Fusion gene caller for Iso-Seq sequencing data.
Authors: Roger Volden, Zev Kronenberg, Daniel Baker, Khi Pin Chua
Please refer to our official pbbioconda page for information on Installation, Support, License, Copyright, and Disclaimer.
pbfusion can be installed from bioconda
conda install -c bioconda pbfusion
Binaries are also availible in the github releases.
pbfusion has two subcommands: pbfusion discover and pbfusion gff-cache.
pbfusion gff-cache is not required, but recommended when running pbfusion multiple times. pbfusion gff-cache will serialize the input gtf/gff file and preprocess into exonic intervals ahead of time, which is fairly slow to do on the fly.
You can find GENCODE annotation files here.
Usage: pbfusion gff-cache [OPTIONS] --gtf <ReferenceAnnotation>
Options:
-g, --gtf <ReferenceAnnotation> Input GTF file
-b, --gtf-out <BinaryReferenceAnnotation> Output binary GTF file [default: *]
-v, --verbose... Enable verbose output
-h, --help Print help information
-V, --version Print version information
pbfusion requires two input files and one option:
- Aligned Iso-Seq HiFi data in a BAM file format. The data should be aligned with
pbmm2using theISOSEQpreset and the--sortflag enabled.pbfusionaccepts Iso-Seq reads or polished transcripts (the output ofisoseq3 cluster). - Reference gene annotations in GTF format. This GTF file must match the reference genome used for alignments.
- The output prefix.
pbfusionwrites multiple files, prefixed with the user specified string.
Identify fusion genes in aligned PacBio Iso-Seq data
Usage: pbfusion discover [OPTIONS] --gtf <REF> --output-prefix <OUTPUT> [FILE]...
Arguments:
[FILE]...
Options:
-b <ADDITIONAL_BAMS>
Aligned Iso-Seq data in BAM format. Accepts a path to a bam, a url (if compiled with curl support), or a fofn (file-of-filenames) file with one filename or url per line
-g, --gtf <REF>
Reference gene annotations in GTF format. We also accept `gtf.bin` files as built by `pbfusion gff-cache`. This file must have `bin` as its suffix to be recognized. We also support gtf.bin.xz and gtf.bin.gz, compressed by xz and gzip, respectively. Recognition is based entirely on filename. Warning: the binary cached format has been altered since 0.3.3. You may need to re-generate your binary annotations.
-o, --output-prefix <OUTPUT>
Output prefix [default: none]
-Q, --min-fusion-quality <MIN_FUSION_QUALITY>
Determine the minimum fusion quality to emit. Choices: must be LOW or MEDIUM [default: MEDIUM]
-t, --threads <THREADS>
Number of threads. Defaults to available parallelism [default: 0]
-c, --min-coverage <MIN_COVERAGE>
Real-cell filtering for single-cell data. Iso-Seq reads annotated with zero "rc" tag value will be filtered. Assigns "low confidence" to fusion calls with read coverage below the minimum coverage threshold [default: 2]
-i, --min-mean-identity <MIN_MEAN_IDENTITY>
Assigns "low confidence" to fusion calls where the mean alignment identity is below the threshold [default: 0.93]
-p, --min-mean-mapq <MIN_MEAN_MAPQ>
Assigns "low confidence" to fusion calls where the mean mapq is below the threshold [default: 10]
-M, --min-fusion-read-fraction <MIN_FUSION_READ_FRACTION>
Remove breakpoint pairs from groups if they have gene alignments which fewer than \[arg\] reads in group have [default: 0.25]
-s, --max-variability <MAX_VARIABILITY>
Assigns "low confidence" to fusion calls with the mean breakpoint distance is above the threshold [default: 1000]
-a, --max-readthrough <MAX_READTHROUGH>
Assigns "low confidence" to fusion calls spanning two genes below the readthrough threshold. [default: 100000]
-m, --max-genes-in-event <MAX_GENES_IN_EVENT>
Mark fusion groups involving > \[arg\] genes as low quality. This is a common source of false positives [default: 3]
-r, --real-cell-filtering
--allow-immune
Permit fusion events identified involving primarily immunological genes and their pseudogenes. These are a common source of false positives and we mark them low-quality by default.
--allow-mito
Permit fusion events identified involving mitochondrial genes. These are a common source of false positives and we mark them low-quality by default.
