Fixing Empty BAM Errors In PacBio Long-Read ScRNA-seq Data

by Alex Johnson 59 views

Dealing with next-generation sequencing data, especially long-read technologies like PacBio for single-cell RNA sequencing (scRNA-seq), can sometimes present unique challenges. One such hurdle, particularly within the nf-core/scnanoseq pipeline, involves the tag_barcodes.py script. Users have reported that this script can produce an empty tagged.bam file when processing PacBio long-read data. This seemingly small issue can cascade into significant problems downstream, specifically causing the BAMTOOLS_SPLIT process to fail because it expects BAM files to be present. This article dives deep into this specific bug, exploring its causes, offering potential solutions, and providing context for those encountering it.

Understanding the Problem: The Empty tagged.bam Issue

The core of the problem lies in bin/tag_barcodes.py generating an empty tagged.bam file. This occurs even when running the pipeline on smaller subsets of data, suggesting it's not a resource limitation but rather an issue related to how the script interacts with the PacBio long-read data format or specific data characteristics. The failure point is clearly identified within the pipeline: NFCORE_SCNANOSEQ:SCNANOSEQ:PROCESS_LONGREAD_SCRNA_GENOME:DEDUP_UMIS:BAMTOOLS_SPLIT. The error message, Missing output file(s) *.bam expected by process, directly points to the absence of expected BAM files. The underlying command, bamtools merge -in TEST_PB_100k.tagged.bam | bamtools split -stub TEST_PB_100k -reference, exits without error (status 0), but because the input tagged.bam is empty, bamtools split has nothing to process and thus produces no output BAM files, leading to the pipeline's subsequent failure.

What's particularly perplexing is that this issue appears to be data-specific. The official demo or test datasets for nf-core/scnanoseq run successfully on the same system, indicating that the pipeline itself and the underlying system are generally functional. This points towards a potential incompatibility or a specific data characteristic in the user's PacBio long-read data that tag_barcodes.py cannot handle. The pipeline version in question is nf-core/scnanoseq: 1.2.1, running on Nextflow 24.10.5 with a Slurm executor and Singularity profile. The problem has been observed across different data sizes, from the full PacBio dataset down to a 100k-read subsample, reinforcing the idea that it's not a scale issue but a fundamental processing problem. This raises critical questions for researchers: Is tag_barcodes.py expected to work seamlessly with PacBio long-read 10X data in its current configuration? Are there specific pre-processing requirements, such as BAM sort order, or are there known limitations that need to be considered? The user's willingness to provide additional logs and a minimal reproducible example is a crucial step in debugging such issues.

Investigating the tag_barcodes.py Script and PacBio Data

To effectively address the empty tagged.bam output from tag_barcodes.py when working with PacBio long-read data, it's essential to delve into the script's functionality and the specifics of PacBio data. The tag_barcodes.py script is typically designed to parse and extract barcode information from sequencing reads and embed it into the BAM file, often as auxiliary tags. This is a critical step for single-cell applications where cellular barcodes (and sometimes UMI barcodes) are used to demultiplex reads belonging to different cells. For PacBio long reads, which are inherently different from short reads generated by Illumina, the structure and format of the FASTQ/BAM files might vary, and the ways barcodes are appended or encoded could differ. For instance, PacBio HiFi reads have high accuracy, but their length distribution and the presence of poly(A) tails or specific adapter sequences could influence how barcode information is parsed.

The nf-core/scnanoseq pipeline, particularly the PROCESS_LONGREAD_SCRNA_GENOME module, aims to adapt these analyses for long-read scRNA-seq. However, the tag_barcodes.py script might have been primarily developed or optimized for short-read data. When applied to long reads, especially those from platforms like PacBio with different read structures and potential barcode integration methods (e.g., barcodes within the read sequence itself, or in associated metadata), the script might fail to find the expected barcode patterns. This could result in no barcodes being identified for any read, leading to an empty output BAM file after processing.

One critical aspect to consider is the format of the input BAM file before it reaches tag_barcodes.py. While the pipeline might handle the initial input, the specific BAM format, including header information, read naming conventions, and the presence (or absence) of certain tags, could be influencing the script's behavior. If the script expects reads to be in a specific format or order, and the PacBio BAM files don't conform, it might simply not find any barcodes to tag. The BAMTOOLS_SPLIT process, which relies on the tagged.bam file having valid entries, will naturally fail if the input is empty. Therefore, a thorough investigation would involve:

  1. Examining the input BAM file: Before tag_barcodes.py runs, inspect the BAM file. Are the reads correctly formatted? Can barcode information be manually extracted or identified using other tools?
  2. Understanding tag_barcodes.py logic: Review the source code of tag_barcodes.py to understand its assumptions about barcode location, format, and the expected input BAM structure. Does it rely on specific read group information, or does it parse sequence directly?
  3. Investigating PacBio data conventions: Research how barcodes are typically embedded or represented in PacBio scRNA-seq datasets. Are there standard adapters or sequence patterns that need to be accounted for?

Without understanding these specifics, it's challenging to pinpoint the exact reason for tag_barcodes.py failing to populate the tagged.bam. The fact that the demo data works suggests that either the demo data uses a format that tag_barcodes.py can handle, or it's a specific characteristic of the user's PacBio data that is not represented in the demo.

