r1cheu/imputation

imputation pipeline for low-coverage sequencing in hybrid rice

Overview

Latest release: None, Last update: 2026-10-10

Share link: https://snakemake.github.io/snakemake-workflow-catalog?wf=r1cheu/imputation

Quality control: linting: failed formatting: passed

Deployment

Step 1: Install Snakemake and Snakedeploy

Snakemake and Snakedeploy are best installed via the Conda package manager. It is recommended to install conda via Miniforge. Run

conda create -c conda-forge -c bioconda -c nodefaults --name snakemake snakemake snakedeploy

to install both Snakemake and Snakedeploy in an isolated environment. For all following commands ensure that this environment is activated via

conda activate snakemake

For other installation methods, refer to the Snakemake and Snakedeploy documentation.

Step 2: Deploy workflow

With Snakemake and Snakedeploy installed, the workflow can be deployed as follows. First, create an appropriate project working directory on your system and enter it:

mkdir -p path/to/project-workdir
cd path/to/project-workdir

In all following steps, we will assume that you are inside of that directory. Then run

snakedeploy deploy-workflow https://github.com/r1cheu/imputation . --tag None

Snakedeploy will create two folders, workflow and config. The former contains the deployment of the chosen workflow as a Snakemake module, the latter contains configuration files which will be modified in the next step in order to configure the workflow to your needs.

Step 3: Configure workflow

To configure the workflow, adapt config/config.yml to your needs following the instructions below.

Step 4: Run workflow

The deployment method is controlled using the --software-deployment-method (short --sdm) argument.

To run the workflow using apptainer/singularity, use

snakemake --cores all --sdm apptainer

To run the workflow using a combination of conda and apptainer/singularity for software deployment, use

snakemake --cores all --sdm conda apptainer

To run the workflow with automatic deployment of all required software via conda/mamba, use

snakemake --cores all --sdm conda

Snakemake will automatically detect the main Snakefile in the workflow subfolder and execute the workflow module that has been defined by the deployment in step 2.

For further options such as cluster and cloud execution, see the docs.

Step 5: Generate report

After finalizing your data analysis, you can automatically generate an interactive visual HTML report for inspection of results together with parameters and code inside of the browser using

snakemake --report report.zip

Configuration

The following section is imported from the workflow’s config/README.md.

Configuration

config.yaml

Key

Meaning

sample_sheet

TSV listing samples (see below)

reference.fasta

Reference genome FASTA (e.g. IRGSP-1.0). Indices are built by the workflow.

panel.full_template

Per-chromosome phased reference panel template (VCF.GZ or BCF, with .csi alongside), e.g. resources/ref_panel/{chrom}.vcf.gz.

chromosomes

List of chromosome names. Must match both reference and panel.

genetic_map.template

Per-chromosome genetic map path template, e.g. resources/map/{chrom}.map. Format expected by GLIMPSE2: pos chr cM.

bam_filter.min_depth

Drop samples whose mapped depth (mapped bases after duplicate removal / reference length) is below this (default 1.0).

bam_filter.max_unmapped_frac

Drop samples whose fraction of unmapped reads is above this (default 0.8).

phase.batch_size

Maximum number of BAMs imputed in one GLIMPSE2_phase job (default 200).

fastp.extra

Extra flags passed verbatim to fastp (e.g. quality cutoffs)

Threads/memory per rule are hard-coded in workflow/rules/*.smk (tuned for ~600 cores / ~5000 samples). Override by editing those resources: blocks.

samples.tsv

Tab-separated, one sample per row.

Column

Required

Meaning

sample

yes

unique sample id (used as RG ID/SM)

platform

yes

sequencing platform string (RG PL), e.g. ILLUMINA

fq1

yes

path to read 1 fastq.gz

fq2

yes

path to read 2 fastq.gz

Workflow parameters

The following table is automatically parsed from the workflow’s config.schema.y(a)ml file.

