FAQ¶

Frequently asked questions about the aa-tRNA-seq pipeline.

General Questions¶

What species can I analyze?¶

The pipeline is designed for Saccharomyces cerevisiae tRNAs by default. To use with other species:

1. Create a reference FASTA with your tRNA sequences + adapters
2. Train or adapt the Remora charging model for your species
3. Update the config to point to your reference

What is the charging threshold?¶

The default threshold is ML ≥ 200 = charged. This is currently hardcoded in the get_cca_trna_cpm rule. Values range from 0-255.

Can I change the charging threshold?¶

Yes, but requires editing the rule. In workflow/rules/aatrnaseq-charging.smk:

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params:
    ml_thresh=200,  # Change this value

What modifications are detected?¶

The pipeline calls these modifications via Dorado:

• pseU - Pseudouridine
• m5C - 5-methylcytosine
• m6A - N6-methyladenosine
• inosine - Inosine

How accurate is charging classification?¶

Accuracy depends on:

• Signal quality
• Full-length read retention
• Model training data match

The 6-nucleotide kmer (CCAGGC) provides the classification signal.

Data Requirements¶

What input format is required?¶

POD5 format from Oxford Nanopore sequencing. The pipeline does not accept:

• FAST5 (convert with pod5 convert)
• FASTQ (signal data required)
• BAM (unless from Dorado with move tables)

How should I organize my data?¶

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run_directory/
├── pod5_pass/    # Passed reads (required)
│   └── *.pod5
├── pod5_fail/    # Failed reads (optional)
│   └── *.pod5
└── pod5/         # Alternative name
    └── *.pod5

Can I merge multiple runs per sample?¶

Yes! List the same sample ID multiple times in your samples file:

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sample1    /data/run1
sample1    /data/run2

POD5 files from both runs will be merged.

What's the minimum read count?¶

There's no hard minimum, but:

• 100+ reads per tRNA recommended for reliable CPM estimates
• 1000+ reads recommended for statistical analysis

Execution Questions¶

How long does the pipeline take?¶

Typical times per sample (GPU):

CPU-only is 10-100x slower.

Can I run without a GPU?¶

Yes, but not recommended. Dorado and Remora will fall back to CPU:

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export CUDA_VISIBLE_DEVICES=""
pixi run snakemake --cores 12 --configfile=config/config.yml

Expect significant slowdown.

How much disk space is needed?¶

Per sample (approximate):

Plan for ~50-100 GB per sample during processing. Clean intermediate files after.

Can I resume a failed run?¶

Yes! Snakemake automatically resumes:

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pixi run snakemake --profile cluster/lsf --configfile=config/config.yml

Only incomplete rules will re-run.

Output Questions¶

What does the charging_prob table contain?¶

Per-read charging likelihood scores:

What does the CPM table contain?¶

Per-tRNA aggregated counts:

Why are CL/CM tags used instead of ML/MM?¶

The original Remora tags (ML/MM) are renamed to CL/CM to avoid conflicts with standard SAM modification tags used by Dorado.

How do I visualize results?¶

Load output files in:

• IGV: BAM files, BedGraph coverage
• R/Python: TSV tables for statistical analysis
• Downstream repo: github.com/rnabioco/aa-tRNA-seq

Configuration Questions¶

What's the difference between config files?¶

Your config only needs to override what's different.

How do I add a new sample?¶

1. Add to samples.tsv:

   Text Only
   1	new_sample /path/to/data

2. Run pipeline:

   Bash
   1	pixi run snakemake --configfile=config/config.yml

Only new samples will be processed.

Can I use a custom reference?¶

Yes, set in your config:

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fasta: "path/to/your/reference.fa"

The reference should include adapter sequences matching your library prep.

Demultiplexing Questions¶

When should I use demultiplexing?¶

When samples were pooled with WarpDemuX barcodes during library preparation.

Which barcode kit should I use?¶

Why are my demux reads unbalanced?¶

Common causes:

1. Library concentration differences
2. Barcode ligation efficiency variation
3. Loading bias

Check demux_summary.tsv.gz for distribution.

Can I mix demux and direct samples?¶

Yes, in YAML format use ~ for direct samples:

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runs:
  - path: /data/pooled
    samples:
      pooled_sample: "barcode03"
  - path: /data/direct
    samples:
      direct_sample: ~

Troubleshooting Questions¶

Why did my job fail?¶

1. Check the log file:

   Bash
   1	cat results/logs/<rule>/<sample>

2. Check cluster job output:

   Bash
   1 2	# LSF bjobs -l <job_id>

How do I force re-run a rule?¶

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pixi run snakemake --forcerun <rule_name> --configfile=config/config.yml

How do I clean up and start fresh?¶

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# Remove all outputs
rm -rf results/

# Or remove specific intermediate files
rm -rf results/bam/rebasecall results/bam/aln results/fq

Where can I get help?¶

• GitHub Issues: github.com/rnabioco/aa-tRNA-seq-pipeline/issues
• Documentation: This site
• Downstream analysis: github.com/rnabioco/aa-tRNA-seq