Pipeline title/name
gap-flow
Keywords
proteogenomics, proteomics, genome annotation, mass spectrometry, novel protein-coding genes, splicing
What is it about?
A pipeline for validation and refinement of genome annotations using raw mass spectrometry-derived peptide evidence. It provides an end-to-end workflow that connects data generation, gene prediction, and downstream analysis by integrating FragPipe for peptide identification, Tiberius or Helixer for deep-learning-based ab initio gene prediction, VARUS for RNA-seq-guided annotation, and a proteogenomic pipeline, GAP-MS (Gene model Assessment using Peptides from Mass Spectrometry).
Please provide a schematic diagram of the proposed pipeline
What would a minimal first release of this pipeline include?
The minimal version would include all the steps visualized in the workflow diagram: proteogenomic analysis using FragPipe, gene prediction using Tiberius or Helixer, optional RNA-seq retrieval using VARUS, and gene model assessment using GAP-MS. It would also include the supporting tools required for data preparation, including: StringTie, gffread, Philosopher, and TransDecoder. All tools would be provided as pipeline containers, with an additional Singularity definition file to build the VARUS container image.
I confirm my proposed pipeline will follow nf-core guidelines. Most importantly, my pipeline will:
Why do we need a new pipeline?
Accurate genome annotation is fundamental to modern biology, yet distinguishing authentic protein-coding sequences from prediction artifacts remains challenging. Computational gene prediction methods can not guarantee a complete and fully accurate gene model.
The Gene model Assessment using Peptide from Mass Spectrometry (GAP-MS) is a recently developed tool that enables the evaluation of predicted gene models using protein-level evidence derived from mass spectrometry. However, GAP-MS requires several compatible input datasets generated through multiple independent software tools and manual preparation steps.
The pipeline provides the first reproducible Nextflow workflow that integrates gene prediction, proteomics analysis, optional RNA-seq evidence, and automated preparation of GAP-MS inputs into a single standardized pipeline, enabling validation and improvement of genome annotation.
Who would be interested?
Proteogenomics and genome annotation researchers. Researchers conducting resequencing studies in both well-annotated and newly assembled genomes.
What has been done so far
The GitHub repository contains a working implementation of the pipeline: github.com/qussai96/GAP-MS-nextflow.
The pipeline currently contains:
-
FragPipe tools setup: Some FragPipes tools (MSFrgagger, DiaTracer and IonQunat) are distributed under their own academic licenses and must be obtained from the official source. The user can either provide the paths to locally installed tools or, if the tools are not available locally, complete an authentication process through the terminal, after which the required tools are downloaded automatically from their official sources.
-
Gene prediction: The user can choose between Helixer, Tiberius, or provide their own GFF file. The resulting gene models are then processed and used in downstream analysis.
-
RNA-se retrieval: The user can optionally choose VARUS to retrieves RNA-seq data for the analysis.
-
Proteogenomic analysis: The FASTA database used by FragPipe is generated by combining protein sequences derived from the selected GFF file, an optional user-provided FASTA file, and, when VARUS is used, the VARUS output converted to FASTA. The resulting database is prepared and subsequently provided as input to GAP-MS.
The pipeline will still need to be adapted to meet nf-core requirements and will likely be restructured using the nf-core template.
The development of the pipeline is currently supervised by Qussai Abbas, the creator of GAP-MS: https://github.com/qussai96/GAP-MS
URL to existing work (if applicable)
https://github.com/qussai96/GAP-MS-nextflow
Are there any similar existing nf-core pipelines?
None that I am aware of
Pipeline title/name
gap-flow
Keywords
proteogenomics, proteomics, genome annotation, mass spectrometry, novel protein-coding genes, splicing
What is it about?
A pipeline for validation and refinement of genome annotations using raw mass spectrometry-derived peptide evidence. It provides an end-to-end workflow that connects data generation, gene prediction, and downstream analysis by integrating FragPipe for peptide identification, Tiberius or Helixer for deep-learning-based ab initio gene prediction, VARUS for RNA-seq-guided annotation, and a proteogenomic pipeline, GAP-MS (Gene model Assessment using Peptides from Mass Spectrometry).
Please provide a schematic diagram of the proposed pipeline
What would a minimal first release of this pipeline include?
The minimal version would include all the steps visualized in the workflow diagram: proteogenomic analysis using FragPipe, gene prediction using Tiberius or Helixer, optional RNA-seq retrieval using VARUS, and gene model assessment using GAP-MS. It would also include the supporting tools required for data preparation, including: StringTie, gffread, Philosopher, and TransDecoder. All tools would be provided as pipeline containers, with an additional Singularity definition file to build the VARUS container image.
I confirm my proposed pipeline will follow nf-core guidelines. Most importantly, my pipeline will:
Why do we need a new pipeline?
Accurate genome annotation is fundamental to modern biology, yet distinguishing authentic protein-coding sequences from prediction artifacts remains challenging. Computational gene prediction methods can not guarantee a complete and fully accurate gene model.
The Gene model Assessment using Peptide from Mass Spectrometry (GAP-MS) is a recently developed tool that enables the evaluation of predicted gene models using protein-level evidence derived from mass spectrometry. However, GAP-MS requires several compatible input datasets generated through multiple independent software tools and manual preparation steps.
The pipeline provides the first reproducible Nextflow workflow that integrates gene prediction, proteomics analysis, optional RNA-seq evidence, and automated preparation of GAP-MS inputs into a single standardized pipeline, enabling validation and improvement of genome annotation.
Who would be interested?
Proteogenomics and genome annotation researchers. Researchers conducting resequencing studies in both well-annotated and newly assembled genomes.
What has been done so far
The GitHub repository contains a working implementation of the pipeline: github.com/qussai96/GAP-MS-nextflow.
The pipeline currently contains:
FragPipe tools setup: Some FragPipes tools (MSFrgagger, DiaTracer and IonQunat) are distributed under their own academic licenses and must be obtained from the official source. The user can either provide the paths to locally installed tools or, if the tools are not available locally, complete an authentication process through the terminal, after which the required tools are downloaded automatically from their official sources.
Gene prediction: The user can choose between Helixer, Tiberius, or provide their own GFF file. The resulting gene models are then processed and used in downstream analysis.
RNA-se retrieval: The user can optionally choose VARUS to retrieves RNA-seq data for the analysis.
Proteogenomic analysis: The FASTA database used by FragPipe is generated by combining protein sequences derived from the selected GFF file, an optional user-provided FASTA file, and, when VARUS is used, the VARUS output converted to FASTA. The resulting database is prepared and subsequently provided as input to GAP-MS.
The pipeline will still need to be adapted to meet nf-core requirements and will likely be restructured using the nf-core template.
The development of the pipeline is currently supervised by Qussai Abbas, the creator of GAP-MS: https://github.com/qussai96/GAP-MS
URL to existing work (if applicable)
https://github.com/qussai96/GAP-MS-nextflow
Are there any similar existing nf-core pipelines?
None that I am aware of