metapi 0.9.2

a pipeline to construct a genome catalogue from metagenomics data

metapi

A pipeline to construct a genome catalogue from metagenomics data.

Installation

metapi works with Python 3.6+. You can install it via bioconda:

# [WIP]
$ conda install -c bioconda metapi
# or [Complete]
$ conda install -c ohmeta metapi

Or via pip:

$ pip install metapi

Run

help

$ metapi --help

  .___  ___.  _______ .___________.    ___      .______    __
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  |  \  /  | |  |__   `---|  |----`  /  ^  \    |  |_)  | |  |
  |  |\/|  | |   __|      |  |      /  /_\  \   |   ___/  |  |
  |  |  |  | |  |____     |  |     /  _____  \  |  |      |  |
  |__|  |__| |_______|    |__|    /__/     \__\ | _|      |__|

            Omics for All, Open Source for All

  A pipeline to construct a genome catalogue from metagenomics data

    optional arguments:
    -h, --help     show this help message and exit
    -v, --version  print software version and exit

    available subcommands:

    init         init project
    denovo_wf    denovo_wf pipeline

init

$ metapi init --help

  usage: metapi init [-h] [-d WORKDIR] [-s SAMPLES]
                    [-b {simulate,trimmingrmhost,assembly}]

  arguments:
      -h, --help            show this help message and exit
      -d, --workdir WORKDIR
                            project workdir, default: ./ (default: ./)
      -s, --samples SAMPLES
                            desired input:
                            samples list, tsv format required.

                            if begin from trimming, rmhost, or assembly:
                                if it is fastq:
                                    the header is [id, fq1, fq2]
                                if it is sra:
                                    the header is [id, sra]
                            if begin from simulate:
                                the header is [id, genome, abundance, reads_num, model]

    -b, --begin {simulate,trimming,rmhost,assembly}
                            pipeline starting point (default: trimming)

denovo_wf

$ metapi denovo_wf --help

  usage: metapi denovo_wf [-h] [-d WORKDIR] [--config CONFIG] [--cores CORES]
                          [--jobs JOBS] [--list] [--run] [--debug] [--dry_run]
                          [--qsub] [--wait WAIT] [--use_conda]
                          [--conda_crate_envs_only] [--snake [SNAKEMAKEARGS]]
                          [TASK]

  positional arguments:
    TASK                  pipeline end point. Allowed values are
                          simulate_all, prepare_reads_all,
                          raw_fastqc_all, raw_report_all, raw_all,
                          trimming_oas1_all, trimming_sickle_all, trimming_fastp_all,
                          trimming_report_all, trimming_all,
                          rmhost_bwa_all, rmhost_bowtie2_all,
                          rmhost_report_all, rmhost_all, qcreport_all,
                          assebmly_megahit_all, assembly_idba_ud_all,
                          assembly_metaspades_all, assembly_spades_all,
                          assembly_metaquast_all, assembly_report_all, assembly_all,
                          coassembly_megahit_all, coassembly_all,
                          alignment_base_depth_all, alignment_all,
                          binning_metabat2_coverage_all,
                          binning_metabat2_all, binning_maxbin2_all,
                          binning_concoct_all, binning_dastools_all,
                          binning_report_all, binning_all, cobinning_all,
                          predict_scaftigs_gene_prodigal_all,
                          predict_scaftigs_gene_prokka_all,
                          predict_bins_gene_prodigal_all,
                          predict_bins_gene_prokka_all,
                          predict_scafitgs_gene_all, predict_bins_gene_all,
                          predict_all, checkm_link_bins, checkm_all,
                          dereplicate_drep_all, dereplicate_all,
                          classify_short_reads_kraken2_all,
                          classify_hmq_bins_gtdbtk_all, classify_all,
                          profiling_metaphlan2_all, profiling_metaphlan3_all,
                          profiling_jgi_all, profiling_bracken_all,
                          profiling_humann2_all, profiling_all,
                          upload_sequencing_all, upload_assembly_all,
                          upload_all, all

  arguments:
    -h, --help            show this help message and exit
    -d WORKDIR, --workdir WORKDIR
                          project workdir, default: ./
    --config CONFIG       config.yaml, default: ./config.yaml
    --cores CORES         CPU cores, default: 8
    --jobs JOBS           qsub job numbers, default: 80
    --list                list pipeline rules
    --run                 run pipeline
    --debug               debug pipeline
    --dry_run             dry run pipeline
    --qsub                qsub pipeline
    --wait WAIT           wait given seconds
    --use_conda           use conda environment
    --conda_create_envs_only
                          conda create environments only
    --snake [SNAKEMAKEARGS]
                          other snakemake command options, if want --touch, just
                          --snake touch

