sjcab_peak2anno_db

Gene-backed peak-to-annotation BED resources for SJ/CAB workflows.

The package installs bundled resources and can also download/generate from GENCODE/UCSC/Ensembl gtf, Roadmap ChromHMM, blacklist, and CpG island BED files. Generated user data defaults to ~/.sjcab_peak2anno_db, override it with SJCAB_PEAK2ANNO_DB_PATH or with command-level --db-path/-d flags.

# Install the package and its default resource set.
pip install sjcab_peak2anno_db
conda install stjudecab::sjcab_peak2anno_db

export SJCAB_PEAK2ANNO_DB_PATH=~/.sjcab_peak2anno_db
sjcab-peak2anno-db install # install default for "GeneBEDs FeatureBEDs blacklists cgi"
sjcab-peak2anno-db install def # same default installation
sjcab-peak2anno-db install default # same default installation
sjcab-peak2anno-db install genebed feature # install selected default group
sjcab-peak2anno-db list def # list default GeneBED bundle
sjcab-peak2anno-db list genebed # list all GeneBED resources
sjcab-peak2anno-db list feature # list installed FeatureBED folders
sjcab-peak2anno-db list blacklists # list bundled blacklist resources
sjcab-peak2anno-db list cgi # list bundled CGI resources
sjcab-peak2anno-db list chromhmm # list installed ChromHMM resources
sjcab-peak2anno-db list segway # list installed Segway resources

sjcab-peak2anno-db download-genebed hg38 v31 -o annotations # download Gencode hg38 v31 GeneBEDs to annotation folder
sjcab-peak2anno-db install-genebed hg19 v31lift37 # install Gencode hg19 v31lift37 to --db-path
sjcab-peak2anno-db download-feature mm10 vM22 -g gencode.vM22.annotation.gtf.gz -o annotations # convert own gtf file to GeneBED and FeatureBEDs
sjcab-peak2anno-db install-feature cat 116 # install GeneBEDs/FeatureBEDs for cat ensemble release 116
sjcab-peak2anno-db install-feature equcab2 # install GeneBEDs/FeatureBEDs for horse UCSC version equCab2
sjcab-peak2anno-db install-feature zea_mays # install GeneBEDs/FeatureBEDs for corn ensemble current release(empty/def/default/current/latest are the same)

• Supported bundled GeneBED species are hg19, hg38, mm10, mm9, mm39(Gencode) and sacCer3.
• All other GeneBED/FeatureBED resources for species/version are automatically generated from GENCODE/UCSC/Ensembl GTFs.
• CGI: hg38, hg19, mm10, mm9, and mm39
• blacklists: readme.md

All download first checks the db-path/cache folder(> output directory > working directory) before downloading. Every downloaded file appends the source URL to {db-path}/download_urls.log

Command conventions and common options

The general difference between command pairs is the destination:

• install-*: install or generate resources under the configured db_path directory.
• download-*: write downloaded/generated resources under --output-dir (default workdir).
• *-feature: installs/downloads both GeneBEDs and FeatureBEDs
• blacklists|cgi|chromhmm|segway: if there isn't available resource for selected species from UCSC, liftover script can be provided based on chain file from UCSC.

Common option styles:

• SPECIES, -s, --species SPECIES: select a species or genome build. It would match exact by Gencode > UCSC > Ensemble, if no exact match, will print the matches from Ensembl(assembly > latin name > common name) for you to copy and rerun with.
• VERSION, -v, --ver VERSION: select a release/version, such as v31, vM39, empty|def|default|current|latest, where supported. The option form can replace
• species1:version1,species2:version2: Species and versions accept comma-separated values or a .lst/.list file with one choice per row. A two-column list supplies species version pairs; species:version values are also accepted. A single species or version is broadcast across the other list, while equal-length lists are paired in order.
• -d, --db-path DIR: override the database/cache directory for commands that read or install the database.
• -name NAME: store the selected GeneBEDs or FeatureBEDs under a custom species name and record the assembly-to-name mapping in custom.name.tsv.
• -o, --output-dir DIR: select a staging/output directory for download commands. Default for download-* use working directory as default.
• -n, --no-overwrite: preserve existing files. --overwrite rewrites them where supported.
• --clean-cache [DAYS]: remove cache files older than DAYS after BED generation. The default is 90 days; using it without a value removes the current cached GTF immediately, and negative values have the same immediate cleanup behavior.
• --sizes-clean [0|1]: create .sizes.clean files by default. Use --sizes-clean 0 to disable them.
• -j, --processes N: use N workers, one GTF per worker. GTF downloads are sequential with a short randomized pause; conversion within each worker is single-process. Default is 4, capped at the CPUs allocated to the job.
• -n, --workers N: on dedup-bed and filter-bed, use N workers for independent TSS/TES annotation outputs. Default is 2.
• -g, --gtf-path FILE: use an existing local GTF instead of downloading one.
• -u, --url URL: override the resolved GTF URL.
• --ucsc-source {ens,refseq}: select the UCSC gene-table source when a UCSC build is used.
• -dry-run: resolve and print downloadable URLs without downloading, where supported.

