Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
日本語の概要は準備中です。原文の説明を表示しています。
Polish genome assemblies to reduce errors using short reads (Pilon), long reads (Racon), or ONT-specific tools (medaka). Essential for improving long-read assembly accuracy. Use when improving assembly accuracy with polishing tools.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Reference examples tested with: BWA 0.7.17+, QUAST 5.2+, minimap2 2.26+, samtools 1.19+
Before using code patterns, verify installed versions match. If versions differ:
<tool> --version then <tool> --help to confirm flagsIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
"Polish my genome assembly" → Iteratively correct base-level errors in a draft assembly using short-read or long-read alignments.
pilon --genome draft.fa --frags short_reads.bam (short-read), medaka_polish (ONT), racon (long-read)| Tool | Input Reads | Best For |
|---|---|---|
| Pilon | Illumina | Final polishing |
| medaka | ONT | ONT assemblies |
| Racon | Long reads | Quick polishing |
| NextPolish | Both | Combined approach |
conda install -c bioconda pilon
# Map short reads to assembly
bwa index assembly.fasta
bwa mem -t 16 assembly.fasta R1.fq.gz R2.fq.gz | samtools sort -o aligned.bam
samtools index aligned.bam
# Run Pilon
pilon --genome assembly.fasta --frags aligned.bam --output polished
| Option | Description |
|---|---|
--genome | Input assembly |
--frags | Paired-end BAM |
--output | Output prefix |
--changes | Write changes file |
--vcf | Write VCF of changes |
--fix | What to fix (snps, indels, gaps, all) |
--threads | Threads for alignment |
--mindepth | Min depth for correction |
#!/bin/bash
ASSEMBLY=$1
R1=$2
R2=$3
ROUNDS=${4:-3}
current=$ASSEMBLY
for i in $(seq 1 $ROUNDS); do
echo "=== Pilon round $i ==="
bwa index $current
bwa mem -t 16 $current $R1 $R2 | samtools sort -o round${i}.bam
samtools index round${i}.bam
pilon --genome $current --frags round${i}.bam --output pilon_round${i} --changes
current=pilon_round${i}.fasta
changes=$(wc -l < pilon_round${i}.changes)
echo "Changes made: $changes"
if [ $changes -eq 0 ]; then
echo "No more changes, stopping"
break
fi
done
cp $current final_polished.fasta
# Only fix SNPs and small indels
pilon --genome assembly.fa --frags aligned.bam --output polished --fix snps,indels
# Only fill gaps
pilon --genome assembly.fa --frags aligned.bam --output polished --fix gaps
conda install -c bioconda medaka
medaka_consensus -i reads.fastq.gz -d assembly.fasta -o medaka_output -t 8
| Option | Description |
|---|---|
-i | Input reads |
-d | Draft assembly |
-o | Output directory |
-t | Threads |
-m | Model name |
# List available models
medaka tools list_models
# Use specific model (match your basecaller)
medaka_consensus -i reads.fq.gz -d assembly.fa -o output -m r1041_e82_400bps_sup_v5.1.0
| Chemistry | Model |
|---|---|
| R10.4.1 + SUP | r1041_e82_400bps_sup_* |
| R10.4.1 + HAC | r1041_e82_400bps_hac_* |
| R9.4.1 + SUP | r941_sup_* |
medaka_output/
├── consensus.fasta # Polished assembly
├── calls_to_draft.bam # Alignments
└── *.hdf # Intermediate files
conda install -c bioconda racon
# Map reads to assembly
minimap2 -ax map-ont assembly.fasta reads.fastq.gz > aligned.sam
# Polish
racon -t 16 reads.fastq.gz aligned.sam assembly.fasta > polished.fasta
#!/bin/bash
ASSEMBLY=$1
READS=$2
ROUNDS=${3:-3}
current=$ASSEMBLY
for i in $(seq 1 $ROUNDS); do
echo "=== Racon round $i ==="
minimap2 -ax map-ont $current $READS > round${i}.sam
racon -t 16 $READS round${i}.sam $current > racon_round${i}.fasta
current=racon_round${i}.fasta
done
cp $current racon_polished.fasta
| Option | Description |
|---|---|
-t | Threads |
-m | Match score (default: 3) |
-x | Mismatch score (default: -5) |
-g | Gap penalty (default: -4) |
-w | Window size (default: 500) |
Goal: Maximize assembly accuracy through iterative multi-tool polishing.
