Introduction

nf-core/scalpel is a bioinformatics pipeline for transcript isoform quantification and alternative polyadenylation (APA) characterization from 3'-tagged single-cell RNA-seq data. It takes paired-end FASTQ files together with pre-computed Cell Ranger outputs, a genome FASTA, a transcriptome FASTA, a GTF annotation, and an internal priming reference track. The pipeline selects representative transcript isoforms, maps reads to them, filters internal priming artifacts, and produces single-cell isoform-resolved digital gene expression (DGE) matrices.

The pipeline is built using Nextflow DSL2 and consists of three main subworkflows:

1. Annotation Processing — Builds a Salmon index, quantifies transcript abundance across samples, averages TPM values, and selects/collapses representative isoforms based on 3'-end profiles.
2. Reads Processing — Converts Cell Ranger BAM files to BED format, maps reads to annotated transcripts, and filters internal priming events using a reference track.
3. Isoform Quantification — Computes read-to-isoform probability distributions, runs an Expectation-Maximization (EM) algorithm per cell, and generates final DGE count matrices.

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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.

Samplesheet input

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

samplesheet.csv:

sample,fastq_1,fastq_2,cranger_path
SRR6129050,/path/to/SRR6129050_S1_L001_R1_001.fastq.gz,/path/to/SRR6129050_S1_L001_R2_001.fastq.gz,/path/to/cellranger/SRR6129050
SRR6129051,/path/to/SRR6129051_S1_L001_R1_001.fastq.gz,/path/to/SRR6129051_S1_L001_R2_001.fastq.gz,/path/to/cellranger/SRR6129051

Column	Description
sample	Unique sample identifier (no spaces).
fastq_1	Path to R1 FASTQ file (.fastq.gz).
fastq_2	Path to R2 FASTQ file (.fastq.gz).
cranger_path	Path to the Cell Ranger output directory for the corresponding sample.

Running the pipeline

nextflow run nf-core/scalpel \
   -profile <docker/singularity/.../institute> \
   --samplesheet samplesheet.csv \
   --genome /path/to/genome.fa \
   --transcriptome /path/to/transcriptome.fa \
   --gtf /path/to/annotation.gtf \
   --ip_reference /path/to/ip_reference.track.gz \
   --outdir <OUTDIR>

Optional parameters

Parameter	Default	Description
--sequencing_platform	chromium	Sequencing platform (chromium or dropseq).
--barcodes_whitelist	null	CSV file mapping samples to custom barcode whitelist files.
--distance_profile	600	Distance threshold for 3'-end profile-based isoform collapsing.
--distance_3end	50	Distance threshold for 3'-end merging.
--distance_ip	60	Distance threshold for internal priming filtering.
--gene_fraction	98%	Gene fraction threshold for isoform quantification.
--binsize	20	Bin size for probability distribution computation.
--libtype	A	Salmon library type (automatic detection by default).

Reference files

SCALPEL requires an internal priming (IP) annotation file. Pre-built references are available:

• Mouse IP annotation (mm10)
• Human IP annotation (GRCh38)
• GENCODE annotations — for genome FASTA, transcriptome FASTA, and GTF files

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.

For more details and further functionality, please refer to the usage documentation and the parameter documentation.

Pipeline output

The pipeline produces the following output directories:

Directory	Description
salmon/	Salmon quantification results (transcript-level TPM).
transcript/	Selected and collapsed transcript annotations.
bed/	BED files from BAM-to-BED conversion.
reads/	Transcript-mapped and filtered reads.
ip/	Internal priming filtering results.
probability/	Read-to-isoform probability distributions.
fragment/	Fragment-level probability assignments.
cell/	Per-cell grouped fragment probabilities.
em/	EM algorithm output (isoform quantification per cell).
dge/	Digital gene expression count matrices per sample.
isoform/	Isoform selection summary statistics.
pipeline_info/	Execution reports, timelines, and software versions.

For more details about the output files and reports, please refer to the output documentation.

Downstream analysis

For tutorials on working with SCALPEL output:

• 10X Chromium scRNA-seq example
• Drop-seq scRNA-seq example
• 10X Chromium downstream analysis
• Drop-seq downstream analysis

Credits

nf-core/scalpel was originally written by Franz AKE.

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

• Marcel Schilling
• Sandra M. Fernández-Moya
• Akshay Jaya Ganesh
• Ana Gutiérrez-Franco
• Lei Li
• Mireya Plass

Contributions and Support

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

For further information or help, don't hesitate to get in touch on the Slack #scalpel channel (you can join with this invite).

Citations

If you use nf-core/scalpel for your analysis, please cite:

Franz Ake, Marcel Schilling, Sandra M. Fernández-Moya, Akshay Jaya Ganesh, Ana Gutiérrez-Franco, Lei Li, Mireya Plass. Quantification of transcript isoforms at the single-cell level using SCALPEL. Nat Commun 16, 6402 (2025). doi: 10.1038/s41467-025-61118-0

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