User configuration

If no RC file exists, the default XDG RC template is created automatically; all settings in the generated template are commented out. Existing RC files are completed with any missing commented settings; commented configuration variables use #SJCAB_... with no space after #. RC files are read from ~/.sjcab_peak2anno.rc and $XDG_CONFIG_HOME/sjcab_peak2anno/.sjcab_peak2anno.rc (the XDG file takes precedence). Set SJCAB_PEAK2ANNO_CONFIG to add a specific RC file; that file has the highest RC precedence. Explicit command-line arguments take precedence over configuration; environment variables beginning with SJCAB_PEAK2ANNO_DB_ take precedence over RC values.

Environment variable defaults are:

SJCAB_PEAK2ANNO_DB_PATH=~/.sjcab_peak2anno_db
SJCAB_PEAK2ANNO_DB_INSTALL_OPTIONS=genebed,feature,blacklists,cgi
SJCAB_PEAK2ANNO_DB_INSTALL_SPECIES=hg38,hg19,mm10,mm39
SJCAB_PEAK2ANNO_DB_INSTALL_VERSIONS=v31,v31lift37,vM22,vM39
SJCAB_PEAK2ANNO_DB_VERSION_STALE_DAYS=90
SJCAB_PEAK2ANNO_DB_SIZESCLEAN=1
SJCAB_PEAK2ANNO_DB_CLEANCACHE=90
SJCAB_PEAK2ANNO_DB_PEAK_TXT_DELIMITER=:-*/^;_%$,
SJCAB_PEAK2ANNO_DB_BED_SCORE_COLUMN=5
SJCAB_PEAK2ANNO_DB_TXT_SCORE_COLUMN=2

RC keys use the same names as the environment variables; the SJCAB_PEAK2ANNO_DB_ prefix is optional for backwards-compatible short keys. SJCAB_PEAK2ANNO_DB_PATH sets the default data directory. INSTALL_OPTIONS accepts genebed, feature, blacklists, and cgi. INSTALL_SPECIES and INSTALL_VERSIONS control the default species/version pairs used by the feature installer. They accept comma-separated values or .lst/.list files, with single-value broadcasting and equal-length pairing.

VERSION_STALE_DAYS controls refresh of the Ensembl VERSION, species_EnsemblVertebrates.txt, and species.txt catalogs. SIZESCLEAN=1 creates .sizes.clean files by default; set it to 0 to disable them. The --sizes-clean 0 option is available on feature-generation and install commands. CLEANCACHE is an age in days: the default is 90, so cache files older than 90 days are removed after BED generation. A negative value removes the newly used cached GTF immediately. The command-line form is --clean-cache [DAYS]; using --clean-cache without a value means immediate cleanup.

PEAK_TXT_DELIMITER controls delimiters inside text-mode peak regions. Selector files are auto-detected once per file from the first data row: BED selectors use the configured fifth-column score, while text selectors use the configured second-column score. Override these with BED_SCORE_COLUMN and TXT_SCORE_COLUMN in the environment or RC file.

GeneBEDs

GeneBED resources provide transcript-level gene annotations. Two isoform sets are generated:

• all: all transcript isoforms.
• deduplong: one longest isoform per gene, grouped by gene symbol by default.

The def directory points to the registry-defined default version for each species, not necessarily the newest parsed GENCODE release.

install-genebed / download-genebed

# Install or regenerate annotation BED resources.
sjcab-peak2anno-db install-genebed

sjcab-peak2anno-db install-genebed mm10 vM22 -g gencode.vM22.annotation.gtf.gz
sjcab-peak2anno-db install-genebed cat
sjcab-peak2anno-db install-genebed dog 100
sjcab-peak2anno-db install-genebed hg38 -name human
sjcab-peak2anno-db download-genebed hg38 v31 -o annotations
sjcab-peak2anno-db download-genebed hg19 v31lift37 -o annotations

For Ensembl releases, def, default, current, and latest resolve the current release metadata. The separate links are {db-path}/ensembl/vertebrates/def -> {release} and {db-path}/ensembl/genomes/def -> {release}. Ensembl Genomes determines its current release from https://ftp.ebi.ac.uk/pub/ensemblgenomes/VERSION.

