scraps

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scraps extracts mRNA polyadenylation sites from “TVN”-primed single-cell RNA-seq libraries at near-nucleotide resolution.

scraps (Single Cell RNA PolyA Site Discovery) is currently implemented as a Snakemake pipeline for 10X Genomics 3’ end v2/3 libraries (and other platforms with similar library structure, including Drop-seq, Microwell-seq, and BD Rhapsody). If long Read1 is available (estimated ~6% of SRA-deposited data, or now planning new experiments), positional information will be calculated from paired realignment; otherwise, the less optimal anchored Read2 approach is used. scraps will eventually be expanded for analyzing a range of RNA processing changes in single-cell RNA-seq data.

For additional discussions and usage cases, please see bioRxiv preprint.

Example usage

scraps requires the following as input (defined in config.yaml):

Quick Start

  1. Set up conda environment: 
    conda env create -f scraps_conda.yml
conda activate scraps_conda

  2. Configure your samples in config.yaml under the SAMPLES section

  3. Run the pipeline: 
    snakemake --configfile config.yaml --resources total_impact=5 --keep-going

Detailed Usage

To run test data, simply execute:

snakemake --snakefile Snakefile \
  --configfile config.yaml \
  --resources total_impact=5 \
  --keep-going

DAG steps illustration

submit jobs in cluster mode

Notes: total_impact is set to 5 for each sample, change this to control how many samples are processed in parallel

Configuration

scraps uses two main configuration files for flexible pipeline setup:

config.yaml

Main pipeline configuration file containing:

Sample Configuration Example:

SAMPLES:
  sample_name:
    basename: sample-           # FASTQ file prefix
    platform: illumina          # Sequencing platform
    chemistry: chromiumV3       # Platform chemistry
    alignments:                 # Optional: override default alignments
      - R2
      - paired

chemistry.yaml

Platform and chemistry-specific parameters organized hierarchically. Each chemistry type (chromiumV3, chromiumV2, dropseq, microwellseq, bd, indrop) contains:

Configuration Hierarchy

The pipeline uses hierarchical configuration lookup to determine parameters for each sample:

┌─────────────────────────────────────────────────────────┐
│  1. Sample-specific settings (config.yaml SAMPLES)     │
│     Highest priority - overrides everything             │
└────────────────────┬────────────────────────────────────┘
                     │ If not found ↓
┌─────────────────────────────────────────────────────────┐
│  2. Chemistry + Platform (chemistry.yaml)               │
│     e.g., chromiumV3 → illumina → STAR_R1               │
└────────────────────┬────────────────────────────────────┘
                     │ If not found ↓
┌─────────────────────────────────────────────────────────┐
│  3. Chemistry defaults (chemistry.yaml)                 │
│     e.g., chromiumV3 → bc_whitelist                     │
└────────────────────┬────────────────────────────────────┘
                     │ If not found ↓
┌─────────────────────────────────────────────────────────┐
│  4. Global defaults (config.yaml DEFAULTS)              │
│     Lowest priority - fallback values                   │
└─────────────────────────────────────────────────────────┘

This allows platform-specific customization (e.g., Illumina vs Ultima Genomics) while maintaining chemistry-specific defaults.

Supported scRNA-seq platforms

| Platform | Library (BC+UMI+A) | Setting | Test data | | :——–|:————| :————| :———| | 10x Chromium V3 | 16 + 12 + 30 | chromiumV3 | ✓ | | 10x V3 - Ultima Genomics | adapter + 16 + 9 + 3 ignored + 8 | chromiumV3UG | | | 10x Chromium V2 | 16 + 10 + 30 | chromiumV2 | ✓ | | 10x Chromium Visium | 16 + 10 + 30 | visium | | | Drop-seq | 12 + 8 + 30 | dropseq | ✓ | | Microwell-seq | 6x3 + 6 + 30 | microwellseq | ✓ | | BD Rhapsody | 9x3 + 8 + 18 | bd | | | inDrop | 8 + 6 + 18 | indrop | |

Custom chemistry supported, by editing chemistry.yaml. Also see synthetic FASTQ tool.