--prom-filter <PROM_FILTER>
Filter rarer events involving genes with high numbers of fusion partners. These are a common source of false positives. Disable by setting `--prom-filter 0`. [default: 8]
-v, --verbose...
Enable verbose output
--log-level <LOG_LEVEL>
Alternative to repeated -v/--verbose: set log level via key.
Values: "error", "warn" (default), "info", "debug", "trace".
Enabling any level higher than "warn" also emits verbose output, including extra output files.
If -v/--verbose is set, this option is ignored.
Equivalence to -v/--verbose:
=> "WARN"
-v => "INFO"
-vv => "DEBUG"
-vvv => "TRACE" [default: error]
-h, --help
Print help information (use `-h` for a summary)
-V, --version
Print version information
Copyright (C) 2004-2023 Pacific Biosciences of California, Inc.
This program comes with ABSOLUTELY NO WARRANTY; it is intended for
Research Use Only and not for use in diagnostic procedures.
pbfusion discover produces one output file designed for end users: {prefix}.breakpoints.groups.bed. All other files are auxiliary and usually used for diagnostic purposes.
If verbose output is enabled (-v), five output files sharing the same prefix are emitted.
| File | Description |
|---|---|
| {prefix}.breakpoints.bed | All detected breakpoints, BED format, aux output |
| {prefix}.transcripts | All transcripts with a breakpoint, plain text, aux output |
| {prefix}.breakpoints.groups.bed | Clustered breakpoint calls, BEDPE format, main output |
| {prefix}.unannotated.bed | unannotated aligned segments, BED format, aux output |
| {prefix}.unannotated.clusters.bed | clustered unannotated aligned segments, BED format, aux output |
The clustered breakpoint call file is BEDPE file formatted including header lines.
Notes:
- The cluster score is set to "MEDIUM" unless the group is considered low-quality, in which case it is "LOW"
- Scores may be set to "LOW" depending on:
- Low coverage
- Low alignment identity
- High breakpoint variability
- Majority immunoglobulin genes
- Mitochondrial genes
- Having many gene partners across the dataset
- Scores may be set to "LOW" depending on:
- You can
grepthe output for specific gene names or gene IDs if you're fishing for a specific fusion gene pair
Filtering is now primarily done through adjusting the allowed score [default "MEDIUM"].
The primary filtering options to reduce false positives are --min-coverage (-c), --max-readthrough (-a), --min-mean-identity (-i), --min-mean-mapq (-p), --min-fusion-read-fraction (-M), and --max-variability (-s).
--min-coverage just filters out breakpoints based on their read support, where the default value is 2 to filter out singletons.
--max-readthrough is used to discard reads that align to two genes next to each other in the genome [default 100kb].
--min-mean-identity will assign low confidence to fusions with mean mapping identity lower than the threshold [0.85]. Sometimes in hard to map regions of the genome, the aligner will force an alignment through a region and incur a high edit distance.
--min-mean-mapq default is set to 10 to reduce mapping errors.
--min-fusion-read-fraction is used to filter long chains of genes.
An example of this would be an IG alignment, where maybe 100 reads align to various annotated regions (eg. IGHA1, IGHV3-23, IGHV3-7, IGHG3, IGHJ5, IGHJ4, IGHJ3, IGHGP, IGHJ2, IGHJ6).
Given a read coverage of these genes like [100, 100, 90, 90, 20, 10, 10, 5, 5, 5], we would by default filter genes with coverage lower than 25% of the total read count for this fusion.
With that filter, you're left with this coverage: [100, 100, 90, 90], which with the read filtering brings it down to [80, 80, 70, 70].
Because this fusion still has >3 genes in it, it would get filtered out.
--max-variability allows you to filter based on breakpoint variability [default 1000].
--min-fusion-fraction default is set to 0.01 This is XC/TC, and represents the fusion fraction relative to the transcript counts of both donor genes.
--gtf-transcript-allow-lncRNA allows lncRNAs to be considered.
As of v0.4.0, the default behavior is to mark entries with simple majority of immune genes as LOW.
We use the GENCODE gene_type field to classify annotations as immune.
This filter can be disabled by setting the --allow-immune option.
Additionally, we mark entries with mitochondrial genes as LOW, which can be disabled with the --allow-mito option.
Lastly, we have implemented a promiscuity filter to help decrease false positives.
This filter works by taking all of the MEDIUM fusion entries and tracking how many different gene partners each gene has.