Troubleshooting Steps and Potential Solutions

When faced with the frustrating scenario of tag_barcodes.py producing an empty tagged.bam file for your PacBio long-read data within the nf-core/scnanoseq pipeline, several troubleshooting steps and potential solutions can be explored. The key is to systematically identify where the process breaks down and to adapt either the data or the script's handling. Given that the pipeline runs successfully with demo data but fails with your specific PacBio input, the focus should be on the differences between these datasets and how tag_barcodes.py might be sensitive to them.

1. Inspect the Input Data and Barcode Format

  • Examine the raw reads: Before processing, look at the raw FASTQ or BAM files. How are the cellular and UMI barcodes encoded? Are they at the beginning of the read sequence, in adapter sequences, or elsewhere? The tag_barcodes.py script likely makes assumptions about their location and format. For PacBio 10X data, the barcode sequences are often part of the adapter ligated to the poly(A) tail of the mRNA. If tag_barcodes.py expects them in a different location (e.g., in the header or a standard adapter sequence), it won't find them.
  • Verify barcode_format parameter: The pipeline uses the --barcode_format parameter. Ensure it's correctly set for your specific PacBio 10X v3 chemistry (10X_3v3). If this parameter is incorrect, the script will search for the wrong patterns.
  • Check BAM Preprocessing: The tag_barcodes.py script operates on a BAM file. Ensure the BAM file it receives as input is correctly formatted and contains the reads as expected. Sometimes, upstream processes might alter read names or sequences in ways that confuse barcode-parsing scripts.

2. Modify or Adapt tag_barcodes.py

  • Code Inspection: If you have the technical expertise, examine the tag_barcodes.py script itself. Look for hardcoded assumptions about read structure, adapter sequences, or barcode positions. You might need to adjust regular expressions or logic to correctly identify barcodes in your PacBio data.
  • Custom Script: Consider if a custom script, specifically tailored to your PacBio data's barcode structure, would be more appropriate. This could involve using PacBio-specific tools or libraries to extract barcodes before they are passed to the nf-core/scnanoseq pipeline, or modifying the script to handle your data's unique format.

3. Pipeline Configuration and Parameters

  • Input File Format: While the error points to tagged.bam, ensure the initial input to the pipeline (likely FASTQ or un-tagged BAM) is correctly specified and compatible with the pipeline's expectations for PacBio data.
  • dedup_tool and quantifier settings: Although the error occurs before the main UMI deduplication and quantification, check if the chosen tools (umitools and isoquant in your case) have any specific requirements or known issues with PacBio data that might indirectly affect upstream steps.

4. Input Data Requirements (BAM Sort Order)

  • BAM Sort Order: The error message doesn't explicitly mention sort order, but BAM processing tools can sometimes be sensitive to it. While bamtools merge and bamtools split are generally robust, it's worth confirming if the input tagged.bam (even if empty) or the BAM file before tagging needs to be in a specific sort order (e.g., by coordinate). If tag_barcodes.py internally relies on sorted data or specific read ordering, this could be a factor.

5. Community and Support

  • Check the nf-core/scnanoseq GitHub Issues: Search the nf-core/scnanoseq GitHub repository for similar issues. Other users may have encountered and solved this problem with PacBio data. If not, consider opening a new issue to report your findings and seek help from the developers and community.
  • Consult PacBio Documentation: Review PacBio's own documentation and best practices for scRNA-seq library preparation and data analysis, especially concerning barcode handling.

6. Minimal Reproducible Example

As the user offered, creating a minimal reproducible example is highly valuable. This involves providing a small subset of the problematic PacBio data, the exact command used, and the pipeline configuration. This allows developers to directly test and debug the issue on their end without needing access to large, proprietary datasets. If you can isolate the specific reads or conditions that cause tag_barcodes.py to fail, it significantly speeds up the debugging process.

By systematically working through these steps, you increase the chances of identifying the root cause of the empty tagged.bam file and finding a viable solution to enable your PacBio long-read scRNA-seq analysis.

Conclusion: Moving Forward with PacBio Long-Read Analysis

The issue of tag_barcodes.py generating an empty tagged.bam file on PacBio long-read data within the nf-core/scnanoseq pipeline is a specific yet critical bug that can halt analysis. It highlights the complexities of adapting short-read-centric bioinformatics tools and pipelines to the unique characteristics of long-read sequencing technologies. While the pipeline's success with demo data suggests its general integrity, the failure with PacBio input underscores the need for careful consideration of data format, barcode encoding, and tool compatibility.

Our exploration has revealed that the problem likely stems from mismatches between the assumptions made by tag_barcodes.py regarding barcode location and format, and how these are actually represented in PacBio long-read scRNA-seq data. Potential solutions range from meticulous inspection of input data and pipeline parameters to, if necessary, adapting the tag_barcodes.py script or implementing custom preprocessing steps. The importance of community support, detailed issue reporting, and creating minimal reproducible examples cannot be overstated in resolving such data-specific challenges.

For researchers working with PacBio long-read scRNA-seq, it's crucial to remain vigilant, especially during the initial data processing stages. Thoroughly understanding your data's specific barcode structure and consulting platform-specific best practices are essential. When encountering issues like this, leveraging the collective knowledge of the bioinformatics community through platforms like GitHub and dedicated forums can provide invaluable assistance.

If you are facing similar challenges, consider exploring the resources available from PacBio for guidance on data processing and analysis best practices for their long-read technologies. Additionally, the nf-core community provides excellent support and documentation for their pipelines, which can be a great place to seek advice and share solutions.