%global _empty_manifest_terminate_build 0
Name: python-spliceai
Version: 1.3.1
Release: 1
Summary: SpliceAI: A deep learning-based tool to identify splice variants
License: GPLv3
URL: https://github.com/illumina/SpliceAI
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/4d/a5/40f44452f56a5061ae729912451ecd3f2972c22a099c153b95c9a97e15d5/spliceai-1.3.1.tar.gz
BuildArch: noarch
Requires: python3-keras
Requires: python3-pyfaidx
Requires: python3-pysam
Requires: python3-numpy
Requires: python3-pandas
Requires: python3-tensorflow
Requires: python3-tensorflow-gpu
%description
## SpliceAI: A deep learning-based tool to identify splice variants
[](https://img.shields.io/badge/release-v1.3-orange.svg)
[](https://img.shields.io/badge/license-GPLv3-green.svg)
[](https://pepy.tech/badge/spliceai)
This package annotates genetic variants with their predicted effect on splicing, as described in [Jaganathan *et al*, Cell 2019 in press](https://doi.org/10.1016/j.cell.2018.12.015).
**Update**: The annotations for all possible substitutions, 1 base insertions, and 1-4 base deletions within genes are available [here](https://basespace.illumina.com/s/5u6ThOblecrh) for download.
### Installation
The simplest way to install SpliceAI is through pip or conda:
```sh
pip install spliceai
# or
conda install -c bioconda spliceai
```
Alternately, SpliceAI can be installed from the [github repository](https://github.com/Illumina/SpliceAI.git):
```sh
git clone https://github.com/Illumina/SpliceAI.git
cd SpliceAI
python setup.py install
```
SpliceAI requires ```tensorflow>=1.2.0```, which is best installed separately via pip or conda (see the [TensorFlow](https://www.tensorflow.org/) website for other installation options):
```sh
pip install tensorflow
# or
conda install tensorflow
```
### Usage
SpliceAI can be run from the command line:
```sh
spliceai -I input.vcf -O output.vcf -R genome.fa -A grch37
# or you can pipe the input and output VCFs
cat input.vcf | spliceai -R genome.fa -A grch37 > output.vcf
```
Required parameters:
- ```-I```: Input VCF with variants of interest.
- ```-O```: Output VCF with SpliceAI predictions `ALLELE|SYMBOL|DS_AG|DS_AL|DS_DG|DS_DL|DP_AG|DP_AL|DP_DG|DP_DL` included in the INFO column (see table below for details). Only SNVs and simple INDELs (REF or ALT is a single base) within genes are annotated. Variants in multiple genes have separate predictions for each gene.
- ```-R```: Reference genome fasta file. Can be downloaded from [GRCh37/hg19](http://hgdownload.cse.ucsc.edu/goldenPath/hg19/bigZips/hg19.fa.gz) or [GRCh38/hg38](http://hgdownload.cse.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz).
- ```-A```: Gene annotation file. Can instead provide `grch37` or `grch38` to use GENCODE V24 canonical annotation files included with the package. To create custom gene annotation files, use `spliceai/annotations/grch37.txt` in repository as template.
Optional parameters:
- ```-D```: Maximum distance between the variant and gained/lost splice site (default: 50).
- ```-M```: Mask scores representing annotated acceptor/donor gain and unannotated acceptor/donor loss (default: 0).
Details of SpliceAI INFO field:
| ID | Description |
| -------- | ----------- |
| ALLELE | Alternate allele |
| SYMBOL | Gene symbol |
| DS_AG | Delta score (acceptor gain) |
| DS_AL | Delta score (acceptor loss) |
| DS_DG | Delta score (donor gain) |
| DS_DL | Delta score (donor loss) |
| DP_AG | Delta position (acceptor gain) |
| DP_AL | Delta position (acceptor loss) |
| DP_DG | Delta position (donor gain) |
| DP_DL | Delta position (donor loss) |
Delta score of a variant, defined as the maximum of (DS_AG, DS_AL, DS_DG, DS_DL), ranges from 0 to 1 and can be interpreted as the probability of the variant being splice-altering. In the paper, a detailed characterization is provided for 0.2 (high recall), 0.5 (recommended), and 0.8 (high precision) cutoffs. Delta position conveys information about the location where splicing changes relative to the variant position (positive values are downstream of the variant, negative values are upstream).
### Examples
A sample input file and the corresponding output file can be found at `examples/input.vcf` and `examples/output.vcf` respectively. The output `T|RYR1|0.00|0.00|0.91|0.08|-28|-46|-2|-31` for the variant `19:38958362 C>T` can be interpreted as follows:
* The probability that the position 19:38958360 (=38958362-2) is used as a splice donor increases by 0.91.
* The probability that the position 19:38958331 (=38958362-31) is used as a splice donor decreases by 0.08.
Similarly, the output `CA|TTN|0.07|1.00|0.00|0.00|-7|-1|35|-29` for the variant `2:179415988 C>CA` has the following interpretation:
* The probability that the position 2:179415981 (=179415988-7) is used as a splice acceptor increases by 0.07.