If Gene_A has entries with genes B, C, ..., K, then Gene_A will be subject to the promiscuity filter [default is 8 gene partners].
For genes with many partners, we calculate the expected coverage for these entries as sum(read_coverage) // n_partners.
Entries that do not pass this expected coverage will get marked as LOW.
Serializing the input gtf file:
pbfusion gff-cache \
--gtf gencode.v38.annotation.gtf \
--gtf-out gencode.v38.annotation.gtf.bin
Running pbfusion on aligned reads:
pbfusion discover \
--reference-gtf gencode.v38.annotation.gtf.bin \
--output-prefix isoseq \
--threads 8 \
isoseq.aligned.bam
You may find substantial space savings by compressing your annotation bin file. pbfusion will accept the smaller file. Being substantially smaller (5M vs 140M), this makes artefact management much easier.
> xz -c -9 --extreme gencode.v38.annotation.gtf.bin > gencode.v38.annotation.gtf.bin.xz
> ls -oh gencode.v38.annotation.gtf.bin* | awk '{print $4, $NF}'
140M gencode.v38.annotation.gtf.bin
5.6M gencode.v38.annotation.gtf.bin.xz
There are two scripts included in this repo for convenience: visualize_fusion.py and extract_tag.py.
visualize_fusion.py will produce a genome browser plot when given an annotation, a single-line BEDPE file, and a mapped BAM.
Optionally, you can specify a path to a pickle file for easier rerunning when refining a figure.
Usage:
python3 visualize_fusion.py \
-o fusion_browser_shot.png \
-a gencode.v38.annotation.gtf \
-f isoseq.breakpoints.groups.bed \
-b isoseq.mapped.bam
Visualization dependencies:
- matplotlib
- pysam
This script is deprecated now that cell barcodes are emitted automatically
The main use case for this script is to output associated cell barcodes for fusion gene reads.
extract_tag.py will take a BAM file, BAM tag, and a list of read names (can be taken from the output BEDPE and edited for one readname per line).
The output is tab delimited with the read name and its associated cell barcode.
This can be used to extract any BAM tag, but it will look for the CB tag by default.
Usage:
python3 extract_tag.py \
-b isoseq.aligned.bam \
-t CB \
-i readnames.txt \
>cell_associations.tsv
Eukaryotic RNA processing complexity introduces a number of modes for transcriptional abnormalities which are not true fusion events. These include trans splicing, read-through events, and sense-antisense chimeras. Additionally, due to overlapping genes/exons, annotating the precise gene combinations correctly cannot always be solved.
To handle this, we classify fusions by quality (LOW, MEDIUM, and HIGH) as well as by event type.
Different mechanisms lead to differing signatures in transcriptional data.
We classify events as belonging to one of several categories.
ReadthroughOverlapSenseAntisensePotentialTransSplicingUnannotatedFusion
Readthrough events result from the polymerase beginning transcription in one region and continuing into a successive gene.
Some are functional, but many are just noise. By default, these events are marked as LOW quality and annotated with CL=Readthrough.
We use <100kb apart on the same chromosome, in the same orientation, where the first gene is upstream of the second gene.
100kb can be tuned by the --max-readthrough CLI flag.
Downgrading to LOW quality can be disabled by --emit-readthrough.
Overlap is assigned when a candidate event is discovered between two genes which overlap on the same chromosome, strand, and region.
This is a common source of false positives, and we annotate them as CL=Overlap.
Downgrading to LOW quality can be disabled by --emit-overlapping.
SenseAntisense is assigned when a read aligns to both strands in the same region. This may have false positives in palindromic sequences, but is functional in some cases. The kallikreins are well-known examples of this which have relevance in cancer.
These events are marked SenseAntisense, but are not marked low quality as they may have biological meaning.
These can be downgraded to LOW quality with --disable-sense-antisense.
Unannotated events involve segments aligned to unannotated regions of the genome, possibly novel exons. These are marked low quality by default.
Downgrading to LOW quality can be disabled by --emit-novel-exons.
Some genes have large numbers of candidate fusion partners. One possible explanation is trans splicing.
We mark events with many (>8) genes as PotentialTransSplicing, and then downgrade to LOW quality if the partner gene coverages are not expected.
Fusion is a category of exclusion; events which are not classified in any of the others are marked as Fusions.
