生成图文并茂的分析报告/实验报告/质检报告(固定模版)。当用户要求"生成报告/总结报告/实验报告/质检报告/QC报告",或分析流程收尾需要交付报告时使用。报告必须基于真实分析输出,包含固定章节结构、关键指标表格和真实结果图。
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。
Generate consensus sequences and manage reference files using samtools. Use when creating consensus from alignments, indexing references, or creating sequence dictionaries.
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: GATK 4.5+, bcftools 1.19+, pysam 0.22+, samtools 1.19+
Before using code patterns, verify installed versions match. If versions differ:
pip show <package> then help(module.function) to check signatures<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.
Generate consensus sequences and manage reference files using samtools.
"Prepare a reference genome" -> Index the FASTA and create a sequence dictionary for downstream tools.
samtools faidx ref.fa + samtools dict ref.fa -o ref.dictpysam.FastaFile('ref.fa') (auto-uses .fai index)"Build a consensus from BAM" -> Derive the most-supported base at each position from aligned reads.
samtools consensus input.bam -o consensus.faCreate index for random access to reference sequences.
samtools faidx reference.fa
# Creates reference.fa.fai
samtools faidx reference.fa chr1:1000-2000
samtools faidx reference.fa chr1:1000-2000 chr2:3000-4000
samtools faidx reference.fa chr1
samtools faidx reference.fa chr1:1000-2000 > region.fa
samtools faidx -i reference.fa chr1:1000-2000
chr1 248956422 6 60 61
chr2 242193529 253404903 60 61
Columns: name, length, offset, line bases, line width
Create SAM header dictionary for reference (used by GATK, Picard).
samtools dict reference.fa -o reference.dict
samtools dict -a GRCh38 -s "Homo sapiens" reference.fa -o reference.dict
@HD VN:1.6 SO:unsorted
@SQ SN:chr1 LN:248956422 M5:6aef897c3d6ff0c78aff06ac189178dd UR:file:reference.fa
@SQ SN:chr2 LN:242193529 M5:f98db672eb0993dcfdabafe2a882905c UR:file:reference.fa
The M5: (MD5) tag is the only definitive reference-identity check -- two references named "GRCh38" with different decoy/alt content have different M5s. CRAM enforces M5 match on read-back. See alignment-validation for BAM-vs-reference M5 cross-check.
| Reference flavor | ALT | Decoy | EBV | HLA | Use case |
|---|---|---|---|---|---|
| GRCh38 no-alt | no | no | no | no | Conservative analyses |
| GRCh38 + decoy + EBV (1000G analysis set) | no | yes | yes | no | Cohort projects |
| GRCh38 ALT + decoy + EBV + HLA (Broad / hs38DH) | yes | yes | yes | yes | GATK Best Practices |
| T2T-CHM13 v2.0 | n/a | n/a | n/a | n/a | Distinct coordinates -- NOT interchangeable |
Mixing no-alt and ALT-aware BAMs in one cohort produces inconsistent multi-mapping behavior at HLA, KIR, and segmental-duplication regions. Standardize before joint calling.
| Convention | Source | chr1 | mitochondrion |
|---|---|---|---|
| UCSC (hg19, hg38) | UCSC Genome Browser | chr1 | chrM |
| Ensembl (GRCh37, GRCh38) | Ensembl, ENA | 1 | MT |
| NCBI RefSeq (recent) | NCBI | chr1 | chrM |
| 1000G analysis sets | 1000G Phase II/III | chr1 | chrM |
A BAM with @SQ SN:chr1 cannot be analyzed against a 1-named reference (and vice versa). Detect:
samtools view -H sample.bam | grep '^@SQ' | head -3
samtools dict ref.fa | head -3
Convert: bcftools annotate --rename-chrs for VCF; for BAM there is no clean conversion -- re-align.
