kentsislab/proteomegenerator3

Introduction

kentsislab/proteomegenerator3 is a bioinformatics pipeline that can be used to create sample-specific, proteogenomics search databases from long-read RNAseq data. It takes in a samplesheet and aligned long-read RNAseq data as input, performs guided, de novo transcript assembly, ORF prediction, and then produces a protein fasta file suitable for use with computational proteomics search platforms (e.g, Fragpipe, DIA-NN).

1. Pre-processing of aligned reads to create transcript read classes with bambu which can be re-used in future analyses. Optional filtering:
   1. Filtering on MAPQ and read length with samtools
2. Transcript assembly, quantification, and filtering with bambu. Option to merge multiple samples into a unified transcriptome.
3. ORF prediction with Transdecoder. Option to provide fusion contigs from JAFFAL.
4. Formatting of ORFs into a fasta file which can be used for computational proteomics searchs with Fragpipe, DIA-NN, Spectronaut.
5. MultiQC to collate package versions used (MultiQC)

Usage

[!NOTE] If you are new to Nextflow and nf-core, please refer to this page on how to set-up Nextflow. Make sure to test your setup with -profile test before running the workflow on actual data. When using the profile, it will run on a minimal test dataset that can be run in 5-10 minutes on most modern laptops.

First, prepare a samplesheet with your input data that looks as follows:

samplesheet.csv:

csv sample,bam,bai,rcFile,jaffal_fasta,jaffal_table CONTROL_REP1,AEG588A1_S1_L002_R1_001.bam,AEG588A1_S1_L002_R1_001.bam.bai,,jaffal_results.fasta,jaffal_results.csv

Each row represents a long-read RNAseq sample. The columns are as follows:

1. sample: name of the sample
2. bam: aligned, sorted long-read RNAseq bam
3. bai: index file for bam
4. rcFile: read class file from Bambu if you've already done some pre-processing; you can provide this and then use the --skip_preprocessing flag to speed up run time and re-analyze previous samples
5. jaffal_fasta: Fusion contigs which are output from JAFFAL (see description here).
6. jaffal_table: Fusion table which is output from JAFFAL (see description here)

To produce the necessary files, we recommend using the nf-core/nanoseq pipeline, which will run both alignment and call fusions with JAFFAL.

Now, you can run the pipeline using:

bash nextflow run kentsislab/proteomegenerator3 -r 1.0.0dev \ -profile <docker/singularity/.../institute> \ --input samplesheet.csv \ --fasta <REF_GENOME> \ --gtf <REF_GTF> \ --outdir <OUTDIR>

Where REF_GENOME and REF_GTF are the reference genome and transcriptome respectively. These can be from GENCODE or Ensembl, but should match the reference used to align the data.

[!WARNING] Please provide pipeline parameters via the CLI or Nextflow -params-file option. Custom config files including those provided by the -c Nextflow option can be used to provide any configuration except for parameters; see docs.

Additional parameters

To see all optional parameters that could be used with the pipeline and their explanations, use the help menu:

bash nextflow run kentsislab/proteomegenerator3 -r 1.0.0dev --help

This options can be run using flags. For example:

bash nextflow run kentsislab/proteomegenerator3 -r 1.0.0dev \ -profile <docker/singularity/.../institute> \ --input samplesheet.csv \ --fasta <REF_GENOME> \ --gtf <REF_GTF> \ --outdir <OUTDIR> \ --filter_reads

Will pre-filter the bam file before transcript assembly is performed on mapq and read length.

As another example, you can use the following flag to perform ORF calling on fusion contigs:

bash nextflow run kentsislab/proteomegenerator3 -r 1.0.0dev \ -profile <docker/singularity/.../institute> \ --input samplesheet.csv \ --fasta <REF_GENOME> \ --gtf <REF_GTF> \ --outdir <OUTDIR> \ --fusions

To run with the latest version, which may not be stable you can use the -r main -latest flags:

bash nextflow run kentsislab/proteomegenerator3 -r main -latest --help

I have highlighted the following options here:

1. filter_reads: use this flag to pre-filter reads using mapq and read length
2. mapq: min mapq for read filtering [default: 20]
3. read_len: min read length for read filtering [default: 500]
4. filter_acc_reads: filter reads on accessory chromosomes; sometimes causes issues for bambu
5. skip_preprocessing: use previously generated bambu read classes
6. NDR: modulate bambu's novel discovery rate [default: 0.1]
7. recommended_NDR: run bambu with recommended NDR (as determined by bambu's algorithm)
8. single_sample: Run bambu on samples individually, and skip merging of transcriptomes; if you provide a single sample or fusions, this will be automatically run.
9. skip_multisample: skip multisample transcript assembly (see #8).
10. fusions: Perform ORF predictions on fusions from JAFFAL [default: false]
11. multiple_orfs: Allow for multiple ORFs per transcript (this is in beta-testing)

Credits

kentsislab/proteomegenerator3 was originally written by Asher Preska Steinberg.

We thank the following people for their extensive assistance in the development of this pipeline:

Contributions and Support

If you would like to contribute to this pipeline, please see the contributing guidelines.

Citations

If you use kentsislab/proteomegenerator3 for your analysis, please cite our manuscript:

End-to-end proteogenomics for discovery of cryptic and non-canonical cancer proteoforms using long-read transcriptomics and multi-dimensional proteomics

Katarzyna Kulej, Asher Preska Steinberg, Jinxin Zhang, Gabriella Casalena, Eli Havasov, Sohrab P. Shah, Andrew McPherson, Alex Kentsis.

BioRXiv. 2025 Aug 28. doi: 10.1101/2025.08.23.671943.

An extensive list of references for the tools used by the pipeline can be found in the CITATIONS.md file.

This pipeline uses code and infrastructure developed and maintained by the nf-core community, reused here under the MIT license.

The nf-core framework for community-curated bioinformatics pipelines.

Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.

Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.