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oracle-rna-seq

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RNA-seq analysis from FASTQ to biology: STAR alignment, gene-level counts, DESeq2 differential expression, and GO/KEGG/GSEA. Metadata-driven design with human/mouse/rat support.

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- - static-checks.yml
- extensions.json
- settings.json
- config.yaml
- samples.tsv
- species.yaml
- BEST_PRACTICES.md
- HOW_TO_RUN.md
- R_SCRIPT_AUDIT.md
- - 00_pkg_mgmt.R
  - 01_qc_plots.R
  - 02_de_helpers.R
  - 03_enrichment.R
  - 04_species_config.R
- deseq2_pipeline.R
- download_references.sh
- fetch_fastq_screen_genomes.sh
- git_bootstrap.ps1
- test_count_merge.py
- test_preflight.py
- test_strandedness.py
- - coreutils.yaml
  - deeptools.yaml
  - deseq2.yaml
  - fastq_screen.yaml
  - multiqc.yaml
  - pandas.yaml
  - picard.yaml
  - preseq.yaml
  - qc.yaml
  - qualimap.yaml
  - rseqc.yaml
  - salmon.yaml
  - samtools.yaml
  - star.yaml
  - stringtie.yaml
  - subread.yaml
  - ucsc.yaml
- - preflight.py
- - 01_qc_raw.smk
  - 01b_fastq_screen.smk
  - 02_align_star.smk
  - 03_post_align_qc.smk
  - 04_quant_featurecounts.smk
  - 05_quant_salmon.smk
  - 06_stringtie.smk
  - 07_export_for_r.smk
  - 08_multiqc.smk
  - 09_run_r.smk
- - infer_strandedness.py
  - make_counts_for_r.py
  - utils.py
  - write_resolved_config.py
- common.smk
- envs.smk
- Snakefile
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CITATION.cff
environment.runner.yml
environment.yml
LICENSE
prepare_references.sh
README.md
run.sh
setup.sh
setup_env.sh
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THIRD_PARTY_LICENSES.md
TUTORIAL.md

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ORACLE is an end-to-end workflow for **paired-end bulk RNA-seq**. It starts with
FASTQ files and produces quality-control reports, aligned reads, gene-level
counts, differential-expression results, pathway enrichment, genome-browser
tracks, and exploratory transcript assemblies.

The workflow supports human, mouse, and rat references and runs through
Snakemake with isolated Conda environments. New users should begin with the
[complete tutorial](TUTORIAL.md); experienced users can use the short
[run guide](docs/HOW_TO_RUN.md).

## Scope

This pipeline is designed for:

- paired-end bulk RNA-seq;
- replicated, metadata-driven differential-expression experiments;
- human, mouse, or rat reference genomes;
- gene-level DESeq2 analysis from featureCounts or Salmon/tximport; and
- GO, KEGG, and preranked GSEA interpretation.

It does **not** currently implement single-end processing, single-cell RNA-seq,
transcript-level differential expression, differential transcript usage, or
within-species sample-identity testing. Salmon produces transcript estimates,
but the implemented DE path summarizes them to genes. StringTie assemblies are
exploratory annotations, not a transcript-level statistical test.

## Workflow

```text
paired-end FASTQ
  |
  |-- 01  FastQC + fastp                    raw-read QC and trimming
  |-- 01b FastQ Screen, optional/default-off genome-panel screening
  v
trimmed FASTQ
  |
  |-- 02  STAR two-pass alignment
  v
coordinate-sorted BAM
  |
  |-- 03  duplicate marking, RSeQC, Picard, Qualimap, preseq, flagstat
  |        + confidence-aware strandedness inference
  |
  |-- 04  featureCounts                     gene-level counts
  |-- 05  Salmon                            transcript estimates; optional DE source
  |-- 06  StringTie + gffcompare            strand-aware exploratory assembly
  |-- 07  export_for_r                      counts, metadata, tx2gene, resolved config
  |-- 08  MultiQC                           aggregate QC report
  `-- 09  DESeq2 + shrinkage + GO/KEGG/GSEA
```

Important safeguards include:

- strict validation of sample IDs, FASTQ pairs, design variables, reference
  levels, comparisons, replicate structure, and design-matrix rank;
- a fully resolved configuration saved with the results and passed unchanged to
  the R analysis;
- confidence-aware strand inference: ambiguous automatic calls stop the run
  instead of silently becoming unstranded;
- paired-fragment featureCounts settings that require both mates and exclude
  chimeric pairs (`-B -C`);
- fail-closed Salmon selection: requesting Salmon counts cannot silently fall
  back to featureCounts;
- shrinkage that must succeed rather than silently returning unshrunk effects;
- GO/KEGG over-representation tests using the tested-gene universe; and
- GSEA ranked by the signed Wald statistic by default.

## Quick start

```bash
git clone https://github.com/challagandla/oracle-rna-seq.git
cd oracle-rna-seq

bash setup.sh
bash prepare_references.sh human

# Replace the template rows and review the full analysis design.
${EDITOR:-nano} config/samples.tsv
${EDITOR:-nano} config/config.yaml

bash run.sh --dry-run
bash run.sh --cores 8
```

Do not treat the quick start as permission to skip experimental-design review.
Before the dry run, confirm that biological replicates are independent, batches
are not confounded with the comparison, and every modeled variable is recorded
correctly in the sample sheet. The tutorial provides a full preflight.

## Installation

`setup.sh` uses an existing Conda installation or installs a checksum-verified
Miniforge release without editing shell startup files. It creates the small
`oracle-rna-runner` environment and smoke-tests the 17 rule environments under
`workflow/envs/`.

```bash
bash setup.sh                 # install runner and rule environments
bash setup.sh --check         # verify an existing installation
bash setup.sh --runner-only   # install the runner; create rule envs on demand
```

No shell activation is required. `run.sh` locates the runner, selects the
appropriate Snakemake Conda-deployment option, and prevents personal R/Python
library overrides from leaking into the analysis. The environment YAML files
are the declared software specifications; they are not exact platform-specific
lockfiles.