Example

# init project
$ metapi init -d . -s samples.tsv -b trimming

# crate conda environments (need connect to internet)
$ metapi denovo_wf --conda_create_envs_only

# run pipeline with conda
# metapi denovo_wf all --use_conda

# run raw_fastqc
$ metapi denovo_wf raw_fastqc_all --run

# run trimming
$ metapi denovo_wf trimming_all --run

# run rmhost
$ metapi denovo_wf rmhost_all --run 

# run qc report
$ metapi denovo_wf qcreport_all --run

# run assembly
$ metapi denovo_wf assembly_all --run

# run co-assembly
$ metapi denovo_wf coassembly_all --run

# run binning 
$ metapi denovo_wf binning_all --run

# run gene predict
$ metapi denovo_wf predict_all --run

# run MAGs checkm
$ metapi denovo_wf checkm_all --run

# run MAGs classify
$ metapi denovo_wf classify_all --run

# run MetaPhlAn2 profiling
$ metapi denovo_wf profiling_metaphlan2_all --run --use_conda

# run MetaPhlAn3 profiling
$ metapi denovo_wf profiling_metaphlan3_all --run

# run MAGs jgi profling (using jgi_summarize_bam_contig_depths)
$ metapi denovo_wf profiling_jgi_all --run

# run HUMAnN2 profiling
$ metapi denovo_wf profiling_humann2_all --run --use_conda

# run all
$ metapi denovo_wf --run

input requirements

The input samples file: samples.tsv format:

Note: If id col contain same id, then the reads of each sample will be merged. Note: The fastq need gzip compress.

• begin from trimming, rmhost or assembly:

  • Paired-end fastq

  id	fq1	fq2
  s1	aa.1.fq.gz	aa.2.fq.gz
  s2	bb.1.fq.gz	bb.2.fq.gz
  s2	cc.1.fq.gz	cc.2.fq.gz
  s3	dd.1.fq.gz	dd.2.fq.gz

  • Single-end fastq

  id	fq1	fq2
  s1	aa.1.fq.gz
  s2	bb.1.fq.gz
  s2	cc.1.fq.gz
  s3	dd.1.fq.gz

  • SRA:

  SRA can be dumpped to Paired-end fastq reads

  id	sra
  s1	aa.sra
  s2	bb.sra
  s2	cc.sra
  s3	dd.sra

• begin from simulate

  id	genome	abundance	reads_num	model
  s1	g1.fa	1.0	1M	hiseq
  s2	g1.fa	0.5	2M	hiseq
  s2	g2.fa	0.5	2M	hiseq
  s3	g1.fa	0.2	3M	hiseq
  s3	g2.fa	0.3	3M	hiseq
  s3	g3.fa	0.5	3M	hiseq

It means:

The sample s1 contain 1M reads which come from g1, the relatative abundance of species g1 is 1.0.

The sample s2 contain 2M reads, 1M reads come from g1 and 1M reads come from g2. the relatative abundance of species g1 is 0.5, the relatative abundance of species g2 is 0.5.

The sample s3 contain 3M reads, 0.6M reads come from g1, 0.9M reads come from g2 and 1.5M reads come from g3, the relatative abundance of species g1 is 0.2, the relatative abundance of species g2 is 0.3, the relatative abundance of species g3 is 0.5.

Then metapi will use InSilicoSeq to generate metagenomics shutgun reads.

FAQ

• You know what you want to do, so you know how to configure config.yaml
• You know snakemake, so you know how to hack metapi

Getting help

If you want to report a bug or issue, or have problems with installing or running the software, please create a new issue. This is the preferred way of getting support. Alternatively, you can mail me.

Contributing

Contributions welcome! Send me a pull request or get in touch.

When contributing a PR, please use the dev branch. For style, code will be checked using flake8, black, and snakefmt. These modules can be installed via conda, conda install black flake8 flake8-bugbear snakefmt or via pip pip install black flake8 flake8-bugbear snakefmt.

Contributors

• Jie Zhu - @alienzj
• Fangming Yang - @yangfangming
• Yanmei Ju - @juyanmei

License

This module is licensed under the terms of the GPLv3 license.