➤ mkdir -p ~/.conda/envs
➤ wget https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-Linux-x86_64.sh
➤ bash Mambaforge-Linux-x86_64.sh
# set the install path to ~/.conda/envs/quantpi-env
# init shell, then relogin
➤ mamba config --add channels nodefaults
➤ mamba config --add channels bioconda
➤ mamba config --add channels conda-forge
➤ mamba config --set channel_priority strict
# then activate mambaforge
➤ mamba activate quantpi-env
# install snakemake
➤ mamba install snakemake fd-find seqkit ruamel.yaml pandas numpy natsort openpyxl biopython seaborn matplotlib executor➤ mamba install quantpi=0.2.0
# or
➤ pip install quantpi=0.2.0
# or latest version
➤ git clone https://github.com/ohmeta/quantpi
➤ echo "export PYTHONPATH=/path/to/quantpi:$PYTHONPATH" >> ~/.bashrc
# relogin➤ mamba activate quantpi-env
➤ quantpi --help
# or
➤ python /path/to/quantpi/run_quantpi.py --help
██████ ██ ██ █████ ███ ██ ████████ ██████ ██
██ ██ ██ ██ ██ ██ ████ ██ ██ ██ ██ ██
██ ██ ██ ██ ███████ ██ ██ ██ ██ ██████ ██
██ ▄▄ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██
██████ ██████ ██ ██ ██ ████ ██ ██ ██
▀▀
Omics for All, Open Source for All
A general profiling system focus on robust microbiome research
optional arguments:
-h, --help show this help message and exit
-v, --version print software version and exit
available subcommands:
init init project
simulate_wf simulate reads
profiling_wf metagenome-profiling pipeline
sync quantpi sync project➤ quantpi init --help
# or
➤ python /path/to/quantpi/run_quantpi.py init --help
usage: quantpi init [-h] [-d WORKDIR] [--check-samples] [-s SAMPLES] [-b {simulate,trimming,rmhost,profiling}] [--trimmer {oas1,sickle,fastp}] [--rmhoster {bwa,bowtie2,minimap2,kraken2,kneaddata}]
options:
-h, --help show this help message and exit
-d, --workdir WORKDIR
project workdir (default: ./)
--check-samples check samples, default: False
-s, --samples SAMPLES
desired input:
samples list, tsv format required.
if begin from trimming, rmhost, or assembly:
if it is fastq:
the header is: [sample_id, fq1, fq2]
if it is sra:
the header is: [sample_id, sra]
if begin from simulate:
the header is: [id, genome, abundance, reads_num, model]
-b, --begin {simulate,trimming,rmhost,profiling}
pipeline starting point (default: trimming)
--trimmer {oas1,sickle,fastp}
which trimmer used (default: fastp)
--rmhoster {bwa,bowtie2,minimap2,kraken2,kneaddata}
which rmhoster used (default: bowtie2)
Step 1: download LMAS data
A set of simulated samples were generated from the genomes in the ZymoBIOMICS standard though the InSilicoSeq sequence simulator (version 1.5.2), including both even and logarithmic distribution, with and without Illumina error model. The number of read pairs generated matches the number of read pairs in the real data for each distribution. The following samples are available in Zenodo:
- ENN - Evenly distributed sample with no error model
- EMS - Evenly distributed sample with Illumina MiSeq error model
- LNN - Log distributed sample with no error model
- LHS - Log distributed sample with Illumina HiSeq error model
