[{"_1":2,"_154":-5,"_155":-5},"loaderData",{"_3":4,"_13":14,"_21":22,"_133":134},"root",{"_5":-5,"_6":-7,"_7":8,"_9":-7,"_10":11,"_12":-7},"currentUser","primaryEmail","siteVerification","6LeIxAcTAAAAAJcZVRqyHh71UMIEGNQ_MXjiZKhI","toast","enableDev",false,"incidentStatus","routes/_layout",{"_15":-5,"_16":17,"_18":17,"_19":11,"_20":11},"username","isAskGordonEnabled",true,"isUsingNewTemplate","showMinimalLanding","showHoneypotTrap","routes/_layout.r.$namespace.$reponame",{"_23":24,"_25":-5,"_26":11,"_27":11,"_28":29,"_30":31,"_32":-5,"_33":11,"_34":35,"_84":-5,"_85":86,"_124":-5,"_125":11,"_126":17,"_127":11,"_128":-5,"_129":11,"_130":-5,"_131":132},"canonicalUrl","https://hub.docker.com/r/rnabioco/scraps/tags","mcpServer","canSeeDockerfile","canSeeAutobuilds","namespace","rnabioco","reponame","scraps","cloudBuildSource","isAutobuildsAvailable","repo",{"_36":37,"_38":39,"_41":42,"_43":44,"_45":11,"_46":47,"_48":49,"_50":51,"_53":31,"_28":29,"_54":55,"_56":40,"_57":58,"_59":60,"_61":62,"_63":-5,"_64":58,"_65":66,"_67":11,"_68":29,"_69":70,"_75":11,"_76":77,"_81":29,"_82":83},"categories",[],"content_types",[40],"image","date_registered","2023-03-21T14:30:23.799976Z","description","","is_private","last_updated","2026-01-28T08:11:10.119711Z","last_modified","2026-01-28T08:11:41.525028Z","media_types",[52],"application/vnd.docker.container.image.v1+json","name","pull_count",918,"repository_type","star_count",0,"status",1,"status_description","active","source","collaborator_count","full_description","# scraps

\n\n\n![Snakemake Check](https://github.com/rnabioco/scraps/actions/workflows/snakemake_run.yml/badge.svg)\n![Github Clones](https://img.shields.io/badge/dynamic/json.svg?label=Downloads&url=https://raw.githubusercontent.com/raysinensis/clone_counts_public/main/scraps.json&query=downloads&colorB=brightgreen)\n\n\n### scraps extracts mRNA polyadenylation sites from \"TVN\"-primed single-cell RNA-seq libraries at near-nucleotide resolution.\n\nscraps (**S**ingle **C**ell **R**N**A** **P**olyA **S**ite Discovery) is currently implemented as a Snakemake pipeline for \n10X Genomics 3' end v2/3 libraries (and other platforms with similar library structure, including Drop-seq, \nMicrowell-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 \nchanges in single-cell RNA-seq data.\n\nFor additional discussions and usage cases, please see [bioRxiv preprint](https://www.biorxiv.org/content/10.1101/2022.08.22.504859v1).\n\n

\n\n---\n- [Example usage](#example-usage)\n- [Configuration](#configuration)\n- [Supported scRNA-seq platforms](#supported-scrna-seq-platforms)\n- [Output](#output)\n- [Setup](#setup)\n- [Dependencies](#dependencies)\n- [For Developers](#for-developers)\n- [Extended function](#bonus-function)\n \n## Example usage\n\nscraps requires the following as input (defined in config.yaml):\n\n - 10X Genomics 3' v2/3 single-cell FASTQs or other platforms (with names \"_R1.fastq.gz\"\" and \"_R2.fastq.gz\"\")\n - A STAR genome index (must be generated with STAR 2.7.4a and above)\n - Whitelist for cell barcodes (optional but recommended to speed up run time)\n - A featureCounts reference (SAF-formatted polya_db, hg38 and mm10 files are included in [ref](https://github.com/rnabioco/scraps/tree/master/ref) subdirectory)\n\n### Quick Start\n\n1. Set