Approach: Apply Racon with long reads, then medaka for ONT-specific error correction, then Pilon with short reads for final accuracy.
#!/bin/bash
set -euo pipefail
ASSEMBLY=$1 # Flye assembly
ONT_READS=$2 # ONT reads
ILLUMINA_R1=$3 # Illumina R1
ILLUMINA_R2=$4 # Illumina R2
OUTDIR=$5
mkdir -p $OUTDIR
# Step 1: Racon polishing (2 rounds)
echo "=== Racon Polishing ==="
current=$ASSEMBLY
for i in 1 2; do
minimap2 -ax map-ont $current $ONT_READS > ${OUTDIR}/racon_${i}.sam
racon -t 16 $ONT_READS ${OUTDIR}/racon_${i}.sam $current > ${OUTDIR}/racon_${i}.fasta
current=${OUTDIR}/racon_${i}.fasta
done
# Step 2: medaka polishing
echo "=== medaka Polishing ==="
medaka_consensus -i $ONT_READS -d $current -o ${OUTDIR}/medaka -t 8
current=${OUTDIR}/medaka/consensus.fasta
# Step 3: Pilon polishing (2 rounds)
echo "=== Pilon Polishing ==="
for i in 1 2; do
bwa index $current
bwa mem -t 16 $current $ILLUMINA_R1 $ILLUMINA_R2 | samtools sort -o ${OUTDIR}/pilon_${i}.bam
samtools index ${OUTDIR}/pilon_${i}.bam
pilon --genome $current --frags ${OUTDIR}/pilon_${i}.bam --output ${OUTDIR}/pilon_${i}
current=${OUTDIR}/pilon_${i}.fasta
done
cp $current ${OUTDIR}/final_polished.fasta
echo "Done: ${OUTDIR}/final_polished.fasta"
conda install -c bioconda nextpolish
# Create config file
cat > run.cfg << EOF
[General]
job_type = local
job_prefix = nextPolish
task = best
rewrite = yes
rerun = 3
parallel_jobs = 2
multithread_jobs = 8
genome = assembly.fasta
genome_size = auto
workdir = ./01_rundir
[lgs_option]
lgs_fofn = lgs.fofn
lgs_options = -min_read_len 1k -max_depth 100
lgs_minimap2_options = -x map-ont
[sgs_option]
sgs_fofn = sgs.fofn
sgs_options = -max_depth 100
EOF
# File of filenames
ls reads.fastq.gz > lgs.fofn
ls R1.fq.gz R2.fq.gz > sgs.fofn
# Run
nextPolish run.cfg
Goal: Measure the accuracy improvement from polishing.
Approach: Compare original and polished assemblies with QUAST against a reference, and check alignment error rates.
After polishing, assess improvement:
# Compare to reference (if available)
quast.py -r reference.fa original.fa polished.fa -o quast_comparison
# Check error rate
minimap2 -ax map-ont polished.fa reads.fq.gz | samtools stats | grep "error rate"
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
日本語の概要は準備中です。原文の説明を表示しています。
Validate protein designs using AlphaFold2 structure prediction. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm. For QC thresholds, use protein-qc.
日本語の概要は準備中です。原文の説明を表示しています。
AlphaFold2 / AlphaFold-Multimer structure prediction for validation and confidence scoring. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm2-sequence-scoring. For QC thresholds, use protein-design-qc.
日本語の概要は準備中です。原文の説明を表示しています。
Workflow for event-level and isoform-level splicing analysis with sashimi-ready outputs and splice QC.
日本語の概要は準備中です。原文の説明を表示しています。
ATAC-seq processing with assay QC, MACS3 peak calling, consensus peak matrices, differential accessibility, and motif or footprint follow-up.
日本語の概要は準備中です。原文の説明を表示しています。
End-to-end binder design using BindCraft hallucination. Use this skill when: (1) Designing protein binders with built-in AF2 validation, (2) Running production-quality binder campaigns, (3) Using different design protocols (fast, default, slow), (4) Need joint backbone and sequence optimization, (5) Want high experimental success rate. For backbone-only generation, use rfdiffusion. For QC thresholds, use protein-qc. For tool selection guidance, use binder-design.
日本語の概要は準備中です。原文の説明を表示しています。