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tasks/edna-metabarcoding/run_script.sh

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+#!/usr/bin/env bash
+set -euo pipefail
+
+# ============================================================
+# eDNA Metabarcoding Pipeline: Aquatic Biodiversity Assessment
+# ============================================================
+# DAG structure (depth=10, convergence=4):
+#
+#  sample_R1.fq.gz + sample_R2.fq.gz (x6 samples)
+#         │
+#  [cutadapt primer removal] ─── per sample         Level 1
+#         │
+#  ┌──────┼──────────┐
+#  │      │          │
+# [vsearch  [fastqc  [seqkit                        Level 2
+#  merge     QC]      stats]
+#  pairs]
+#  │      │          │
+#  └──────┼──────────┘
+#         │
+#  [CONVERGENCE 1: QC + merged reads]               Level 3
+#         │
+#  [vsearch quality filter]                          Level 4
+#         │
+#  [pool samples + vsearch dereplicate]              Level 5
+#         │
+#  ┌──────┼──────────┐
+#  │      │          │
+# [vsearch [swarm   [vsearch                        Level 6
+#  cluster  cluster] denoise
+#  97%]              UNOISE3]
+#  │      │          │
+#  └──────┼──────────┘
+#         │
+#  [CONVERGENCE 2: select consensus + chimera removal] Level 7
+#         │
+#  ┌──────┼──────────┐
+#  │                 │
+# [BLAST           [vsearch                          Level 8
+#  taxonomy]        usearch_global
+#                   taxonomy]
+#  │                 │
+#  └────────┬────────┘
+#           │
+#  [CONVERGENCE 3: LCA consensus taxonomy]           Level 9
+#           │
+#  ┌────────┼──────────┐
+#  │        │          │
+# [species [R/vegan   [detection                     Level 9
+#  list]    diversity]  probability]
+#  │        │          │
+#  └────────┼──────────┘
+#           │
+#  [CONVERGENCE 4: final report with QC]             Level 10
+#
+# Longest path: primer_removal -> merge -> QC_convergence ->
+#   quality_filter -> dereplicate -> cluster -> chimera_removal ->
+#   BLAST -> LCA -> diversity -> report = depth 10
+# ============================================================
+
+THREADS=$(( $(nproc) > 8 ? 8 : $(nproc) ))
+WORKDIR="$(cd "$(dirname "$0")" && pwd)"
+DATA="${WORKDIR}/data"
+REF="${WORKDIR}/reference"
+OUT="${WORKDIR}/outputs"
+RESULTS="${WORKDIR}/results"
+
+mkdir -p "${OUT}"/{trimmed,merged,filtered,qc,derep,clusters,chimera,taxonomy,community}
+mkdir -p "${RESULTS}"
+
+# Sample list
+SAMPLES=(DRR205394 DRR205395 DRR205396 DRR205397 DRR205398 DRR205399)
+
+# MiFish-U primer sequences (Miya et al. 2015)
+FWD_PRIMER="GTCGGTAAAACTCGTGCCAGC"
+REV_PRIMER="CATAGTGGGGTATCTAATCCCAGTTTG"
+# Reverse complement of reverse primer
+REV_PRIMER_RC=$(echo "$REV_PRIMER" | tr ACGTacgt TGCAtgca | rev)
+
+# ============================================================
+# Level 1: Primer removal with cutadapt (per sample)
+# ============================================================
+echo "=== Level 1: Primer removal ==="
+for S in "${SAMPLES[@]}"; do
+  if [ ! -f "${OUT}/trimmed/${S}_R1.fastq.gz" ]; then
+    cutadapt \
+      -g "${FWD_PRIMER}" \
+      -G "${REV_PRIMER}" \
+      --discard-untrimmed \
+      --minimum-length 50 \
+      -j ${THREADS} \
+      -o "${OUT}/trimmed/${S}_R1.fastq.gz" \
+      -p "${OUT}/trimmed/${S}_R2.fastq.gz" \
+      "${DATA}/${S}_R1.fastq.gz" \
+      "${DATA}/${S}_R2.fastq.gz" \
+      > "${OUT}/trimmed/${S}_cutadapt.log" 2>&1
+    echo "  ${S}: trimmed"
+  fi
+done
+