Create consensus sequence from alignments.
samtools consensus input.bam -o consensus.fa
samtools consensus -r chr1:1000-2000 input.bam -o region_consensus.fa
# FASTA (default)
samtools consensus -f fasta input.bam -o consensus.fa
# FASTQ (includes quality)
samtools consensus -f fastq input.bam -o consensus.fq
# Minimum depth to call base
samtools consensus -d 5 input.bam -o consensus.fa
# Call all positions (including low coverage)
samtools consensus -a input.bam -o consensus.fa
# Emit IUPAC codes (R, Y, S, W, K, M, B, D, H, V, N) for heterozygous columns
# --ambig is REQUIRED -- without it, output is restricted to A,C,G,T,N,*
samtools consensus --ambig --het-fract 0.2 --call-fract 0.5 input.bam -o consensus.fa
--het-fract controls the fraction of the second-most-common base relative to the most common required to call a heterozygote (default ~0.15). Without --ambig, columns where the second base passes --het-fract resolve to N rather than the IUPAC code. --show-ins / --show-del control insertion / deletion display, not ambiguity.
# Default: Bayesian Illumina profile
samtools consensus -f fasta input.bam -o consensus.fa
# Platform-specific profiles (samtools 1.21+; verify via samtools consensus --help for installed version)
samtools consensus --config hifi input.bam -o consensus.fa # PacBio HiFi
samtools consensus --config ont input.bam -o consensus.fa # ONT R10.4+
samtools consensus --config ultima input.bam -o consensus.fa # Ultima Genomics
samtools consensus --config illumina input.bam -o consensus.fa # default
# Report ref base where consensus unavailable (low coverage)
samtools consensus -T ref.fa input.bam -o consensus.fa
Different operations -- conflating them produces nonsense:
| Tool | Input | Output | Use case |
|---|---|---|---|
samtools consensus | BAM | Consensus FASTA derived from reads (Bayesian) | Viral, de novo / amplicon, low-coverage species |
bcftools consensus | reference + VCF | Reference with VCF variants applied | Apply called variants (haplotype reconstruction, custom ref for re-mapping) |
For viral consensus from BAM:
# Modern: samtools consensus
samtools consensus --config illumina -d 10 --het-fract 0.5 \
--show-ins yes --show-del yes input.bam -o consensus.fa
# Apply called variants to reference (different question)
bcftools consensus -f reference.fa variants.vcf.gz -o sample_consensus.fa
bcftools consensus -f reference.fa -H 1 phased.vcf.gz -o haplotype1.fa # phased haplotype 1
For bacterial / phage assembly polishing, prefer Pilon (short-read) or medaka (ONT); samtools consensus is not iterative.
import pysam
with pysam.FastaFile('reference.fa') as ref:
seq = ref.fetch('chr1', 999, 2000) # 0-based
print(seq)
with pysam.FastaFile('reference.fa') as ref:
for name in ref.references:
length = ref.get_reference_length(name)
print(f'{name}: {length:,} bp')
with pysam.FastaFile('reference.fa') as ref:
for chrom in ref.references:
seq = ref.fetch(chrom)
print(f'>{chrom}')
print(seq[:100] + '...')
import pysam
from collections import Counter
def consensus_at_position(bam, chrom, pos):
bases = Counter()
for pileup in bam.pileup(chrom, pos, pos + 1, truncate=True):
if pileup.pos == pos:
for read in pileup.pileups:
if not read.is_del and not read.is_refskip:
bases[read.alignment.query_sequence[read.query_position]] += 1
if bases:
return bases.most_common(1)[0][0]
return 'N'
with pysam.AlignmentFile('input.bam', 'rb') as bam:
consensus = consensus_at_position(bam, 'chr1', 1000000)
print(f'Consensus at chr1:1000000 = {consensus}')
The Python majority-vote consensus below is illustrative, NOT production. samtools consensus is Bayesian, quality-aware, and platform-aware; majority vote ignores base qualities and produces wrong calls on low-coverage / low-quality regions. Use for teaching pileup iteration mechanics; use samtools consensus for any real consensus.