➤ mkdir -p test
➤ mkdir -p test/fastq
➤ cd test
# - mock even distributed data no error
➤ wget -c https://zenodo.org/record/5579145/files/ENN_1.fq.gz -P ./fastq/
➤ wget -c https://zenodo.org/record/5579145/files/ENN_2.fq.gz -P ./fastq/
# - mock even distributed data illumina miseq error
➤ wget -c https://zenodo.org/record/5579145/files/EMS_1.fq.gz -P ./fastq/
➤ wget -c https://zenodo.org/record/5579145/files/EMS_2.fq.gz -P ./fastq/
# - mock log distributed data no error
➤ wget -c https://zenodo.org/record/5579145/files/LNN_1.fq.gz -P ./fastq/
➤ wget -c https://zenodo.org/record/5579145/files/LNN_2.fq.gz -P ./fastq/
# - mock log distributed data illumina hiseq error
➤ wget -c https://zenodo.org/record/5579145/files/LHS_1.fq.gz -P ./fastq/
➤ wget -c https://zenodo.org/record/5579145/files/LHS_2.fq.gz -P ./fastq/
➤ fd -a fq.gz fastq/ | sort | uniq | paste - - | awk -F'[/_]' 'BEGIN{print "sample_id\tfq1\tfq2"};{print $(NF-1) "\t" $0}' > samples.tsv samples.tsv looks like below:
| sample_id | fq1 | fq2 |
|---|---|---|
| ENN | /full/path/to/ENN_1.fq.gz | /full/path/to/ENN_2.fq.gz |
| EMS | /full/path/to/EMS_1.fq.gz | /full/path/to/EMS_2.fq.gz |
| LNN | /full/path/to/LNN_1.fq.gz | /full/path/to/LNN_2.fq.gz |
| LHS | /full/path/to/LHS_1.fq.gz | /full/path/to/LHS_2.fq.gz |
➤ quantpi init -d . -s samples.tsv
# or
➤ python /path/to/quantpi/run_quantpi.py init -d . -s samples.tsv➤ quantpi profiling_wf --list
# or
➤ python /path/to/quantpi/run_quantpi.py profiling_wf --list
Running quantpi profiling_wf:
snakemake --snakefile /path/to/quantpi/quantpi/snakefiles/profiling_wf.smk --configfile ./config.yaml --cores 240 --keep-going --printshellcmds --reason --until all --list
all
prepare_long_reads_all
prepare_reads_all
prepare_short_reads
prepare_short_reads_all
profiling_alignment_bam_postprocess
profiling_alignment_bowtie2
profiling_all
profiling_bracken_all
profiling_genome_coverm_all
profiling_genomecov_all
profiling_humann2_all
profiling_humann35
profiling_humann35_all
profiling_humann35_config
profiling_humann35_join
profiling_humann35_postprocess
profiling_humann35_split_stratified
profiling_humann38
profiling_humann38_all
profiling_humann38_config
profiling_humann38_join
profiling_humann38_postprocess
profiling_humann38_split_stratified
profiling_humann39
profiling_humann39_all
profiling_humann39_config
profiling_humann39_join
profiling_humann39_postprocess
profiling_humann39_split_stratified
profiling_kmcp_all
profiling_kraken2_all
profiling_metaphlan2_all
profiling_metaphlan3
profiling_metaphlan3_all
profiling_metaphlan3_merge
profiling_metaphlan40
profiling_metaphlan40_all
profiling_metaphlan40_merge
profiling_metaphlan41
profiling_metaphlan41_all
profiling_metaphlan41_merge
profiling_strainphlan3_all
profiling_strainphlan40_all
profiling_strainphlan41_all
qcreport_all
qcreport_plot
qcreport_summary
raw_all
raw_fastqc_all
raw_report
raw_report_all
raw_report_merge
raw_report_refine
rmhost_alignment_report
rmhost_all
rmhost_bowtie2
rmhost_bowtie2_all
rmhost_bowtie2_index
rmhost_bwa_all
rmhost_kneaddata_all