up conda environment:\n```bash\nconda env create -f scraps_conda.yml\nconda activate scraps_conda\n```\n\n2. Configure your samples in `config.yaml` under the `SAMPLES` section\n\n3. Run the pipeline:\n```bash\nsnakemake --configfile config.yaml --resources total_impact=5 --keep-going\n```\n\n### Detailed Usage\n\nTo run test data, simply execute:\n```\nsnakemake --snakefile Snakefile \\\n --configfile config.yaml \\\n --resources total_impact=5 \\\n --keep-going\n```\n[DAG steps illustration](https://raw.githack.com/rnabioco/scraps/master/inst/dag.pdf)\n\n[submit jobs in cluster mode](https://snakemake.readthedocs.io/en/stable/executing/cluster.html)\n\nNotes: `total_impact` is set to 5 for each sample, change this to control how many samples are processed in parallel\n\n---\n\n## Configuration\n\nscraps uses two main configuration files for flexible pipeline setup:\n\n### config.yaml\n\nMain pipeline configuration file containing:\n\n- **DATA**: Directory containing input FASTQ files\n- **RESULTS**: Output directory for pipeline results\n- **STAR_INDEX**: Path to STAR genome index directory\n- **POLYA_SITES**: PolyA database reference file (SAF format, provided in `ref/`)\n- **DEFAULTS**: Default chemistry and platform settings\n - `platform`: Default sequencing platform (e.g., illumina, element, ultima)\n - `chemistry`: Default chemistry type (e.g., chromiumV3, chromiumV2, dropseq)\n - `alignments`: Which alignment modes to run ([R1, R2, paired])\n- **SAMPLES**: Per-sample configuration\n\n#### Sample Configuration Example:\n```yaml\nSAMPLES:\n sample_name:\n basename: sample- # FASTQ file prefix\n platform: illumina # Sequencing platform\n chemistry: chromiumV3 # Platform chemistry\n alignments: # Optional: override default alignments\n - R2\n - paired\n```\n\n### chemistry.yaml\n\nPlatform and chemistry-specific parameters organized hierarchically. Each chemistry type (chromiumV3, chromiumV2, dropseq, microwellseq, bd, indrop) contains:\n\n- **bc_whitelist**: Path to barcode whitelist file (optional)\n- **bc_cut**: Adapter sequences for complex barcode extraction (optional)\n- **Platform-specific settings**: Nested configurations for different sequencing platforms\n - `cutadapt_R1` / `cutadapt_paired`: Adapter trimming parameters\n - `STAR_R1` / `STAR_R2`: STAR alignment parameters (UMI/barcode positions)\n\n### Configuration Hierarchy\n\nThe pipeline uses hierarchical configuration lookup to determine parameters for each sample:\n\n```\n┌─────────────────────────────────────────────────────────┐\n│ 1. Sample-specific settings (config.yaml SAMPLES) │\n│ Highest priority - overrides everything │\n└────────────────────┬────────────────────────────────────┘\n │ If not found ↓\n┌─────────────────────────────────────────────────────────┐\n│ 2. Chemistry + Platform (chemistry.yaml) │\n│ e.g., chromiumV3 → illumina → STAR_R1 │\n└────────────────────┬────────────────────────────────────┘\n │ If not found ↓\n┌─────────────────────────────────────────────────────────┐\n│ 3. Chemistry defaults (chemistry.yaml) │\n│ e.g., chromiumV3 → bc_whitelist │\n└────────────────────┬────────────────────────────────────┘\n │ If not found ↓\n┌─────────────────────────────────────────────────────────┐\n│ 4. Global defaults (config.yaml DEFAULTS) │\n│ Lowest priority - fallback values │\n└─────────────────────────────────────────────────────────┘\n```\n\nThis allows platform-specific customization (e.g., Illumina vs Ultima Genomics) while maintaining chemistry-specific defaults.