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18 changes: 9 additions & 9 deletions README.md
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Expand Up @@ -6,10 +6,10 @@ This repository contains the data and code used to generate the JASPAR UCSC Geno

## Content
* The `genomes` folder contains scripts to download and process different genome assemblies
* The `profiles` folder contains the output from the script [`get_profiles.py`](https://github.com/wassermanlab/JASPAR-UCSC-tracks/blob/master/profiles/get_profiles.py), which downloads the JASPAR CORE profiles for different taxons
* The file [`environment.yml`](https://github.com/wassermanlab/JASPAR-UCSC-tracks/blob/master/environment.yml) contains the conda environment used to generate the genomic tracks for JASPAR 2020 (see installation)
* The `profiles` folder contains the output from the script [`get-profiles.py`](https://github.com/wassermanlab/JASPAR-UCSC-tracks/blob/master/profiles/get_profiles.py), which downloads the JASPAR CORE profiles for different taxons
* The file [`environment.yml`](https://github.com/wassermanlab/JASPAR-UCSC-tracks/blob/master/environment.yml), within the `conda` folder, contains the conda environment used to generate the genomic tracks for JASPAR 2022 (see installation)
* The script [`install-pwmscan.sh`](https://github.com/wassermanlab/JASPAR-UCSC-tracks/blob/master/install-pwmscan.sh) downloads and installs PWMscan and places its binaries in the in the `bin` folder.
* The script [`scan_sequence.py`](https://github.com/wassermanlab/JASPAR-UCSC-tracks/blob/master/scan_sequence.py) takes as its input the `profiles` folder and a nucleotide sequence in [FASTA format](https://en.wikipedia.org/wiki/FASTA_format)</br>(*e.g.* a genome), and outputs TFBS predictions
* The script [`scan-sequence.py`](https://github.com/wassermanlab/JASPAR-UCSC-tracks/blob/master/scan_sequence.py) takes as its input the `profiles` folder and a nucleotide sequence in [FASTA format](https://en.wikipedia.org/wiki/FASTA_format)</br>(*e.g.* a genome), and outputs TFBS predictions
* The script [`scans2bigBed`](https://github.com/wassermanlab/JASPAR-UCSC-tracks/blob/master/scans2bigBed) creates a [bigBed track file](https://genome.ucsc.edu/goldenPath/help/bigBed.html) from TFBS predictions

The original scripts used for the publication of [JASPAR 2018](https://doi.org/10.1093/nar/gkx1126) have been placed in the folder [`version-1.0`](https://github.com/wassermanlab/JASPAR-UCSC-tracks/tree/master/version-1.0).
Expand All @@ -26,26 +26,26 @@ To install PWMScan, execute the script [`install-pwmscan.sh`](https://github.com

The remaining dependencies can be installed through the [conda](https://docs.conda.io/en/latest/) package manager:
```
conda env create -f ./environment.yml
conda env create -f ./conda/environment.yml
```

## Availability
Genomic tracks and TFBS predictions for human and six other model organisms are available online:
* [http://expdata.cmmt.ubc.ca/JASPAR/downloads/UCSC_tracks/2020/](http://expdata.cmmt.ubc.ca/JASPAR/downloads/UCSC_tracks/2020/)
Genomic tracks and TFBS predictions for human and **seven** other model organisms, covering **11** genome assemblies, are available online:
* [http://expdata.cmmt.ubc.ca/JASPAR/downloads/UCSC_tracks/2022/](http://expdata.cmmt.ubc.ca/JASPAR/downloads/UCSC_tracks/2022/)

## Usage
To illustrate how the genomic tracks are generated, we provide an example for the [baker's yeast genome](https://www.ncbi.nlm.nih.gov/assembly/GCF_000146045.2/):
* Download the genome sequence and chromosome sizes (automated in this [script](https://github.com/wassermanlab/JASPAR-UCSC-tracks/blob/master/genomes/sacCer3/sacCer3.sh))
* Scan the genome sequence using [**all** fungi profiles from the JASPAR CORE](http://jaspar.genereg.net/search?q=&collection=CORE&tax_group=fungi)
```
./scan_sequence.py --fasta-file ./genomes/sacCer3/sacCer3.fa --profiles-dir ./profiles/ \
./scan-sequence.py --fasta-file ./genomes/sacCer3/sacCer3.fa --profiles-dir ./profiles/ \
--output-dir ./tracks/sacCer3/ --threads 4 --latest --taxon fungi
```
For this example, the scanning step should take no longer than a minute. For human and other similar genomes, this step is usually finished within a few hours (the final amount of time will depend on the number of `--threads` specified).
* Create the genomic track
```
./scans2bigBed -c ./genomes/sacCer3/sacCer3.chrom.sizes -i ./tracks/sacCer3/ -o ./tracks/sacCer3.bb -t 4
./scans2bigBed -c ./genomes/sacCer3/sacCer3.fa.sizes -i ./tracks/sacCer3/ -o ./tracks/sacCer3.bb -t 4
```
TFBS predictions from the previous step are merged into a [bigBed track file](https://genome.ucsc.edu/goldenPath/help/bigBed.html). In column five, we use as scores the <i>p</i>-values from PWMScan (scaled between 0-1000, where 0 corresponds to <i>p</i>-value = 1 and 1000 to <i>p</i>-value ≤ 10-10). This allows for comparison of prediction confidence across TFBSs. Again, for this example, this step should be completed within a few minutes, while for larger genomes it can take a few hours.

**Important note:** both disk space and memory requirements for large genomes (*i.e.* danRer11, hg19, hg38 and mm10) are substantial. In these cases, we highly recommend allocating at least 1Tb of disk space and 512Gb of ram.
**Important note:** disk space requirements for large genomes (*i.e.* danRer11, hg19, hg38, mm10, and mm39) are substantial. In these cases, we highly recommend allocating at least 1Tb of disk space.
1 change: 1 addition & 0 deletions environment.yml → conda/environment.yml
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Expand Up @@ -14,6 +14,7 @@ dependencies:
- pip=19.2.2
- pyfaidx=0.5.5.2
- python=3.7.3
- seaborn=0.11.2
- setuptools=41.0.1
- tqdm=4.35.0
- ucsc-bedtobigbed
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Expand Up @@ -6,7 +6,7 @@ if ! [ -f araTha1/araTha1.fa.sizes ]; then
wget https://genome-test.gi.ucsc.edu/~hiram/hubs/Plants/araTha1/araTha1.2bit
twoBitToFa araTha1.2bit araTha1.fa
faidx -x araTha1.fa
faidx araTha1.fa -i chromsizes > araTha1.chrom.sizes
faidx araTha1.fa -i chromsizes > araTha1.fa.sizes
rm araTha1.2bit chr*.fa
cd ..
fi
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