rmhost_kraken2_all
rmhost_minimap2_all
rmhost_report
rmhost_report_all
rmhost_report_merge
rmhost_report_refine
trimming_all
trimming_fastp
trimming_fastp_all
trimming_fastp_multiqc
trimming_report
trimming_report_all
trimming_report_merge
trimming_report_refine
trimming_sickle_all
trimming_trimmomatic_all
Real running cmd:
snakemake --snakefile /path/to/quantpi/quantpi/snakefiles/profiling_wf.smk --configfile ./config.yaml --cores 240 --keep-going --printshellcmds --reason --until all --list➤ cat config.yaml
params:
reads_layout: pe
interleaved: false
have_long: false
fq_encoding: sanger
begin: trimming
samples: samples.tsv
simulate:
do: false
threads: 8
raw:
do: true
threads: 8
check_paired: true
save_reads: true
fastqc:
do: false
trimming:
save_reads: false
threads: 8
sickle:
do: false
quality_type: sanger
sickle_pe: ''
length_cutoff: 51
quality_cutoff: 20
fastp: # recommand
do: true
use_slide_window: false # strict when using slide window
disable_adapter_trimming: false
detect_adapter_for_se: true # If activated, adapter_sequence will not used
detect_adapter_for_pe: true # If activated, adapter_sequence and adapter_sequence_r2 will not used
adapter_sequence: AAGTCGGAGGCCAAGCGGTCTTAGGAAGACAA # MGI adapter 3
adapter_sequence_r2: AAGTCGGATCGTAGCCATGTCGTTCTGTGAGCCAAGGAGTTG # MGI adapter 5
# "AGATCGGAAGAGCACACGTCTGAACTCCAGTCA" # eg: Illumina TruSeq adapter 3
# "AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT" # eg: Illumina TruSeq adapter 5
compression: 6
cut_front_window_size: 4
cut_front_mean_quality: 20
cut_tail_window_size: 4
cut_tail_mean_quality: 20
cut_right_window_size: 4
cut_right_mean_quality: 20
length_required: 51
n_base_limit: 5
dedup: false
dup_calc_accuracy: 3 # [1, 2, 3, 4, 5, 6] # only used when dedup: True
trimmomatic:
do: false
phred: -phred33
save_unpaired: false
trimmomatic_options: MINLEN:50 ILLUMINACLIP:/path/to/adapters.fa:2:40:15 SLIDINGWINDOW:4:20 # eg: adapters: /path/to/TruSeq3-PE-2.fa
rmhost:
host_fasta: /home/jiezhu/databases/genomics/human/CHM13/chm13v2.0_plusY/chm13v2.0.fa
threads: 8
save_reads: true
save_bam: false
compression: 6
bwa:
do: false
algorithms: mem # "mem2"
index_prefix: /home/jiezhu/databases/genomics/human/CHM13/chm13v2.0_plusY/bwa_index/chm13v2.0.fa
minimum_seed_length: 23
bowtie2: # recommand
do: true
index_prefix: /home/jiezhu/databases/genomics/human/CHM13/chm13v2.0_plusY/chm13v2.0.fa
presets: --very-sensitive
minimap2:
do: false
split_size: 4G
preset: sr
index: /home/jiezhu/databases/genomics/human/CHM13/chm13v2.0_plusY/minimap_index/chm13v2.0.fa
kraken2:
do: false
database: /home/jiezhu/databases/kraken/minikraken2_v2_8GB_201904_UPDATE
host_taxid: 9606
# must include human reference genome
confidence: 0
min_base_quality: 0
min_hit_groups: 2
kneaddata:
do: false
do_trf: false
do_trimmomatic: false
trimmomatic_options: MINLEN:50 ILLUMINACLIP:/path/to/adapters.fa:2:40:15 SLIDINGWINDOW:4:20 # eg: adapters: /path/to/TruSeq3-PE-2.fa
sequencer_source: TruSeq3 # ["NexteraPE", "TruSeq2", "TruSeq3", "none"]
do_bmtagger: false
do_bowtie2: true
