From 86cc03f4d138cd690cce1779ba96d5c14c0cbff3 Mon Sep 17 00:00:00 2001 From: Mitchell Robert Vollger Date: Mon, 23 Dec 2024 10:18:04 -0800 Subject: [PATCH] Working to the next release (#30) This is a major refactor of the FIRE pipeline and the naming of its outputs and will represent the v0.1.0 of the software. There are no changes to the underlying methods or ideas, but I have made significant efforts to speed up the software and reduce dependencies while increasing reliability. --- .github/workflows/main.yml | 57 +- .github/workflows/release-please.yml | 17 - .gitignore | 7 +- CHANGELOG.md | 3 + INSTALL.md | 11 +- README.md | 45 +- _config.yml | 20 - config/README.md | 5 + fire | 47 - pixi.lock | 5866 +++++++++++++++++ pixi.toml | 68 + profiles/slurm-executor/config.yaml | 1 + workflow/Snakefile | 111 +- workflow/envs/env.yaml | 7 +- workflow/envs/python.yaml | 8 +- workflow/envs/runner.yaml | 12 +- workflow/profiles/default/config.yaml | 5 +- workflow/rules/apply-model.smk | 253 +- workflow/rules/common.smk | 40 +- workflow/rules/coverages.smk | 58 +- workflow/rules/decorated-reads.smk | 65 +- .../rules/{FDR-peaks.smk => fire-peaks.smk} | 210 +- workflow/rules/levio.smk | 102 + workflow/rules/{peak-stats.smk => stats.smk} | 36 +- workflow/rules/track-hub.smk | 83 +- workflow/scripts/cov.py | 2 - workflow/scripts/decorated-bed12.py | 163 - workflow/scripts/fdr-table.py | 312 + workflow/scripts/fire-null-distribution.py | 565 -- workflow/scripts/merge_fire_peaks.py | 9 +- workflow/scripts/percent-in-clusters.sh | 8 +- workflow/scripts/trackhub.py | 199 +- workflow/templates/fire-description.html | 98 + 33 files changed, 6955 insertions(+), 1538 deletions(-) delete mode 100644 .github/workflows/release-please.yml create mode 100644 CHANGELOG.md delete mode 100644 _config.yml delete mode 100755 fire create mode 100644 pixi.lock create mode 100644 pixi.toml rename workflow/rules/{FDR-peaks.smk => fire-peaks.smk} (62%) create mode 100644 workflow/rules/levio.smk rename workflow/rules/{peak-stats.smk => stats.smk} (66%) delete mode 100755 workflow/scripts/decorated-bed12.py create mode 100644 workflow/scripts/fdr-table.py delete mode 100755 workflow/scripts/fire-null-distribution.py create mode 100644 workflow/templates/fire-description.html diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 8876a3676c..8af20c95b0 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -1,54 +1,19 @@ -name: Tests +name: CI on: push: - branches: [ main ] + branches: [main] pull_request: - branches: [ main ] - + branches: [main] jobs: - Formatting: + # pixi test + Test: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 - - name: Formatting - uses: github/super-linter@v4 - env: - VALIDATE_ALL_CODEBASE: false - DEFAULT_BRANCH: main - GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} - VALIDATE_SNAKEMAKE_SNAKEFMT: true - - Linting: - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v2 - - name: Lint workflow - uses: snakemake/snakemake-github-action@v1.24.0 - with: - directory: . - snakefile: workflow/Snakefile - args: "--lint" - - Testing: - runs-on: ubuntu-latest - needs: - - Linting - - Formatting - steps: - - uses: actions/checkout@v2 - - - name: Test workflow - uses: snakemake/snakemake-github-action@v1.24.0 - with: - directory: .test - snakefile: workflow/Snakefile - args: "--use-conda --show-failed-logs --cores 3 --conda-cleanup-pkgs cache --all-temp --configfile config.yml" - -# - name: Test report -# uses: snakemake/snakemake-github-action@v1.24.0 -# with: -# directory: .test -# snakefile: workflow/Snakefile -# args: "--report report.zip" + - uses: actions/checkout@v4 + - uses: prefix-dev/setup-pixi@v0.8.1 + with: + pixi-version: v0.37.0 + cache: true + - run: pixi run test diff --git a/.github/workflows/release-please.yml b/.github/workflows/release-please.yml deleted file mode 100644 index 19cfd041c7..0000000000 --- a/.github/workflows/release-please.yml +++ /dev/null @@ -1,17 +0,0 @@ -on: - push: - branches: - - main - -name: release-please - -jobs: - release-please: - runs-on: ubuntu-latest - steps: - - - uses: GoogleCloudPlatform/release-please-action@v2 - id: release - with: - release-type: go # just keep a changelog, no version anywhere outside of git tags - package-name: diff --git a/.gitignore b/.gitignore index c9000ba079..78e4c88367 100644 --- a/.gitignore +++ b/.gitignore @@ -142,7 +142,7 @@ figures/ old/ phasing/ run_scripts/ -trackHub/ +trackHub-{v}/ trackHub_altius/ XCI/ altius/ @@ -158,7 +158,6 @@ config/** .vscode/* .DS_Store - # stats Rdata/ Rplots.pdf @@ -177,4 +176,6 @@ misc/ new-agg-test/ scATAC/ test/ - +test-data/ +bigtools-test/ +fire-test-data/ diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 0000000000..303530a57d --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,3 @@ +# v0.1.0 + +First major release of the FIRE pipeline. This release includes a refactor to reduce the computation by increased use of ft, changes to the output file names to include the fire version among other things, and finally a new launching method for the pipeline that uses pixi. Results are very similar to v0.0.7 of the pipeline; however, there are minor differences in the peak calls and the output names. diff --git a/INSTALL.md b/INSTALL.md index 1016ad6a0d..540e90a7a0 100644 --- a/INSTALL.md +++ b/INSTALL.md @@ -1,18 +1,17 @@ # Install -You will need **snakemake** which you can install using conda/mamba, e.g.: -``` -mamba create -c conda-forge -c bioconda -n snakemake 'snakemake>=8.4' -``` +If you wish to distribute jobs across a cluster you will need to install the appropriate [snakemake executor plugin](https://snakemake.github.io/snakemake-plugin-catalog/). For example, to use SLURM you can install the `snakemake-executor-slurm` plugin using pip: -Finally, if you wish to distribute jobs across a cluster you will need to install the appropriate [snakemake executor plugin](https://snakemake.github.io/snakemake-plugin-catalog/). For example, to use SLURM you can install the `snakemake-executor-slurm` plugin using pip: -``` +``` +pixi shell pip install snakemake-executor-plugin-slurm ``` We recommend adding a snakemake conda prefix to your `bashrc`, e.g. in the Stergachis lab add: + ```bash export SNAKEMAKE_CONDA_PREFIX=/mmfs1/gscratch/stergachislab/snakemake-conda-envs export APPTAINER_CACHEDIR=/mmfs1/gscratch/stergachislab/snakemake-conda-envs/apptainer-cache ``` + Then snakemake installs all the additional requirements as conda envs in that directory. diff --git a/README.md b/README.md index 8ff9af3b50..234d1166d1 100644 --- a/README.md +++ b/README.md @@ -1,47 +1,10 @@ # 🔥 **FIRE**: Fiber-seq Inferred Regulatory Elements [![DOI](https://zenodo.org/badge/561430995.svg)](https://zenodo.org/doi/10.5281/zenodo.10023811) +[![Actions Status](https://github.com/fiberseq/FIRE/workflows/CI/badge.svg)](https://github.com/mrvollger/FIRE/actions) -A Snakemake workflow for calling Fiber-seq Inferred Regulatory Elements (FIREs) on single molecules. For a more detailed description and methods see the [docs](/docs/README.md), or [watch](https://youtu.be/RiZrMltAiWM?si=sSo64goaNQxgyfcc) my lab meeting on FIRE. +A Snakemake workflow for calling Fiber-seq Inferred Regulatory Elements (FIREs) on single molecules. -## Install +## Installation and Usage -Please install `snakemake` and all the UCSC Kent utilities. For detailed instructions see the [installation README](/INSTALL.md). - -## Configuring - -See the [configuration README](/config/README.md), the example [configuration file](/config/config.yaml), and the example [manifest file](/config/config.tbl) for configuration options. - - -## Run - -We have a run script that executes the FIRE snakemake called `fire`, and any extra parameters are passed directly to snakemake. For example: - -```bash -./fire --configfile config/config.yaml -``` - -If you want to do a dry run: - -```bash -./fire --configfile config/config.yaml -n -``` - -If you want to execute across a cluster (modify `profiles/slurm-executor` as needed for distributed execution): - -```bash -./fire --configfile config/config.yaml --profile profiles/slurm-executor -``` - -You can also run snakemake directly, e.g.: - -```bash -snakemake \ - --configfile config/config.yaml \ - --profile profiles/slurm-executor \ - --local-cores 8 -k -``` - -## Test data - -You can find input data to test against at [this url](https://s3-us-west-2.amazonaws.com/stergachis-public1/index.html?prefix=FIRE/test-data/). +See the [docs](https://fiberseq.github.io/fire/run.html) for detailed installation and usage instructions. diff --git a/_config.yml b/_config.yml deleted file mode 100644 index ac3cdded7d..0000000000 --- a/_config.yml +++ /dev/null @@ -1,20 +0,0 @@ -#theme: jekyll-theme-minimal -#theme: minima -remote_theme: jekyll/minima -#logo: /assets/img/fiber_tools_grey.png - -minima: - skin: dark - -defaults: - - scope: - path: "README.md" - values: - permalink: "index.html" - -exclude: - - target - - models - - dists - - tmp.* - - temp diff --git a/config/README.md b/config/README.md index 0d85d6cf14..2dda19989c 100644 --- a/config/README.md +++ b/config/README.md @@ -17,6 +17,11 @@ manifest: config/config.tbl ## Optional input options +Specify that the input BAM file is an ONT Fiber-seq file. Default is `False`. +``` +ont: True +``` + Max number of threads to use in very distributed steps: ``` max_t: 4 diff --git a/fire b/fire deleted file mode 100755 index e5a9611669..0000000000 --- a/fire +++ /dev/null @@ -1,47 +0,0 @@ -#!/usr/bin/env bash - -# check for conda location -if [[ -z "${SNAKEMAKE_CONDA_PREFIX}" ]]; then - printf "Warning:\n\tSNAKEMAKE_CONDA_PREFIX is not set. Please set this env variable to the location of your group's shared snakemke conda enviroments.\n\tSee --conda-prefix at https://snakemake.readthedocs.io/en/stable/executing/cli.html#conda for more information.\n\n" 1>&2 -fi - -set -euo pipefail -SRC_DIR=$( - cd "$(dirname "${BASH_SOURCE[0]}")" - pwd -P -) - -ARGS=$(echo "$@") - -# check for required arguments -has_config=false -while test $# -gt 0; do - case "$1" in - --configfile) - has_config=true - ;; - esac - shift -done - -if [[ "${has_config}" == false ]]; then - printf "Error:\n\t--configfile argument required.\n" - exit 1 -fi - -# check for executables -for x in snakemake; do - if [[ ! $(type -P "${x}") ]]; then - echo "Error: ${x} not found in PATH, but it is required for FIRE." - exit 1 - fi -done - -# n cpus -CPUS=$(getconf _NPROCESSORS_ONLN 2>/dev/null) - -#echo "Arguments passed to snakemake: ${ARGS}" -snakemake \ - -s "${SRC_DIR}/workflow/Snakefile" \ - --local-cores "${CPUS}" \ - ${ARGS} diff --git a/pixi.lock b/pixi.lock new file mode 100644 index 0000000000..d798a10b36 --- /dev/null +++ b/pixi.lock @@ -0,0 +1,5866 @@ +version: 5 +environments: + default: + channels: + - 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disk_mb=4096 - mem_mb=16096 - runtime=120 + - slurm_partition=cpu-g2 # you can change the default account or partition by them to the default-resources. # Without these options it will default to stergachislab and compute when using hyak. # - slurm_partition=ckpt diff --git a/workflow/Snakefile b/workflow/Snakefile index e7213536fc..3322c700a3 100644 --- a/workflow/Snakefile +++ b/workflow/Snakefile @@ -4,18 +4,27 @@ # TODO get the container working # container: "docker://continuumio/miniconda3" + import pandas as pd import math import sys import os from snakemake.utils import min_version -min_version("8.12.0") +min_version("8.21.0") + # setup shared functions include: "rules/common.smk" +# load the version from the environment +VERSION = f"v{os.environ.get("PIXI_PROJECT_VERSION", "UNK")}" + +# thread options +MAX_THREADS = config.get("max_threads", 4) +SORT_THREADS = config.get("sort_threads", 8) + # reference genome and reference regions REF = get_ref() FAI = get_fai() @@ -42,32 +51,57 @@ else: MAX_PEAK_FDR = 1.0 MIN_FRAC_ACCESSIBLE = config.get("min_frac_accessible", 0) +# data filtering +FILTER_FLAG = config.get("samtools-filter-flag", "260") # 2308 + +# Misc options +NZOOMS = config.get("nzooms", 10) + # Misc sets of wildcards haps = ["all", "hap1", "hap2", "unk"] not_unk = ["all", "hap1", "hap2"] +all_only = ["all"] types = ["fdr", "acc", "link", "nuc"] types_to_col = {"fdr": 4, "acc": 5, "link": 6, "nuc": 7} bw_types = ["log_FDR"] # "score", "FDR", -bw_types = bw_types + [f"{t}_H1" for t in bw_types] + [f"{t}_H2" for t in bw_types] el_types = ["fire", "linker", "nucleosome"] + +# DSA options +DSA_CHAIN = config.get("chain", None) +DSA = DSA_CHAIN is not None +LEVIO_EXE = config.get("levio_exe", "leviosam2") + # developer options FT_EXE = config.get("ft_exe", "ft") +if FT_EXE != "ft": + print(f"INFO: Using FT_EXE: {FT_EXE}", file=sys.stderr) + ONT = config.get("ont", False) -USE_ONT = "" if ONT: - USE_ONT = " --ont --ml 225 " + ont_ml = config.get("ont_ml", 0) + if ont_ml != 0: + USE_ONT = f" --ont --ml {ont_ml} " + else: + USE_ONT = " --ont " +else: + USE_ONT = "" MIN_UNRELIABLE_COVERAGE_LEN = config.get("min_unreliable_coverage_len", 50) include: "rules/apply-model.smk" include: "rules/coverages.smk" -include: "rules/FDR-peaks.smk" -include: "rules/peak-stats.smk" +include: "rules/fire-peaks.smk" +include: "rules/stats.smk" include: "rules/decorated-reads.smk" include: "rules/track-hub.smk" +if DSA: + + include: "rules/levio.smk" + + wildcard_constraints: chrom="|".join(get_chroms()), call="|".join(["msp", "m6a"]), @@ -77,6 +111,7 @@ wildcard_constraints: hp="|".join(haps), col="|".join(bw_types), el_type="|".join(el_types), + v=VERSION, localrules: @@ -86,37 +121,51 @@ localrules: rule all: input: # coverage information - expand(rules.genome_bedgraph.output, sm=MANIFEST.index), - expand(rules.coverage.output, sm=MANIFEST.index), - expand(rules.exclude_from_shuffle.output, sm=MANIFEST.index), - expand(rules.unreliable_coverage_regions.output, sm=MANIFEST.index), - # model results - expand(rules.fire_sites.output, sm=MANIFEST.index), - # fiber