+# ============================================================
+# Level 2: QC + merge + stats (parallel branches)
+# ============================================================
+echo "=== Level 2: QC + merge + stats ==="
+
+# Branch 2a: FastQC on trimmed reads
+if [ ! -f "${OUT}/qc/fastqc_done" ]; then
+  for S in "${SAMPLES[@]}"; do
+    fastqc -t ${THREADS} -o "${OUT}/qc/" \
+      "${OUT}/trimmed/${S}_R1.fastq.gz" \
+      "${OUT}/trimmed/${S}_R2.fastq.gz" \
+      > /dev/null 2>&1
+  done
+  touch "${OUT}/qc/fastqc_done"
+  echo "  FastQC done"
+fi
+
+# Branch 2b: seqkit stats on trimmed reads
+if [ ! -f "${OUT}/qc/seqkit_stats.tsv" ]; then
+  seqkit stats -T -j ${THREADS} "${OUT}/trimmed/"*.fastq.gz > "${OUT}/qc/seqkit_stats.tsv" 2>/dev/null
+  echo "  seqkit stats done"
+fi
+
+# Branch 2c: vsearch merge pairs (per sample)
+for S in "${SAMPLES[@]}"; do
+  if [ ! -f "${OUT}/merged/${S}.fastq" ]; then
+    vsearch --fastq_mergepairs "${OUT}/trimmed/${S}_R1.fastq.gz" \
+      --reverse "${OUT}/trimmed/${S}_R2.fastq.gz" \
+      --fastqout "${OUT}/merged/${S}.fastq" \
+      --fastq_maxdiffs 10 \
+      --fastq_minovlen 50 \
+      --threads ${THREADS} \
+      --label_suffix ";sample=${S}" \
+      > "${OUT}/merged/${S}_merge.log" 2>&1
+    echo "  ${S}: merged"
+  fi
+done
+
+# ============================================================
+# Level 3: CONVERGENCE 1 — QC + merged reads available
+# ============================================================
+echo "=== Level 3: Convergence 1 (QC + merged) ==="
+# MultiQC aggregation
+if [ ! -f "${OUT}/qc/multiqc_report.html" ]; then
+  multiqc "${OUT}/qc/" "${OUT}/trimmed/" -o "${OUT}/qc/" --force > /dev/null 2>&1 || true
+  echo "  MultiQC done"
+fi
+
+# ============================================================
+# Level 4: Quality filter (per sample)
+# ============================================================
+echo "=== Level 4: Quality filtering ==="
+for S in "${SAMPLES[@]}"; do
+  if [ ! -f "${OUT}/filtered/${S}.fasta" ]; then
+    vsearch --fastq_filter "${OUT}/merged/${S}.fastq" \
+      --fastq_maxee 1.0 \
+      --fastq_minlen 100 \
+      --fastq_maxlen 300 \
+      --fastaout "${OUT}/filtered/${S}.fasta" \
+      --relabel "${S}." \
+      > "${OUT}/filtered/${S}_filter.log" 2>&1
+    echo "  ${S}: filtered"
+  fi
+done
+
+# ============================================================
+# Level 5: Pool samples + dereplicate
+# ============================================================
+echo "=== Level 5: Pool + dereplicate ==="
+if [ ! -f "${OUT}/derep/all_derep.fasta" ]; then
+  # Pool all filtered sequences
+  cat "${OUT}/filtered/"*.fasta > "${OUT}/derep/all_pooled.fasta"
+
+  # Dereplicate
+  vsearch --derep_fulllength "${OUT}/derep/all_pooled.fasta" \
+    --output "${OUT}/derep/all_derep.fasta" \
+    --sizein --sizeout \
+    --minuniquesize 2 \
+    --uc "${OUT}/derep/all_derep.uc" \
+    > "${OUT}/derep/derep.log" 2>&1
+  echo "  Dereplication done"
+fi
+
+UNIQUE_COUNT=$(grep -c "^>" "${OUT}/derep/all_derep.fasta" || true)
+echo "  Unique sequences: ${UNIQUE_COUNT}"
+
+# ============================================================
+# Level 6: Three parallel clustering methods
+# ============================================================
+echo "=== Level 6: Clustering (3 methods) ==="
+
+# Method 6a: vsearch OTU clustering at 97%
+if [ ! -f "${OUT}/clusters/otu97_centroids.fasta" ]; then
+  vsearch --cluster_size "${OUT}/derep/all_derep.fasta" \
+    --id 0.97 \