For a species not built into the resolver, the command checks separate cached catalogs under {db-path}/ensembl/vertebrates/{release}/ and {db-path}/ensembl/genomes/{release}/, downloading the missing catalog only when needed. The def directory links to the current release. Each catalog stores assembly, species, division, name, and assembly_accession in that order; assembly or species or assembly_accession can be passed back as the species ID as long as they are unique. A release-specific reduced catalog is stored at {db-path}/ensembl/vertebrates/{release}/ or {db-path}/ensembl/genomes/{release}/; each catalog's def link points to its own release directory.

For automatic GTF selection, the order is GENCODE, a recognized UCSC build with a UCSC GTF, Ensembl Vertebrates, then Ensembl Genomes. UCSC build IDs and liftOver chain file URL are cached in {db-path}/ucsc/gtf_builds.tsv after reading the UCSC downloads page and liftOver would refresh if older than VERSION_STALE_DAYS.

{species}.sizes for UCSC were based on bigZips/{build}.chrom.sizes, otherwise based on ensembl assembly report. {species}.sizes.clean is based on ensembl assembly report only kept primary chromosomes All generated liftOver scripts store chain files in {db-path}/cache/chains/ and reuse an existing non-empty chain file.

Default layout:

{db-path}/genebed/{species}/{version}/all.gene.bed
{db-path}/genebed/{species}/{version}/deduplong.gene.bed
{db-path}/genebed/{species}/def -> {version}
{db-path}/sizes/{species}.sizes
{db-path}/sizes/{species}.sizes.clean

Python API:

import sjcab_peak2anno_db as db

db.download_and_convert_gencode_gtf("hg38", "v31", output_dir="annotations")
db.write_tss("all.gene.bed", "all.tss.bed")
db.write_tes("all.gene.bed", "all.tes.bed")
db.write_deduplong("all.gene.bed", "deduplong.gene.bed")

dedup-bed / filter-bed

Both commands select one isoform per gene from an all-isoform GENCODE BED and write {prefix}.gene.bed, {prefix}.tss.bed, and {prefix}.tes.bed.

dedup-bed falls back to the longest isoform for genes without selector support (each gene from original would have at least one transcript). filter-bed uses the same selector logic but omits genes without selector support (could remove a lot genes without supports).

The default dedup-bed selector is longcol5: it selects the isoform with the largest numeric BED column 5. Use long to select by interval length (end - start):

Detailed selector input examples, including input and output BED content, are in README.DEDUP.md.

# Select one transcript per gene using the default column-5 selector.
sjcab-peak2anno-db dedup-bed hg38 v31 -b all.gene.bed -o annotations
# Select one transcript per gene using genomic interval length.
sjcab-peak2anno-db dedup-bed hg38 v31 -m long -b all.gene.bed -o annotations
# Select transcripts using peak scores around their TSS.
sjcab-peak2anno-db dedup-bed hg38 v31 -m peak -i h3k4me3_peaks.bed -o annotations
# Select transcript IDs explicitly.
sjcab-peak2anno-db dedup-bed mm10 vM22 -m isoID -i isoforms.txt -K ensid -o annotations
# Keep genes whose promoters overlap the selector file.
sjcab-peak2anno-db filter-bed -b annotations/genebed/hg38/v31/all.gene.bed -m perover -i active_chromhmm.bed -o annotations
# Select by expression and require exact selector matches.
sjcab-peak2anno-db filter-bed hg38 v31 -m isoexp -i isoform_expression.tsv --exclusive -o annotations

Deduplicate Selector methods:

• longcol5: no selector is needed; select the isoform with the largest numeric value in BED column 5. This is the default for dedup-bed.
• long: no selector is needed; select the isoform with the largest end - start.
• peak: selector is a peak BED file with peak score in column 5; the isoform whose TSS +/- promoter window has the highest peak score is selected. Text selectors are also accepted, for example chr1:100-200 10.
• isoID: selector is a transcript ID list, one ID per line.
• isoexp: selector is a two-column table: transcript ID, then expression.
• perover: selector is a BED file, often user-filtered ChromHMM active states; the isoform promoter with the highest percent overlap is selected. Text selectors are also accepted, for example chr1:100-200 or chr1_100_200.