decontaminate_pairs: strict # ["strict", "lenient", "unpaired"]
bowtie2_options: --very-sensitive --dovetail
bowtie2_database: /home/jiezhu/databases/genomics/human/CHM13/chm13v2.0_plusY/ # directory, not bowtie2 index prefix
qcreport:
do: true
seqkit:
threads: 4
profiling:
threads: 8
# DNA-to-DNA
kraken2:
do: false
database: /home/jiezhu/databases/ecogenomics/Struo2/GTDB_release202/kraken2
taxonomy: /home/jiezhu/databases/ecogenomics/Struo2/GTDB_release202/kraken2/taxonomy
quick: false
memory_mapping: false
use_names: true
use_mpa_style: false
report_zero_counts: false
confidence: 0
min_base_quality: 0
min_hit_groups: 2
unclassified_out: false
classified_out: false
save_table: false
bracken:
do: false
reads_len: 100
level: [D, P, C, O, F, G, S, S1]
# DNA-to-DNA
kmcp:
do:
bacteriome: false
mycobiome: false
virome: false
database:
bacteriome: /home/jiezhu/databases/ecogenomics/KMCP/bacteriome/GTDB_r202/gtdb.kmcp
mycobiome: /home/jiezhu/databases/ecogenomics/KMCP/mycobiome/RefSeq_r208/refseq-fungi.kmcp
virome: /home/jiezhu/databases/ecogenomics/KMCP/virome/GenBank_246/genbank-viral.kmcp
taxdump: /home/jiezhu/.taxonkit
search:
threads: 24
reads_mode: Single-end # "Paired-end" # Recommended Single-end
min_query_len: 30
min_query_cov: 0.55
external_opts: ''
profile:
threads: 4 # recommand
mode: [0, 1, 2, 3, 4, 5]
level: strain # ["species", "strain", "assembly"]
# reference: https://bioinf.shenwei.me/kmcp/tutorial/profiling/
# kmcp profile mode details
# 0: for pathogen detection
# 1: higher recall
# 2: high recall
# 3: default
# 4: high precision
# 5: higher precision
# options m=0 m=1 m=2 m=3 m=4 m=5
# --------------------------- ---- --- --- ---- --- ----
# -r/--min-chunks-reads 1 5 10 50 100 100
# -p/--min-chunks-fraction 0.2 0.6 0.7 0.8 1 1
# -d/--max-chunks-depth-stdev 10 2 2 2 2 1.5
# -u/--min-uniq-reads 1 2 5 20 50 50
# -U/--min-hic-ureads 1 1 2 5 10 10
# -H/--min-hic-ureads-qcov 0.55 0.7 0.7 0.75 0.8 0.8
# -P/--min-hic-ureads-prop 0.01 0.1 0.2 0.1 0.1 0.15
# --keep-main-matches true
# --max-qcov-gap 0.4
# If you want to overide some parameter setted by preset mode, just add it to external_opts below.
metaphlan_report_version: 3
disable_two_stage_taxonomy_assignment: true # --no-amb-corr
external_opts: ''
# DNA-to-marker
metaphlan:
do_v2: False # metaphlan2
do_v3: False # metaphlan3
do_v40: True # metaphlan v4.0.*
do_v41: True # metaphlan v4.1.*
bowtie2db_v2: "/home/jiezhu/databases/ecogenomics/MetaPhlAn/mpa_v20/bowtie2_index"
bowtie2db_v3: "/home/jiezhu/databases/ecogenomics/MetaPhlAn/mpa_v30/bowtie2_index"
bowtie2db_v40: "/home/jiezhu/databases/ecogenomics/MetaPhlAn/mpa_vOct22/bowtie2_index"
bowtie2db_v41: "/home/jiezhu/databases/ecogenomics/MetaPhlAn/mpa_vJun23/bowtie2_index"
index_prefix_v2: "v20_m200"
index_prefix_v3: "mpa_v30_CHOCOPhlAn_201901"
index_prefix_v40: "mpa_vOct22_CHOCOPhlAnSGB_202212"
index_prefix_v41: "mpa_vJun23_CHOCOPhlAnSGB_202403"
bowtie2_presets: "very-sensitive"
taxonomic_level: "a"
min_cu_len: 2000
read_min_len: 70