+    --centroids "${OUT}/clusters/otu97_centroids.fasta" \
+    --uc "${OUT}/clusters/otu97.uc" \
+    --sizein --sizeout \
+    --threads ${THREADS} \
+    > "${OUT}/clusters/otu97.log" 2>&1
+  echo "  OTU 97% clustering done"
+fi
+
+# Method 6b: SWARM clustering
+if [ ! -f "${OUT}/clusters/swarm_centroids.fasta" ]; then
+  # swarm needs dereplicated sequences sorted by abundance
+  vsearch --sortbysize "${OUT}/derep/all_derep.fasta" \
+    --output "${OUT}/clusters/sorted_for_swarm.fasta" \
+    --sizein --sizeout 2>/dev/null
+
+  swarm -d 1 -z \
+    -w "${OUT}/clusters/swarm_centroids.fasta" \
+    -o "${OUT}/clusters/swarm_otus.txt" \
+    -s "${OUT}/clusters/swarm_stats.txt" \
+    -t ${THREADS} \
+    "${OUT}/clusters/sorted_for_swarm.fasta" \
+    > "${OUT}/clusters/swarm.log" 2>&1
+  echo "  SWARM clustering done"
+fi
+
+# Method 6c: vsearch UNOISE3 denoising (ASVs)
+if [ ! -f "${OUT}/clusters/unoise3_asvs.fasta" ]; then
+  vsearch --cluster_unoise "${OUT}/derep/all_derep.fasta" \
+    --centroids "${OUT}/clusters/unoise3_asvs.fasta" \
+    --sizein --sizeout \
+    --minsize 2 \
+    > "${OUT}/clusters/unoise3.log" 2>&1
+  echo "  UNOISE3 denoising done"
+fi
+
+OTU97_COUNT=$(grep -c "^>" "${OUT}/clusters/otu97_centroids.fasta" || true)
+SWARM_COUNT=$(grep -c "^>" "${OUT}/clusters/swarm_centroids.fasta" || true)
+UNOISE3_COUNT=$(grep -c "^>" "${OUT}/clusters/unoise3_asvs.fasta" || true)
+echo "  OTU97: ${OTU97_COUNT}, SWARM: ${SWARM_COUNT}, UNOISE3: ${UNOISE3_COUNT}"
+
+# ============================================================
+# Level 7: CONVERGENCE 2 — Select consensus + chimera removal
+# ============================================================
+echo "=== Level 7: Convergence 2 (consensus + chimera removal) ==="
+
+# Use UNOISE3 ASVs as primary (most conservative denoising method)
+# Then apply chimera removal
+if [ ! -f "${OUT}/chimera/clean_asvs.fasta" ]; then
+  vsearch --uchime_denovo "${OUT}/clusters/unoise3_asvs.fasta" \
+    --nonchimeras "${OUT}/chimera/clean_asvs.fasta" \
+    --chimeras "${OUT}/chimera/chimeras.fasta" \
+    --sizein --sizeout \
+    > "${OUT}/chimera/chimera.log" 2>&1
+  echo "  Chimera removal done"
+fi
+
+CLEAN_COUNT=$(grep -c "^>" "${OUT}/chimera/clean_asvs.fasta" || true)
+CHIMERA_COUNT=$(grep -c "^>" "${OUT}/chimera/chimeras.fasta" 2>/dev/null || true)
+CHIMERA_COUNT=${CHIMERA_COUNT:-0}
+echo "  Clean ASVs: ${CLEAN_COUNT}, Chimeras removed: ${CHIMERA_COUNT}"
+
+# ============================================================
+# Level 7.5: Build BLAST database from reference
+# ============================================================
+echo "=== Building BLAST database ==="
+if [ ! -f "${REF}/mitofish_12S.ndb" ]; then
+  makeblastdb -in "${REF}/mitofish_12S.fasta" \
+    -dbtype nucl \
+    -out "${REF}/mitofish_12S" \
+    -parse_seqids \
+    > /dev/null 2>&1
+  echo "  BLAST DB built"
+fi
+
+# ============================================================
+# Level 8: Taxonomy assignment (two parallel methods)
+# ============================================================
+echo "=== Level 8: Taxonomy assignment ==="
+
+# Method 8a: BLAST against MitoFish 12S
+if [ ! -f "${OUT}/taxonomy/blast_hits.tsv" ]; then
+  # Strip size annotations from headers for BLAST
+  sed 's/;size=[0-9]*//' "${OUT}/chimera/clean_asvs.fasta" > "${OUT}/taxonomy/query.fasta"
+
+  blastn -query "${OUT}/taxonomy/query.fasta" \
+    -db "${REF}/mitofish_12S" \
+    -out "${OUT}/taxonomy/blast_hits.tsv" \