path

Use path to print an installed resource path. Add --install/-I when the resource should be installed first.

sjcab-peak2anno-db path hg38 gene

Python API:

import sjcab_peak2anno_db as db

print(db.path("hg38", "gene"))

FeatureBEDs

FeatureBED resources are CAB-style merged region BEDs derived from GTF and GeneBED input. The default prefix is the promoter size label, usually 2kb.

install-feature / download-feature

# Install or generate merged FeatureBED resources.
sjcab-peak2anno-db install-feature all all
sjcab-peak2anno-db install-feature hg38 v31
sjcab-peak2anno-db install-feature hg38 v31 -o feature_downloads

sjcab-peak2anno-db download-feature hg38 v31 -o feature_downloads
sjcab-peak2anno-db download-feature all all -o feature_downloads
sjcab-peak2anno-db download-feature hg38 v31 -o feature_downloads -p 2kb -D 50kb -e 2kb
# Resolve and print the GTF URL without downloading.
sjcab-peak2anno-db download-feature horse def -dry-run
# Store one species under a custom name and record the mapping.
sjcab-peak2anno-db install-feature horse def -name horse_custom

For install-feature, omitting the version is equivalent to using def. The -name mapping is written to custom.name.tsv in the selected data directory.

-name also works with install-genebed and stores the selected GeneBEDs under the custom species directory.

install-feature -o DIR reuses preprocessed feature files from DIR when order.lst and the expected FeatureBED files are already present. Like download-genebed, feature generation reuses the cached GTF, then an expected GTF from the output directory or current working directory before downloading. install-feature reuses existing generated files by default; pass --overwrite to rebuild the installed annotation BED prerequisite and feature files.

For UCSC short genome IDs, select the UCSC gene table when needed:

# Use the UCSC RefSeq gene annotation for the T2T human build.
sjcab-peak2anno-db download-genebed hs1 1 --ucsc-source refseq
# Use the UCSC RefSeq gene annotation for the alpaca build.
sjcab-peak2anno-db download-genebed vicPac2 1 --ucsc-source refseq
# Use the UCSC Ensembl gene annotation for the yeast build.
sjcab-peak2anno-db download-genebed sacCer3 R64-1-1 --ucsc-source ens

UCSC files are selected from bigZips/genes/; ens uses ensGene and refseq uses ncbiRefSeq or refGene. GENCODE is preferred when the genome has a configured GENCODE URL; otherwise recognized UCSC builds are tried before Ensembl Vertebrates and Ensembl Genomes. See the UCSC downloads page.

Default installed layout:

{db-path}/feature/{species}/{version}/{prefix}/{prefix}.promoter.up.bed
{db-path}/feature/{species}/{version}/{prefix}/{prefix}.5utr.bed
{db-path}/feature/{species}/{version}/{prefix}/{prefix}.3utr.bed
{db-path}/feature/{species}/{version}/{prefix}/{prefix}.promoter.down.bed
{db-path}/feature/{species}/{version}/{prefix}/{prefix}.exon.bed
{db-path}/feature/{species}/{version}/{prefix}/{prefix}.intron.bed
{db-path}/feature/{species}/{version}/{prefix}/{prefix}.tes.bed
{db-path}/feature/{species}/{version}/{prefix}/{prefix}.dis5.bed
{db-path}/feature/{species}/{version}/{prefix}/{prefix}.dis3.bed
{db-path}/feature/{species}/{version}/{prefix}/{prefix}.intergenic.bed
{db-path}/feature/{species}/{version}/{prefix}/order.lst
{db-path}/feature/{species}/{version}/{prefix}/order.utr.lst
{db-path}/feature/{species}/def -> {version}/{prefix}

Feature installation records the source GTF basename in {db-path}/installed.tsv (species, version, and gtf columns). list feature uses this mapping for its source column.

order.lst and order.utr.lst contain tab-separated rows with the BED filename, the feature name, and its full name:

{prefix}.promoter.up.bed  Promoter.Up   Promoter_Upstream
{prefix}.5utr.bed         5UTR          Five_Prime_Untranslated_Regions
{prefix}.3utr.bed         3UTR          Three_Prime_Untranslated_Regions
{prefix}.promoter.down.bed Promoter.Down Promoter_Downstream
{prefix}.exon.bed         Exon          Exons
{prefix}.intron.bed       Intron        Introns
{prefix}.tes.bed          TES           Transcription_End_Sites
{prefix}.dis5.bed         Dis5          Distal_5_Prime
{prefix}.dis3.bed         Dis3          Distal_3_Prime
{prefix}.intergenic.bed   Intergenic    Intergenic_Regions

Both files use the UTR-aware priority order: Promoter.Up > 5utr > 3utr > Promoter.Down > exon > intron > TES > Dis5 > Dis3 > Intergenic.

