index.md

RNA: Overview

Thin, modular wrapper over the external amalgkit CLI with a plan/execute workflow.

Quick Links

• Main Documentation Index - Complete documentation map
• API Reference - Complete function documentation
• Workflow Guide - Workflow planning and execution
• Step Documentation - All 11 step guides
• Function Index - Quick function lookup

flowchart LR
  AworkflowConfig[Workflow Config] --> BplanWorkflow[plan_workflow]
  B --> CorderedSteps[Ordered Steps]
  C --> DexecuteWorkflow[execute_workflow]
  D --> EamalgkitSubcommands[amalgkit subcommands]
  E --> Flogs+Manifest[Logs + Manifest]

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See: amalgkit/README.md, workflow.md, CONFIGURATION.md.

Advanced: step runners in metainformant.rna.engine.workflow_steps provide a stable call surface per subcommand. See: Step Documentation

Notes

• Ensure amalgkit is installed and on PATH; verify using check_cli_available
• execute_workflow writes per-step logs to work_dir/logs and a JSONL manifest to work_dir/amalgkit.manifest.jsonl
• To parameterize steps by species/tissues and output layout, see Configuration Guide

MetaInformAnt Extensions

These tools provide automation and precision beyond the standard amalgkit capabilities:

• Tissue Patching: Metadata correction system (tissue_patches.yaml).
• Ortholog Generation: Automated cross-species mapping.
• Streaming Orchestration: High-efficiency ENA download/quant pipeline.
• Troubleshooting & Hacks: Solutions for common bottlenecks.

Transcriptomic meta-analysis (concept)

Meta-analysis integrates RNA-seq data across studies to detect robust gene expression patterns with improved power. A typical flow:

• Data collection and QC: gather SRA/ENA records, assess read quality (e.g., FastQC), trim adapters (e.g., Trimmomatic/fastp)
• Alignment/quantification: map or quasi-map reads (e.g., HISAT2/STAR or Salmon/RSEM)
• Normalization and batch handling: account for depth/length and correct study-level effects (e.g., ComBat, limma removeBatchEffect)
• Differential expression: within-study DE (e.g., DESeq2, edgeR)
• Meta-analysis of DE: combine evidence across studies (e.g., metaRNASeq; p-value or effect-size aggregation)
• Functional interpretation: GO/pathway enrichment

Mapping to the amalgkit-based workflow

The metainformant.rna wrappers organize these concerns into reproducible steps under a single working directory (default under output/).

• Discovery, planning, selection: metadata → integrate → config → select
• Acquisition: getfastq (downloads raw FASTQ into work_dir/fastq by convention)
• Quantification: quant (e.g., Salmon) producing per-sample abundances under work_dir/quant
• Aggregation: merge yielding a merged table such as work_dir/merged_abundance.tsv
• Normalization: cstmm (count scaling) under work_dir/cstmm
• Curation and QC: curate, csca (sample clustering/assessment), sanity (final checks)
• Downstream DE/meta-analysis: export merged counts/TPMs to R/Python tools (e.g., DESeq2/edgeR/metaRNASeq) for study-level DE and cross-study meta-analysis

See workflow.md for planning/execution and Step Documentation for per-step details and expected artifacts.

See Also

Getting Started

• GETTING_STARTED.md - Installation and setup
• README.md - Complete documentation index

Core Documentation

• workflow.md - Workflow planning and execution
• Step Documentation - Individual step documentation
• CONFIGURATION.md - Configuration management
• API.md - Complete API reference

Orchestration

• ORCHESTRATION.md - Orchestrator overview

References

• metaRNASeq: RNA-seq meta-analysis methodology (arXiv)
• metaTP: automated metatranscriptomic pipeline with Snakemake (BMC Bioinformatics)
• SMAGEXP: Galaxy suite unifying metaMA/metaRNASeq (arXiv)
• Flimma: federated limma-voom for privacy-preserving DE (arXiv)