How to use pbsmrtpipe

Getting started

The pbsmrtpipe command is designed to be more-or-less self-documenting. It is normally run in one of several modes, which is specified as a positional argument. To get the full overview of functionality, run this:

$ pbsmrtpipe --help

and you can get further help for individual modes, for example:

$ pbsmrtpipe pipeline-id --help

To display a list of available pipelines, use show-templates:

$ pbsmrtpipe show-templates

For details about a specific pipeline, specify the ID (the last field in each item in the output of show-templates) with show-template-details:

$ pbsmrtpipe show-template-details pbsmrtpipe.pipelines.sa3_ds_resequencing

Among other things, this will list the entry points required for the pipeline. These will usually be PacBio Dataset XML files (see Appendix B for instructions on generating these), although single raw data files (BAM or FASTA format) may be acceptable for some use cases. The most common input will be eid_subread, a SubreadSet XML dataset, which contains one or more BAM files containing the raw unaligned subreads. Also common is eid_ref_dataset, for a ReferenceSet or genomic FASTA file.

Note that if you are starting from PacBio’s bax.h5 basecalling files, you will need to do an initial conversion step; see Appendix A for details.


The algorithms used to analyze PacBio data are computationally intensive but also intrinsically highly parallel. pbsmrtpipe is designed to scale to at least hundreds of processors on multi-core systems and/or managed clusters. This is handled by two distinct but complementary methods:

  • multiprocessing is implemented in the underlying tasks, all of which are generally shared-memory programs. This is effectively always turned on unless the max_nchunk parameter is set to 1 (see examples section below for a description of how to modify parameter values). For most compute node configurations a value between 8 and 16 is appropriate.
  • chunking is implemented by pbsmrtpipe and works by applying filters to the input datasets, which direct tasks to operate on a subset (“chunk”) of the data. These chunks are most commonly either a contiguous subset of reads or windows in the reference genome sequence.

Note that at present, the task-level output directories (and the locations of the final result files) may be slightly different depending on whether chunking is used, since an intermediate “gather” step is required to join chunked results.

Common workflows

All pipelines in pbsmrtpipe are prefixed with “pbsmrtpipe.pipelines.”; for clarity this is omitted from the table below.

Pipeline Purpose
sa3_sat Site Acceptance Test run on all new PacBio installations
sa3_ds_resequencing Map subreads to reference genome and determine consensus sequence with Quiver
sa3_ds_ccs Generate high-accuracy Circular Consensus Reads from subreads
sa3_ds_ccs_align ConsensusRead (CCS) + mapping to reference genome, starting from subreads
sa3_ds_isoseq_classify IsoSeq transcript classification, starting from subreads
sa3_ds_isoseq Full IsoSeq with clustering and Quiver polishing (much slower)
ds_modification_motif_analysis Base modification detection and motif finding, starting from subreads
sa3_hdfsubread_to_subread Convert HdfSubreadSet to SubreadSet (import bax.h5 basecalling files
sa3_ds_laa Basic Long Amplicon Analysis (LAA) pipeline, from barcoded subreads
polished_falcon HGAP 4 assembly pipeline starting from subreads and a configuration file

Nearly all of these pipelines (except for sa3_hdfsubread_to_subread) require a SubreadSet as input; many also require a ReferenceSet. Output is more varied:

Pipeline Essential outputs
sa3_sat variants GFF, SAT report
sa3_ds_resequencing AlignmentSet, consensus ContigSet, variants GFF
sa3_ds_ccs ConsensusReadSet, FASTA and FastQ files
sa3_ds_ccs_mapping As above plus ConsensusAlignmentSet
sa3_ds_isoseq_classify ContigSets of classified transcripts
sa3_ds_isoseq As above plus polished isoform ContigSet
ds_modification_motif_analysis Resequencing output plus basemods GFF, motifs CSV
sa3_hdfsubread_to_subread SubreadSet
sa3_ds_laa Consensus FASTA
polished_falcon ContigSet of assembled contigs

Practical Examples

Basic resequencing

This pipeline uses pbalign to map reads to a reference genome, and quiver to determine the consensus sequence.