locations - expand(rules.fiber_locations.output, sm=MANIFEST.index), - expand(rules.filtered_and_shuffled_fiber_locations.output, sm=MANIFEST.index), - # coverage of elements + expand(rules.coverage.output, sm=MANIFEST.index, v=VERSION), + expand(rules.exclude_from_shuffle.output, sm=MANIFEST.index, v=VERSION), + expand(rules.unreliable_coverage_regions.output, sm=MANIFEST.index, v=VERSION), expand( - rules.element_coverages.output, + rules.pileup.output.bed, sm=MANIFEST.index, - hp=not_unk, - el_type=el_types, + v=VERSION, ), - # FDR results - expand(rules.fdr_track.output, sm=MANIFEST.index), - expand(rules.fdr_peaks_by_fire_elements.output, sm=MANIFEST.index), - expand(rules.wide_fdr_peaks.output, sm=MANIFEST.index), - expand(rules.peaks_vs_percent.output, sm=MANIFEST.index), - expand(rules.one_percent_fdr_peaks.output, sm=MANIFEST.index), - expand(rules.fires_in_peaks.output.txt, sm=MANIFEST.index), + # model results + expand(rules.fire.output.cram, sm=MANIFEST.index, v=VERSION), + expand(rules.fire_sites.output, sm=MANIFEST.index, v=VERSION), + # Stats and Tables + expand(rules.fires_in_peaks.output.txt, sm=MANIFEST.index, v=VERSION), + expand(rules.ft_qc.output.tbl, sm=MANIFEST.index, v=VERSION), + # FIRE peaks + expand(rules.fdr_table.output.tbl, sm=MANIFEST.index, v=VERSION), + expand(rules.pileup.output.bed, sm=MANIFEST.index, v=VERSION), + expand(rules.fire_peaks.output.bed, sm=MANIFEST.index, v=VERSION), + expand(rules.wide_fire_peaks.output.bed, sm=MANIFEST.index, v=VERSION), + expand(rules.one_percent_fire_peaks.output.bed, sm=MANIFEST.index, v=VERSION), + expand(rules.peaks_vs_percent.output.fig1, sm=MANIFEST.index, v=VERSION), + # haplotype differences + expand(rules.hap_differences.output.fig1, sm=MANIFEST.index, v=VERSION), + expand(rules.hap_differences.output.fig2, sm=MANIFEST.index, v=VERSION), + expand(rules.hap_differences.output.bed, sm=MANIFEST.index, v=VERSION), # trackhub - expand(rules.fdr_track_to_bw.output.bw, sm=MANIFEST.index, col=bw_types), - expand(rules.fdr_peaks_by_fire_elements_to_bb.output.bb, sm=MANIFEST.index), - expand(rules.percent_accessible.output.bw, hp=not_unk, sm=MANIFEST.index), + expand(rules.fire_peaks_bb.output.bb, sm=MANIFEST.index, v=VERSION), + expand( + rules.percent_accessible.output.bw, + hp=not_unk, + sm=MANIFEST.index, + v=VERSION, + ), + expand(rules.decorate_fibers_1.output.bb, sm=MANIFEST.index, v=VERSION), + expand(rules.decorate_fibers_2.output.bb, sm=MANIFEST.index, v=VERSION), + expand(rules.hap_differences_track.output.bb, sm=MANIFEST.index, v=VERSION), expand( rules.element_coverages_bw.output.bw, sm=MANIFEST.index, - hp=not_unk, + hp=all_only, el_type=el_types, + v=VERSION, ), - expand(rules.trackhub.output, sm=MANIFEST.index), + expand(rules.trackhub.output.hub, sm=MANIFEST.index, v=VERSION), + + +# UNUSED +# expand(rules.fdr_track_to_bw.output.bw, sm=MANIFEST.index, col=bw_types), diff --git a/workflow/envs/env.yaml b/workflow/envs/env.yaml index 3aed0b9e72..04b2f46b27 100644 --- a/workflow/envs/env.yaml +++ b/workflow/envs/env.yaml @@ -7,15 +7,10 @@ dependencies: - samtools==1.19.1 - htslib==1.19.1 - bedtools==2.31 - - bioconda::fibertools-rs==0.5.3 - - bioconda::gia - - seqtk + - bioconda::fibertools-rs==0.6 - hck>=0.9.2 - bioawk - ripgrep - csvtk - - datamash - - parallel - mosdepth==0.3.7 - - bedops - bioconda::bigtools==0.5.4 diff --git a/workflow/envs/python.yaml b/workflow/envs/python.yaml index 8d338cbca7..b72cd8234a 100644 --- a/workflow/envs/python.yaml +++ b/workflow/envs/python.yaml @@ -5,13 +5,13 @@ channels: - bioconda - defaults dependencies: - - tqdm - defopt - numpy==1.24 # use this version to avoid cxx errors - - numba::numba==0.60 - pandas==1.4 # pandas versions later than this have cxx errors - - pysam==0.21 - htslib==1.19.1 - pip - pip: - - polars[pyarrow]==0.19 + - polars-lts-cpu[pyarrow]==1.7.1 +#- tqdm +#- numba::numba==0.60 +#- pysam==0.21 diff --git a/workflow/envs/runner.yaml b/workflow/envs/runner.yaml index 211d7e503d..dddf584ec6 100644 --- a/workflow/envs/runner.yaml +++ b/workflow/envs/runner.yaml @@ -1,13 +1,11 @@ name: runner channels: - - numba - conda-forge - bioconda - - defaults dependencies: - - tqdm - numpy - - pandas - - pip - - pip: - - pysam + - pandas==2.2.3 +#- tqdm +#- pip +#- pip: +#- pysam==0.22.1 diff --git a/workflow/profiles/default/config.yaml b/workflow/profiles/default/config.yaml index 0634954bd4..54c9b54ac2 100644 --- a/workflow/profiles/default/config.yaml +++ b/workflow/profiles/default/config.yaml @@ -1,11 +1,12 @@ rerun-incomplete: True show-failed-logs: True rerun-triggers: mtime -restart-times: 1 +restart-times: 0 software-deployment-method: - apptainer - conda -printshellcmds: True show-failed-logs: True cores: 32 local-cores: 4 +# printshellcmds: True +# quiet: rules # all rules or progress, cannot use all because it hides the unlock error message (and others?) \ No newline at end of file diff --git a/workflow/rules/apply-model.smk b/workflow/rules/apply-model.smk index d8c5a8a19d..9e6ad95590 100644 --- a/workflow/rules/apply-model.smk +++ b/workflow/rules/apply-model.smk @@ -4,131 +4,63 @@ rule fire: input: bam=ancient(get_input_bam), + ref=ancient(REF), output: - bam=temp("temp/{sm}/fire/{chrom}.fire.bam"), - threads: 8 + cram="results/{sm}/{sm}-fire-{v}-filtered.cram", + crai="results/{sm}/{sm}-fire-{v}-filtered.cram.crai", + threads: 32 resources: - mem_mb=8 * 1024, + mem_mb=32 * 1024, + runtime=600, params: min_msp=config.get("min_msp", 10), min_ave_msp_size=config.get("min_ave_msp_size", 10), use_ont=USE_ONT, + flag=FILTER_FLAG, + benchmark: + "results/{sm}/additional-outputs-{v}/benchmarks/{sm}-fire-bam.txt" conda: DEFAULT_ENV shell: """ - samtools view -F 2308 -u -@ {threads} {input.bam} {wildcards.chrom} \ - | {FT_EXE} fire -t {threads} \ + samtools view -@ {threads} -u -F {params.flag} {input.bam} \ + | {FT_EXE} fire -F {params.flag} -t {threads} \ {params.use_ont} \ --min-msp {params.min_msp} \ --min-ave-msp-size {params.min_ave_msp_size} \ --skip-no-m6a \ - - {output.bam} - """ - - -rule merged_fire_bam: - input: - ref=ancient(REF), - fai=ancient(FAI), - bams=expand(rules.fire.output.bam, chrom=get_chroms(), allow_missing=True), - output: - cram="results/{sm}/fire/{sm}.fire.cram", - crai="results/{sm}/fire/{sm}.fire.cram.crai", - threads: 16 - resources: - mem_mb=16 * 1024, - runtime=300, - conda: - DEFAULT_ENV - benchmark: - "results/{sm}/benchmarks/merged_fire_bam/{sm}.txt" - shell: - """ - samtools merge -@ {threads} -u {input.bams} -o - \ - | samtools view \ - -C -@ {threads} \ - -T {input.ref} \ + - - \ + | samtools view -C -@ {threads} -T {input.ref} \ + --output-fmt-option embed_ref=1 \ + | samtools view -C -@ {threads} -T {input.ref} \ --output-fmt-option embed_ref=1 \ - --write-index \ - -o {output.cram} + --input-fmt-option required_fields=0x1bff \ + --write-index -o {output.cram} """ -rule extract_from_fire: +rule fire_sites_chrom: input: - bam=rules.fire.output.bam, + cram=rules.fire.output.cram, output: - bed=temp("temp/{sm}/chrom/{chrom}.sorted.bed.gz"), + bed=temp("temp/{sm}/chrom/{v}-{chrom}.sorted.bed.gz"), threads: 4 conda: DEFAULT_ENV resources: mem_mb=16 * 1024, - priority: 10 - shell: - """ - {FT_EXE} fire -t {threads} --all --extract {input.bam} \ - | LC_ALL=C sort \ - --parallel={threads} \ - -k1,1 -k2,2n -k3,3n -k4,4 \ - | bgzip -@ {threads} \ - > {output.bed} - """ - - -rule merge_model_results: - input: - beds=expand( - rules.extract_from_fire.output.bed, chrom=get_chroms(), allow_missing=True - ), - output: - bed=temp("temp/{sm}/fiber-calls/model.results.bed.gz"), - threads: 8 - conda: - DEFAULT_ENV - params: - n_chunks=len(get_chroms()) + 1, - benchmark: - "results/{sm}/benchmarks/merge_model_results/{sm}.txt" - priority: 20 - shell: - """ - cat {input.beds} > {output.bed} - """ - - -rule index_model_results: - input: - bed=rules.merge_model_results.output.bed, - output: - tbi=temp(rules.merge_model_results.output.bed + ".tbi"), - conda: - DEFAULT_ENV - shell: - """ - tabix -p bed {input.bed} - """ - - -rule fire_sites_chrom: - input: - bed=rules.merge_model_results.output.bed, - tbi=rules.index_model_results.output.tbi, - output: - bed=temp("temp/{sm}/fiber-calls/{chrom}/FIRE.bed.gz"), - threads: 4 - conda: - DEFAULT_ENV params: min_fdr=MIN_FIRE_FDR, shell: """ - tabix {input.bed} {wildcards.chrom} \ - | bioawk -tc hdr '$10<={params.min_fdr}' \ - | (grep '\\S' || true) \ - | (grep -v '^#' || true) \ - | bgzip -@{threads} \ + samtools view -@ {threads} -u {input.cram} {wildcards.chrom} \ + | {FT_EXE} fire -t {threads} --extract - \ + | LC_ALL=C sort --parallel={threads} \ + -k1,1 -k2,2n -k3,3n -k4,4 \ + | bioawk -tc hdr '$10<={params.min_fdr}' \ + | (grep '\\S' || true) \ + | (grep -v '^#' || true) \ + | bgzip -@ {threads} \ > {output.bed} """ @@ -139,12 +71,10 @@ rule fire_sites: rules.fire_sites_chrom.output.bed, chrom=get_chroms(), allow_missing=True ), output: - bed="results/{sm}/fiber-calls/FIRE.bed.gz", - threads: 8 + bed="results/{sm}/additional-outputs-{v}/fire-peaks/{sm}-{v}-fire-elements.bed.gz", + threads: 1 conda: DEFAULT_ENV - params: - min_fdr=MIN_FIRE_FDR, shell: """ cat {input.beds} > {output.bed} @@ -163,126 +93,3 @@ rule fire_sites_index: """ tabix -p bed {input.bed} """ - - -rule split_by_hap_per_chrom: - input: - bed=rules.merge_model_results.output.bed, - tbi=rules.index_model_results.output.tbi, - fai=ancient(FAI), - output: - both=pipe("temp/{sm}/coverage/all/{chrom}.bed"), - H1=pipe("temp/{sm}/coverage/hap1/{chrom}.bed"), - H2=pipe("temp/{sm}/coverage/hap2/{chrom}.bed"), - conda: - DEFAULT_ENV - resources: - disk_mb=100, - runtime=240, - mem_mb=4 * 1024, - shell: - """ - tabix {input.bed} {wildcards.chrom} | tee \ - >( (rg -w H1 || true) > {output.H1} ) \ - >( (rg -w H2 || true) > {output.H2} ) \ - > {output.both} - """ - - -rule split_hap_by_element_type_per_chrom: - input: - bed="temp/{sm}/coverage/{hp}/{chrom}.bed", - fai=ancient(FAI), - output: - fire=temp("temp/{sm}/coverage/{hp}/fire_{chrom}.bed.gz"), - link=temp("temp/{sm}/coverage/{hp}/linker_{chrom}.bed.gz"), - nuc=temp("temp/{sm}/coverage/{hp}/nucleosome_{chrom}.bed.gz"), - params: - min_fire_fdr=MIN_FIRE_FDR, - threads: 2 - conda: - DEFAULT_ENV - resources: - disk_mb=100, - mem_mb=8 * 1024, - shell: - """ - cat {input.bed} | tee \ - >( awk '$10<={params.min_fire_fdr}' \ - | bedtools genomecov -bg -i - -g {input.fai} \ - | bgzip > {output.fire} \ - ) \ - >( awk '$10<=1.0 && $10>{params.min_fire_fdr}' \ - | bedtools genomecov -bg -i - -g {input.fai} \ - | bgzip > {output.link} \ - ) \ - | awk '$10>1.0' \ - | bedtools genomecov -bg -i - -g {input.fai} \ - | bgzip > {output.nuc} - - # check if files are empty and if they are add a fake data line - for f in {output.fire} {output.link} {output.nuc}; do - HAS_LINES=$(zcat $f | head | grep -cv '^#') || true - if [ $HAS_LINES -eq 0 ]; then - printf "{wildcards.chrom}\\t0\\t1\\t0\\n" \ - | bgzip -@{threads} > $f - fi - done - """ - - -rule element_coverages_per_chrom: - input: - beds=expand( - "temp/{sm}/coverage/{hp}/{el_type}_{chrom}.bed.gz", - el_type=el_types, - allow_missing=True, - ), - output: - bed=temp("temp/{sm}/coverage/{hp}_{chrom}_element_coverages.bed.gz"), - conda: - DEFAULT_ENV - params: - names="\t".join(el_types), - resources: - runtime=300, - threads: 2 - shell: - """ - HAS_LINES=$(zcat {input.beds} | head | grep -cv '^#') || true - if [ $HAS_LINES -eq 0 ]; then - echo "No element coverages found for {wildcards.sm} {wildcards.hp} {wildcards.chrom}" - printf "#chrom\\tstart\\tend\\t{params.names}\\n{wildcards.chrom}\\t0\\t1\\t0\\t0\\t0\\n" \ - | bgzip -@{threads} \ - > {output.bed} - else - # bedtools unionbedg -header -i {input.beds} -names {params.names} - # | sed 's/^chrom/#chrom/' - ( \ - printf "#chrom\\tstart\\tend\\t{params.names}\\n"; \ - gia unionbedg -s -i {input.beds} \ - ) \ - | bgzip -@ {threads} \ - > {output.bed} - fi - """ - - -rule element_coverages: - input: - beds=expand( - rules.element_coverages_per_chrom.output.bed, - chrom=get_chroms(), - allow_missing=True, - ), - output: - bed="results/{sm}/coverage/{hp}_element_coverages.bed.gz", - tbi="results/{sm}/coverage/{hp}_element_coverages.bed.gz.tbi", - conda: - DEFAULT_ENV - threads: 1 - shell: - """ - cat {input.beds} > {output.bed} - tabix -p bed {output.bed} - """ diff --git a/workflow/rules/common.smk b/workflow/rules/common.smk index bcf267d4ba..2dec22b795 100644 --- a/workflow/rules/common.smk +++ b/workflow/rules/common.smk @@ -11,7 +11,7 @@ def get_ref(): ref = config["ref"] if not os.path.exists(ref): raise ValueError(f"FIRE: reference file {ref} does not exist") - return ref + return os.path.abspath(ref) def get_fai(): @@ -25,8 +25,8 @@ def get_excludes(): excludes = config.get("excludes", []) if REF_NAME == "hg38" or REF_NAME == "GRCh38": files = [ - "../annotations/hg38.blacklist.ENCFF356LFX.bed.gz", "../annotations/hg38.gap.bed.gz", + "../annotations/hg38.blacklist.ENCFF356LFX.bed.gz", "../annotations/SDs.merged.hg38.bed.gz", ] excludes += [workflow.source_path(file) for file in files] @@ -105,34 +105,32 @@ def get_load(wc): return 50 -def grep_command_for_el_type(wc): - if wc.el_type == "nucleosome": - return "awk '$10>1.0'" - elif wc.el_type == "linker": - return f"awk '$10<=1.0 && $10>{MIN_FIRE_FDR}'" - elif wc.el_type == "fire": - return f"awk '$10<={MIN_FIRE_FDR}'" - else: - raise ValueError(f"Unknown element type {wc.el_type}") - - -def hap_grep_term(wc): +def get_hap_col_suffix(wc): if wc.hp == "all": - return '""' + return "" elif wc.hp == "hap1": - return "H1" + return "_H1" elif wc.hp == "hap2": - return "H2" + return "_H2" else: raise ValueError(f"Unknown haplotype {wc.hp}") -def hap_hck_columns(wc): +def pileup_cut_cmd(wc): if wc.hp == "all": - return "-F fire_coverage -F coverage" + tail = "" elif wc.hp == "hap1": - return "-F fire_coverage_H1 -F coverage_H1" + tail = "_H1" elif wc.hp == "hap2": - return "-F fire_coverage_H2 -F