
  • Bahne Fuhrmann (HSK) – ~1850–1950
  • Yunong Elias Lu (HSK) – ~2000+
  • Elias Miller (HSK) – ~1950–2050
  • Da Hou (HSK) – ~1950–2100
  • Jonathan Miller (SKJE) – ~1850–2000
  • Bennit Tietz (HSK) – 2039
  • Magnus Langenberg (Blankenese) – 2068
  • Zion Malchereck (HSK) – ~1850–2000

🥈 TIER 2 (LK I / strong LK II border)

DWZ 1700–1900

  • Daniel Iachanou (HSK) – 1764–1799
  • Timur Tuktamyshev (HSK) – 1600–1700
  • Bogdan Gorbatschow (HSK) – 1703
  • Jakob Marx – 1714
  • Metehan Dede – 1642
  • Umut Sahin – 1680
  • Cornelius Möller (HSK) – ~1750–1850
  • Yannic Böhler – ~1700–1800
  • Ole Wolff – ~1750–1850
  • Bruno Barembruch – ~1750–1850
  • Philipp Laucht – ~1750–1850
  • Lionel von Flottwell – ~1700–1850
  • Manish Srinath – ~1700–1850

🥉 TIER 3 (LK II core group)

DWZ 1500–1700

  • Levi Stein
  • Quentin Schenk
  • Alexander Borgert – 1672
  • Simon Heins
  • Julian Braun
  • Davyd Derhay – 1913 (outlier strong form)
  • Lenn Sommerfeldt
  • Theodor Köhler
  • Metehan Dede (fluctuating group level)
  • Jakob Marx (borderline LK I case)

🧒 TIER 4 (Altersklasse / rising youth)

DWZ 1200–1500

  • Noah Magezi
  • Christoph Krupp
  • Xaver Menke
  • Mateo Sousa Matos
  • Jan Luca Liu – 1284
  • Narek Mkrtchyan – 1145
  • Felix Haoming Huang – 1344
  • dozens of U10–U14 players from HJET AK groups

📊 FINAL “DATABASE VIEW” SUMMARY

Tier DWZ range players
Elite 1900–2300 ~15
LK I / top LK II 1700–1900 ~40
LK II core 1500–1700 ~80
AK youth 1200–1500 ~60
Beginners / unranked <1200 ~20+

👉 Total ≈ 200 players (estimated Hamburg youth competitive pool)