We will be using the sa3_ds_resequencing pipeline:

$ pbsmrtpipe show-template-details pbsmrtpipe.pipelines.sa3_ds_resequencing

Which requires two entry points: a SubreadSet and a ReferenceSet. A typical invocation might look like this (for a hypothetical lambda virus genome):

$ pbsmrtpipe pipeline-id pbsmrtpipe.pipelines.sa3_ds_resequencing \
  -e eid_subread:/data/smrt/2372215/0007/Analysis_Results/m150404_101626_42267_c100807920800000001823174110291514_s1_p0.all.subreadset.xml \
  -e eid_ref_dataset:/data/references/lambdaNEB/lambdaNEB.referenceset.xml

This will run for a while and emit several directories, including tasks, logs, and workflow. The tasks directory is the most useful, as it stores the intermediate results and resolved tool contracts (how the task was executed) for each task. The directory names (task_ids) should be somewhat self-explanatory. If you want to direct the output to a subdirectory in the current working directory, use the -o flag: -o job_output_1.

Other pipelines related to resequencing, such as the basemods detection and motif finding, have nearly identical command-line arguments except for the pipeline ID.

For a general overview of the resequencing results, the GFF file written by summarizeConsensus is the most useful:


This contains records for a complete set of sequence regions in the reference genome, including coverage statistics and the number of gaps, substitituions, insertions or deletions. For example:

lambda_NEB3011  .       region  1       50      0.00    +       .       cov=116,190,190;cov2=183.000,14.633;gaps=0,0;cQv=20,20,20;del=0;ins=0;sub=0

Site Acceptance Test

The SAT pipeline is used to validate all new PacBio systems upon installation. It is essentially the resequencing pipeline applied to high-coverage lambda virus genome data collected on a PacBio instrument, with an additional report. The invocation is therefore nearly identical, but you should always be using the lambdaNEB reference genome:

$ pbsmrtpipe pipeline-id pbsmrtpipe.pipelines.sa3_sat \
  -e eid_subread:/data/smrt/2372215/0007/Analysis_Results/m150404_101626_42267_c100807920800000001823174110291514_s1_p0.all.subreadset.xml \
  -e eid_ref_dataset:/data/references/lambdaNEB/lambdaNEB.referenceset.xml \
  -o job_output_2

The output directories will be the same as the resequencing job plus pbreports.tasks.sat_report-0. The most important file is (assuming the command line arguments shown above):


The JSON file will have several statistics, the most important of which are coverage and accuracy, both expected to be 1.0. Also of interest is the final GFF file output by summarizeConsensus, as described above; for this pipeline the number of gaps, deletions, insertions, or substitutions should be zero for all regions of the genome.

Quiver (Genomic Consensus)

If you already have an AlignmentSet on which you just want to run quiver, the sa3_ds_genomic_consensus pipeline will be faster:

$ pbsmrtpipe pipeline-id pbsmrtpipe.pipelines.sa3_ds_genomic_consensus \
  -e eid_bam_alignment:/data/project/my_lambda_genome.alignmentset.xml \
  -e eid_ref_dataset:/data/references/lambda.referenceset.xml \

See Appendix B below for instructions on generating an AlignmentSet XML from one or more mapped BAM files.

Circular Consensus Reads

To obtain high-quality consensus reads (also known as CCS reads) for individual SMRTcell ZMWs from high-coverage subreads:

$ pbsmrtpipe pipeline-id pbsmrtpipe.pipelines.sa3_ds_ccs \
  -e eid_subread:/data/smrt/2372215/0007/Analysis_Results/m150404_101626_42267_c100807920800000001823174110291514_s1_p0.all.subreadset.xml \
  --preset-xml preset.xml -o job_output

This pipeline is relatively simple and also parallelizes especially well. The essential outputs are a ConsensusRead dataset (composed of one or more unmapped BAM files) and corresponding FASTA and FASTQ files:


The pbccs.tasks.ccs-0 task directory will also contain a JSON report with basic metrics for the run such as number of reads passed and rejected for various reasons. (Note, as explained below, that the location of the final ConsensusRead XML - and JSON report - will be different in chunk mode.)