Reference indexing is resource intensive. A mammalian STAR index commonly needs
tens of gigabytes of RAM and the complete reference plus indexes needs
substantial disk space. Actual requirements depend on the assembly and local
Conda package cache.

## Required inputs

`config/samples.tsv` must contain one row per biological library and these
columns:

```text
sample_id    fastq_r1    fastq_r2
```

Add every variable used by the statistical model, such as `condition`, `batch`,
`donor`, `sex`, or `age`. Categorical and continuous variables are declared
explicitly in `config/config.yaml`; numeric-looking category codes must not be
allowed to change meaning accidentally.

Set `de.primary_factor` to the main categorical factor of interest. It anchors
the reference level and is the default factor for comparisons that omit one.

The checked-in configuration defaults to human. Build matching references with:

```bash
bash prepare_references.sh human
```

Use `mouse` or `rat` only after selecting the same species in
`config/config.yaml`. Reference paths are defined in `config/species.yaml`.

## Optional FastQ Screen

FastQ Screen is disabled by default because its reference panel is large and is
not shipped with the repository. To use it:

```bash
bash run.sh --cores 8 fastq_screen_genomes
# Then set fastq_screen.enabled: true in config/config.yaml.
bash run.sh --cores 8 contamination
```

It can reveal reads matching other species, rRNA, vectors, PhiX, or other panel
references. It cannot establish that a human sample belongs to a particular
human donor and therefore cannot rule out within-species sample swaps.

## Common commands

```bash
bash run.sh --dry-run
bash run.sh --cores 16
bash run.sh --cores 16 --rerun-incomplete

bash run.sh --cores 8 qc_raw
bash run.sh --cores 8 contamination
bash run.sh --cores 16 align_star
bash run.sh --cores 16 quant
bash run.sh --cores 16 novel_transcripts
bash run.sh --cores 8 export_for_r
bash run.sh --cores 4 run_r
bash run.sh --cores 8 multiqc

bash run.sh --cores 8 --configfile path/to/project-config.yaml
```

`export_for_r` materializes the gene-count matrix, metadata, transcript-to-gene
map, and resolved configuration. `run_r` runs the statistical stage and does not
require bigWig generation. When `de.use_salmon: true`, it also requires each
configured sample's completed `quant.sf`.

## Main outputs

- `results/multiqc/multiqc_report.html` — aggregate technical QC.
- `results/qc/strandedness.tsv` — per-sample strand fractions, calls, and
  confidence information.
- `results/r_inputs/` — the exact inputs handed to the statistical analysis,
  including `resolved_config.yaml`.
- `results/r_outputs/DE_<comparison>.csv` — complete per-gene DESeq2 result for
  each configured comparison.
- `results/r_outputs/analysis_summary.csv` — comparison-level summary.
- `results/r_outputs/design_matrix.tsv` and `metadata_used.tsv` — the validated
  model matrix and ordered metadata actually analyzed.
- `results/r_outputs/results_names.txt` and `count_source.txt` — resolved DESeq2
  result names and the featureCounts or Salmon/tximport source.
- `results/r_outputs/sessionInfo.txt` — R and package session record.
- `results/stringtie/gffcompare.*` — exploratory assembled-transcript comparison.
- `results/tracks/<sample>.bw` — normalized genome-browser coverage.

The R rule log captures both standard output and errors. A completed command is
only the start of interpretation: review MultiQC and sample-level diagnostics
before trusting differential-expression or enrichment results.

## Interpreting DE results

`padj` controls the false-discovery rate across the tested genes. The configured
absolute log2-fold-change cutoff is a separate biological effect-size screen; it
does not change the null hypothesis tested by DESeq2. Report both the adjusted
p-value and effect size, and inspect expression/QC context rather than reducing
the result to a binary label.

GO/KEGG over-representation analysis uses all tested, mappable genes as the
background. GSEA uses the signed Wald statistic by default and asks whether a
gene set is concentrated toward either end of the full ranked list. Neither
method proves pathway activation or causality.

## Supported species

| Species | Assembly/annotation | OrgDb | KEGG code |
|---|---|---|---|
| Human | GRCh38 / GENCODE v46 | `org.Hs.eg.db` | `hsa` |
| Mouse | GRCm39 / GENCODE vM34 | `org.Mm.eg.db` | `mmu` |
| Rat | mRatBN7.2 / Ensembl 112 | `org.Rn.eg.db` | `rno` |

Adding another species requires more than a path block: add reference metadata,
download/index logic, the correct OrgDb package to the DE environment, KEGG and
identifier mappings, then validate reference preparation, DAG resolution, and a
small end-to-end run.

## Documentation

- [Complete beginner-to-advanced tutorial](TUTORIAL.md)
- [Concise run guide](docs/HOW_TO_RUN.md)
- [Best-practice checklist](docs/BEST_PRACTICES.md)

## License and usage

The pipeline's own code is MIT — see [LICENSE](LICENSE). It bundles no
third-party source code or reference data; tools are installed through Conda and
references are downloaded at run time. See
[THIRD_PARTY_LICENSES.md](THIRD_PARTY_LICENSES.md) for the dependency and data
license breakdown.

KEGG pathway data and UCSC command-line tools have commercial-use restrictions.
Academic and non-commercial users should cite the relevant sources. Commercial
users must obtain the applicable licenses or replace/disable those components as
described in the third-party license notice.

## Contact

Anil Challagandla — challagandla.anil@gmail.com