Home

Awesome

<h1 align="center"><img width="300px" src="pbfusion_logo.svg"/></h1> <h1 align="center">pbfusion</h1>

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.

Table of Contents

  1. Install
  2. Usage
  3. Output
  4. Examples
  5. Accessory scripts
  6. Documentation
  7. Change log
  8. Help
  9. Disclaimer

Install <a name="install"></a>

install with bioconda

pbfusion can be installed from bioconda

conda install -c bioconda pbfusion

Binaries are also availible in the github releases.

Usage <a name="usage"></a>

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:

  1. Aligned Iso-Seq HiFi data in a BAM file format. The data should be aligned with pbmm2 using the ISOSEQ preset and the --sort flag enabled. pbfusion accepts Iso-Seq reads or polished transcripts (the output of isoseq3 cluster).
  2. Reference gene annotations in GTF format. This GTF file must match the reference genome used for alignments.
  3. The output prefix. pbfusion writes 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.

Output <a name="output"></a>

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.

FileDescription
{prefix}.breakpoints.bedAll detected breakpoints, BED format, aux output
{prefix}.transcriptsAll transcripts with a breakpoint, plain text, aux output
{prefix}.breakpoints.groups.bedClustered breakpoint calls, BEDPE format, main output
{prefix}.unannotated.bedunannotated aligned segments, BED format, aux output
{prefix}.unannotated.clusters.bedclustered unannotated aligned segments, BED format, aux output

Clustered breakpoint calls file format

The clustered breakpoint call file is BEDPE file formatted including header lines.

Notes:

Filtering options

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.

Examples <a name="examples"></a>

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

Accessory scripts <a name="accessory"></a>

There are two scripts included in this repo for convenience: visualize_fusion.py and extract_tag.py.

Visualize fusion

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:

Extract tag

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

Documentation <a name="documentation"></a>

Fusion classification

Introduction

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.

Event Type <a name="type"></a>

Different mechanisms lead to differing signatures in transcriptional data.

We classify events as belonging to one of several categories.

  1. Readthrough
  2. Overlap
  3. SenseAntisense
  4. PotentialTransSplicing
  5. Unannotated
  6. Fusion

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.

Quality

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:

  1. Too many genes (> 3) [--max-genes-in-event]
  2. Too few reads supporting (< 2). [--min-coverage]
  3. Minimum identity on either side of the breakpoint is too low (< 93%) [--min-mean-identity]
  4. 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]
  5. 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 <a name="changelog"></a>

Changelog - PacBio Fusion Detection - pbfusion

v0.5.0 11/29/24

Changes

The main focus on this update is to reduce the number of false positive calls by adjusting default parameters, and introducing new filters.

v0.4.1 3/22/24

Changes

v0.4.0 11/28/23

Changes

v0.3.2 8/17/23

Changes

v0.3.1 8/17/23

Changes

v0.3.0 7/25/23

Changes

v0.2.3 7/11/23

Changes

v0.2.2 6/29/23

Changes

v0.2.1: 6/8/23

Changes

v0.2.0: 5/22/23

Changes

v0.1.2: 5/19/23

Changes

v0.1.1: 4/26/23

Changes

v0.1.0

Changes

Help <a name="help"></a>

Please direct support/help/bug questions to Github issue

Disclaimer <a name="disclaimer">

THIS WEBSITE AND CONTENT AND ALL SITE-RELATED SERVICES, INCLUDING ANY DATA, ARE PROVIDED "AS IS," WITH ALL FAULTS, WITH NO REPRESENTATIONS OR WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, ANY WARRANTIES OF MERCHANTABILITY, SATISFACTORY QUALITY, NON-INFRINGEMENT OR FITNESS FOR A PARTICULAR PURPOSE. YOU ASSUME TOTAL RESPONSIBILITY AND RISK FOR YOUR USE OF THIS SITE, ALL SITE-RELATED SERVICES, AND ANY THIRD PARTY WEBSITES OR APPLICATIONS. NO ORAL OR WRITTEN INFORMATION OR ADVICE SHALL CREATE A WARRANTY OF ANY KIND. ANY REFERENCES TO SPECIFIC PRODUCTS OR SERVICES ON THE WEBSITES DO NOT CONSTITUTE OR IMPLY A RECOMMENDATION OR ENDORSEMENT BY PACIFIC BIOSCIENCES.