\n\n---\n\n## Supported scRNA-seq platforms\n| Platform | Library (BC+UMI+A) | Setting | Test data |\n| :--------|:------------| :------------| :---------|\n| 10x Chromium V3 | [16 + 12 + 30](https://teichlab.github.io/scg_lib_structs/methods_html/10xChromium3.html) | chromiumV3 | ✓ |\n| 10x V3 - Ultima Genomics | [adapter + 16 + 9 + 3 ignored + 8](https://www.nature.com/articles/s41587-022-01452-6/figures/1) | chromiumV3UG | |\n| 10x Chromium V2 | [16 + 10 + 30](https://teichlab.github.io/scg_lib_structs/methods_html/10xChromium3.html) | chromiumV2 | ✓ |\n| 10x Chromium Visium | [16 + 10 + 30](https://teichlab.github.io/scg_lib_structs/methods_html/10xChromium3.html) | visium | |\n| Drop-seq | [12 + 8 + 30](https://teichlab.github.io/scg_lib_structs/methods_html/Drop-seq.html) | dropseq | ✓ |\n| Microwell-seq | [6x3 + 6 + 30](https://teichlab.github.io/scg_lib_structs/methods_html/Microwell-seq.html) | microwellseq | ✓ |\n| BD Rhapsody | [9x3 + 8 + 18](https://teichlab.github.io/scg_lib_structs/methods_html/BD_Rhapsody.html) | bd | |\n| inDrop | [8 + 6 + 18](https://teichlab.github.io/scg_lib_structs/methods_html/inDrop.html) | indrop | |\n\nCustom chemistry supported, by editing [chemistry.yaml](https://github.com/rnabioco/scraps/tree/master/chemistry.yaml). Also see synthetic FASTQ [tool](https://github.com/raysinensis/bc_umi_gen).\n\n---\n\n## Output\n1. bedgraph : TVN-priming site pileup\n```\nchr11 215106 215107 1\nchr11 689216 689217 1\nchr11 812862 812863 1\nchr11 812870 812871 2\nchr11 812871 812872 2\n```\n2. count table : +-10 around PolyA_DB sites, by cell barcode\n```\ngene cell count\nAC135178.2_NA_ENSG00000263809_chr17_8377523_-_Intron,RPL26_6154_ENSG00000161970_chr17_8377523_-_3'UTR(M) AACTCCCGTTCCTCCA 1\nAC135178.2_NA_ENSG00000263809_chr17_8377523_-_Intron,RPL26_6154_ENSG00000161970_chr17_8377523_-_3'UTR(M) CCCATACGTTAAAGAC 1\nAC135178.2_NA_ENSG00000263809_chr17_8377523_-_Intron,RPL26_6154_ENSG00000161970_chr17_8377523_-_3'UTR(M) CGTCCATTCGACAGCC 1\nACTG1_71_ENSG00000184009_chr17_81509999_-_3'UTR(M) ACATCAGGTGATGTCT 1\nADRM1_11047_ENSG00000130706_chr20_62308862_+_3'UTR(M) CAGCGACTCTGCCCTA 1\n```\n3. [html report](https://raw.githack.com/rnabioco/scraps/master/inst/test_output/report/multiqc_report.html) : various metrics from steps in the pipeline\n\n[R functions](https://github.com/rnabioco/scraps/tree/master/inst/scripts/R) 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\")`](https://github.com/rnabioco/scrapR)\n\n

\n \n \n

\n\n___\n\n## Setup\n\n### 1. Clone repository\n```bash\ngit clone https://github.com/rnabioco/scraps\ncd scraps\n```\n\n### 2. Set up conda environment (recommended)\n```bash\nconda env create -f scraps_conda.yml\nconda activate scraps_conda\n```\n\nAlternatively, ensure all [dependencies](#dependencies) are installed and available in your PATH.\n\n### 3. Prepare reference files\n\n#### STAR genome index\nPlace STAR index in the `ref/` directory or specify custom path in config.yaml (`STAR_INDEX`)\n\nDownload link (extract after download):\n- [GRCh38 index](https://scrapsaccessory.s3.us-west-2.amazonaws.com/GRCh38_cr2020A_star.tar.gz)\n\n#### Barcode whitelists (optional but recommended)\nWhitelist paths are configured per chemistry in `chemistry.yaml`. Place downloaded whitelists in the `ref/` directory.