We classify events as LOW, MEDIUM, or HIGH quality.
The default assignment is MEDIUM. Events as described in types above may downgrade a candidate fusion to LOW.
There is additional filtering as well.
We filter them by default, but --min-fusion-quality LOW causes all events to be emitted. This can be important for some fusions.
These tests are:
- Too many genes (
> 3) [--max-genes-in-event] - Too few reads supporting (
< 2). [--min-coverage] - Minimum identity on either side of the breakpoint is too low (
< 93%) [--min-mean-identity] - Breakpoint median distance is too high - this means the breakpoint isn't well-defined, or there are multiple events with nearby breakpoints being grouped together. (
> 1000) [--max-variability] - Minimum mapq on either side (disabled by default with 0). [
--min-min-mapq]
These parameters can be changed by CLI interface.
A candidate is marked as MEDIUM if it is not in any of the failing cases.
Changelog - PacBio Fusion Detection - pbfusion
The main focus on this update is to reduce the number of false positive calls by adjusting default parameters, and introducing new filters.
- Feature: Addition of minimum fusion fraction filter (--min-fusion-fraction). Increased specificity at a small cost to sensitivity.
- Feature: Addition of lncRNA filtering (--gtf-transcript-allow-lncRNA). Increased specificity at a small cost to sensitivity.
- Feature: Increasing min map quality to 10
- Feature: Increasing min identity to 93%
- Feature: Adding transcript count for fusion support (XC), and total count (TC) to output.
- Bug fix: Fix fofn hang
- Bug fix: Ensure unique fusion IDs
- Bug fix: consistent ordering between gene ids and gene names when dealing with ambiguity.
- Bug fix: rely on breakpoint ordering in visualization script.
- Improvement: gene names are assigned automatically in visualization script.
- Improvement: visualization script can now take SAM files as input.
- Improvement: visualization script can now take a breakpoint number (these aren't always unique, so it's best to still give a single entry).
- Improvement: Improved ambiguous gene resolution.
- Improvement: read lzma-compressed annotations in text and binary format.
- Improvement: Support multiple bam/fofn (File-Of-FileNames) inputs.
- Improvement: reduced false-positive fusions.
- Improvement: Additional metadata for putative fusion candidates.
- Improvement: Updated cached binary format. WARNING: this is a breaking change. Old binary annotation files will need to be re-generated.
- Improvement: Maintain read ordering in reporting fusions.
- Bug fix: Update reported breakpoints so that the interval described is the last-in-read and the second is first-soft-clipped.
- Improvement: Add gene names to annotation in order they occur in reads.
- Improvement: Report all breakpoint coordinates for reads in a breakpoint cluster, add as a new tag.
- Improvement: Update read-through annotation such that we define it based on the breakpoint distance instead of the distance between genes.
- Alteration: reduced chaining distance for clustering breakpoints.
- Alteration: emit sense-antisense events by default.
- Bug fix: resolving query position sorting.
- Bug fix: resolving strand-related breakpoint errors.
- Bug fix: GTF parsing of overlapping annotations.
- Bug fix: annoting gtf records without parent gene entries.
- Add sample name, sequencing platform, and read group information to output BED file header.
- Command-line interface, filtering, and formatting changes.
- Option to filter by average mapping quality on either side of a breakpoint.
- Option to filter reads from groups based on gene coverage (as a %-age of total reads for that group).
- Filters out entries where the number of genes is 1 or >3.
- We now mark events by quality, and then filter by quality levels instead of directly filtering events.
These events can be emitted with
--min-fusion-quality LOWinstead of the defaultMEDIUM. - Simplified output. Fewer categories, simpler identifiers, and a new header format that is easier to parse.
- Number of reads filter now checks the
imbam tag for clustered input. Now, the number of original reads is used for filtering instead of the number of clusters.
- Add options to filter segments by length and error rate.
- Join pbfusion and pbgffcache into one executable
pbfusionwith subcommandspbfusion discoverandpbfusion gff-cachecorresponding to the previous.
- Add edit distance per segment as a feature of Interval for filtering.
- Add cell barcodes if found, and original read names if reads are clustered to output file. The first tag set in
CB,XC, andCRis used. - Add
--max-mean-breakpoint-distanceflag, which marks breakpoint groups as low quality if the mean distance of breakpoints in a cluster exceeds this threshold.
- Initial release
Please direct support/help/bug questions to Github issue
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