Main feature parameters:

• --promoter-bp/-p: promoter flank size, default 2kb.
• --promoter-down: downstream promoter flank; defaults to --promoter-bp.
• --distal-bp/-D: distal flank size, default 50kb.
• --distal-down: downstream distal flank; defaults to --distal-bp.
• --tes-bp/-e: TES flank size, default 2kb.
• --tes-up: upstream TES flank; defaults to --tes-bp.
• --prefix/-P: output prefix, default is the promoter size label.
• --gene-bed/-b: use an existing GeneBED for region generation.
• --gtf-path/-g: use an existing local GTF.

Python API:

import sjcab_peak2anno_db as db

db.download_feature("hg38", "v31", "feature_downloads")

db.write_tss_flank_region_unions(
    "annotations/genebed/hg38/v31/all.gene.bed",
    "feature_downloads",
    promoter_bp="2kb",
    distal_bp="50kb",
)

ChromHMM

ChromHMM commands download Roadmap dense state BED files and write normalized metadata.

install-chromhmm / download-chromhmm

# Install Roadmap ChromHMM resources for selected genomes and samples.
sjcab-peak2anno-db install-chromhmm -m 18 -s hg19 -i E001,E063
sjcab-peak2anno-db install-chromhmm -m 18 -s hg38 -i E063

sjcab-peak2anno-db download-chromhmm -o chromhmm_downloads -m 25 -s hg19 -c GM12878
sjcab-peak2anno-db download-chromhmm -o chromhmm_downloads -m 18 -s hg38 -i E063
sjcab-peak2anno-db download-chromhmm -o chromhmm_downloads -m 15 -t brain

ChromHMM defaults to Roadmap hg19 BED files. Use --species hg38/-s hg38 for Roadmap lifted-over BEDs. Other UCSC builds use --yes-liftover and the hg38 chain files listed at UCSC liftOver. Select records with epigenome IDs (--ids/-i), fuzzy tissue/group text (--tissue/-t), or fuzzy sample/cell-line text (--cellline/-c).

Output layout:

{db-path}/chromhmm/{genome}/{model}state/{epigenome_id}_{model}_{model_label}_dense.bed.gz
{db-path}/chromhmm/metadata.tsv

metadata.tsv includes the downloaded path, genome (hg19 or hg38), state model, Roadmap epigenome ID, and normalized sample metadata.

Python API:

import sjcab_peak2anno_db as db

db.download_chromhmm(model=18, genome="hg38", ids="E063")
db.download_chromhmm(model=25, genome="hg19", cellline="GM12878")

Segway

Segway commands download the hg19 Segway encyclopedia of human regulatory elements by trying the ENCODE publication page first, then falling back to Segway Lab Site. If ENCODE returns a human-verification page or no Segway download links, the CLI prints a fallback message and records only the source that actually provided the downloaded files.

install-segway / download-segway

With no selector, the command downloads segway_encyclopedia.bed.gz. Existing files are skipped by default; pass --overwrite to replace them.

# Install or download Segway annotation resources.
sjcab-peak2anno-db install-segway
sjcab-peak2anno-db install-segway -i GM12878,H1-HESC --include-label-info

sjcab-peak2anno-db download-segway -o segway_downloads -i GM12878
sjcab-peak2anno-db download-segway -o segway_downloads -t brain
sjcab-peak2anno-db download-segway -o segway_downloads --include-caas

Segway source files are hg19 only. If another genome is requested, the CLI asks whether to download hg19 and write a UCSC liftOver helper script. In non-interactive runs, use --yes-liftover.

sjcab-peak2anno-db download-segway -o segway_downloads -s hg38 --yes-liftover -i GM12878

The generated script first tries to activate an existing segway-liftover environment with micromamba, conda, or mamba. If activation succeeds, the script does not install or modify packages inside it. If activation fails for all available tools, the script creates an environment with bioconda::ucsc-liftover first and falls back to CrossMap if that package cannot be installed. It downloads the UCSC hg19To<Genome>.over.chain.gz file from https://hgdownload.soe.ucsc.edu/goldenPath/hg19/liftOver/ and lifts over downloaded .bed.gz files. For install-segway, lifted files are staged and then copied into the installed Segway cache under ~/.sjcab_peak2anno_db/segway/{genome} unless -d selects another data directory.