Output

  1. bedgraph : TVN-priming site pileup 
    chr11   215106  215107  1
chr11   689216  689217  1
chr11   812862  812863  1
chr11   812870  812871  2
chr11   812871  812872  2
  2. count table : +-10 around PolyA_DB sites, by cell barcode 
    gene    cell    count
AC135178.2_NA_ENSG00000263809_chr17_8377523_-_Intron,RPL26_6154_ENSG00000161970_chr17_8377523_-_3'UTR(M)        AACTCCCGTTCCTCCA        1
AC135178.2_NA_ENSG00000263809_chr17_8377523_-_Intron,RPL26_6154_ENSG00000161970_chr17_8377523_-_3'UTR(M)        CCCATACGTTAAAGAC        1
AC135178.2_NA_ENSG00000263809_chr17_8377523_-_Intron,RPL26_6154_ENSG00000161970_chr17_8377523_-_3'UTR(M)        CGTCCATTCGACAGCC        1
ACTG1_71_ENSG00000184009_chr17_81509999_-_3'UTR(M)      ACATCAGGTGATGTCT        1
ADRM1_11047_ENSG00000130706_chr20_62308862_+_3'UTR(M)   CAGCGACTCTGCCCTA        1
  3. html report : various metrics from steps in the pipeline

R functions available for importing results into Seurat object, and finding differential PA site usage. Alternatively, a package of the same functions can be installed with remotes::install_github("rnabioco/scrapR")

Setup

1. Clone repository

git clone https://github.com/rnabioco/scraps
cd scraps

conda env create -f scraps_conda.yml
conda activate scraps_conda

Alternatively, ensure all dependencies are installed and available in your PATH.

3. Prepare reference files

STAR genome index

Place STAR index in the ref/ directory or specify custom path in config.yaml (STAR_INDEX)

Download link (extract after download):

Whitelist paths are configured per chemistry in chemistry.yaml. Place downloaded whitelists in the ref/ directory.

Download links (extract after download):

Update chemistry.yaml with the correct paths:

chromiumV3:
  bc_whitelist: ref/3M-february-2018.txt
chromiumV2:
  bc_whitelist: ref/737K-august-2016.txt

4. Configure your samples

Edit config.yaml to specify:

Example:

SAMPLES:
  my_sample:
    basename: SRR9887775_        # Matches SRR9887775_R1.fastq.gz, SRR9887775_R2.fastq.gz
    chemistry: chromiumV3
    platform: illumina

Note: SRA accessions (e.g., SRR9887775) can be used directly as basenames for automatic download.

5. Run the pipeline

# Dry-run to check configuration
snakemake -npr --configfile config.yaml

# Run pipeline
snakemake --configfile config.yaml --resources total_impact=5 --keep-going

# Or with specific core count
snakemake -j 8 --configfile config.yaml

Sample test results can be found at inst/test_output/

Dependencies

scraps requires the following executables in your PATH:

Recommended: Use Conda to manage these dependencies:

conda env create -f scraps_conda.yml
conda activate scraps_conda

All required dependencies (including zsh) will be installed automatically.

Docker image for automated deployment can also be found at https://hub.docker.com/r/rnabioco/scraps.

Please also see the Snakemake documentation for general information on executing and manipulating snakemake pipelines.

For Developers

For detailed development guidelines including code style conventions, testing procedures, and instructions for adding new rules or chemistry configurations, see AGENTS.md.

Key resources:

Extended function

1) Measuring internal priming as indicator of apoptotic cytoplasmic poly(A) RNA decay

(Based on widespread RNA decay during apoptosis: Liu and Fu et al.) Use SAF (hg38 version provided in ref subdirectory) file marking all gene regions (5’UTR, intron, CDS, 3’UTR), and helper R functions to process output. Please see Rmarkdown notebook for more.

2) Accurate intron/exon quantification for RNA velocity

(See discussions on quantification approaches and pitfalls: Soneson et al.)

Consideration scraps
Avoid feature double-counting ✓
Take strandedness into account ✓
Avoid count substraction ✓
Resolve spliced vs unspliced target ✓
Speed ✓
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