import pysam
from collections import Counter
def build_consensus(bam_path, chrom, start, end, min_depth=3):
consensus = []
with pysam.AlignmentFile(bam_path, 'rb') as bam:
for pileup in bam.pileup(chrom, start, end, truncate=True):
bases = Counter()
for read in pileup.pileups:
if not read.is_del and not read.is_refskip:
base = read.alignment.query_sequence[read.query_position]
bases[base] += 1
if sum(bases.values()) >= min_depth:
consensus.append(bases.most_common(1)[0][0])
else:
consensus.append('N')
return ''.join(consensus)
import pysam
def create_dict_header(fasta_path):
header = {'HD': {'VN': '1.6', 'SO': 'unsorted'}, 'SQ': []}
with pysam.FastaFile(fasta_path) as ref:
for name in ref.references:
length = ref.get_reference_length(name)
header['SQ'].append({'SN': name, 'LN': length})
return header
header = create_dict_header('reference.fa')
for sq in header['SQ'][:5]:
print(f'{sq["SN"]}: {sq["LN"]:,} bp')
Goal: Set up a reference genome with all indices needed by common analysis tools.
Approach: Create FASTA index (.fai), sequence dictionary (.dict), and aligner-specific indices in sequence.
# 1. Index FASTA for samtools/pysam
samtools faidx reference.fa
# 2. Create sequence dictionary for GATK/Picard
samtools dict reference.fa -o reference.dict
# 3. Pre-populate CRAM REF_CACHE (for offline HPC nodes)
seq_cache_populate.pl -root $REF_CACHE_DIR reference.fa
For aligner-specific indices (BWA, Bowtie2, STAR, minimap2, Salmon), see read-alignment.
# Verify FAI exists
ls -la reference.fa.fai
# Verify dict exists
head reference.dict
# Test fetch
samtools faidx reference.fa chr1:1-100
samtools faidx reference.fa chr1 > chr1.fa
samtools faidx chr1.fa # Index the subset
cut -f1,2 reference.fa.fai > chrom.sizes
samtools faidx reference.fa chr1 chr2 chr3 > subset.fa
samtools faidx subset.fa
# Generate consensus
samtools consensus input.bam -o consensus.fa
# Align consensus back to reference
minimap2 -a reference.fa consensus.fa > comparison.sam
| Task | Command |
|---|---|
| Index FASTA | samtools faidx ref.fa |
| Fetch region | samtools faidx ref.fa chr1:1-1000 |
| Create dict | samtools dict ref.fa -o ref.dict |
| Build consensus | samtools consensus in.bam -o out.fa |
| Chrom sizes | cut -f1,2 ref.fa.fai |
bcftools consensus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
生成图文并茂的分析报告/实验报告/质检报告(固定模版)。当用户要求"生成报告/总结报告/实验报告/质检报告/QC报告",或分析流程收尾需要交付报告时使用。报告必须基于真实分析输出,包含固定章节结构、关键指标表格和真实结果图。
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。
Predicts ADMET properties using ADMETlab 3.0 (119 endpoints with uncertainty), ADMET-AI, DeepChem MolNet, and chemprop D-MPNN with explicit handling of OECD QSAR principles, applicability domain assessment, calibration, hERG/CYP/AMES gold-standard endpoints, and PAINS / Lipinski / Ro5 / Veber / BBB druglikeness filters. Use when filtering compounds for drug-likeness, prioritizing leads by predicted safety, or building an in-house ADMET QSAR model.
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。
Trim PCR primers from aligned reads in amplicon-panel BAMs using samtools ampliconclip. Use when processing SARS-CoV-2 ARTIC, hereditary cancer panels, ctDNA hot-spot panels, or any amplicon assay where primer-derived bases would falsely confirm reference at primer footprints.
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。
Filter alignments by flags, mapping quality, and regions using samtools view and pysam. Use when extracting specific reads, removing low-quality alignments, or subsetting to target regions.
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。
Create and use BAI/CSI indices for BAM/CRAM files using samtools and pysam. Use when enabling random access to alignment files or fetching specific genomic regions.
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。
Read, write, and convert multiple sequence alignment files using Biopython Bio.AlignIO. Supports Clustal, PHYLIP, Stockholm, FASTA, Nexus, and other alignment formats for phylogenetics and conservation analysis. Use when reading, writing, or converting alignment file formats.
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。