Bioinformatics Tips | Direct RNA Sequencing – Signal File Handling and Visualization

At a glance:

• Why POD5 Matters for Direct RNA Sequencing
• Installation and Environment Preparation
• Integrity and Summary Inspection
• File Manipulation: Merge, Filter, Subset, and Repack
• Format Conversion Between POD5 and FAST5
• Performance Tuning and Capacity Planning
• Quality Control Checklist for Signal Files
• End to End Example Pipeline with Outputs
• Troubleshooting Common Issues
• Signal Visualization with Squigualiser
• Installation Options
• Typical Workflow
• Output Interpretation
• From Signal Files to Visualization
• Frequently Asked Questions
• Glossary

Nanopore sequencers generate raw electrical signals that encode RNA sequence information. In direct RNA sequencing workflows, these signals are written as high volume binary files. Most modern instruments and pipelines use the POD5 format for storage and transfer. POD5 supports streaming writes and efficient random access during downstream analysis. Correct handling of these files is mission critical for reliable results and stable pipelines. This article explains practical steps, provides command examples, and includes output snippets. You will learn how to inspect, merge, filter, subset, repack, and convert signal files. We also cover performance tuning, quality control, troubleshooting, and workflow integration. Each section uses clear language for scientists and engineers who manage sequencing projects.

Why POD5 Matters for Direct RNA Sequencing

POD5 replaces legacy FAST5 for most production environments today. The format couples compact storage with reliable metadata indexing. It enables streaming from the acquisition software to persistent storage. That behavior reduces temporary bottlenecks and minimizes partial writes. POD5 relies on a columnar memory model that accelerates analytics. Fast reads make integrity checks and targeted extraction practical at scale. Large projects benefit because parallel workers can access distinct chunks safely. Service providers value predictable throughput and simpler file lifecycle management.

• POD5 allows streaming writes during active runs, improving reliability under load.
• Columnar layouts support fast queries and efficient random access for analytics.
• Stable metadata schemas reduce parsing errors across tools and versions.
• Compression settings balance storage cost against downstream compute speed.
• Consistent directory design simplifies collaboration across teams and vendors.

Installation and Environment Preparation

Install the POD5 toolkit using Python packaging. Use virtual environments for isolation.

pip install pod5

Confirm that the command line interface is available and versioned.

pod5 --version

Record the toolkit version in your run logs and analysis notebooks.

• Pin versions for reproducibility across cohorts and reruns.
• Document hardware, driver, and operating system details for audits.
• Store checksums and read counts next to each primary POD5 file.

Integrity and Summary Inspection

Start with quick summaries that surface obvious problems before basecalling.

Use pod5 view to build a compact table containing essential fields only.

pod5 view input.pod5 --include "read_id,channel,num_samples,end_reason" --output summary.tsv --separator "\t"

Typical output shows read identifiers, channels, sample counts, and end reasons.

read_id channel num_samples end_reason

00000000-0000-0000-0000-000000000001 23 45000 COMPLETE

00000000-0000-0000-0000-000000000002 24 45210 UNBLOCK

Inspect global integrity metrics and logs with the summary mode.

pod5 inspect summary input.pod5

Drill into individual reads when specific anomalies require deeper review.

pod5 inspect read input.pod5 00000000-0000-0000-0000-000000000001

Capture screenshots to document anomalies and share them with collaborators.

Consistent output archives make regression analysis fast during method updates.

File Manipulation: Merge, Filter, Subset, and Repack

Merging simplifies downstream scheduling when many POD5 fragments exist.

pod5 merge *.pod5 -o merged.pod5 --duplicate-ok

Filtering extracts reads of interest using a deterministic list of identifiers.

pod5 filter input.pod5 --output filtered.pod5 --ids read_ids.txt

Subsetting creates groups by barcode or quality status for organized processing.

pod5 subset -s sequencing_summary.txt --columns pod5 barcode pod5/ --template pod5_{pod5}/{barcode}/{pod5}.{barcode}.pod5

Repacking improves I O patterns and reduces fragmentation in heavy pipelines.

pod5 repack pod5s/*.pod5 repacked_pods/

• Run repack before GPU basecalling to reduce stalls during high throughput.
• Keep original files until validation completes for every repacked batch.
• Log file sizes and processing times to guide future capacity plans.

Format Conversion Between POD5 and FAST5

Convert between formats when legacy tools require FAST5 inputs or outputs.

pod5 convert fast5 ./fast5/ --output pod5/ --one-to-one ./fast5/

Produce FAST5 from POD5 when specific utilities remain unported to POD5.

pod5 convert to_fast5 input.pod5 --output fast5/

• Test conversions on small samples to validate integrity and performance.
• Preserve directory structure to keep provenance clear across conversions.
• Track read counts and checksums before and after every conversion step.

Performance Tuning and Capacity Planning

Signal pipelines stress storage and compute concurrently. Plan resources carefully.

• Use fast local SSD for temporary stages and merge outputs to network storage.
• Batch operations by barcode or lane to balance load across nodes.
• Adopt memory mapped reads when supported by your runtime environment.
• Schedule repack and conversion during low traffic windows to avoid contention.
• Profile I O throughput and CPU wait states to eliminate silent bottlenecks.