coverage_H2" + tail = "_H2" else: raise ValueError(f"Unknown haplotype {wc.hp}") + if wc.el_type == "nucleosome": + col = f"$nuc_coverage{tail}" + elif wc.el_type == "linker": + col = f"$msp_coverage{tail}-$fire_coverage{tail}" + elif wc.el_type == "fire": + col = f"$fire_coverage{tail}" + else: + raise ValueError(f"Unknown element type {wc.el_type}") + return f"bioawk -tc hdr '{{print $1,$2,$3,{col}}}'" diff --git a/workflow/rules/coverages.smk b/workflow/rules/coverages.smk index 990416c9af..05f537c45b 100644 --- a/workflow/rules/coverages.smk +++ b/workflow/rules/coverages.smk @@ -5,22 +5,20 @@ rule genome_bedgraph: input: ref=ancient(REF), fai=ancient(FAI), - cram=rules.merged_fire_bam.output.cram, - crai=rules.merged_fire_bam.output.crai, + cram=rules.fire.output.cram, + crai=rules.fire.output.crai, output: - bg="results/{sm}/coverage/{sm}.bed.gz", - tbi="results/{sm}/coverage/{sm}.bed.gz.tbi", + bg=temp("temp/{sm}/coverage/{sm}-{v}.bed.gz"), + tbi=temp("temp/{sm}/coverage/{sm}-{v}.bed.gz.tbi"), threads: 16 shadow: "minimal" conda: DEFAULT_ENV - benchmark: - "results/{sm}/benchmarks/genome_bedgraph/{sm}.txt" shell: """ mosdepth -f {input.ref} -t {threads} tmp {input.cram} - zcat tmp.per-base.bed.gz \ + bgzip -cd tmp.per-base.bed.gz \ | LC_ALL=C sort --parallel={threads} -k1,1 -k2,2n -k3,3n -k4,4 \ | bgzip -@ {threads} \ > {output.bg} @@ -32,16 +30,16 @@ rule coverage: input: bg=rules.genome_bedgraph.output.bg, output: - cov="results/{sm}/coverage/{sm}.median.coverage.txt", - minimum="results/{sm}/coverage/{sm}.minimum.coverage.txt", - maximum="results/{sm}/coverage/{sm}.maximum.coverage.txt", + cov="results/{sm}/additional-outputs-{v}/coverage/{sm}-{v}-median-coverage.txt", + minimum="results/{sm}/additional-outputs-{v}/coverage/{sm}-{v}-minimum-coverage.txt", + maximum="results/{sm}/additional-outputs-{v}/coverage/{sm}-{v}-maximum-coverage.txt", conda: "../envs/python.yaml" threads: 1 resources: mem_mb=64 * 1024, benchmark: - "results/{sm}/benchmarks/coverage/{sm}.txt" + "results/{sm}/additional-outputs-{v}/benchmarks/coverage/{sm}.txt" params: coverage_within_n_sd=COVERAGE_WITHIN_N_SD, min_coverage=MIN_COVERAGE, @@ -55,17 +53,17 @@ rule coverage: # rule fiber_locations_chromosome: input: - cram=rules.merged_fire_bam.output.cram, - crai=rules.merged_fire_bam.output.crai, + cram=rules.fire.output.cram, + crai=rules.fire.output.crai, output: - bed=temp("temp/{sm}/coverage/{chrom}.fiber-locations.bed.gz"), - threads: 8 + bed=temp("temp/{sm}/coverage/{v}-{chrom}.fiber-locations.bed.gz"), + threads: 4 conda: DEFAULT_ENV shell: """ # get fiber locations - (samtools view -@ {threads} -F 2308 -u {input.cram} {wildcards.chrom} \ + (samtools view -@ {threads} -u {input.cram} {wildcards.chrom} \ | {FT_EXE} extract -t {threads} -s --all - \ | hck -F '#ct' -F st -F en -F fiber -F strand -F HP ) \ | (grep -v "^#" || true) \ @@ -85,13 +83,19 @@ rule fiber_locations: minimum=rules.coverage.output.minimum, maximum=rules.coverage.output.maximum, output: - bed="results/{sm}/coverage/fiber-locations.bed.gz", - bed_tbi="results/{sm}/coverage/fiber-locations.bed.gz.tbi", - filtered="results/{sm}/coverage/filtered-for-coverage/fiber-locations.bed.gz", - filtered_tbi="results/{sm}/coverage/filtered-for-coverage/fiber-locations.bed.gz.tbi", + bed=temp("temp/{sm}/coverage/{v}-fiber-locations.bed.gz"), + bed_tbi=temp("temp/{sm}/coverage/{v}-fiber-locations.bed.gz.tbi"), + filtered=temp( + "temp/{sm}/coverage/filtered-for-coverage/{v}-fiber-locations.bed.gz" + ), + filtered_tbi=temp( + "temp/{sm}/coverage/filtered-for-coverage/{v}-fiber-locations.bed.gz.tbi" + ), threads: 4 conda: DEFAULT_ENV + params: + max_frac_overlap=0.2, shell: """ cat {input.fibers} > {output.bed} @@ -100,9 +104,9 @@ rule fiber_locations: # get filtered fiber locations MIN=$(cat {input.minimum}) MAX=$(cat {input.maximum}) - bedtools intersect -header -sorted -v -f 0.2 \ + bedtools intersect -header -sorted -v -f {params.max_frac_overlap} \ -a {output.bed} \ - -b <(zcat {input.bg} | awk -v MAX="$MAX" -v MIN="$MIN" '$4 <= MIN || $4 >= MAX') \ + -b <(bgzip -cd {input.bg} | awk -v MAX="$MAX" -v MIN="$MIN" '$4 <= MIN || $4 >= MAX') \ | bgzip -@ {threads} \ > {output.filtered} tabix -f -p bed {output.filtered} @@ -117,7 +121,7 @@ rule exclude_from_shuffle: filtered=rules.fiber_locations.output.filtered, fai=ancient(FAI), output: - bed="results/{sm}/coverage/exclude-from-shuffles.bed.gz", + bed="results/{sm}/additional-outputs-{v}/coverage/exclude-from-shuffles.bed.gz", threads: 4 conda: DEFAULT_ENV @@ -145,9 +149,9 @@ rule unreliable_coverage_regions: maximum=rules.coverage.output.maximum, fai=ancient(FAI), output: - bed="results/{sm}/coverage/unreliable-coverage-regions.bed.gz", - bed_tbi="results/{sm}/coverage/unreliable-coverage-regions.bed.gz.tbi", - bb="results/{sm}/trackHub/bb/unreliable-coverage-regions.bb", + bed="results/{sm}/additional-outputs-{v}/coverage/unreliable-coverage-regions.bed.gz", + bed_tbi="results/{sm}/additional-outputs-{v}/coverage/unreliable-coverage-regions.bed.gz.tbi", + bb="results/{sm}/trackHub-{v}/bb/unreliable-coverage-regions.bb", threads: 4 params: min_len=MIN_UNRELIABLE_COVERAGE_LEN, @@ -158,7 +162,7 @@ rule unreliable_coverage_regions: """ MIN=$(cat {input.minimum}) MAX=$(cat {input.maximum}) - zcat {input.bg} \ + bgzip -cd {input.bg} \ | awk '$4>0' \ | awk -v MAX="$MAX" -v MIN="$MIN" '$4 <= MIN || $4 >= MAX' \ | bedtools merge -i - \ diff --git a/workflow/rules/decorated-reads.smk b/workflow/rules/decorated-reads.smk index d05a9fceea..3442185966 100644 --- a/workflow/rules/decorated-reads.smk +++ b/workflow/rules/decorated-reads.smk @@ -1,13 +1,19 @@ +# Number of items to bundle in r-tree [default: 256] +BLOCK_SIZE = 256 * 8 +# Number of data points bundled at lowest level [default: 1024] +ITEMS_PER_SLOT = 1024 * 8 + + rule decorate_fibers_chromosome: input: - cram=rules.merged_fire_bam.output.cram, - crai=rules.merged_fire_bam.output.crai, + cram=rules.fire.output.cram, + crai=rules.fire.output.crai, output: - bed=temp("temp/{sm}/decorate/{chrom}.bed.gz"), - decorated=temp("temp/{sm}/decorate/{chrom}.dec.bed.gz"), - threads: 8 + bed=temp("temp/{sm}/decorate/{v}-{chrom}.bed.gz"), + decorated=temp("temp/{sm}/decorate/{v}-{chrom}.dec.bed.gz"), + threads: 4 resources: - mem_mb=get_large_mem_mb, + mem_mb=get_mem_mb, conda: DEFAULT_ENV shell: @@ -29,23 +35,29 @@ rule decorate_fibers_1: ), fai=ancient(FAI), output: - bed="results/{sm}/fiber-calls/fire-fibers.bed.gz", - bb="results/{sm}/trackHub/bb/fire-fibers.bb", + #bed=temp("temp/{sm}/fiber-calls/fire-fibers.bed.gz"), + bb="results/{sm}/trackHub-{v}/bb/fire-fibers.bb", benchmark: - "results/{sm}/benchmarks/decorate_fibers_1/{sm}.txt" - threads: 1 + "results/{sm}/additional-outputs-{v}/benchmarks/decorate_fibers_1/{sm}.txt" + threads: 8 resources: runtime=240, conda: DEFAULT_ENV params: bed_as=workflow.source_path("../templates/bed12_filter.as"), + nzooms=NZOOMS, + items_per_slot=ITEMS_PER_SLOT, + block_size=BLOCK_SIZE, shell: + # bigtools version """ - cat {input.bed} > {output.bed} - - bgzip -cd -@ {threads} {output.bed} \ + cat {input.bed} \ + | bgzip -cd -@ {threads} \ | bigtools bedtobigbed \ + --inmemory \ + --block-size {params.block_size} --items-per-slot {params.items_per_slot} \ + --nzooms {params.nzooms} \ -s start -a {params.bed_as} \ - {input.fai} {output.bb} """ @@ -60,22 +72,43 @@ rule decorate_fibers_2: ), fai=ancient(FAI), output: - bb="results/{sm}/trackHub/bb/fire-fiber-decorators.bb", + bb="results/{sm}/trackHub-{v}/bb/fire-fiber-decorators.bb", + #bed=temp("temp/{sm}/trackHub-{v}/bb/fire-fiber-decorators.bed.gz"), benchmark: - "results/{sm}/benchmarks/decorate_fibers_2/{sm}.txt" - threads: 1 + "results/{sm}/additional-outputs-{v}/benchmarks/decorate_fibers_2/{sm}.txt" + threads: 8 resources: runtime=60 * 16, conda: DEFAULT_ENV params: dec_as=workflow.source_path("../templates/decoration.as"), + nzooms=NZOOMS, + items_per_slot=ITEMS_PER_SLOT, + block_size=BLOCK_SIZE, shell: + # bigtools version + # for some reason filtering out NUCs removes the display bug for bigtools + # at least in my test cases """ cat {input.decorated} \ | bgzip -cd -@ {threads} \ | rg -v '^#' \ + | rg -vw 'NUC' \ | bigtools bedtobigbed \ + --inmemory \ + --block-size {params.block_size} --items-per-slot {params.items_per_slot} \ + --nzooms {params.nzooms} \ -a {params.dec_as} -s start \ - {input.fai} {output.bb} """ + + +if False: + # UCSC version + """ + cat {input.decorated} > {output.bed} + bedToBigBed \ + -allow1bpOverlap -type=bed12+ -as={params.dec_as} \ + {output.bed} {input.fai} {output.bb} + """ diff --git a/workflow/rules/FDR-peaks.smk b/workflow/rules/fire-peaks.smk similarity index 62% rename from workflow/rules/FDR-peaks.smk rename to workflow/rules/fire-peaks.smk index bbe1fa331f..6d5f2428d7 100644 --- a/workflow/rules/FDR-peaks.smk +++ b/workflow/rules/fire-peaks.smk @@ -1,16 +1,18 @@ rule filtered_and_shuffled_fiber_locations_chromosome: input: filtered=rules.fiber_locations.output.filtered, + filtered_tbi=rules.fiber_locations.output.filtered_tbi, exclude=rules.exclude_from_shuffle.output.bed, fai=ancient(FAI), output: - shuffled=temp("temp/{sm}/coverage/{chrom}.fiber-locations-shuffled.bed.gz"), + shuffled=temp("temp/{sm}/shuffle/{v}-{chrom}.fiber-locations-shuffled.bed.gz"), threads: 4 conda: DEFAULT_ENV shell: """ - tabix -h {input.filtered} {wildcards.chrom} \ + tabix {input.filtered} {wildcards.chrom} \ + | bioawk -t '{{print $1,$2,$3,$4,$2}}' \ | bedtools shuffle -chrom -seed 42 \ -excl {input.exclude} \ -i - \ @@ -21,21 +23,42 @@ rule filtered_and_shuffled_fiber_locations_chromosome: """ -rule filtered_and_shuffled_fiber_locations: +rule shuffled_pileup_chromosome: input: - shuffled=expand( - rules.filtered_and_shuffled_fiber_locations_chromosome.output.shuffled, + cram=rules.fire.output.cram, + shuffled=rules.filtered_and_shuffled_fiber_locations_chromosome.output.shuffled, + output: + bed=temp("temp/{sm}/shuffle/{v}-{chrom}.pileup.bed.gz"), + threads: 4 + conda: + DEFAULT_ENV + shell: + """ + {FT_EXE} pileup {input.cram} {wildcards.chrom} -t {threads} \ + --fiber-coverage --shuffle {input.shuffled} \ + --no-msp --no-nuc \ + | bgzip -@ {threads} \ + > {output.bed} + """ + + +rule shuffled_pileup: + input: + beds=expand( + rules.shuffled_pileup_chromosome.output.bed, chrom=get_chroms(), allow_missing=True, ), output: - shuffled="results/{sm}/coverage/filtered-for-coverage/fiber-locations-shuffled.bed.gz", - threads: 1 + bed=temp("temp/{sm}/shuffle/{v}-shuffled-pileup.bed.gz"), + tbi=temp("temp/{sm}/shuffle/{v}-shuffled-pileup.bed.gz.tbi"), + threads: 4 conda: DEFAULT_ENV shell: """ - cat {input.shuffled} > {output.shuffled} + cat {input.beds} > {output.bed} + tabix -p bed {output.bed} """ @@ -44,65 +67,70 @@ rule filtered_and_shuffled_fiber_locations: # rule fdr_table: input: - fire=rules.fire_sites.output.bed, - fiber_locations=rules.fiber_locations.output.filtered, - shuffled=rules.filtered_and_shuffled_fiber_locations.output.shuffled, - fai=ancient(FAI), + shuffled=rules.shuffled_pileup.output.bed, + minimum=rules.coverage.output.minimum, + maximum=rules.coverage.output.maximum, output: - tbl="results/{sm}/FDR-peaks/FIRE.score.to.FDR.tbl", - benchmark: - "results/{sm}/benchmarks/fdr_table/{sm}.txt" - threads: 8 + tbl="results/{sm}/additional-outputs-{v}/fire-peaks/{sm}-{v}-fire-score-to-fdr.tbl", conda: "../envs/python.yaml" params: - script=workflow.source_path("../scripts/fire-null-distribution.py"), + script=workflow.source_path("../scripts/fdr-table.py"), resources: mem_mb=get_mem_mb, shell: """ - python {params.script} -v 1 {input.fire} {input.fiber_locations} {input.fai} -s {input.shuffled} -o {output.tbl} + MIN=$(cat {input.minimum}) + MAX=$(cat {input.maximum}) + python {params.script} -v 1 {input.shuffled} {output.tbl} --max-cov $MAX --min-cov $MIN + """ + + +# Colnames made by this +# #chrom start end +# coverage fire_coverage score nuc_coverage msp_coverage +# coverage_H1 fire_coverage_H1 score_H1 nuc_coverage_H1 msp_coverage_H1 +# coverage_H2 fire_coverage_H2 score_H2 nuc_coverage_H2 msp_coverage_H2 +rule pileup_chromosome: + input: + bam=rules.fire.output.cram, + output: + bed=temp("temp/{sm}/{v}-{chrom}.pileup.bed.gz"), + threads: 4 + conda: + DEFAULT_ENV + shell: + """ + {FT_EXE} pileup -t {threads} \ + --haps --fiber-coverage \ + {input.bam} {wildcards.chrom} \ + | bgzip -@ {threads} \ + > {output.bed} """ rule fdr_track_chromosome: input: - fire=rules.fire_sites.output.bed, - fire_tbi=rules.fire_sites_index.output.tbi, - fiber_locations=rules.fiber_locations.output.bed, - fai=ancient(FAI), + pileup=rules.pileup_chromosome.output.bed, fdr_tbl=rules.fdr_table.output.tbl, output: - fire=temp("temp/{sm}/FDR-peaks/{chrom}-fire.bed"), - fiber=temp("temp/{sm}/FDR-peaks/{chrom}-fiber.bed"), - bed=temp("temp/{sm}/FDR-peaks/{chrom}-FDR.track.bed"), - threads: 8 + bed=temp("temp/{sm}/fire-peaks/{v}-{chrom}-FDR.track.bed"), + threads: 4 conda: "../envs/python.yaml" params: - script=workflow.source_path("../scripts/fire-null-distribution.py"), + script=workflow.source_path("../scripts/fdr-table.py"), resources: mem_mb=get_mem_mb_xl, shell: """ - tabix -h {input.fire} {wildcards.chrom} > {output.fire} - tabix -h {input.fiber_locations} {wildcards.chrom} > {output.fiber} - - # check if file is empty - if [ ! -s {output.fire} ]; then - echo "No FIRE sites for {wildcards.chrom}" - touch {output} - exit 0 - fi - python {params.script} -v 1 \ - {output.fire} {output.fiber} \ - {input.fai} -f {input.fdr_tbl} \ - -o {output.bed} + --fdr-table {input.fdr_tbl} \ + {input.pileup} {output.bed} """ -rule fdr_track: +rule pileup: input: beds=expand( rules.fdr_track_chromosome.output.bed, @@ -110,10 +138,10 @@ rule fdr_track: allow_missing=True, ), output: - fofn=temp("temp/{sm}/FDR-peaks/FDR.track.fofn"), - bed="results/{sm}/FDR-peaks/FDR.track.bed.gz", - tbi="results/{sm}/FDR-peaks/FDR.track.bed.gz.tbi", - threads: 8 + fofn=temp("temp/{sm}/fire/fire-{v}-pileup.fofn"), + bed="results/{sm}/{sm}-fire-{v}-pileup.bed.gz", + tbi="results/{sm}/{sm}-fire-{v}-pileup.bed.gz.tbi", + threads: 4 conda: DEFAULT_ENV shell: @@ -137,40 +165,14 @@ rule fdr_track: """ -rule fdr_track_filtered: - input: - bed=rules.fdr_track.output.bed, - minimum=rules.coverage.output.minimum, - maximum=rules.coverage.output.maximum, - output: - bed="results/{sm}/FDR-peaks/FDR.track.coverage.filtered.bed.gz", - tbi="results/{sm}/FDR-peaks/FDR.track.coverage.filtered.bed.gz.tbi", - threads: 8 - conda: - DEFAULT_ENV - shell: - """ - MIN=$(cat {input.minimum}) - MAX=$(cat {input.maximum}) - - ( \ - zcat {input.bed} | head -n 1 || true; \ - zcat {input.bed} | bioawk -tc hdr -v MAX=$MAX -v MIN=$MIN '$coverage > MIN && $coverage < MAX' \ - ) \ - | bgzip -@ {threads} \ - > {output.bed} - tabix -f -p bed {output.bed} - """ - - rule helper_fdr_peaks_by_fire_elements: input: - bed=rules.fdr_track.output.bed, - tbi=rules.fdr_track.output.tbi, + bed=rules.pileup.output.bed, + tbi=rules.pileup.output.tbi, fire=rules.fire_sites.output.bed, fire_tbi=rules.fire_sites_index.output.tbi, output: - bed=temp("temp/{sm}/FDR-peaks/{chrom}-FDR-FIRE-peaks.bed.gz"), + bed=temp("temp/{sm}/fire-peaks/{v}-{chrom}-fire-peaks.bed.gz"), threads: 2 conda: DEFAULT_ENV @@ -179,7 +181,7 @@ rule helper_fdr_peaks_by_fire_elements: min_per_acc_peak=MIN_PER_ACC_PEAK, shell: """ - HEADER=$(zcat {input.bed} | head -n 1 || true) + HEADER=$(bgzip -cd {input.bed} | head -n 1 || true) NC=$(echo $HEADER | awk '{{print NF}}' || true) FIRE_CT=$((NC+1)) FIRE_ST=$((NC+2)) @@ -193,10 +195,10 @@ rule helper_fdr_peaks_by_fire_elements: ( \ printf "$OUT_HEADER\\n"; \ tabix -h {input.bed} {wildcards.chrom} \ - | rg -w "#chrom|True" \ + | bioawk -tc hdr '(NR==1)||($is_local_max=="true")' \ | csvtk filter -tT -C '$' -f "FDR<={params.max_peak_fdr}" \ | csvtk filter -tT -C '$' -f "fire_coverage>1" \ - | bioawk -tc hdr 'NR==1 || ($fire_coverage/$coverage>={params.min_per_acc_peak})' \ + | bioawk -tc hdr '(NR==1)||($fire_coverage/$coverage>={params.min_per_acc_peak})' \ | bedtools intersect -wa -wb -sorted -a - \ -b <(tabix {input.fire} {wildcards.chrom} \ | cut -f 1-3 \ @@ -220,8 +222,8 @@ rule fdr_peaks_by_fire_elements_chromosome: minimum=rules.coverage.output.minimum, maximum=rules.coverage.output.maximum, output: - bed=temp("temp/{sm}/FDR-peaks/grouped-{chrom}-FDR-FIRE-peaks.bed.gz"), - threads: 8 + bed=temp("temp/{sm}/fire-peaks/{v}-grouped-{chrom}-fire-peaks.bed.gz"), + threads: 4 conda: "../envs/python.yaml" params: @@ -229,7 +231,7 @@ rule fdr_peaks_by_fire_elements_chromosome: min_frac_accessible=MIN_FRAC_ACCESSIBLE, shell: """ - zcat {input.bed} \ + bgzip -cd {input.bed} \ | python {params.script} -v 1 \ --max-cov $(cat {input.maximum}) \ --min-cov $(cat {input.minimum}) \ @@ -239,7 +241,7 @@ rule fdr_peaks_by_fire_elements_chromosome: """ -rule fdr_peaks_by_fire_elements: +rule fire_peaks: input: beds=expand( rules.fdr_peaks_by_fire_elements_chromosome.output.bed, @@ -247,10 +249,10 @@ rule fdr_peaks_by_fire_elements: allow_missing=True, ), output: - fofn=temp("temp/{sm}/FDR-peaks/FDR-FIRE-peaks.fofn"), - bed="results/{sm}/FDR-peaks/FDR-FIRE-peaks.bed.gz", - tbi="results/{sm}/FDR-peaks/FDR-FIRE-peaks.bed.gz.tbi", - threads: 8 + fofn=temp("temp/{sm}/fire-peaks/{sm}-fire-{v}-peaks.fofn"), + bed="results/{sm}/{sm}-fire-{v}-peaks.bed.gz", + tbi="results/{sm}/{sm}-fire-{v}-peaks.bed.gz.tbi", + threads: 4 conda: DEFAULT_ENV shell: @@ -271,15 +273,15 @@ rule fdr_peaks_by_fire_elements: """ -rule wide_fdr_peaks: +rule wide_fire_peaks: input: - bed=rules.fdr_peaks_by_fire_elements.output.bed, - track=rules.fdr_track.output.bed, + bed=rules.fire_peaks.output.bed, + track=rules.pileup.output.bed, fai=ancient(FAI), output: - bed="results/{sm}/FDR-peaks/FDR-wide-peaks.bed.gz", - tbi="results/{sm}/FDR-peaks/FDR-wide-peaks.bed.gz.tbi", - bb="results/{sm}/trackHub/bb/FDR-wide-peaks.bb", + bed="results/{sm}/additional-outputs-{v}/fire-peaks/{sm}-fire-{v}-wide-peaks.bed.gz", + tbi="results/{sm}/additional-outputs-{v}/fire-peaks/{sm}-fire-{v}-wide-peaks.bed.gz.tbi", + bb="results/{sm}/trackHub-{v}/bb/fire-wide-peaks.bb", conda: DEFAULT_ENV threads: 4 @@ -291,7 +293,7 @@ rule wide_fdr_peaks: shell: """ ( \ - zcat {input.bed}; \ + bgzip -cd {input.bed}; \ bioawk -tc hdr 'NR==1 || $FDR<={params.max_peak_fdr}' {input.track} \ | bioawk -tc hdr 'NR==1 || $coverage>0' \ | bioawk -tc hdr 'NR==1 || ($fire_coverage/$coverage>={params.min_frac_acc})' \ @@ -311,15 +313,15 @@ rule wide_fdr_peaks: """ -rule one_percent_fdr_peaks: +rule one_percent_fire_peaks: input: - bed=rules.fdr_peaks_by_fire_elements.output.bed, - track=rules.fdr_track.output.bed, + bed=rules.fire_peaks.output.bed, + track=rules.pileup.output.bed, output: - bed="results/{sm}/FDR-peaks/one-percent-FDR/FDR-01-FIRE-peaks.bed.gz", - tbi="results/{sm}/FDR-peaks/one-percent-FDR/FDR-01-FIRE-peaks.bed.gz.tbi", - wide="results/{sm}/FDR-peaks/one-percent-FDR/FDR-01-FIRE-wide-peaks.bed.gz", - wide_tbi="results/{sm}/FDR-peaks/one-percent-FDR/FDR-01-FIRE-wide-peaks.bed.gz.tbi", + bed="results/{sm}/additional-outputs-{v}/fire-peaks/one-percent-FDR/{sm}-fire-{v}-01-fire-peaks.bed.gz", + tbi="results/{sm}/additional-outputs-{v}/fire-peaks/one-percent-FDR/{sm}-fire-{v}-01-fire-peaks.bed.gz.tbi", + wide="results/{sm}/additional-outputs-{v}/fire-peaks/one-percent-FDR/{sm}-fire-{v}-01-fire-wide-peaks.bed.gz", + wtbi="results/{sm}/additional-outputs-{v}/fire-peaks/one-percent-FDR/{sm}-fire-{v}-01-fire-wide-peaks.bed.gz.tbi", threads: 4 conda: DEFAULT_ENV @@ -327,14 +329,14 @@ rule one_percent_fdr_peaks: nuc_size=config.get("nucleosome_size", 147), shell: """ - zcat {input.bed} \ + bgzip -cd {input.bed} \ | csvtk filter -tT -C '$' -f "FDR<=0.01" \ | bgzip -@ {threads} \ > {output.bed} tabix -f -p bed {output.bed} ( \ - zcat {output.bed}; \ + bgzip -cd {output.bed}; \ bioawk -tc hdr '$FDR<=0.01' {input.track} \ ) \ | cut -f 1-3 \ @@ -348,13 +350,13 @@ rule one_percent_fdr_peaks: rule peaks_vs_percent: input: - bed=rules.fdr_peaks_by_fire_elements.output.bed, + bed=rules.fire_peaks.output.bed, output: fig1=report( - "results/{sm}/FDR-peaks/{sm}.peaks-vs-percent.pdf", + "results/{sm}/additional-outputs-{v}/figures/{sm}-fire-{v}-peaks-vs-percent.pdf", category="Peak calls", ), - threads: 8 + threads: 4 conda: "../envs/R.yaml" script: diff --git a/workflow/rules/levio.smk b/workflow/rules/levio.smk new file mode 100644 index 0000000000..e6d4b1b16c --- /dev/null +++ b/workflow/rules/levio.smk @@ -0,0 +1,102 @@ +# +# Index the chain file for leviosam2. +# +# Input here is a chain file that defines the alignment between the DSA and the reference genome +# at a contig level (>100 kbp of alignment). +# +# The output is a special leviosam2 index file that is used to lift over the alignments from the DSA to the reference genome. +# +rule leviosam2_index: + input: + chain=DSA_CHAIN, + fai=FAI, + output: + index=temp("temp/{sm}/leviosam2-index/index.clft"), + conda: + DEFAULT_ENV + threads: 1 + resources: + mem_mb=64 * 1024, + runtime=16 * 60, + shell: + """ + {LEVIO_EXE} index \ + -p results/leviosam2-index/index \ + -c {input.chain} \ + -F {input.fai} + """ + + +# +# Lift over the alignments from the DSA to the reference genome using the chain file / leviosam2 index. +# +# This is not a realignment, but a lift over of the reads from the DSA to the reference genome. +# +rule leviosam2: + input: + bam=rules.fire.output.bam, + levio_index=rules.leviosam2_index.output.index, + ref=REF, + output: + lifted=temp("temp/{sm}/leviosam2/{sm}-{chrom}-committed.bam"), + deferred=temp("temp/{sm}/leviosam2/{sm}-{chrom}-deferred.bam"), + unliftable=temp("temp/{sm}/leviosam2/{sm}-{chrom}-unliftable.bam"), + threads: MAX_THREADS + resources: + mem_mb=MAX_THREADS * 4 * 1024, + runtime=16 * 60, + conda: + DEFAULT_ENV + params: + # maximum number of CIGAR opts to change, also the max gap size that can be spanned + G=config.get("levio_G", 100_000), + # Using -S clipped_frac 0.05 means when a read has >5% clipped bases, it is deferred. A lower value is more stringent (by deferring more reads). + # aln_score is the minumum score before the alignment is lifted over + S=config.get( + "levio_S", + f"-S mapq:0 -S hdist:{100_000} -S isize:{100_000} -S clipped_frac:0.95 -S aln_score:100", + ), + # number of reads per thread + T=config.get("levio_T", 4 * 256), + shell: + """ + PRE="temp/{wildcards.sm}/leviosam2/{wildcards.sm}-{wildcards.chrom}" + {LEVIO_EXE} lift -t {threads} -a {input.cram} \ + -T {params.T} -G {params.G} {params.S} \ + -C {input.levio_index} -p $PRE -f {input.ref} -m -O bam + """ + + +# +# This step sorted the leviosam2 output and fixes some tags in the CRAM file. +# +# Specifically, the MAPQ is reset to 60 for all reads that were previously aligned to the DSA. +# And the XS tag is set to zero for all reads that were aligned to the DSA. +# This is a hueristic that we may need to return to in the future. +# +# Other tags and fields like CIGAR, bitflags, and MD are correctly updated by +# leviosam2 during liftover. +# +rule leviosam2_sorted: + input: + lifted=rules.leviosam2.output.lifted, + ref=REF, + output: + bam=temp("temp/{sm}/leviosam2/{sm}-{chrom}-sorted.bam"), + threads: SORT_THREADS + resources: + mem_mb=SORT_THREADS * 4 * 1024, + runtime=16 * 60, + conda: + DEFAULT_ENV + shell: + """ + samtools sort {input.lifted} \ + -@ {threads} -m 3G \ + -o {output.bam} + """ + + +# params: +# reset_mapq=workflow.source_path("../scripts/reset-mapq.py"), +# python {params.reset_mapq} -t {threads} {input.lifted} \ diff --git a/workflow/rules/peak-stats.smk b/workflow/rules/stats.smk similarity index 66% rename from workflow/rules/peak-stats.smk rename to workflow/rules/stats.smk index 2348e42373..366eca87e8 100644 --- a/workflow/rules/peak-stats.smk +++ b/workflow/rules/stats.smk @@ -9,7 +9,7 @@ rule clustering_vs_null: tmp=temp("temp/{sm}/tmp.pre.calls.bed"), null=temp("temp/{sm}/null.calls.bed"), bed="results/{sm}/clustering-vs-null.bed.gz", - threads: 8 + threads: 4 conda: DEFAULT_ENV shell: @@ -29,11 +29,11 @@ rule fires_in_peaks: input: fire=rules.fire_sites.output.bed, exclude=rules.unreliable_coverage_regions.output.bed, - peaks=rules.fdr_peaks_by_fire_elements.output.bed, + peaks=rules.fire_peaks.output.bed, output: - tmp=temp("temp/{sm}/tmp.FIREs-in-peaks.bed"), - txt="results/{sm}/tables/FIREs-in-peaks.txt", - threads: 8 + tmp=temp("temp/{sm}/tmp.FIREs-{v}-in-peaks.bed"), + txt="results/{sm}/additional-outputs-{v}/fire-peaks/{sm}-{v}-fires-in-peaks.txt", + threads: 4 conda: DEFAULT_ENV params: @@ -50,21 +50,35 @@ rule fires_in_peaks: """ +rule ft_qc: + input: + cram=rules.fire.output.cram, + output: + tbl="results/{sm}/{sm}-fire-{v}-qc.tbl.gz", + conda: + DEFAULT_ENV + threads: 16 + shell: + """ + {FT_EXE} qc --acf -t {threads} {input.cram} {output.tbl} + """ + + rule hap_differences: input: - bed=rules.fdr_peaks_by_fire_elements.output.bed, + bed=rules.fire_peaks.output.bed, output: fig1=report( - "results/{sm}/hap1-vs-hap2/hap1-vs-hap2.pdf", + "results/{sm}/additional-outputs-{v}/figures/{sm}-{v}-hap1-vs-hap2.pdf", category="Haplotype selectivity", ), fig2=report( - "results/{sm}/hap1-vs-hap2/hap1-vs-hap2-volcano.pdf", + "results/{sm}/additional-outputs-{v}/figures/{sm}-{v}-hap1-vs-hap2-volcano.pdf", category="Haplotype selectivity", ), - bed="results/{sm}/hap1-vs-hap2/FIRE.hap.differences.bed", - bed9="results/{sm}/hap1-vs-hap2/FIRE.hap.differences.bed9", - threads: 8 + bed="results/{sm}/{sm}-fire-{v}-hap-differences.bed.gz", + bed9=temp("temp/{sm}/hap1-vs-hap2/FIRE-{v}.hap.differences.bed9"), + threads: 4 conda: "../envs/R.yaml" script: diff --git a/workflow/rules/track-hub.smk b/workflow/rules/track-hub.smk index 4a2eb5f868..b67902249a 100644 --- a/workflow/rules/track-hub.smk +++ b/workflow/rules/track-hub.smk @@ -1,85 +1,91 @@ rule percent_accessible: input: - bed=rules.fdr_track.output.bed, + bed=rules.pileup.output.bed, fai=ancient(FAI), output: - tmp=temp("temp/{sm}/{hp}/percent.accessible.bed"), - bw="results/{sm}/trackHub/bw/{hp}.percent.accessible.bw", - bed="results/{sm}/{hp}/percent.accessible.bed.gz", - tbi="results/{sm}/{hp}/percent.accessible.bed.gz.tbi", + tmp=temp("temp/{sm}/{hp}/{v}-percent.accessible.bed"), + bw="results/{sm}/trackHub-{v}/bw/{hp}.percent.accessible.bw", threads: 4 conda: DEFAULT_ENV resources: mem_mb=get_mem_mb, params: - cols=hap_hck_columns, + suffix=get_hap_col_suffix, + nzooms=NZOOMS, + chrom=get_chroms()[0], shell: """ - zcat {input.bed} \ - | hck -f 1-3 {params.cols} \ - | grep -v "^#" \ - | awk -v OFS='\t' '$5 > 0 {{print $1,$2,$3,$4*100/$5}}' \ + bgzip -cd {input.bed} \ + | bioawk -tc hdr '$coverage{params.suffix}>0' \ + | bioawk -tc hdr \ + 'NR>1{{print $1,$2,$3,100*$fire_coverage{params.suffix}/$coverage{params.suffix}}}' \ > {output.tmp} - # skip if the file is empty + # add fake if file is empty if [[ -s {output.tmp} ]]; then - bigtools bedgraphtobigwig \ - --nzooms 10 -s start \ - {output.tmp} {input.fai} {output.bw} + echo "File is not empty" else - touch {output.bw} + echo "File is empty" + printf "{params.chrom}\t0\t1\t0\\n" > {output.tmp} fi - - bgzip -@{threads} -c {output.tmp} > {output.bed} - tabix -p bed {output.bed} + + + bigtools bedgraphtobigwig \ + --nzooms {params.nzooms} -s start \ + {output.tmp} {input.fai} {output.bw} """ rule element_coverages_bw: input: - bed=rules.element_coverages.output.bed, + bed=rules.pileup.output.bed, fai=ancient(FAI), output: - bw="results/{sm}/trackHub/bw/{hp}.{el_type}.coverage.bw", + bw="results/{sm}/trackHub-{v}/bw/{hp}.