Output layout:

{db-path}/segway/hg19/segway_encyclopedia.bed.gz
{db-path}/segway/hg19/caas.bed.gz
{db-path}/segway/hg19/label_info.tab
{db-path}/segway/hg19/{sample}.bed.gz
{db-path}/segway/metadata.tsv
{db-path}/segway/liftover_hg19_to_{genome}.sh

Lifted BED files include lift in the genome suffix, for example:

{db-path}/segway/hg38/PERIPHERAL_BLOOD_MONONUCLEAR_PRIMARY_CELLS.hg38lift.bed.gz

Python API:

import sjcab_peak2anno_db as db

db.download_segway(names="GM12878,H1-HESC")
db.download_segway(tissue="brain")
db.write_segway_liftover_script(target_genome="hg38")

Blacklists And CGI

Blacklist files are bundled. CGI files are bundled for install and can also be freshly downloaded from UCSC cpgIslandExt.

install-blacklists

install-blacklists installs bundled blacklist BED files into the user data directory. There is no separate blacklist download command.

sjcab-peak2anno-db install-blacklists
sjcab-peak2anno-db install blacklists

Output layout:

{db-path}/blacklists/{name}.bed.20230411
{db-path}/blacklists/{name}.bed

The current {name}.bed path is refreshed as a symlink when supported by the filesystem.

Python API:

import sjcab_peak2anno_db as db

db.install_blacklists()

install-cgi / download-cgi

install-cgi installs packaged CGI BED files. download-cgi downloads fresh UCSC CpG island tables and writes BED files.

sjcab-peak2anno-db install-cgi
sjcab-peak2anno-db install cgi

sjcab-peak2anno-db download-cgi
sjcab-peak2anno-db download-cgi -d cgi_downloads --species hg38

Output layout:

{db-path}/cgi/{species}_cgi.bed

Python API:

import sjcab_peak2anno_db as db

db.install_cgi()
db.download_cgi(species="hg38")

Command Examples

sjcab-peak2anno-db list def
sjcab-peak2anno-db list genebed
sjcab-peak2anno-db list feature
sjcab-peak2anno-db install
sjcab-peak2anno-db install def
sjcab-peak2anno-db install default
sjcab-peak2anno-db install genebed feature blacklists cgi
sjcab-peak2anno-db install -c genebed -c feature
sjcab-peak2anno-db install --overwrite

sjcab-peak2anno-db install-genebed
sjcab-peak2anno-db download-genebed hg38 v31 -o annotations
sjcab-peak2anno-db download-genebed mm10 vM22 -g gencode.vM22.annotation.gtf.gz -o annotations
sjcab-peak2anno-db dedup-bed hg38 v31 -m peak -i h3k4me3_peaks.bed -o annotations
sjcab-peak2anno-db dedup-bed mm10 vM22 -m isoID -i isoforms.txt -K ensid -o annotations
sjcab-peak2anno-db filter-bed -b annotations/genebed/hg38/v31/all.gene.bed -m perover -i active_chromhmm.bed -o annotations
sjcab-peak2anno-db filter-bed hg38 v31 -m isoexp -i isoform_expression.tsv --exclusive -o annotations

sjcab-peak2anno-db install-feature all all
sjcab-peak2anno-db install-feature hg38 v31 -o feature_downloads
sjcab-peak2anno-db download-feature hg38 v31 -o feature_downloads
sjcab-peak2anno-db download-feature all all -o feature_downloads

sjcab-peak2anno-db install-chromhmm -m 18 -s hg19 -i E001,E063
sjcab-peak2anno-db install-chromhmm -m 18 -s hg38 -i E063
sjcab-peak2anno-db download-chromhmm -o chromhmm_downloads -m 25 -s hg19 -c GM12878
sjcab-peak2anno-db download-chromhmm -o chromhmm_downloads -m 18 -s hg38 -i E063

sjcab-peak2anno-db install-segway -i GM12878,H1-HESC
sjcab-peak2anno-db download-segway -o segway_downloads -t brain
sjcab-peak2anno-db download-segway -o segway_downloads -s hg38 --yes-liftover -i GM12878

sjcab-peak2anno-db install-blacklists
sjcab-peak2anno-db install-cgi
sjcab-peak2anno-db download-cgi
sjcab-peak2anno-db download-cgi -d cgi_downloads --species hg38

sjcab-peak2anno-db path hg38 gene