Parameter

Type

Description

Required

Default

sample_sheet

string

yes

config/samples.tsv

reference

yes

. fasta

string

path to the reference genome FASTA

yes

panel

yes

. full_template

string

per-chrom phased panel VCF/BCF template with .csi alongside, e.g. “reference/panel_{chrom}.bcf”

yes

chromosomes

array

chromosome names matching the reference and panel

yes

genetic_map

yes

. template

string

per-chromosome genetic map path template, e.g. “maps/{chrom}.gmap”

yes

bam_filter

. min_depth

number

samples with mapped depth (after dedup) below this are dropped

1.0

. max_unmapped_frac

number

samples with a fraction of unmapped reads above this are dropped

0.8

phase

. batch_size

integer

maximum number of BAMs imputed in one GLIMPSE2_phase job

200

fastp

. extra

string

Linting and formatting

Linting results
 1/home/runner/work/snakemake-workflow-catalog/snakemake-workflow-catalog/.pixi/envs/default/lib/python3.13/site-packages/google/auth/transport/grpc.py:43: FutureWarning: grpcio < 1.83.0 does not support Post-Quantum Cryptography (PQC). Support for non-PQC environments is deprecated. In April 2027, google-auth will raise its minimum requirements to enforce grpcio >= 1.83.0. For more details on Google Cloud's post-quantum security migration, visit: https://cloud.google.com/security/resources/post-quantum-cryptography
 2  warnings.warn(
 3Workflow defines that rule minibwa_index is eligible for caching between workflows (use the --cache argument to enable this).
 4Workflow defines that rule samtools_faidx is eligible for caching between workflows (use the --cache argument to enable this).
 5Lints for snakefile /tmp/tmpr3dxytw2/workflow/rules/imputation.smk:
 6    * Path composition with '+' in line 8:
 7      This becomes quickly unreadable. Usually, it is better to endure some
 8      redundancy against having a more readable workflow. Hence, just repeat
 9      common prefixes. If path composition is unavoidable, use pathlib or
10      (python >= 3.6) string formatting with f"...".
11
12Lints for rule minibwa_align_dedup (line 1, /tmp/tmpr3dxytw2/workflow/rules/align.smk):
13    * Param idx_prefix is a prefix of input or output file but hardcoded:
14      If this is meant to represent a file path prefix, it will fail when
15      running workflow in environments without a shared filesystem. Instead,
16      provide a function that infers the appropriate prefix from the input or
17      output file, e.g.: lambda w, input: os.path.splitext(input[0])[0]
18      Also see:
19      https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#non-file-parameters-for-rules
20      https://snakemake.readthedocs.io/en/stable/tutorial/advanced.html#tutorial-input-functions
21
22Lints for rule filter_samples (line 21, /tmp/tmpr3dxytw2/workflow/rules/qc.smk):
23    * Migrate long run directives into scripts or notebooks:
24      Long run directives hamper workflow readability. Use the script or
25      notebook directive instead. Note that the script or notebook directive
26      does not involve boilerplate. Similar to run, you will have direct access
27      to params, input, output, and wildcards.Only use the run directive for a
28      handful of lines.
29      Also see:
30      https://snakemake.readthedocs.io/en/latest/snakefiles/rules.html#external-scripts
31      https://snakemake.readthedocs.io/en/latest/snakefiles/rules.html#jupyter-notebook-integration
32
33Lints for rule bam_list (line 32, /tmp/tmpr3dxytw2/workflow/rules/imputation.smk):
34    * No log directive defined:
35      Without a log directive, all output will be printed to the terminal. In
36      distributed environments, this means that errors are harder to discover.
37      In local environments, output of concurrent jobs will be mixed and become
38      unreadable.
39      Also see:
40      https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#log-files
41    * Migrate long run directives into scripts or notebooks:
42      Long run directives hamper workflow readability. Use the script or
43      notebook directive instead. Note that the script or notebook directive
44      does not involve boilerplate. Similar to run, you will have direct access
45      to params, input, output, and wildcards.Only use the run directive for a
46      handful of lines.
47      Also see:
48      https://snakemake.readthedocs.io/en/latest/snakefiles/rules.html#external-scripts
49      https://snakemake.readthedocs.io/en/latest/snakefiles/rules.html#jupyter-notebook-integration
Formatting results
All tests passed!