\n\nDownload links (extract after download):\n- [10x V2 barcodes](https://scrapsaccessory.s3.us-west-2.amazonaws.com/737K-august-2016.txt.gz) → `ref/737K-august-2016.txt`\n- [10x V3 barcodes](https://scrapsaccessory.s3.us-west-2.amazonaws.com/3M-february-2018.txt.gz) → `ref/3M-february-2018.txt`\n\nUpdate `chemistry.yaml` with the correct paths:\n```yaml\nchromiumV3:\n bc_whitelist: ref/3M-february-2018.txt\nchromiumV2:\n bc_whitelist: ref/737K-august-2016.txt\n```\n\n### 4. Configure your samples\n\nEdit `config.yaml` to specify:\n- **DATA**: Path to directory containing FASTQ files (with naming pattern: `*_R1.fastq.gz`, `*_R2.fastq.gz`)\n- **RESULTS**: Output directory path\n- **STAR_INDEX**: Path to STAR genome index\n- **POLYA_SITES**: PolyA database reference (provided: `ref/polyadb32.hg38.saf.gz` or `ref/polyadb32.mm10.saf.gz`)\n- **SAMPLES**: Define each sample with:\n - `basename`: FASTQ filename prefix\n - `chemistry`: Platform chemistry type (chromiumV2, chromiumV3, dropseq, microwellseq, bd, indrop)\n - `platform`: Sequencing platform (illumina, element, ultima)\n - `alignments`: Optional list of alignment modes to run\n\nExample:\n```yaml\nSAMPLES:\n my_sample:\n basename: SRR9887775_ # Matches SRR9887775_R1.fastq.gz, SRR9887775_R2.fastq.gz\n chemistry: chromiumV3\n platform: illumina\n```\n\n**Note**: SRA accessions (e.g., `SRR9887775`) can be used directly as basenames for automatic download.\n\n### 5. Run the pipeline\n\n```bash\n# Dry-run to check configuration\nsnakemake -npr --configfile config.yaml\n\n# Run pipeline\nsnakemake --configfile config.yaml --resources total_impact=5 --keep-going\n\n# Or with specific core count\nsnakemake -j 8 --configfile config.yaml\n```\n\nSample test results can be found at [inst/test_output/](inst/test_output/)\n\n___\n\n## Dependencies\n\nscraps requires the following executables in your PATH:\n\n - Python 3 (>= 3.7)\n - Snakemake (>= 5.3.0, < 8.0)\n - UMI-tools (>= 1.1.2)\n - cutadapt (>= 3.4)\n - STAR (>= 2.7.9a)\n - Samtools (>= 1.15)\n - Bedtools (>= 2.30.0)\n - Subread (>= 2.0.1)\n - MultiQC (>= 1.6)\n - pysam (>= 0.16.0)\n - drmaa (>= 0.7.9, for cluster execution)\n - zsh (shell used for pipeline execution)\n \n**Recommended**: Use [Conda](https://docs.conda.io/en/latest/) to manage these dependencies:\n```bash\nconda env create -f scraps_conda.yml\nconda activate scraps_conda\n```\nAll required dependencies (including zsh) will be installed automatically.\n\nDocker image for automated deployment can also be found at https://hub.docker.com/r/rnabioco/scraps.\n\nPlease also see the Snakemake documentation\nfor general information on executing and manipulating snakemake pipelines.\n\n---\n\n## For Developers\n\nFor detailed development guidelines including code style conventions, testing procedures, and instructions for adding new rules or chemistry configurations, see [AGENTS.md](AGENTS.md).\n\nKey resources:\n- **Code style**: Python, R, and Snakemake conventions\n- **Testing**: Using Snakemake dry-run for validation\n- **Adding rules**: How to extend the pipeline\n- **Chemistry config**: Adding new platform support\n- **Debugging**: Common issues and solutions\n\n---\n\n## Extended function\n\n

