Classify sequencing reads from mixed biological samples with Kraken2 to identify represented organisms. Covers reference database setup, paired reads, filtering settings, separating unclassified reads, and summarizing species or genus results before abundance estimation.
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Taxonomic classification of metagenomic reads using Kraken2. Fast k-mer based classification against RefSeq database. Use when performing initial taxonomic classification of shotgun metagenomic reads before abundance estimation with Bracken.
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Reference examples tested with: Kraken2 2.1+, MetaPhlAn 4.1+, pandas 2.2+
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.
"Classify what organisms are in my metagenomic sample" → Assign taxonomic labels to sequencing reads using exact k-mer matching against a reference database for fast initial classification.
kraken2 --db db --paired R1.fastq R2.fastq --report report.txt# Classify reads against standard database
kraken2 --db /path/to/kraken2_db \
--output output.kraken \
--report report.txt \
reads.fastq.gz
kraken2 --db /path/to/kraken2_db \
--paired \
--output output.kraken \
--report report.txt \
reads_R1.fastq.gz reads_R2.fastq.gz
kraken2 --db /path/to/kraken2_db \
--threads 8 \ # CPU threads
--confidence 0.1 \ # Confidence threshold
--minimum-base-quality 20 \ # Quality filter
--output output.kraken \
--report report.txt \
--use-names \ # Add taxon names to output
--gzip-compressed \ # Input is gzipped
reads.fastq.gz
# For systems with limited RAM
kraken2 --db /path/to/kraken2_db \
--memory-mapping \ # Use disk-based database
--output output.kraken \
--report report.txt \
reads.fastq.gz
# Save space by not writing per-read classifications
kraken2 --db /path/to/kraken2_db \
--report report.txt \
--report-zero-counts \ # Include taxa with 0 counts
reads.fastq.gz
# Separate classified and unclassified reads
kraken2 --db /path/to/kraken2_db \
--classified-out classified#.fq \ # # replaced by 1/2 for PE
--unclassified-out unclassified#.fq \
--output output.kraken \
--report report.txt \
--paired \
reads_R1.fastq.gz reads_R2.fastq.gz
Goal: Create a custom Kraken2 database with specific organism libraries for targeted classification.
Approach: Download NCBI taxonomy, add desired library sequences (bacteria, archaea, viral), build the k-mer index, and clean up intermediate files.
# Download taxonomy
kraken2-build --download-taxonomy --db custom_db
# Download specific libraries
kraken2-build --download-library bacteria --db custom_db
kraken2-build --download-library archaea --db custom_db
kraken2-build --download-library viral --db custom_db
# Build database
kraken2-build --build --db custom_db --threads 8
# Clean up intermediate files
kraken2-build --clean --db custom_db
# Add FASTA sequences to library
kraken2-build --add-to-library custom_genomes.fasta --db custom_db
# Then build
kraken2-build --build --db custom_db
# View database contents
kraken2-inspect --db /path/to/kraken2_db | head -50
17.45 1745 1745 U 0 unclassified
82.55 8255 48 R 1 root
82.07 8207 2 R1 131567 cellular organisms
81.99 8199 132 D 2 Bacteria
76.23 7623 178 P 1224 Proteobacteria
Columns:
import pandas as pd
report = pd.read_csv('report.txt', sep='\t', header=None,
names=['pct', 'reads_clade', 'reads_taxon', 'rank', 'taxid', 'name'])
report['name'] = report['name'].str.strip()
species = report[report['rank'] == 'S']
species_sorted = species.sort_values('pct', ascending=False)
species_sorted.head(20)
# Get only species-level classifications
awk '$4 == "S"' report.txt > species_report.txt
# Get genus level
awk '$4 == "G"' report.txt > genus_report.txt
| Parameter | Default | Description |
|---|---|---|
| --db | required | Database path |
| --threads | 1 | CPU threads |
| --confidence | 0.0 | Confidence threshold (0-1) |
| --minimum-base-quality | 0 | Phred quality threshold |
| --memory-mapping | false | Use disk-based database |
| --paired | false | Paired-end mode |
| --use-names | false | Include taxon names |
| --report-zero-counts | false | Include 0-count taxa |
| Library | Content |
|---|---|
| bacteria | RefSeq complete bacterial genomes |
| archaea | RefSeq complete archaeal genomes |
| viral | RefSeq complete viral genomes |
| plasmid | RefSeq plasmid nucleotide sequences |
| human | GRCh38 human genome |
| fungi | RefSeq fungi |
| protozoa | RefSeq protozoa |
| UniVec_Core | Common vector sequences |
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