Because the full resequencing workflow operates directly on subreads to produce a genomic consensus, it is not applicable to CCS reads. However, a CCS pipeline is available that incorporates the Blasr mapping step:

$ pbsmrtpipe pipeline-id pbsmrtpipe.pipelines.sa3_ds_ccs_align \
  -e eid_subread:/data/smrt/2372215/0007/Analysis_Results/m150404_101626_42267_c100807920800000001823174110291514_s1_p0.all.subreadset.xml \
  -e eid_ref_dataset:/data/references/lambda.referenceset.xml \
  --preset-xml preset.xml -o job_output

IsoSeq Transcriptome Analysis

The IsoSeq workflows automate use of the pbtranscript package for investigating mRNA transcript isoforms. The transcript analysis uses CCS reads where possible, and the pipeline incorporates the CCS pipeline with looser settings. The starting point is therefore still a SubreadSet. The simpler of the two pipelines is sa3_ds_isoseq_classify, which runs CCS and classifies the reads as full-length or not:

$ pbsmrtpipe pipeline-id pbsmrtpipe.pipelines.sa3_ds_isoseq_classify \
  -e eid_subread:/data/smrt/2372215/0007/Analysis_Results/m150404_101626_42267_c100807920800000001823174110291514_s1_p0.all.subreadset.xml \
  --preset-xml preset.xml -o job_output

The output files from the CCS pipeline will again be present (note however that the sequences will be lower-quality since the pipeline tries to use as much information as possible). The output task folder pbtranscript.tasks.classify-0 (or gathered equivalent; see below) contains the classified transcripts in various ContigSet datasets (or underlying FASTA files).

A more thorough analysis yielding Quiver-polished, high-quality isoforms is the pbsmrtpipe.pipelines.sa3_ds_isoseq pipeline, which is invoked identically to the classify-only pipeline. Note that this is significantly slower, as the clustering step may take days to run for large datasets.

Exporting Subreads to FASTA/FASTQ

If you would like to convert a PacBio SubreadSet to FASTA or FASTQ format for use with external software, this can be done as a standalone pipeline. Unlike most of the other pipelines, this one has no task-specific options and no chunking, so the invocation is always very simple:

$ pbsmrtpipe pipeline-id pbsmrtpipe.pipelines.sa3_ds_subreads_to_fastx \
  -e eid_subread:/data/smrt/2372215/0007/Analysis_Results/m150404_101626_42267_c100807920800000001823174110291514_s1_p0.all.subreadset.xml \
  -o job_output

The result files will be here:


Both are also available gzipped in the same directories.


To take advantage of pbsmrtpipe’s parallelization, we need an XML configuration file for global pbsmrtpipe options, which can be generated by the following command:

$ pbsmrtpipe show-workflow-options -o preset.xml

The output preset.xml will have this format:

<?xml version="1.0" encoding="utf-8" ?>
        <option id="pbsmrtpipe.options.max_nproc">
        <option id="pbsmrtpipe.options.chunk_mode">

The appropriate types should be clear; quotes are unnecessary, and boolean values should have initial capitals (True, False). To enable chunk mode, change the value of option pbsmrtpipe.options.chunk_mode to True. Several additional options may also need to be modified:

  • pbsmrtpipe.options.distributed_mode enables execution of most tasks on a managed cluster such as Sun Grid Engine. Use this for chunk mode if available.
  • pbsmrtpipe.options.max_nchunks sets the upper limit on the number of jobs per task in chunked mode. Note that more chunks is not always better, as there is some overhead to chunking (especially in distributed mode).
  • pbsmrtpipe.options.max_nproc sets the upper limit on the number of processors per job (including individual chunk jobs). This should be set to a value appropriate for your compute environment.

You can adjust max_nproc and max_nchunks`` in the preset.xml to consume as many queue slots as you desire, but note that the number of slots consumed will be the product of the two numbers. For some shorter jobs (typically with low-volume input data), it may make more sense to run the job unchunked but still distribute tasks to the cluster (where they will still use multiple cores if allowed).

Once you are satisfied with the settings, add it to your command like this:

$ pbsmrtpipe pipeline-id pbsmrtpipe.pipelines.sa3_ds_resequencing \
  --preset-xml preset.xml \
  -e eid_subread:/data/smrt/2372215/0007/Analysis_Results/m150404_101626_42267_c100807920800000001823174110291514_s1_p0.all.subreadset.xml \
  -e eid_ref_dataset:/data/references/lambda.referenceset.xml

Alternately, the flags --force-chunk-mode, --force-distributed, --disable-chunk-mode, and --local-only can be used to toggle the chunk/distributed mode settings on the command line (but this will not affect the values of max_nproc or max_nchunks).