Create dashboards that track throughput, error rates, and queue depth over time.

Share weekly performance reports with stakeholders to align on capacity upgrades.

Quality Control Checklist for Signal Files

• Verify file headers, version fields, and channel counts on import.
• Confirm that read counts match acquisition summaries and run logs.
• Scan end_reason distributions for abnormal spikes that suggest hardware issues.
• Compare num_samples distributions across channels to detect stuck pores.
• Check barcode balance before downstream demultiplexing and alignment.
• Record coverage targets and achieved values for audit and reporting.
• Archive original files with read only permissions after initial QC passes.

End to End Example Pipeline with Outputs

The following sequence demonstrates a compact intake routine for one run.

# Summaries

pod5 view run1.pod5 --include "read_id,channel,num_samples" > run1_summary.tsv

# Integrity logs

pod5 inspect summary run1.pod5 > run1_integrity.log

# Merge shards

pod5 merge run1_barcode01.pod5 run1_barcode02.pod5 -o run1_merged.pod5

# Repack for performance

pod5 repack run1_merged.pod5 repacked_run1/

# Convert for legacy tools

pod5 convert to_fast5 repacked_run1/run1_merged.pod5 --output fast5_out/

Representative output snippets are included for documentation and training.

POD5 file version: 0.3.28

Reads: 1245678

Channels: 512

Integrity: OK

read_id channel num_samples

00000000-0000-0000-0000-000000000001 23 47892

00000000-0000-0000-0000-000000000002 24 48010

Troubleshooting Common Issues

• Merge failures: confirm file permissions and available disk space before retry.
• Filter errors: validate UUID formatting and remove empty lines in ID lists.
• Subset mismatches: ensure column names match the sequencing summary headers.
• Slow repack: move temporary directories to faster storage and retry the batch.
• Conversion crashes: test a minimal file, then scale using a controlled loop.

Maintain a runbook that documents symptoms, root causes, and durable fixes.

Share lessons across teams to reduce repeated investigation time during sprints.

Signal Visualization with Squigualiser

Once POD5 files have been processed and quality checked, the next step in Direct RNA Sequencing workflows is visualizing the raw electrical signal. Visualization bridges machine output and human interpretation, helping to validate basecalling, detect motif-associated signal patterns, and explore RNA modifications. Squigualiser is one of the most widely used tools for this purpose.

Installation Options

Option 1. Precompiled binary release:

wget https://github.com/hiruna72/squigualiser/releases/download/squigualiser-v0.6.1/squigualiser-v0.6.1-linux-x86-64-binaries.tar.gz -O squigualiser.tar.gz

tar xf squigualiser.tar.gz

cd squigualiser

./squigualiser --help

Option 2. Python installation via pip:

pip install squigualiser

Test the installation with sample data:

wget https://hiruna72.github.io/squigualiser/docs/sample_dataset.tar.gz

tar xf sample_dataset.tar.gz

squigualiser plot_pileup -f ref.fasta -s reads.blow5 -a eventalign.bam -o dir_out --region chr1:92,778,040-92,782,120 --tag_name "test_0"

Typical Workflow

Step 1. Basecall with Dorado using --emit-moves:

dorado basecaller dna_r10.4.1_e8.2_400bps_sup@v5.0.0 input.pod5 --emit-moves > basecalls.bam

Step 2. Reform BAM for plotting:

squigualiser reform --sig_move_offset 0 --kmer_length 1 -c --bam basecalls.bam -o reform_output.paf

Step 3. Extract sequences for alignment:

samtools fasta basecalls.bam > pass.fasta

Step 4. Align sequences to the reference genome:

minimap2 -t 16 -ax map-ont ref.fa pass.fa > mapped.bam

Step 5. Convert POD5 to SLOW5/BLOW5 format:

blue-crab p2s input.pod5 -o input.blow5

Step 6. Plot signal–read graphs:

squigualiser plot --file pass.fasta --slow5 input.blow5 --alignment mapped.bam

Output Interpretation

The generated plots display:

- X-axis: nucleotide positions, color-coded by base.

- Y-axis: current intensity values.

- Multiple aligned reads stacked together to reveal consistent patterns or deviations.

This visualization is valuable for validating new basecalling models, identifying motif-linked artifacts, or training new researchers.

From Signal Files to Visualization

By combining POD5 file management with Squigualiser visualization, researchers ensure both technical integrity and intuitive confirmation of their sequencing data. Clean, repacked files reduce computational noise, while signal-level plots highlight whether basecalling and modification signatures are reliable. This workflow forms the foundation for downstream RNA modification detection and differential methylation analysis.

Frequently Asked Questions

Glossary

• POD5: A modern binary format for nanopore signal data and metadata.
• FAST5: A legacy HDF5 based format formerly used for nanopore signal files.
• Channel: An individual nanopore that contributes signal during a run.
• End reason: A categorical label that describes why a read ended.
• Repack: An operation that rewrites a file to optimize layout and access.

Recommend reading

Direct RNA Sequencing Analysis: From Raw Signals to Differential Methylation

Direct RNA Sequencing Analysis: Guide to RNA Modification and DMR Analysis