{el_type}.coverage.bw", conda: DEFAULT_ENV + params: + nzooms=NZOOMS, + cut_cmd=pileup_cut_cmd, shell: """ - zcat {input.bed} \ - | hck -f 1-3 -F {wildcards.el_type} \ + bgzip -cd {input.bed} \ + | {params.cut_cmd} \ | grep -v "^#" \ | bigtools bedgraphtobigwig \ - -s start --nzooms 10 \ + -s start --nzooms {params.nzooms} \ - {input.fai} {output.bw} """ rule fdr_track_to_bw: input: - bed=rules.fdr_track.output.bed, + bed=rules.pileup.output.bed, fai=ancient(FAI), output: - bw="results/{sm}/trackHub/bw/{col}.bw", + bw="results/{sm}/trackHub-{v}/bw/{col}.bw", threads: 4 conda: DEFAULT_ENV + params: + nzooms=NZOOMS, shell: """ hck -z -f 1-3 -F {wildcards.col} {input.bed} \ | grep -v "^#" \ | bigtools bedgraphtobigwig \ - -s start --nzooms 10 \ + -s start --nzooms {params.nzooms} \ - {input.fai} {output.bw} """ -rule fdr_peaks_by_fire_elements_to_bb: +rule fire_peaks_bb: input: - bed=rules.fdr_peaks_by_fire_elements.output.bed, + bed=rules.fire_peaks.output.bed, fai=ancient(FAI), output: - bb="results/{sm}/trackHub/bb/FDR-FIRE-peaks.bb", + bb="results/{sm}/trackHub-{v}/bb/fire-peaks.bb", threads: 4 conda: DEFAULT_ENV @@ -87,7 +93,7 @@ rule fdr_peaks_by_fire_elements_to_bb: bedfmt=workflow.source_path("../templates/fire_peak.as"), shell: """ - zcat {input.bed} \ + bgzip -cd {input.bed} \ | bioawk -tc hdr '{{print $1,$2,$3,"peak-"NR,int($score*10),".",$score,"-1",$log_FDR,int($start/2+$end/2)-$peak_start}}' \ | bioawk -tc hdr '$5<=1000' \ | rg -v '^#' \ @@ -102,8 +108,8 @@ rule hap_differences_track: bed9=rules.hap_differences.output.bed9, fai=ancient(FAI), output: - bb="results/{sm}/trackHub/bb/hap_differences.bb", - threads: 1 + bb="results/{sm}/trackHub-{v}/bb/hap_differences.bb", + threads: 4 resources: mem_mb=get_mem_mb, conda: @@ -125,15 +131,10 @@ rule hap_differences_track: rule trackhub: input: - fai=ancient(FAI), - fire=rules.fdr_peaks_by_fire_elements_to_bb.output.bb, cov=rules.coverage.output.cov, - hap_diffs=rules.hap_differences_track.output.bb, - wide=rules.wide_fdr_peaks.output.bb, - decorators_1=rules.decorate_fibers_1.output.bb, - decorators_2=rules.decorate_fibers_2.output.bb, output: - hub="results/{sm}/trackHub/hub.txt", + hub="results/{sm}/trackHub-{v}/hub.txt", + description="results/{sm}/trackHub-{v}/fire-description.html", resources: load=get_load, threads: 4 @@ -142,11 +143,13 @@ rule trackhub: params: ref=REF_NAME, script=workflow.source_path("../scripts/trackhub.py"), + description=workflow.source_path("../templates/fire-description.html"), shell: """ python {params.script} -v 2 \ - --trackhub-dir results/{wildcards.sm}/trackHub \ + --trackhub-dir results/{wildcards.sm}/trackHub-{wildcards.v} \ --reference {params.ref} \ --sample {wildcards.sm} \ --average-coverage $(cat {input.cov}) + cp {params.description} {output.description} """ diff --git a/workflow/scripts/cov.py b/workflow/scripts/cov.py index 6058fc1669..4c69c48c43 100644 --- a/workflow/scripts/cov.py +++ b/workflow/scripts/cov.py @@ -1,7 +1,5 @@ import pandas as pd -import numpy as np import sys -import os import math import polars as pl diff --git a/workflow/scripts/decorated-bed12.py b/workflow/scripts/decorated-bed12.py deleted file mode 100755 index 59669c6e50..0000000000 --- a/workflow/scripts/decorated-bed12.py +++ /dev/null @@ -1,163 +0,0 @@ -#!/usr/bin/env python -import defopt -import sys -import gc -import logging -from pathlib import Path -from typing import Optional -import pandas as pd -import polars as pl -import numpy as np -from numba import njit -import io - -# from pyinstrument import Profiler - - -def chunker(seq, size): - return (seq[pos : pos + size] for pos in range(0, len(seq), size)) - - -def make_decorator(ct, fiber, score, strand, color, el_type, hp, st, en, starts, ends): - start = starts[0] - end = ends[-1] - lengths = ",".join(map(str, ends - starts)) - offsets = ",".join(map(str, starts - start)) - block_count = len(starts) - # chr1 12 9985 block 1000 + 12 9985 200,0,150 - # 382, 1 , 1 - # chr1:1-10000:LongRead - # block 255,0,0,180 Ignored TypeA - # 1 is transparent - prime_color = "200,200,200,1" - return ( - # bed9 - f"{ct}\t{start}\t{end}\t{el_type}\t{score}\t{strand}\t{start}\t{end}\t{prime_color}\t" - # bed12 - f"{block_count}\t{lengths}\t{offsets}\t" - # read tag for the decorator - f"{ct}:{st}-{en}:{fiber}\t" - # decorator - f"block\t{color},0\tIgnored\t{el_type}" - ) - - -def subgroup(df, ct, fiber, strand, hp): - st = df["st"].min() - en = df["en"].max() - # tmp = df.filter(pl.col("color") != "230,230,230") - # linker = df.filter(pl.col("color") == "147,112,219") - # fire = df.filter(pl.col("color") == "255,0,0") - # for el_type, tdf in zip(["Linker", "FIRE"], [linker, fire]): - for (color, score), gdf in df.group_by(["color", "score"]): - if gdf.shape[0] == 0 or color == "230,230,230": - continue - elif color == "147,112,219": - el_type = "Linker" - else: - el_type = "FIRE" - decorator = make_decorator( - ct, - fiber, - score, - strand, - color, - el_type, - hp, - st, - en, - gdf["st"], - gdf["en"], - ) - print(decorator) - - return ( - ct, - st, - en, - fiber, - 1, - strand, - 0, - 0, - "0,0,0,200", - 2, - "1,1", - f"0,{en-st-1}", - hp, - ) - - -def process(df, outfile, group_size=5_000): - data = [] - fibers = df["fiber"].unique() - n_fibers = len(fibers) - n = 0 - mode = "w" - for (ct, fiber, hp), gdf in df.group_by( - ["#ct", "fiber", "HP"], maintain_order=True - ): - strand = "." - data.append(subgroup(gdf, ct, fiber, strand, hp)) - n += 1 - if n % group_size == 0 or n == n_fibers: - logging.info(f"processed {n:,} fibers of {n_fibers:,}") - pd.DataFrame(data).sort_values([0, 1, 2]).to_csv( - outfile, sep="\t", header=False, index=False, mode=mode - ) - mode = "a" - data = [] - gc.collect() - - -def main( - infile: str, - outfile: Optional[Path], - *, - verbose: int = 0, -): - """ - Author Mitchell R. Vollger - :param infile: Input file, stdin by default - :param outfile: Output file, stdout by default - :param verbose: Set the logging level of the function - """ - if infile == "-": - infile = io.StringIO(sys.stdin.read()) - - logger = logging.getLogger() - log_format = "[%(levelname)s][Time elapsed (ms) %(relativeCreated)d]: %(message)s" - log_level = 10 * (3 - verbose) - logging.basicConfig(format=log_format) - logger.setLevel(log_level) - - df = pl.read_csv( - infile, - separator="\t", - low_memory=True, - columns=[ - "#ct", - "st", - "en", - "fiber", - "score", - # "strand", - "HP", - "color", - ], - ) - if df.shape[0] == 0: - outfile = open(outfile, "w") - return 0 - # df = df.filter(pl.col("color") != "230,230,230") - - # with Profiler(interval=0.1) as profiler: - logging.info(f"{df}") - process(df, outfile) - # profiler.print() - # profiler.open_in_browser() - return 0 - - -if __name__ == "__main__": - defopt.run(main, show_types=True, version="0.0.1") diff --git a/workflow/scripts/fdr-table.py b/workflow/scripts/fdr-table.py new file mode 100644 index 0000000000..8ec3f023a5 --- /dev/null +++ b/workflow/scripts/fdr-table.py @@ -0,0 +1,312 @@ +#!/usr/bin/env python +import defopt +import logging +from pathlib import Path +from typing import Optional +import pandas as pd +import polars as pl +import numpy as np +import polars.selectors as cs +import gzip + +# from numba import njit +ROLLING_FIRE_SCORE_WINDOW_SIZE = 200 + + +def is_gzipped(path): + with open(path, "rb") as f: + return f.read(2) == b"\x1f\x8b" + + +def find_nearest(array, value): + idx = np.searchsorted(array, value, side="left") + idx[idx < 0] = 0 + idx[idx >= len(array)] = len(array) - 1 + return idx + + +# ['#chrom', 'start', 'end', 'coverage', 'fire_coverage', 'score', 'nuc_coverage', 'msp_coverage', +# 'coverage_H1', 'fire_coverage_H1', 'score_H1', 'nuc_coverage_H1', 'msp_coverage_H1', +# 'coverage_H2', 'fire_coverage_H2', 'score_H2', 'nuc_coverage_H2', 'msp_coverage_H2'] +def read_pileup_file(infile, nrows): + # get the header from the first line of the file + header = pl.read_csv(infile, separator="\t", n_rows=1).columns + + # check that there is at least two lines + open_infile = gzip.open if is_gzipped(infile) else open + with open_infile(infile) as f: + for i, _ in enumerate(f): + if i > 1: + break + if i < 2: + return None + + # add scema overrides for the score columns + schema_overrides = {} + for n in ["score", "score_H1", "score_H2", "score_shuffled"]: + if n in header: + schema_overrides[n] = float + + logging.info(f"Header of the pileup file:\n{header}") + logging.info(f"Schema overrides for the pileup file:\n{schema_overrides}") + + # read the file + pileup = pl.read_csv( + infile, + separator="\t", + has_header=False, + new_columns=header, + comment_prefix="#", + n_rows=nrows, + infer_schema_length=100000, + schema_overrides=schema_overrides, + ) + logging.info(f"Done reading pileup file:\n{pileup}") + return pileup + + +# @njit +def fdr_from_fire_scores(fire_scores): + Vs = [] + Rs = [] + Ts = [] + cur_R = 0.0 + cur_V = 0.0 + pre_score = -1.0 + first = True + for score, is_real, bp in fire_scores.iter_rows(): + # save the counts and thresholds as long as we have counts + if score != pre_score and cur_R > 0 and not first: + Rs.append(cur_R) + Vs.append(cur_V) + Ts.append(pre_score) + # don't add negative scores to the fdr data, since they have no coverage. + if score < 0.0: + break + # update the counts + if is_real: + cur_R += bp + else: + cur_V += bp + # prepare for next iteration + pre_score = score + first = False + + # add the last threshold with an FDR of 1 + Rs.append(1) + Vs.append(1) + Ts.append(-1.0) + + # set up return values + Vs = np.array(Vs) + Rs = np.array(Rs) + Ts = np.array(Ts) + FDRs = Vs / Rs + FDRs[FDRs > 1] = 1.0 + + return (Ts, FDRs, Vs, Rs) + + +def fdr_table_from_scores(fire_scores): + # Calculate FDR thresholds + Ts, FDRs, Vs, Rs = fdr_from_fire_scores(fire_scores) + results = pd.DataFrame( + { + "threshold": Ts, + "FDR": FDRs, + "shuffled_bp": Vs, + "real_bp": Rs, + } + ) + # simplify the results a little, don't want 100,000s of thresholds + results = results.groupby("FDR", sort=False).tail(1).reset_index(drop=True) + results = results.groupby("shuffled_bp", sort=False).tail(1).reset_index(drop=True) + results = results.groupby("real_bp", sort=False).tail(1).reset_index(drop=True) + # limit the number of thresholds that can be in the table + results["threshold"] = results["threshold"].round(2) + results = results.groupby("threshold", sort=False).tail(1).reset_index(drop=True) + # sort the results by threshold so that they are now acceding + # which is needed for the find_nearest function + results = results.sort_values("threshold") + logging.info(f"FDR results\n{results}") + return results + + +def make_fdr_table(infile, outfile, nrows, max_cov=None, min_cov=None): + # read the pileup file + pileup = read_pileup_file(infile, nrows) + # filter on coverages if needed + if max_cov is not None: + pileup = pileup.filter( + pl.col("coverage") <= max_cov, pl.col("coverage_shuffled") <= max_cov + ) + if min_cov is not None: + pileup = pileup.filter( + pl.col("coverage") >= min_cov, pl.col("coverage_shuffled") >= min_cov + ) + + # aggregate by the score and weight the score by the number of bases + fire_scores = ( + pileup.melt( + value_vars=["score", "score_shuffled"], + id_vars=["#chrom", "start", "end"], + variable_name="type", + value_name="score", + ) + .with_columns( + pl.when(pl.col("type") == "score") + .then(True) + .otherwise(False) + .alias("is_real"), + pl.col("end").sub(pl.col("start")).alias("bp"), + ) + .group_by(["score", "is_real"]) + .agg(pl.sum("bp").alias("bp")) + .sort("score", descending=True) + ) + + # count bases in each category + sums = fire_scores.group_by("is_real").agg(pl.sum("bp").alias("Mbp") / 1_000_000) + logging.info(f"Number of Mbp in each category:\n{sums}") + + logging.info(f"Done aggregating pileup file:\n{fire_scores}") + fdr_table = fdr_table_from_scores(fire_scores) + fdr_table.to_csv(outfile, sep="\t", index=False) + return fdr_table + + +def read_fdr_table(infile): + fdr_table = pl.read_csv(infile, separator="\t").to_pandas() + logging.info(f"Read FDR table:\n{fdr_table}") + return fdr_table + + +def apply_fdr_table(infile, outfile, fdr_table, nrows): + pileup = read_pileup_file(infile, nrows) + # there is no input data + if pileup is None: + Path(outfile).touch() + return + + logging.info(f"Applying FDR table to pileup file:\n{pileup}") + # add a new column that reports the largest score in a centered window of with ROLLING_FIRE_SCORE_WINDOW_SIZE number of bases + rolling_max_score = ( + pileup.rolling( + index_column="start", + period=f"{ROLLING_FIRE_SCORE_WINDOW_SIZE}i", + offset=f"-{ROLLING_FIRE_SCORE_WINDOW_SIZE // 2}i", + closed="both", + group_by="#chrom", + ) + .agg( + pl.max("score").alias("max_window_score").fill_null(-1.0), + ) + .drop("#chrom") + ) + + # add the max window score to the pileup + pileup = ( + pileup.with_columns(rolling_max_score) + .with_columns( + pl.when( + (pl.col("score") == pl.col("max_window_score")) + & (pl.col("score") > 0.0) + ) + .then(True) + .otherwise(False) + .alias("is_local_max"), + ) + .with_columns( + pl.col("is_local_max").rle_id().alias("local_max_group"), + ) + ) + + # group by local_max_group and find the midpoint (bp) of the group + middle = ( + pileup.group_by(["#chrom", "local_max_group"]) + .agg( + pl.col("end").sub(pl.col("start")).sum().alias("width"), + pl.min("start").alias("first_start"), + ) + .with_columns( + # find the middle of the group + pl.col("first_start").add(pl.col("width") // 2).alias("middle"), + ) + .drop("width", "first_start") + ) + + # find the middle row of local max groups + pileup = ( + pileup.join(middle, on=["#chrom", "local_max_group"], how="left") + .with_columns( + ( + (pl.col("start") <= pl.col("middle")) + & (pl.col("end") > pl.col("middle")) + ).alias("is_middle"), + ) + .with_columns( + # only keep the local maxes that are in the middle of the group + (pl.col("is_local_max") & pl.col("is_middle")).alias("is_local_max"), + ) + .drop("middle", "is_middle", "local_max_group", "max_window_score") + .drop(cs.ends_with("_shuffled")) + ) + + # find the FDRs for the thresholds + fdr_idx = find_nearest(fdr_table.threshold.values, pileup["score"].to_numpy()) + FDRs = fdr_table.FDR.values[fdr_idx] + pileup = ( + pileup.with_columns( + FDR=FDRs, + ) + .with_columns( + pl.when(pl.col("score") >= 0.0) + .then(pl.col("FDR")) + .otherwise(1.0) + .alias("FDR"), + ) + .with_columns( + (-10 * np.log10(pl.col("FDR"))).alias("log_FDR").replace(float("inf"), 100), + ) + ) + + logging.info(f"Done calculating max window score:\n{pileup}") + # write the pileup to a file + pileup.write_csv(outfile, separator="\t") + + +def main( + infile: Path, + outfile: Path, + *, + fdr_table: Path = None, + nrows: Optional[int] = None, + max_cov: Optional[int] = None, + min_cov: Optional[int] = None, + verbose: int = 0, +): + """ + Author Mitchell R. Vollger + + :param infile: ft pileup track with shuffled fiber data + :param outfile: FIRE score to FDR table + :param verbose: Set the logging level of the function + """ + logger = logging.getLogger() + log_format = "[%(levelname)s][Time elapsed (ms) %(relativeCreated)d]: %(message)s" + log_level = 10 * (3 - verbose) + logging.basicConfig(format=log_format) + logger.setLevel(log_level) + + if fdr_table is not None: + fdr_table = read_fdr_table(fdr_table) + apply_fdr_table(infile, outfile, fdr_table, nrows) + else: + fdr_table = make_fdr_table( + infile, outfile, nrows, min_cov=min_cov, max_cov=max_cov + ) + return 0 + + +if __name__ == "__main__": + defopt.run(main, show_types=True, version="0.0.1") diff --git a/workflow/scripts/fire-null-distribution.py b/workflow/scripts/fire-null-distribution.py deleted file mode 100755 index db9f563c95..0000000000 --- a/workflow/scripts/fire-null-distribution.py +++ /dev/null @@ -1,565 +0,0 @@ -#!