\n\n**1) Measuring internal priming as indicator of apoptotic cytoplasmic poly(A) RNA decay**\n\n(Based on widespread RNA decay during apoptosis: [Liu and Fu et al.](https://www.sciencedirect.com/science/article/pii/S0092867418305105))\nUse SAF (hg38 version provided in [ref](https://github.com/rnabioco/scraps/tree/master/ref) subdirectory) file marking all gene regions (5'UTR, intron, CDS, 3'UTR), and helper [R functions](https://github.com/rnabioco/scraps/tree/master/inst/scripts/R/scraps_priming_region.R) to process output. Please see [Rmarkdown notebook](https://github.com/rnabioco/scraps/blob/master/inst/notebooks/scraps_internalpriming_5fu_2023.Rmd) for more.\n\n---\n\n**2) Accurate intron/exon quantification for RNA velocity**\n\n(See discussions on quantification approaches and pitfalls: [Soneson et al.](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008585))\n\n| Consideration | scraps |\n|:--------|:------------|\n| Avoid feature double-counting | ✓ |\n| Take strandedness into account | ✓ |\n| Avoid count substraction | ✓ |\n| Resolve spliced vs unspliced target | ✓ |\n| Speed | ✓ |\n","has_starred","hub_user","immutable_tags_settings",{"_71":11,"_72":73},"enabled","rules",[74],".*","is_automated","permissions",{"_78":17,"_79":11,"_80":11},"read","write","admin","user","storage_size",6237807251,"orgData","recentTags",[87],{"_88":40,"_89":90,"_91":92,"_93":94,"_95":96,"_97":98,"_46":113,"_114":90,"_115":29,"_116":52,"_53":117,"_118":119,"_120":104,"_121":113,"_122":62,"_123":17},"content_type","creator",16747761,"digest","sha256:ab1819f43ab29a62b4f37e156bab4a402911bc546b17724177d802d2269e2d26","full_size",2467304702,"id",408651773,"images",[99],{"_100":101,"_91":92,"_102":44,"_103":104,"_105":106,"_107":108,"_109":44,"_110":-5,"_111":94,"_59":62,"_112":-5},"architecture","amd64","features","last_pulled","2026-09-25T16:25:04.390907853Z","last_pushed","2026-01-28T08:11:08.341001206Z","os","linux","os_features","os_version","size","variant","2026-01-28T08:11:09.045758Z","last_updater","last_updater_username","media_type","latest","repository",19545097,"tag_last_pulled","tag_last_pushed","tag_status","v2","sbxKitMetadata","isPotentialSpam","isHubSbxKitsEnabled","showSbxLogoOverride","sbxTrademarkText","displaySecurityMetadata","tagAttestations","tagsHaveAttestations",[],"routes/_layout.r.$namespace.$reponame.tags",{"_34":135,"_142":143,"_131":152,"_153":11},{"_36":136,"_38":137,"_41":42,"_43":44,"_45":11,"_46":47,"_48":49,"_50":138,"_53":31,"_28":29,"_54":55,"_56":40,"_57":58,"_59":60,"_61":62,"_63":-5,"_64":58,"_65":66,"_67":11,"_68":29,"_69":139,"_75":11,"_76":141,"_81":29,"_82":83},[],[40],[52],{"_71":11,"_72":140},[74],{"_78":17,"_79":11,"_80":11},"tags",{"_144":60,"_145":-5,"_146":-5,"_147":148},"count","next","previous","results",[149],{"_88":40,"_89":90,"_91":92,"_93":94,"_95":96,"_97":150,"_46":113,"_114":90,"_115":29,"_116":52,"_53":117,"_118":119,"_120":104,"_121":113,"_122":62,"_123":17},[151],{"_100":101,"_91":92,"_102":44,"_103":104,"_105":106,"_107":108,"_109":44,"_110":-5,"_111":94,"_59":62,"_112":-5},[],"canDeleteTags","actionData","errors"]

rnabioco/scraps

Tags for rnabioco/scraps