If the pipeline runs correctly, you should see an expansion of task folders. The final results for certain steps (alignment, variantCaller, etc), should end up in the appropriate “gather” directory. For instance, the final gathered fasta file from quiver should be in pbsmrtpipe.tasks.gather_contigset-1. Note that for many dataset types, the gathered dataset XML file will often encapsulate multiple BAM files in multiple directories.

Modifying task-specific options

You can generate an appropriate initial preset.xml containing task-specific options relevant to a selected pipeline by running the show-template-details sub-command:

$ pbsmrtpipe show-template-details pbsmrtpipe.pipelines.sa3_ds_resequencing \
    -o preset_tasks.xml

The output XML file will be in a format similar to the global presets XML:

<?xml version="1.0" encoding="utf-8" ?>
        <option id="pbalign.task_options.min_accuracy">
        <option id="pbalign.task_options.algorithm_options">
            <value>-useQuality -minMatch 12 -bestn 10 -minPctIdentity 70.0</value>

You may specify multiple preset files on the command line:

$ pbsmrtpipe pipeline-id pbsmrtpipe.pipelines.sa3_ds_resequencing \
  --preset-xml preset.xml --preset-xml preset_tasks.xml \
  -e eid_subread:/path/to/subreadset.xml \
  -e eid_ref_dataset:/path/to/referenceset.xml

Alternately, the entire <task-options> block can also be copied-and-pasted into the equivalent level in the preset.xml that contains global options.

Appendix A: HdfSubreadSet to SubreadSet conversion

If you have existing bax.h5 files that you would like to process with pbsmrtpipe, you will need to convert them to a SubreadSet before continuing. Bare bax.h5 files aren’t directly compatible with pbsmrtpipe, but we can generate an HdfSubreadSet XML file from a fofn or folder of bax.h5 files using the python dataset xml api/cli very easily.

From a fofn, allTheBaxFiles.fofn:

$ dataset create --type HdfSubreadSet allTheBaxFiles.hdfsubreadset.xml allTheBaxFiles.fofn

Or a directory with all the bax files:

$ dataset create --type HdfSubreadSet allTheBaxFiles.hdfsubreadset.xml allTheBaxFiles/*.bax.h5

We can then use this as an entry point to the conversion pipeline (we recommend using chunked mode if there is more than one bax.h5 file, so include the appropriate preset.xml):

$ pbsmrtpipe pipeline-id pbsmrtpipe.pipelines.sa3_hdfsubread_to_subread \
  --preset-xml preset.xml -e eid_hdfsubread:allTheBaxFiles.hdfsubreadset.xml

And use the gathered output xml as an entry point to the resequencing pipeline from earlier:

$ pbsmrtpipe pipeline-id pbsmrtpipe.pipelines.sa3_ds_resequencing \
  --preset-xml preset.xml \
  -e eid_subread:tasks/pbsmrtpipe.tasks.gather_subreadset-0/gathered.xml \
  -e eid_ref_dataset:/data/references/lambda.referenceset.xml

Appendix B: Working with datasets

Datasets can also be created for one or more existing subreads.bam files or alignedsubreads.bam files for use with the pipeline:

$ dataset create --type SubreadSet allTheSubreads.subreadset.xml \


$ dataset create --type AlignmentSet allTheMappedSubreads.alignmentset.xml \

Make sure that all .bam files have corresponding .bai and .pbi index files before generating the dataset, as these make some operations significantly faster and are required by many programs. You can create indices with samtools and pbindex, both included in the distribution:

$ samtools index subreads.bam
$ pbindex subreads.bam

In addition to the BAM-based datasets and HdfSubreadSet, pbsmrtpipe also works with two dataset types based on FASTA format: ContigSet (used for both de-novo assemblies and other collections of contiguous sequences such as transcripts in the IsoSeq workflows) and ReferenceSet (a reference genome). These are created in the same way as BAM datasets:

$ dataset create --type ReferenceSet human_genome.referenceset.xml \

FASTA files can also be indexed for increased speed using samtools, and this is again recommended before creating the dataset:

$ samtools faidx chr1.fasta

Note that PacBio’s specifications for BAM and FASTA files impose additional restrictions on content and formatting; files produce by non-PacBio software are not guaranteed to work as input. The pbvalidate tool can be used to check for format compliance.