+    -outfmt "6 qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue bitscore" \
+    -evalue 1e-10 \
+    -max_target_seqs 10 \
+    -num_threads ${THREADS} \
+    > /dev/null 2>&1
+  echo "  BLAST done"
+fi
+
+# Method 8b: vsearch usearch_global against MitoFish 12S
+if [ ! -f "${OUT}/taxonomy/vsearch_hits.tsv" ]; then
+  vsearch --usearch_global "${OUT}/taxonomy/query.fasta" \
+    --db "${REF}/mitofish_12S.fasta" \
+    --id 0.80 \
+    --maxaccepts 10 \
+    --blast6out "${OUT}/taxonomy/vsearch_hits.tsv" \
+    --threads ${THREADS} \
+    > "${OUT}/taxonomy/vsearch.log" 2>&1
+  echo "  vsearch global search done"
+fi
+
+# ============================================================
+# Level 9: CONVERGENCE 3 — LCA consensus taxonomy
+# ============================================================
+echo "=== Level 9: Convergence 3 (LCA taxonomy) ==="
+
+if [ ! -f "${OUT}/taxonomy/lca_taxonomy.tsv" ]; then
+  python3 << 'PYEOF'
+import csv
+import sys
+from collections import defaultdict
+
+# Load MitoFish taxonomy lookup
+tax_lookup = {}
+with open("reference/mitofish_12S_taxonomy.tsv") as f:
+    reader = csv.DictReader(f, delimiter='\t')
+    for row in reader:
+        acc = row['Accession']
+        tax_lookup[acc] = {
+            'superkingdom': row.get('Superkingdom', ''),
+            'phylum': row.get('Phylum', ''),
+            'class': row.get('Class', ''),
+            'order': row.get('Order', ''),
+            'family': row.get('Family', ''),
+            'genus': row.get('Genus', ''),
+            'species': row.get('Species', '')
+        }
+
+# Read BLAST hits (top hits per query)
+blast_tax = defaultdict(list)
+with open("outputs/taxonomy/blast_hits.tsv") as f:
+    for line in f:
+        parts = line.strip().split('\t')
+        qid, sid, pident = parts[0], parts[1], float(parts[2])
+        if pident >= 97.0 and sid in tax_lookup:
+            blast_tax[qid].append(tax_lookup[sid])
+
+# Read vsearch hits
+vsearch_tax = defaultdict(list)
+with open("outputs/taxonomy/vsearch_hits.tsv") as f:
+    for line in f:
+        parts = line.strip().split('\t')
+        qid, sid, pident = parts[0], parts[1], float(parts[2])
+        if pident >= 97.0 and sid in tax_lookup:
+            vsearch_tax[qid].append(tax_lookup[sid])
+
+# LCA function
+RANKS = ['superkingdom', 'phylum', 'class', 'order', 'family', 'genus', 'species']
+def lca(tax_list):
+    if not tax_list:
+        return {r: '' for r in RANKS}
+    result = {}
+    for rank in RANKS:
+        values = set(t[rank] for t in tax_list if t[rank])
+        if len(values) == 1:
+            result[rank] = values.pop()
+        else:
+            result[rank] = ''
+            break  # stop at first disagreement
+    for rank in RANKS:
+        if rank not in result:
+            result[rank] = ''
+    return result
+
+# Merge BLAST + vsearch via LCA
+all_queries = set(list(blast_tax.keys()) + list(vsearch_tax.keys()))
+with open("outputs/taxonomy/lca_taxonomy.tsv", 'w') as f:
+    f.write("asv_id\tsuperkingdom\tphylum\tclass\torder\tfamily\tgenus\tspecies\n")
+    for qid in sorted(all_queries):
+        combined = blast_tax.get(qid, []) + vsearch_tax.get(qid, [])
+        tax = lca(combined)
+        f.write(f"{qid}\t{tax['superkingdom']}\t{tax['phylum']}\t{tax['class']}\t{tax['order']}\t{tax['family']}\t{tax['genus']}\t{tax['species']}\n")
+
+print(f"LCA taxonomy assigned to {len(all_queries)} ASVs")
+PYEOF
+fi
+
+# ============================================================
+# Level 9 continued: Three parallel community analyses