/usr/bin/env python -import defopt -import sys -import gc -import logging -from pathlib import Path -from typing import Optional -import pandas as pd -import polars as pl -import numpy as np -from numba import njit - -ROLLING_FIRE_SCORE_WINDOW_SIZE = 200 - -FIRE_COLUMNS = [ - "chrom", - "start", - "end", - "fiber", - "score", - "strand", - "thick_start", - "thick_end", - "item_rgb", - "fdr", - "hap", -] -FIBER_COLUMNS = [ - "chrom", - "fiber", - "fiber_start", - "fiber_end", - "null_fiber_start", - "null_fiber_end", -] -HAPS = ["H1", "H2"] - - -def rle(inarray): - """run length encoding. Partial credit to R rle function. - Multi datatype arrays catered for including non Numpy - returns: tuple (runlengths, startpositions, values)""" - ia = np.asarray(inarray) # force numpy - if ia.size == 0: - return (ia, ia, ia) - else: - n = ia.shape[0] - y = ia[1:] != ia[:-1] # pairwise unequal (string safe) - i = np.append(np.where(y), n - 1) # must include last element posi - z = np.diff(np.append(-1, i)) # run lengths - p = np.cumsum(np.append(0, z))[:-1] # positions - return (z, p, ia[i]) - - -def bed_rle(inarray): - run_lengths, starts, scores = rle(inarray) - starts = starts.astype(int) - ends = starts + run_lengths.astype(int) - return np.array([starts, ends, scores]).transpose() - - -@njit -def is_local_max(array): - output = [] - for idx in range(array.shape[0]): - if idx - 1 < 0 or idx + 1 >= array.shape[0]: - output.append(False) - continue - cur_res = False - pre = array[idx - 1] - cur = array[idx] - next = array[idx + 1] - if cur >= pre and cur >= next: - cur_res = True - output.append(cur_res) - - return output - - -@njit -def fire_scores_per_chrom( - starts, - ends, - q_values, - chrom_length, - coverage_array, - min_allowed_q=0.01, - min_coverage=4, -): - fire_scores = np.zeros(int(chrom_length), dtype=np.float64) - - multi = -50.0 # a multi of -50 and a min_allowed_q of 0.01 gives a max score of 100 - max_add = multi * np.log10(min_allowed_q) - q_values_t = multi * np.log10(q_values) - for start, end, q in zip(starts, ends, q_values_t): - if end >= chrom_length: - continue - fire_scores[start:end] += min(q, max_add) - - # correct for coverage - fire_scores = fire_scores / coverage_array - # correct divide by zeros - fire_scores[np.isnan(fire_scores)] = 0.0 - # drop the scores that have no coverage - fire_scores[coverage_array < min_coverage] = -1.0 - return fire_scores - - -@njit -def fdr_from_fire_scores(fire_scores): - Vs = [] - Rs = [] - Ts = [] - cur_R = 0.0 - cur_V = 0.0 - pre_score = -1.0 - first = True - for start, end, score, is_real in fire_scores: - # save the counts and thresholds as long as we have counts - if score != pre_score and cur_R > 0 and not first: - Rs.append(cur_R) - Vs.append(cur_V) - Ts.append(pre_score) - # don't add negative scores to the fdr data, since they have no coverage. - if score < 0.0: - break - # update the counts - counts = end - start - if is_real: - cur_R += counts - else: - cur_V += counts - # prepare for next iteration - pre_score = score - first = False - # set up return values - Vs = np.array(Vs) - Rs = np.array(Rs) - Ts = np.array(Ts) - FDRs = Vs / Rs - FDRs[FDRs > 1] = 1.0 - return (Ts, FDRs, Vs, Rs) - - -@njit -def get_coverage_from_array(starts, ends, coverage_array, stat="median"): - out_coverage = np.zeros(starts.shape[0], dtype=np.float64) - idx = 0 - for start, end in zip(starts, ends): - if stat == "median": - val = np.median(coverage_array[start:end]) - elif stat == "max": - val = np.max(coverage_array[start:end]) - else: - val = np.mean(coverage_array[start:end]) - out_coverage[idx] = val - idx += 1 - return out_coverage - - -@njit -def make_coverage_array(starts, ends, chrom_length): - coverage_array = np.zeros(int(chrom_length), dtype=np.float64) - for start, end in zip(starts, ends): - coverage_array[start:end] += 1 - return coverage_array - - -def fire_tracks(fire, outfile, min_coverage=4): - null_s = [] - fire_s = [] - logging.info(f"Fire data\n{fire}") - # number of elements where start and fiber_start are null - null_count = fire.filter( - pl.col("start").is_null() & pl.col("fiber_start").is_null() - ).shape[0] - if null_count > 0: - logging.warn(f"Null count: {null_count}") - - for chrom, g in fire.group_by("chrom", maintain_order=True): - logging.info(f"Processing {chrom}") - # fibers for this chromosome - fibers = ( - g[FIBER_COLUMNS] - .filter(~pl.col("fiber_start").is_null()) - .unique() - .to_pandas() - ) - # convert to pandas for easier manipulation - g = ( - g.filter(~pl.col("start").is_null()) - .filter(~pl.col("fiber_start").is_null()) - .to_pandas() - ) - logging.debug(f"Grouped fire data\n{g}\n{g.dtypes}") - - if g.shape[0] == 0: - logging.warning(f"No data for {chrom}") - continue - - # get coverage for this chromosome and the shuffled fibers - chrom_length = g.length[0].astype(int) - coverage_array = make_coverage_array( - fibers.fiber_start.values, fibers.fiber_end.values, chrom_length - ) - null_coverage_array = make_coverage_array( - fibers.null_fiber_start.values, fibers.null_fiber_end.values, chrom_length - ) - expected_median_coverage = np.median( - null_coverage_array[null_coverage_array > 0] - ) - - # find offset to use based on the shuffled fiber - g["offset"] = g.null_fiber_start - g.fiber_start - g["null_start"] = g.start + g.offset - g["null_end"] = g.end + g.offset - - logging.info( - f"real bp: {(g.end-g.start).sum():,}\t" - f"null bp: {(g.null_end-g.null_start).sum():,}" - ) - - # - rle_fire_scores = bed_rle( - fire_scores_per_chrom( - g.start.values, - g.end.values, - g.fdr.values, - g.length.max(), - coverage_array, - min_coverage=min_coverage, - ) - ) - rle_null_scores = bed_rle( - fire_scores_per_chrom( - g.null_start.values, - g.null_end.values, - g.fdr.values, - g.length.max(), - null_coverage_array, - min_coverage=min_coverage, - ) - ) - n_bp_considered = (coverage_array >= min_coverage).sum() - logging.info( - f"{chrom}: {n_bp_considered:,} of {chrom_length:,}\t" - f"Max real FIRE score: {rle_fire_scores[:,2].max():,.8}\t" - f"Max null FIRE score: {rle_null_scores[:,2].max():,.8}\t" - f"Expected median coverage: {expected_median_coverage}" - ) - fire_s.append(rle_fire_scores) - null_s.append(rle_null_scores) - - # all data - fire_scores = np.concatenate(fire_s) - null_fire_scores = np.concatenate(null_s) - logging.debug(f"rle fire score shape: {fire_scores.shape}") - logging.info( - f"all: {fire_scores.shape[0]:,}\t" - f"Max real FIRE score: {fire_scores[:,2].max():,.8}\t" - f"Max null FIRE score: {null_fire_scores[:,2].max():,.8}" - ) - - # convert to pandas for easier manipulation - fire_scores = pd.DataFrame(fire_scores, columns=["start", "end", "score"]) - fire_scores["is_real"] = 1.0 - null_fire_scores = pd.DataFrame(null_fire_scores, columns=["start", "end", "score"]) - null_fire_scores["is_real"] = 0.0 - fire_scores = ( - pd.concat([fire_scores, null_fire_scores]) - .sort_values("score", ascending=False) - .to_numpy() - ) - logging.debug(f"Fire scores\n{fire_scores}") - - # Calculate FDR thresholds - Ts, FDRs, Vs, Rs = fdr_from_fire_scores(fire_scores) - results = pd.DataFrame( - { - "threshold": Ts, - "FDR": FDRs, - "shuffled_peaks": Vs, - "peaks": Rs, - } - ) - # simplify the results a little, don't want 100,000s of thresholds - results = results[results.threshold > 0.0] - results = results.groupby("FDR", sort=False).tail(1).reset_index(drop=True) - results = ( - results.groupby("shuffled_peaks", sort=False).tail(1).reset_index(drop=True) - ) - results = results.groupby("peaks", sort=False).tail(1).reset_index(drop=True) - # limit the number of thresholds that can be in the table - results["threshold"] = results["threshold"].round(2) - results = results.groupby("threshold", sort=False).tail(1).reset_index(drop=True) - logging.info(f"FDR results\n{results}") - results.to_csv(outfile, sep="\t", index=False) - - -def make_fdr_table(fire, outfile, min_coverage=4): - logging.info("Starting analysis") - logging.debug(f"Joined fibers\n{fire}") - fire_tracks(fire, outfile, min_coverage=min_coverage) - return 0 - - -def find_nearest(array, value): - idx = np.searchsorted(array, value, side="left") - idx[idx < 0] = 0 - idx[idx >= len(array)] = len(array) - 1 - return idx - - -def write_bed(chrom, output_dict, out, first=True): - chrom_length = output_dict["coverage"].shape[0] - # make df - if first: - header = True - mode = "w" - else: - header = False - mode = "a" - logging.info("Making data frame") - df = pl.DataFrame(output_dict) - del output_dict - gc.collect() - - # finding maxes within windows - df = df.with_columns( - max_window_score=pl.col("score").rolling_max( - window_size=ROLLING_FIRE_SCORE_WINDOW_SIZE, - center=True, - ) - ) - original_columns = df.columns - # find and clear the duplicates - logging.info("Finding duplicates") - # float array that says if a row is different from the previous row - diff = (((df != df.shift(periods=1)).sum(axis=1)) > 0) * 1.0 - # turn the diff array into a group number - logging.info("Merging duplicates") - df = ( - df.with_columns( - diff.cumsum().alias("group"), - ) - .with_row_count(name="end", offset=1) - .unique(keep="last", subset=original_columns + ["group"], maintain_order=True) - .with_columns( - pl.col("end").shift_and_fill(periods=1, fill_value=0).alias("start"), - pl.lit(chrom).alias("#chrom"), - ) - .select(["#chrom", "start", "end"] + original_columns) - .sort(["#chrom", "start", "end"]) - ).to_pandas() - - # can only find local maxes after de duplicating - # window_score = ( - # df.rolling(ROLLING_FIRE_SCORE_WINDOW_SIZE, on="start", center=True) - # .score.max() - # .values - # ) - # df["is_local_max"] = df["score"] == window_score # df["score"].values - df["is_local_max"] = (df["score"] == df["max_window_score"]) & (df["score"] > 0.0) - - logging.info(f"Found {df.is_local_max.sum():,} local maximums.") - - # checks - final_end = df.end.max() - assert final_end == chrom_length, f"{final_end} != {chrom_length}" - - logging.info(f"Writing {chrom}") - df.to_csv(out, mode=mode, header=header, index=False, sep="\t") - logging.info(f"Done writing {chrom}") - return - - -def extra_output_columns(fire, fibers, fdr_table, min_coverage=4): - return_data = {} - # get the inital data - chrom_length = fire.length[0] - - # get fire info per haplotype - for hap in [""] + HAPS: - # select data we are working with - if hap == "": - logging.info("Processing all haplotypes") - tag = "" - cur_fire = fire - cur_fibers = fibers - else: - logging.info(f"Processing {hap}") - tag = f"_{hap}" - cur_fibers = fibers[fibers.hap == hap] - cur_fire = fire[fire.hap == hap] - # if no data in the hap write empty values - if cur_fire.shape[0] == 0: - for x in [ - "fire_coverage", - "coverage", - "score", - "FDR", - "log_FDR", - ]: - return_data[f"{x}{tag}"] = -1 - continue - - cur_coverage_array = make_coverage_array( - cur_fibers.fiber_start.values, cur_fibers.fiber_end.values, chrom_length - ) - # get the FIRE scores in bed format - cur_fire_scores = fire_scores_per_chrom( - cur_fire.start.values, - cur_fire.end.values, - cur_fire.fdr.values, - cur_fire.length.max(), - cur_coverage_array, - min_coverage=min_coverage, - ) - # fire coverage - fire_coverage = make_coverage_array( - cur_fire.start.values, cur_fire.end.values, chrom_length - ) - return_data[f"fire_coverage{tag}"] = fire_coverage - - # total coverage - return_data[f"coverage{tag}"] = cur_coverage_array - - # save the scores - return_data[f"score{tag}"] = cur_fire_scores - - # find the FDRs for the thresholds - fdr_idx = find_nearest(fdr_table.threshold.values, cur_fire_scores) - FDRs = fdr_table.FDR.values[fdr_idx] - return_data[f"FDR{tag}"] = FDRs - - # log the FDRs - tmp_FDR = FDRs.copy() - tmp_FDR[tmp_FDR <= 0] = tmp_FDR[tmp_FDR > 0].min() - log_FDRs = -10 * np.log10(tmp_FDR) - return_data[f"log_FDR{tag}"] = log_FDRs - - for key, data in return_data.items(): - if isinstance(data, np.ndarray): - assert ( - data.shape[0] == chrom_length - ), f"{key} is not the expected size: {data.shape} instead of {chrom_length.shape}." - return return_data - - -def write_scores(fire, fdr_table, outfile, min_coverage=4): - first = True - for chrom, g in fire.group_by("chrom", maintain_order=True): - logging.info(f"Processing {chrom}") - # fibers for this chromosome - fibers = ( - g[["chrom", "fiber", "fiber_start", "fiber_end", "hap"]] - .unique() - .to_pandas() - ) - # convert to pandas for easier manipulation - g = g.to_pandas() - - # get a bunch of extra columns + per haplotype - output_dict = extra_output_columns( - g, fibers, fdr_table, min_coverage=min_coverage - ) - - # write data - write_bed(chrom, output_dict, outfile, first=first) - first = False - - -def main( - infile: Path, - fiber_locations_file: Path, - genome_file: Path, - *, - outfile: Optional[Path] = None, - shuffled_locations_file: Optional[Path] = None, - fdr_table_file: Optional[Path] = None, - n_rows: Optional[int] = None, - min_coverage: Optional[int] = 4, - verbose: int = 0, -): - """ - Author Mitchell R. Vollger - - :param infile: Input file, stdin by default - :param outfile: Output file, stdout by default - :param count: Number of times to display the greeting - :param verbose: Set the logging level of the function - """ - if infile is None: - infile = sys.stdin - if outfile is None: - outfile = sys.stdout - - logger = logging.getLogger() - log_format = "[%(levelname)s][Time elapsed (ms) %(relativeCreated)d]: %(message)s" - log_level = 10 * (3 - verbose) - logging.basicConfig(format=log_format) - logger.setLevel(log_level) - - logging.info(f"Reading FIRE file: {infile}") - fire = pl.read_csv( - infile, - separator="\t", - has_header=False, - columns=[0, 1, 2, 3, 9, 10], - new_columns=["chrom", "start", "end", "fiber", "fdr", "hap"], - comment_char="#", - n_rows=n_rows, - ) - logging.debug(f"FIRE peaks {fire}") - logging.info(f"Reading genome file: {genome_file}") - fai = pl.read_csv( - genome_file, - separator="\t", - has_header=False, - columns=[0, 1], - new_columns=["chrom", "length"], - ) - logging.info(f"Reading fiber locations file: {fiber_locations_file}") - fiber_locations = pl.read_csv( - fiber_locations_file, - separator="\t", - has_header=False, - columns=[0, 1, 2, 3, 5], - new_columns=["chrom", "fiber_start", "fiber_end", "fiber", "hap"], - ).join(fai, on="chrom") - - if shuffled_locations_file is not None: - logging.info( - f"Reading shuffled fiber locations file: {shuffled_locations_file}" - ) - shuffled_locations = pl.read_csv( - shuffled_locations_file, - separator="\t", - has_header=False, - columns=[0, 1, 2, 3], - new_columns=["chrom", "null_fiber_start", "null_fiber_end", "fiber"], - ) - fiber_locations = fiber_locations.join( - shuffled_locations, on=["chrom", "fiber"] - ) - - logging.info("Joining FIRE elements and fibers and then sorting") - fire = fire.join(fiber_locations, on=["chrom", "fiber", "hap"], how="outer").sort( - ["chrom", "start", "end"] - ) - - if shuffled_locations_file is not None: - make_fdr_table(fire, outfile, min_coverage=min_coverage) - else: - fdr_table = ( - pl.read_csv(fdr_table_file, separator="\t") - .to_pandas() - .sort_values("threshold") - ) - write_scores(fire, fdr_table, outfile, min_coverage=min_coverage) - return 0 - - -if __name__ == "__main__": - defopt.run(main, show_types=True, version="0.0.1") diff --git a/workflow/scripts/merge_fire_peaks.py b/workflow/scripts/merge_fire_peaks.py index ce77b67d96..eaf0c33f58 100755 --- a/workflow/scripts/merge_fire_peaks.py +++ b/workflow/scripts/merge_fire_peaks.py @@ -1,14 +1,9 @@ #!/usr/bin/env python -import os import defopt import logging -from pathlib import Path -import numpy as np -from typing import Optional import polars as pl import io import sys -from numba import njit def is_grouped_with_previous( @@ -59,11 +54,11 @@ def group_peaks(df, min_frac_overlap=0.5, min_reciprocal_overlap=0.75): ), ) .with_columns( - (~pl.col("shares_FIREs")).cumsum().alias("group"), + (~pl.col("shares_FIREs")).cum_sum().alias("group"), ) .sort("group") .with_columns( - pl.col("score").max().over("group").suffix("_max"), + pl.col("score").max().over("group").name.suffix("_max"), peak_start=pl.col("peak_start").min().over("group").cast(pl.UInt32), peak_end=pl.col("peak_end").max().over("group").cast(pl.UInt32), local_max_count=pl.col("group").len().over("group"), diff --git a/workflow/scripts/percent-in-clusters.sh b/workflow/scripts/percent-in-clusters.sh index 1d2db9f2e2..01213cf511 100755 --- a/workflow/scripts/percent-in-clusters.sh +++ b/workflow/scripts/percent-in-clusters.sh @@ -9,8 +9,8 @@ if [[ $# != 3 ]]; then exit 1 fi -oe=$(zcat $1 \ - | awk '{ \ +oe=$(zcat $1 | + awk '{ \ coverage[$4][$5]+=$3-$2; \ } END { \ real_bp = 0; \ @@ -26,7 +26,7 @@ n_tests=$(zcat $2 | wc -l) min_fdr=$(echo "-10.0*(l(0.01/${n_tests})/l(10.0))" | bc -lq) # n_peaks=$(zcat $2 | awk -v m="${min_fdr}" '$4 >= m' | wc -l) -printf "percent-of-MSPs-preferentially-clustered-along-the-genome\tmin_fdr\n" > $3 -printf "%s%%\t%s\n" ${oe} ${min_fdr} >> $3 +printf "percent-of-MSPs-preferentially-clustered-along-the-genome\tmin_fdr\n" >$3 +printf "%s%%\t%s\n" ${oe} ${min_fdr} >>$3 exit 0 diff --git a/workflow/scripts/trackhub.py b/workflow/scripts/trackhub.py index 76c8d1216d..f0f3e28f69 100755 --- a/workflow/scripts/trackhub.py +++ b/workflow/scripts/trackhub.py @@ -8,11 +8,12 @@ HUB = """ -hub {sample}-fiberseq -shortLabel {sample}-fiberseq -longLabel {sample}-fiberseq +hub {sample}-FIRE-fiberseq +shortLabel {sample}-FIRE-fiberseq +longLabel {sample}-FIRE-fiberseq genomesFile genomes.txt email mvollger.edu +descriptionUrl fire-description.html """ GENOMES = """ @@ -21,35 +22,11 @@ """ -BW_COMP = """ -track {sample}-{hap}-FDR -compositeTrack on -shortLabel {hap} FDR tracks -longLabel {hap} FDR tracks -type bigWig 0 1000 -autoScale off -viewLimits {FDR_min}:{FDR_max} -maxItems 100000 -maxHeightPixels 50:50:1 -""" - -BW_TEMPLATE = """ - track FDR.{sample}.{hap}.{nm} - parent {sample}-{hap}-FDR - bigDataUrl {file} - shortLabel FDR.{sample}.{hap}.{nm} - longLabel FDR.{sample}.{hap}.{nm} - type bigWig - visibility {viz} - priority {i} - maxHeightPixels 50:50:1 -""" - # transparentOverlay PER_ACC_COMP = """ -track {sample}-percent-accessible -shortLabel {sample}-percent-accessible -longLabel {sample}-percent-accessible +track {sample}-FIRE-percent-accessible +shortLabel {sample}-FIRE-percent-accessible +longLabel {sample}-FIRE-percent-accessible graphTypeDefault points aggregate transparentOverlay container multiWig @@ -63,43 +40,17 @@ visibility full maxHeightPixels 100:50:8 priority 1 +yLineOnOff on +yLineMark 100 +html fire-description.html +gridDefault on """ PER_ACC_TEMPLATE = """ - track {sample}-{hap}-percent-accessible - parent {sample}-percent-accessible - shortLabel {sample}-{hap}-percent-accessible - longLabel {sample}-{hap}-percent-accessible - bigDataUrl {file} - type bigWig - visibility {viz} - color {color} -""" - -FIRE_SCORE_COMP = """ -track {sample}-FIRE-score -shortLabel {sample}-FIRE-score -longLabel {sample}-FIRE-score -graphTypeDefault points -aggregate transparentOverlay -container multiWig -aggregate none -showSubtrackColorOnUi on -type bigWig 0 1000 -alwaysZero on -viewLimits 0:100 -autoScale off -maxItems 100000 -visibility full -maxHeightPixels 100:50:8 -priority 100 -""" - -FIRE_SCORE = """ - track {sample}-{hap}-FIRE-score - parent {sample}-FIRE-score - shortLabel {sample}-{hap}-FIRE-score - longLabel {sample}-{hap}-FIRE-score + track {sample}-{hap}-FIRE-percent-accessible + parent {sample}-FIRE-percent-accessible + shortLabel {sample}-{hap}-FIRE-percent-accessible + longLabel {sample}-{hap}-FIRE-percent-accessible bigDataUrl {file} type bigWig visibility {viz} @@ -108,10 +59,10 @@ MULTI_WIG = """ -track {sample}-{hap}-coverage -parent {sample}-coverage -longLabel {sample}-{hap}-coverage -shortLabel {sample}-{hap}-coverage +track {sample}-{hap}-FIRE-coverage +parent {sample}-FIRE-coverage +longLabel {sample}-{hap}-FIRE-coverage +shortLabel {sample}-{hap}-FIRE-coverage container multiWig aggregate stacked showSubtrackColorOnUi on @@ -119,24 +70,25 @@ autoScale off alwaysZero on viewLimits 0:{upper_coverage} -visibility full +visibility {viz} maxHeightPixels 100:50:8 +html fire-description.html priority 90 - track {sample}-{hap}-accessible - parent {sample}-{hap}-coverage + track {sample}-{hap}-FIRE-accessible + parent {sample}-{hap}-FIRE-coverage bigDataUrl {acc} type bigWig color 139,0,0 - track {sample}-{hap}-linker - parent {sample}-{hap}-coverage + track {sample}-{hap}-FIRE-linker + parent {sample}-{hap}-FIRE-coverage bigDataUrl {link} type bigWig color 147,112,219 - track {sample}-{hap}-nucleosome - parent {sample}-{hap}-coverage + track {sample}-{hap}-FIRE-nucleosome + parent {sample}-{hap}-FIRE-coverage bigDataUrl {nuc} type bigWig color 169,169,169 @@ -148,20 +100,20 @@ compositeTrack on shortLabel {sample}-FIRE-FDR longLabel {sample}-FIRE-FDR -visibility full type bigWig 0 1000 maxItems 100000 maxHeightPixels 100:50:1 alwaysZero on priority 10 +html fire-description.html - track {sample}-log-fdr + track {sample}-log-FIRE-FDR parent {sample}-FIRE-FDR bigDataUrl {fdr} shortLabel {sample} -10log10 FDR longLabel {sample} -10log10 FDR autoScale on - visibility full + visibility hide yLineOnOff on yLineMark {y_line} gridDefault on @@ -169,48 +121,51 @@ TRACK_GROUPS = """ # grouping for fibers -track {sample}-fibers +track {sample}-FIRE-fibers compositeTrack on -shortLabel {sample}-fibers -longLabel {sample}-fibers +shortLabel {sample}-FIRE-fibers +longLabel {sample}-FIRE-fibers type bigBed 12 + maxItems 100000 visibility dense priority 80 +html fire-description.html # grouping for peaks -track {sample}-peaks +track {sample}-FIRE-peaks compositeTrack on -shortLabel {sample}-peaks -longLabel {sample}-peaks +shortLabel {sample}-FIRE-peaks +longLabel {sample}-FIRE-peaks type bigBed 12 + maxItems 100000 visibility dense priority 30 +html fire-description.html # track of unreliable regions just above the peak tracks - track {sample}-unreliable-coverage-regions - parent {sample}-peaks - shortLabel {sample}-unreliable-coverage-regions - longLabel {sample}-unreliable-coverage-regions + track {sample}-unreliable-FIRE-coverage-regions + parent {sample}-FIRE-peaks + shortLabel {sample}-unreliable-FIRE-coverage-regions + longLabel {sample}-unreliable-FIRE-coverage-regions type bigBed bigDataUrl bb/unreliable-coverage-regions.bb visibility dense priority 29 # grouping for coverage -track {sample}-coverage +track {sample}-FIRE-coverage superTrack on show -shortLabel {sample}-coverage -longLabel {sample}-coverage +shortLabel {sample}-FIRE-coverage +longLabel {sample}-FIRE-coverage priority 90 +html fire-description.html """ DECORATED = """ - track {sample}-{hap}-fibers - parent {sample}-fibers - shortLabel {sample}-{hap}-fibers - longLabel {sample}-{hap}-fibers + track {sample}-{hap}-FIRE-fibers + parent {sample}-FIRE-fibers + shortLabel {sample}-{hap}-FIRE-fibers + longLabel {sample}-{hap}-FIRE-fibers visibility dense type bigBed 12 + itemRgb On @@ -224,8 +179,8 @@ # type bigBed 6 + 4 FIRE_TEMPLATE = """ - track {sample}-FIRE-peaks - parent {sample}-peaks + track {sample}-narrow-FIRE-peaks + parent {sample}-FIRE-peaks type bigNarrowPeak bigDataUrl {file} shortLabel {sample}-FIRE-peaks @@ -235,10 +190,10 @@ """ WIDE_TEMPLATE = """ - track {sample}-{name} - parent {sample}-peaks - shortLabel {sample}-{name} - longLabel {sample}-{name} + track {sample}-wide-FIRE-peaks + parent {sample}-FIRE-peaks + shortLabel {sample}-wide-FIRE-peaks + longLabel {sample}-wide-FIRE-peaks type bigBed bigDataUrl {file} visibility dense @@ -247,13 +202,13 @@ """ HAP_TEMPLATE = """ - track {sample}-hap-differences - parent {sample}-peaks + track {sample}-FIRE-hap-differences + parent {sample}-FIRE-peaks type bigBed 9 + itemRgb on bigDataUrl {file} - shortLabel {sample}-hap-differences - longLabel {sample}-hap-differences + shortLabel {sample}-FIRE-hap-differences + longLabel {sample}-FIRE-hap-differences visibility dense maxHeightPixels 25:25:1 """ @@ -267,6 +222,8 @@ def generate_trackhub( ): if ref == "T2Tv2.0": ref = "GCA_009914755.4" + elif ref == "HG002v1.1": + ref = "HG002v1.1.PAT" upper_coverage = int(ave_coverage + 5 * np.sqrt(ave_coverage)) os.makedirs(f"{trackhub_dir}/", exist_ok=True) @@ -276,8 +233,12 @@ def generate_trackhub( trackDb.write(TRACK_GROUPS.format(sample=sample)) for hap in ["all", "hap1", "hap2", "unk"]: + if hap == "all": + viz = "full" + else: + viz = "hide" # add coverage tracks - if hap != "unk": + if hap == "all": acc = f"bw/{hap}.fire.coverage.bw" nuc = f"bw/{hap}.nucleosome.coverage.bw" link = f"bw/{hap}.linker.coverage.bw" @@ -288,27 +249,25 @@ def generate_trackhub( nuc=nuc, sample=sample, hap=hap, + viz=viz, upper_coverage=upper_coverage, ) ) if hap == "all": - file = f"bb/FDR-FIRE-peaks.bb" + file = "bb/fire-peaks.bb" trackDb.write(FIRE_TEMPLATE.format(file=file, sample=sample)) # add hap tracks - file = f"bb/hap_differences.bb" + file = "bb/hap_differences.bb" trackDb.write(HAP_TEMPLATE.format(file=file, sample=sample)) - file = "bb/FDR-wide-peaks.bb" - trackDb.write( - WIDE_TEMPLATE.format(file=file, name="FDR-wide-peaks", sample=sample) - ) + file = "bb/fire-wide-peaks.bb" + trackDb.write(WIDE_TEMPLATE.format(file=file, sample=sample)) # add percent accessible tracks file = f"bw/{hap}.percent.accessible.bw" if hap == "all": color = "0,0,0" trackDb.write(PER_ACC_COMP.format(sample=sample)) - # trackDb.write(FIRE_SCORE_COMP.format(sample=sample, file=f"bw/score.bw")) elif hap == "hap1": color = "0,0,255" elif hap == "hap2": @@ -321,16 +280,6 @@ def generate_trackhub( sample=sample, hap=hap, file=file, color=color, viz=viz ) ) - # zhap = "" if hap == "all" else f"_{hap}".replace("hap", "H") - # trackDb.write( - # FIRE_SCORE.format( - # sample=sample, - # hap=hap, - # file=f"bw/score{zhap}.bw", - # viz=viz, - # color=color, - # ) - # ) # new bin files if hap == "all": @@ -341,8 +290,8 @@ def generate_trackhub( trackDb.write( FIRE_SCORE_AND_FDR.format( sample=sample, - fdr=f"bw/log_FDR.bw", - score=f"bw/score.bw", + fdr="bw/log_FDR.bw", + score="bw/score.bw", y_line=-10 * np.log10(0.05), ) ) diff --git a/workflow/templates/fire-description.html b/workflow/templates/fire-description.html new file mode 100644 index 0000000000..ada2528c85 --- /dev/null +++ b/workflow/templates/fire-description.html @@ -0,0 +1,98 @@ +