analysis_type: "rel_ab_w_read_stats" #[ "rel_ab", "rel_ab_w_read_stats", "reads_map", "clade_profiles", "marker_ab_table", "marker_counts", "marker_pres_table" ]
stat: "tavg_g"
external_opts_v2: ""
external_opts_v3: "--unknown_estimation"
external_opts_v40: "--unclassified_estimation"
external_opts_v41: "--unclassified_estimation"
strainphlan:
do_v3: False # metaphlan3
do_v40: False # metaphlan4
do_v41: False # metaphlan4
reference_genome:
use: False
clades_tsv_v3: "/path/to/clades_v3.tsv" # extrct clade markes from specific clades, two column, [clade\tfna_path]
clades_tsv_v40: "/path/to/clades_v40.tsv" # extrct clade markes from specific clades, two column, [clade\tfna_path]
clades_tsv_v41: "/path/to/clades_v41.tsv" # extrct clade markes from specific clades, two column, [clade\tfna_path]
marker_in_n_samples: "1" # reduce it if meet: Phylogeny can not be inferred. Too many markers were discarded
sample_with_n_markers: "10"
phylophlan_mode: "accurate"
breadth_thres: "80"
external_opts_v3: ""
external_opts_v40: ""
external_opts_v41: ""
# Functional profiling
humann:
do_v2: False # reply on metaphlan2 v2*
do_v35: False # reply on metaphlan v3*
do_v38: True # reply on metaphlan v4.0.*
do_v39: True # reply on metaphlan v4.1.*
# humann v3.5, v3.6, v3.7, v3.8, v3.9 used same database v31
database_nucleotide_v2: "/home/jiezhu/databases/funcgenomics/HUMAnN/v2/chocophlan"
database_nucleotide_v31: "/home/jiezhu/databases/funcgenomics/HUMAnN/v3.1/chocophlan"
database_protein_v2: "/home/jiezhu/databases/funcgenomics/HUMAnN/v2/uniref"
database_protein_v31: "/home/jiezhu/databases/funcgenomics/HUMAnN/v3.1/uniref"
database_utility_mapping_v2: "/home/jiezhu/databases/funcgenomics/HUMAnN/v2/utility_mapping"
database_utility_mapping_v31: "/home/jiezhu/databases/funcgenomics/HUMAnN/v3.1/utility_mapping"
prescreen_threshold: 0.005 # minimum percentage of reads matching a species, default: 0.01
identity_threshold: 50.0
nucleotide_identity_threshold: 0.0 # nucleotide: 0
translated_identity_threshold: 80.0 # uniref90: 80.0; uniref50: 50.0
nucleotide_subject_coverage_threshold: 50.0
translated_subject_coverage_threshold: 50.0
nucleotide_query_coverage_threshold: 90.0
translated_query_coverage_threshold: 90.0
translated-alignment: "diamond" # ["diamond", "usearch", "rapsearch"]
pathways: "metacyc" # ["metacyc", "unipathway"]
normalize_method: "relab"
regroup_method: "sum"
map_database: ["uniref90_go", "uniref90_ko", "uniref90_eggnog", "uniref90_pfam"] # ["uniref90_level4ec", "uniref90_infogo1000", "uniref90_rxn" ]
external_opts_v2: ""
external_opts_v35: ""
external_opts_v38: ""
external_opts_v39: ""
# alignment method
genomecov:
do: false
bowtie2_db_prefix: /home/jiezhu/databases/ecogenomics/virome/virome.fasta
bowtie2_db_fasta: /home/jiezhu/databases/ecogenomics/virome/virome.fasta
gen_contig_cov_script: /home/jiezhu/toolkit/metassemble/scripts/validate/map/gen_contig_cov_per_bam_table.py
# alignment method
coverm:
do: false
genome_dir: /home/jiezhu/databases/ecogenomics/virome/genomes
genome_fasta_extension: fna
methods: [relative_abundance, mean, trimmed_mean, covered_fraction, covered_bases,
variance, rpkm, tpm]