+# ============================================================
+echo "=== Level 9: Community analyses ==="
+
+# Build OTU table (ASV x sample) by mapping reads back
+if [ ! -f "${OUT}/community/otu_table.tsv" ]; then
+  # Map all filtered reads back to clean ASVs
+  vsearch --usearch_global "${OUT}/derep/all_pooled.fasta" \
+    --db "${OUT}/chimera/clean_asvs.fasta" \
+    --id 0.97 \
+    --otutabout "${OUT}/community/otu_table.tsv" \
+    --threads ${THREADS} \
+    > "${OUT}/community/map.log" 2>&1
+  echo "  OTU table built"
+fi
+
+# Branch 9a: Species list
+# Branch 9b: Alpha + beta diversity (R/vegan)
+# Branch 9c: Detection probability
+if [ ! -f "${OUT}/community/diversity_results.tsv" ]; then
+  python3 << 'PYEOF'
+import csv
+import math
+from collections import defaultdict
+
+# Load OTU table
+otu_table = {}  # {asv_id: {sample: count}}
+samples = []
+with open("outputs/community/otu_table.tsv") as f:
+    header = f.readline().strip().split('\t')
+    samples = header[1:]  # first col is OTU ID
+    for line in f:
+        parts = line.strip().split('\t')
+        asv_id = parts[0]
+        counts = [int(x) for x in parts[1:]]
+        otu_table[asv_id] = dict(zip(samples, counts))
+
+# Load taxonomy
+taxonomy = {}
+with open("outputs/taxonomy/lca_taxonomy.tsv") as f:
+    reader = csv.DictReader(f, delimiter='\t')
+    for row in reader:
+        taxonomy[row['asv_id']] = row
+
+# === Branch 9a: Species list ===
+species_set = set()
+genus_set = set()
+family_set = set()
+order_set = set()
+for asv_id, tax in taxonomy.items():
+    if tax['species']:
+        species_set.add(tax['species'])
+    if tax['genus']:
+        genus_set.add(tax['genus'])
+    if tax['family']:
+        family_set.add(tax['family'])
+    if tax['order']:
+        order_set.add(tax['order'])
+
+with open("outputs/community/species_list.tsv", 'w') as f:
+    f.write("species\tgenus\tfamily\torder\n")
+    for sp in sorted(species_set):
+        # find matching taxonomy
+        for asv_id, tax in taxonomy.items():
+            if tax['species'] == sp:
+                f.write(f"{sp}\t{tax['genus']}\t{tax['family']}\t{tax['order']}\n")
+                break
+
+# === Branch 9b: Alpha diversity (Shannon index per sample) ===
+shannon_per_sample = {}
+richness_per_sample = {}
+for s in samples:
+    counts = [otu_table[asv][s] for asv in otu_table if otu_table[asv].get(s, 0) > 0]
+    total = sum(counts)
+    if total == 0:
+        shannon_per_sample[s] = 0.0
+        richness_per_sample[s] = 0
+        continue
+    richness_per_sample[s] = len(counts)
+    shannon = 0.0
+    for c in counts:
+        p = c / total
+        if p > 0:
+            shannon -= p * math.log(p)
+    shannon_per_sample[s] = round(shannon, 4)
+
+with open("outputs/community/diversity_results.tsv", 'w') as f:
+    f.write("sample\tshannon_diversity\tspecies_richness\n")
+    for s in samples:
+        f.write(f"{s}\t{shannon_per_sample[s]}\t{richness_per_sample[s]}\n")
+
+# === Branch 9c: Detection probability ===
+# For each species, proportion of samples where detected
+species_detection = defaultdict(int)
+species_total_reads = defaultdict(int)
+for asv_id in otu_table:
+    sp = taxonomy.get(asv_id, {}).get('species', '')
+    if not sp:
+        continue
+    for s in samples:
+        if otu_table[asv_id].get(s, 0) > 0:
+            species_detection[sp] += 1
+            species_total_reads[sp] += otu_table[asv_id][s]
+
+with open("outputs/community/detection_probability.tsv", 'w') as f:
+    f.write("species\tsamples_detected\tdetection_rate\ttotal_reads\n")
+    for sp in sorted(species_detection.keys()):
+        det_rate = round(species_detection[sp] / len(samples), 4)
+        f.write(f"{sp}\t{species_detection[sp]}\t{det_rate}\t{species_total_reads[sp]}\n")
+
+print(f"Species: {len(species_set)}, Genera: {len(genus_set)}, Families: {len(family_set)}, Orders: {len(order_set)}")
+print(f"Shannon range: {min(shannon_per_sample.values()):.4f} - {max(shannon_per_sample.values()):.4f}")
+PYEOF
+  echo "  Python community analysis done"
+fi
+
+# R/vegan beta diversity
+if [ ! -f "${OUT}/community/beta_diversity.tsv" ]; then
+  cat > "${OUT}/community/run_vegan.R" << 'REOF'
+library(vegan)
+otu <- read.delim("outputs/community/otu_table.tsv", row.names=1, check.names=FALSE)
+otu_t <- t(otu)
+bc <- as.matrix(vegdist(otu_t, method="bray"))
+write.table(bc, "outputs/community/beta_diversity.tsv", sep="\t", quote=FALSE)
+cat("Beta diversity (Bray-Curtis) range:", range(bc[lower.tri(bc)]), "\n")
+cat("Mean Bray-Curtis:", mean(bc[lower.tri(bc)]), "\n")
+REOF
+  Rscript "${OUT}/community/run_vegan.R"
+  echo "  R/vegan beta diversity done"
+fi
+
+# ============================================================
+# Level 10: CONVERGENCE 4 — Final report
+# ============================================================
+echo "=== Level 10: Final report ==="
+
+python3 << 'PYEOF'
+import csv
+import math
+import os
+from collections import defaultdict
+
+# Gather all metrics
+metrics = {}
+
+# --- Raw read counts ---
+total_raw = 0
+for s in ["DRR205394","DRR205395","DRR205396","DRR205397","DRR205398","DRR205399"]:
+    for r in ["R1", "R2"]:
+        fpath = f"outputs/qc/seqkit_stats.tsv"
+        break
+    break
+
+# Count from seqkit stats
+with open("outputs/qc/seqkit_stats.tsv") as f:
+    reader = csv.DictReader(f, delimiter='\t')
+    for row in reader:
+        total_raw += int(row['num_seqs'].replace(',', ''))
+metrics['total_raw_reads'] = total_raw
+
+# --- Merged read counts ---
+total_merged = 0
+for s in ["DRR205394","DRR205395","DRR205396","DRR205397","DRR205398","DRR205399"]:
+    logf = f"outputs/merged/{s}_merge.log"
+    if os.path.exists(logf):
+        with open(logf) as f:
+            for line in f:
+                if "Merged" in line and "pairs" in line:
+                    # Parse vsearch merge log
+                    parts = line.strip().split()
+                    for i, p in enumerate(parts):
+                        if p.isdigit() or p.replace(',','').isdigit():
+                            total_merged += int(p.replace(',',''))
+                            break
+                    break
+
+# Fallback: count merged reads directly
+if total_merged == 0:
+    for s in ["DRR205394","DRR205395","DRR205396","DRR205397","DRR205398","DRR205399"]:
+        mf = f"outputs/merged/{s}.fastq"
+        if os.path.exists(mf):
+            count = sum(1 for line in open(mf)) // 4
+            total_merged += count
+metrics['total_merged_reads'] = total_merged
+
+# Merge rate
+if total_raw > 0:
+    # total_raw is R1+R2, so pairs = total_raw / 2
+    metrics['merge_rate'] = round(total_merged / (total_raw / 2) * 100, 2)
+else:
+    metrics['merge_rate'] = 0.0
+
+# --- Unique sequences ---
+derep_fasta = "outputs/derep/all_derep.fasta"
+unique_count = sum(1 for line in open(derep_fasta) if line.startswith('>'))
+metrics['unique_sequences'] = unique_count
+
+# --- Clustering results ---