Description

+Fiber-seq Inferred Regulatory Elements + +

+ These tracks represent + FIRE peak calls + inferred from regulatory elements in Fiber-seq data. If you are unframiliar + with Fiber-seq please see the references below for a detailed description, but + in short it is useful to think of it as a long-read version of DNaseI/ATAC-seq + that can be used to identify regions of chromatin accessibility. +

+ +

+ FIREs are MTase sensitive patches (MSPs) on Fiber-seq reads that are inferred + to be regulatory elements on single chromatin fibers. To do this we used + semi-supervised machine learning to identify MSPs that are likely to be + regulatory elements using the Mokapot framework and + XGBoost. Every individual FIRE element is associated with a + precision value, which indicates the probability that the FIRE element is a + true regulatory element. Significantly more detail is avalible in our + manuscript and fiberseq website both of which are linked below. +

+ +

Track Descriptions

+

+

  • + FIRE peaks: Peaks are called by identifying FIRE score local-maxima + that have FDR values below a 5% threshold. Once a local-maxima is + identified, the start and end positions of the peak are determined by the + median start and end positions of the underlying FIRE elements. +
    • + +
  • + Wide FIRE peaks: Wide peaks are the union of the FIRE peaks and all + regions below the FDR threshold. We then merge the resulting regions that + are within one nucleosome (147 bp) of one another. +
    • + +
  • + log FIRE FDR: FDR calculation begins by shuffling the locations of + all the fibers across the genome and recalculating the FIRE score for each + position in the genome. The FDR is then defined as the number of bases that + have shuffled FIRE scores above a threshold divided by the number of bases + in the un-shuffled data. Displayed in the track is the -10log10 + transformation of this FDR value so the more significant FIRE scores appear + as higher values. +
    • + +
  • + Unreliable FIRE coverage regions: The unreliable FIRE coverage track + shows regions that were exlcuded from the FDR calculations due to low or + high sequencing depth. Defined as deviating from the median sequencing depth + by 5 or more standard deviations. +
    • + +
  • + FIRE coverage: The FIRE coverage track shows the number of fibers + that are MSPs (purple), FIREs (red), and nucleosomes (gray) at each position + in the genome. The coverage track is calculated by counting the number of + fibers that have a MSP, FIRE, or nucleosome at each position in the genome. +
    • + +
  • + Percent accessible: The percent of fibers that contain a FIRE element + overlaping a given position in the genome. The percent of accessible fibers + for haplotype one is displayed as a red line, haplotype 2 as a blue line, + and both haplotypes as a black line. +
    • + +
  • + FIRE fibers : A decorator track that shows individual Fiber-seq + reads (fibers) along the genome. Each fiber is colored by the MSPs (purple), + FIREs (red), and nucleosomes (gray) that it contains. Optionally, you can + also display the raw 5mC or m6A information using the track configuration. +
    • +

    + +

    Methods

    +

    + Please refer to + https://fiberseq.github.io/ + for more details. +

    + +

    Credits

    +Tracks were generated by Mitchell Vollger (mvollger_at_uw.edu) and Andrew +Stergachis (absterga_at_uw.edu). + +

    References

    +

    + Vollger, M. R., Swanson, et al. (2024). A haplotype-resolved view of human + gene regulation. bioRxiv (p. 2024.06.14.599122). + DOI: https://doi.org/10.1101/2024.06.14.599122 +