min_covered_fraction: 10
contig_end_exclusion: 75
trim_min: 5
trim_max: 95
# custom method
bgi_soap: # for learning and test
do: false
taxonomy: /path/to/mag_taxonomy
index_prefix: /path/to/mag_soap_index
minimal_insert_size: 0
maximal_insert_size: 1000
report_repeat_hits: 1
match_model: 4
align_seed: 30
max_mismatch_num: 5
identity: 0.95
bowtie2: # for learning and test
do: false
taxonomy: /path/to/mag_taxonomy
index_prefix: /path/to/mag_bowtie2_index
jgi: # for learning and test
do: false
metadata: /path/to/mag_metadata
taxonomy: /path/to/mag_taxonomy
index_prefix: /path/to/mag_bowtie2_index
fragment: 1200
memory_limit: 8G
compression_level: 6
output:
simulate: results/00.simulate
raw: results/00.raw
trimming: results/01.trimming
rmhost: results/02.rmhost
qcreport: results/03.qcreport
profiling: results/04.profiling
envs:
simulate: /home/jiezhu/toolkit/quantpi/test/test_dev/envs/simulate.yaml
prepare: /home/jiezhu/toolkit/quantpi/test/test_dev/envs/prepare.yaml
fastqc: /home/jiezhu/toolkit/quantpi/test/test_dev/envs/fastqc.yaml
trimming: /home/jiezhu/toolkit/quantpi/test/test_dev/envs/trimming.yaml
multiqc: /home/jiezhu/toolkit/quantpi/test/test_dev/envs/multiqc.yaml
report: /home/jiezhu/toolkit/quantpi/test/test_dev/envs/report.yaml
align: /home/jiezhu/toolkit/quantpi/test/test_dev/envs/align.yaml
kraken2: /home/jiezhu/toolkit/quantpi/test/test_dev/envs/kraken2.yaml
kneaddata: /home/jiezhu/toolkit/quantpi/test/test_dev/envs/kneaddata.yaml
kmcp: /home/jiezhu/toolkit/quantpi/test/test_dev/envs/kmcp.yaml
biobakery2: /home/jiezhu/toolkit/quantpi/test/test_dev/envs/biobakery2.yaml
biobakery3: /home/jiezhu/toolkit/quantpi/test/test_dev/envs/biobakery3.yaml
biobakery40: /home/jiezhu/toolkit/quantpi/test/test_dev/envs/biobakery40.yaml
biobakery41: /home/jiezhu/toolkit/quantpi/test/test_dev/envs/biobakery41.yaml➤ quantpi profiling_wf all --dry-run
# or
➤ python /path/to/quantpi/run_quantpi.py profiling_wf all --dry-run
Real running cmd:
snakemake --snakefile /home/jiezhu/toolkit/quantpi/quantpi/snakefiles/profiling_wf.smk --configfile ./config.yaml --cores 240 --keep-going --printshellcmds --reason --until all --dry-run
Job stats:
job count min threads max threads
---------------------------------- ------- ------------- -------------
all 1 1 1
prepare_short_reads 4 8 8
profiling_humann39 4 8 8
profiling_humann39_config 1 1 1
profiling_humann39_join 1 1 1
profiling_humann39_postprocess 4 1 1
profiling_humann39_split_stratified 1 1 1
profiling_metaphlan41 4 8 8
profiling_metaphlan41_merge 1 8 8
qcreport_plot 1 1 1
qcreport_summary 1 4 4
raw_report 4 4 4
raw_report_merge 1 4 4
raw_report_refine 4 1 1
rmhost_bowtie2 4 8 8
rmhost_report 4 4 4
rmhost_report_merge 1 4 4
rmhost_report_refine 4 1 1
trimming_fastp 4 8 8
trimming_fastp_multiqc 1 1 1
trimming_report 4 4 4
trimming_report_merge 1 4 4
trimming_report_refine 4 1 1
➤ quantpi profiling_wf all --use-conda --cores 80 --jobs 10 --run-local
# or
➤ python /path/to/quantpi/run_quantpi.py profiling_wf all --use-conda --cores 80 --jobs 10 --run-local➤ tree results