align-condathis-targets

This repo showcases the use of targets and condathis to build a pure R pipeline that stays reproducible while also using non-R command-line interface (CLI) tools. The targets package is “a Make-like pipeline tool for statistics and data science in R”. The condathis package is a CRAN package that lets you run CLI tools in R, ensuring reproducibility across systems and environmental isolation. With condathis, there’s no need for users to manually ensure CLI tools are installed on their systems.

The pipeline in this example processes RNA-seq FASTQ files by running quality control (QC), trimming adapters, aligning the sequences, and ultimately producing sorted BAM files.

Motivation

As bioinformaticians working with omics data, R is incredibly rich in packages. However, most tools for the initial phases of omics analysis are not R-based. The targets package is fantastic for building R pipelines. By combining targets with condathis, we can create pipelines that integrate both R tools and other CLI tools in an R environment, while still maintaining reproducibility.

How condathis is used

The package condathis is used to:

1. Create environments with specific versions of the CLI tools needed. See example below.

## Create an environment with gsutil v5.33
condathis::create_env("gsutil==5.33", env_name = "gsutil-env")

2. Interact with those environments and launch the CLI commands. See example below.

url_cloude_storage <- "gs://gcp-public-data--broad-references/Homo_sapiens_assembly19_1000genomes_decoy/Homo_sapiens_assembly19_1000genomes_decoy.fasta"

# Same as processx::run but needs to indicate the envname
condathis::run(
  "gsutil", "-m", "cp", url_cloude_storage, here::here("data", "outputs"), # where to save the data
  env_name = "gsutil-env"
)

–> R wrapper functions that incorporate specific condathis CLI commands are created to return paths that can be used by tar_files. <–

Folders and files

• All the wrapper functions are stored in code/targets_functions.R.
• config/: Contains the mapping.csv that maps subjects with corresponding files.
• data/raw/: Contains some example FASTQ files (note that the files have been trimmed to contain only a few reads for easy processing).
• data/outputs/: This is where the outputs of the pipeline are created.

Pipeline Overview

• fastp: Quality of FASTQ files is checked and adapters are trimmed.
• gsutil: Reference genome is downloaded.
• minimap2: Aligns the files to the reference.
• samtools: Transforms SAM files to BAM files and sorts

To restore all the project dependencies, run: renv::restore().

All outputs will be generated in data/outputs.

Run Pipeline with targets

library(targets)
# tar_dir()
targets::tar_make()
#> Loading required package: parallelly
#> here() starts at /Users/luciorq/projects/clones/align-condathis-targets
#> ✔ skipped target samtools_env
#> ✔ skipped target minimap_env
#> ✔ skipped target rna_files_file
#> ▶ dispatched target fastp_env
#> ● completed target fastp_env [2.669 seconds, 62 bytes]
#> ✔ skipped target gsutil_env
#> ✔ skipped target wget_env
#> ✔ skipped target fastq_path
#> ✔ skipped target reference_files
#> ✔ skipped target rna_files
#> ✔ skipped target reference_mmi
#> ✔ skipped target rna_mapping
#> ✔ skipped branch trimmed_fastq_qc_ca20143f0fae43e9
#> ✔ skipped branch trimmed_fastq_qc_6f135ab8bf1a9cb5
#> ✔ skipped pattern trimmed_fastq_qc
#> ✔ skipped branch mapped_sams_d241dbb2d8120a23
#> ✔ skipped branch mapped_sams_3c0cda8129e92eb0
#> ✔ skipped pattern mapped_sams
#> ✔ skipped branch bam_files_5c0a6881bee73101
#> ✔ skipped branch bam_files_e73d037ef7aa1ce0
#> ✔ skipped pattern bam_files
#> ✔ skipped branch sorted_bams_bcf10dcc743dc79b
#> ✔ skipped branch sorted_bams_b3b2d89238686ae2
#> ✔ skipped pattern sorted_bams
#> ▶ ended pipeline [3.471 seconds]