+for method, fname in [("clusters_otu97", "outputs/clusters/otu97_centroids.fasta"),
+                       ("clusters_swarm", "outputs/clusters/swarm_centroids.fasta"),
+                       ("clusters_denoised", "outputs/clusters/unoise3_asvs.fasta")]:
+    count = sum(1 for line in open(fname) if line.startswith('>'))
+    metrics[method] = count
+
+# --- Chimera removal ---
+clean_fasta = "outputs/chimera/clean_asvs.fasta"
+chimera_fasta = "outputs/chimera/chimeras.fasta"
+metrics['clean_sequence_count'] = sum(1 for line in open(clean_fasta) if line.startswith('>'))
+chimera_count = 0
+if os.path.exists(chimera_fasta):
+    chimera_count = sum(1 for line in open(chimera_fasta) if line.startswith('>'))
+metrics['chimera_count'] = chimera_count
+
+# --- Taxonomy assignment ---
+assigned = 0
+total_asvs = 0
+with open("outputs/taxonomy/lca_taxonomy.tsv") as f:
+    reader = csv.DictReader(f, delimiter='\t')
+    for row in reader:
+        total_asvs += 1
+        if row['species'] or row['genus'] or row['family']:
+            assigned += 1
+metrics['assigned_sequences'] = assigned
+metrics['unassigned_sequences'] = total_asvs - assigned
+
+# --- Species/genus/family/order counts ---
+species_set = set()
+genus_set = set()
+family_set = set()
+order_set = set()
+with open("outputs/taxonomy/lca_taxonomy.tsv") as f:
+    reader = csv.DictReader(f, delimiter='\t')
+    for row in reader:
+        if row['species']: species_set.add(row['species'])
+        if row['genus']: genus_set.add(row['genus'])
+        if row['family']: family_set.add(row['family'])
+        if row['order']: order_set.add(row['order'])
+metrics['species_count'] = len(species_set)
+metrics['genus_count'] = len(genus_set)
+metrics['family_count'] = len(family_set)
+metrics['order_count'] = len(order_set)
+
+# --- Diversity ---
+shannon_vals = []
+richness_vals = []
+with open("outputs/community/diversity_results.tsv") as f:
+    reader = csv.DictReader(f, delimiter='\t')
+    for row in reader:
+        shannon_vals.append(float(row['shannon_diversity']))
+        richness_vals.append(int(row['species_richness']))
+metrics['mean_shannon_diversity'] = round(sum(shannon_vals)/len(shannon_vals), 4)
+metrics['min_species_richness'] = min(richness_vals)
+metrics['max_species_richness'] = max(richness_vals)
+
+# --- Beta diversity ---
+bc_vals = []
+with open("outputs/community/beta_diversity.tsv") as f:
+    header = f.readline().strip().split('\t')
+    rows = []
+    for line in f:
+        parts = line.strip().split('\t')
+        rows.append([float(x) for x in parts[1:]])
+    for i in range(len(rows)):
+        for j in range(i+1, len(rows)):
+            bc_vals.append(rows[i][j])
+if bc_vals:
+    metrics['mean_beta_diversity'] = round(sum(bc_vals)/len(bc_vals), 4)
+    metrics['min_beta_diversity'] = round(min(bc_vals), 4)
+    metrics['max_beta_diversity'] = round(max(bc_vals), 4)
+
+# --- Detection rate ---
+det_rates = []
+with open("outputs/community/detection_probability.tsv") as f:
+    reader = csv.DictReader(f, delimiter='\t')
+    for row in reader:
+        det_rates.append(float(row['detection_rate']))
+if det_rates:
+    metrics['mean_detection_rate'] = round(sum(det_rates)/len(det_rates), 4)
+
+# === Write report ===
+with open("results/report.csv", 'w') as f:
+    f.write("metric,value\n")
+    for k, v in metrics.items():
+        f.write(f"{k},{v}\n")
+
+print("=== Report generated ===")
+for k, v in metrics.items():
+    print(f"  {k} = {v}")
+PYEOF
+
+echo "=== Pipeline complete ==="