* The probability that the position 2:179415987 (=179415988-1) is used as a splice acceptor decreases by 1.00.
### Contact
Kishore Jaganathan: kishorejaganathan@gmail.com
%package -n python3-spliceai
Summary: SpliceAI: A deep learning-based tool to identify splice variants
Provides: python-spliceai
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-spliceai
## SpliceAI: A deep learning-based tool to identify splice variants
[](https://img.shields.io/badge/release-v1.3-orange.svg)
[](https://img.shields.io/badge/license-GPLv3-green.svg)
[](https://pepy.tech/badge/spliceai)
This package annotates genetic variants with their predicted effect on splicing, as described in [Jaganathan *et al*, Cell 2019 in press](https://doi.org/10.1016/j.cell.2018.12.015).
**Update**: The annotations for all possible substitutions, 1 base insertions, and 1-4 base deletions within genes are available [here](https://basespace.illumina.com/s/5u6ThOblecrh) for download.
### Installation
The simplest way to install SpliceAI is through pip or conda:
```sh
pip install spliceai
# or
conda install -c bioconda spliceai
```
Alternately, SpliceAI can be installed from the [github repository](https://github.com/Illumina/SpliceAI.git):
```sh
git clone https://github.com/Illumina/SpliceAI.git
cd SpliceAI
python setup.py install
```
SpliceAI requires ```tensorflow>=1.2.0```, which is best installed separately via pip or conda (see the [TensorFlow](https://www.tensorflow.org/) website for other installation options):
```sh
pip install tensorflow
# or
conda install tensorflow
```
### Usage
SpliceAI can be run from the command line:
```sh
spliceai -I input.vcf -O output.vcf -R genome.fa -A grch37
# or you can pipe the input and output VCFs
cat input.vcf | spliceai -R genome.fa -A grch37 > output.vcf
```
Required parameters:
- ```-I```: Input VCF with variants of interest.
- ```-O```: Output VCF with SpliceAI predictions `ALLELE|SYMBOL|DS_AG|DS_AL|DS_DG|DS_DL|DP_AG|DP_AL|DP_DG|DP_DL` included in the INFO column (see table below for details). Only SNVs and simple INDELs (REF or ALT is a single base) within genes are annotated. Variants in multiple genes have separate predictions for each gene.
- ```-R```: Reference genome fasta file. Can be downloaded from [GRCh37/hg19](http://hgdownload.cse.ucsc.edu/goldenPath/hg19/bigZips/hg19.fa.gz) or [GRCh38/hg38](http://hgdownload.cse.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz).
- ```-A```: Gene annotation file. Can instead provide `grch37` or `grch38` to use GENCODE V24 canonical annotation files included with the package. To create custom gene annotation files, use `spliceai/annotations/grch37.txt` in repository as template.
Optional parameters:
- ```-D```: Maximum distance between the variant and gained/lost splice site (default: 50).
- ```-M```: Mask scores representing annotated acceptor/donor gain and unannotated acceptor/donor loss (default: 0).
Details of SpliceAI INFO field:
| ID | Description |
| -------- | ----------- |
| ALLELE | Alternate allele |
| SYMBOL | Gene symbol |
| DS_AG | Delta score (acceptor gain) |
| DS_AL | Delta score (acceptor loss) |
| DS_DG | Delta score (donor gain) |
| DS_DL | Delta score (donor loss) |
| DP_AG | Delta position (acceptor gain) |
| DP_AL | Delta position (acceptor loss) |
| DP_DG | Delta position (donor gain) |
| DP_DL | Delta position (donor loss) |
Delta score of a variant, defined as the maximum of (DS_AG, DS_AL, DS_DG, DS_DL), ranges from 0 to 1 and can be interpreted as the probability of the variant being splice-altering. In the paper, a detailed characterization is provided for 0.2 (high recall), 0.5 (recommended), and 0.8 (high precision) cutoffs. Delta position conveys information about the location where splicing changes relative to the variant position (positive values are downstream of the variant, negative values are upstream).
### Examples
A sample input file and the corresponding output file can be found at `examples/input.vcf` and `examples/output.vcf` respectively. The output `T|RYR1|0.00|0.00|0.91|0.08|-28|-46|-2|-31` for the variant `19:38958362 C>T` can be interpreted as follows:
* The probability that the position 19:38958360 (=38958362-2) is used as a splice donor increases by 0.91.
* The probability that the position 19:38958331 (=38958362-31) is used as a splice donor decreases by 0.08.
Similarly, the output `CA|TTN|0.07|1.00|0.00|0.00|-7|-1|35|-29` for the variant `2:179415988 C>CA` has the following interpretation:
* The probability that the position 2:179415981 (=179415988-7) is used as a splice acceptor increases by 0.07.
* The probability that the position 2:179415987 (=179415988-1) is used as a splice acceptor decreases by 1.00.
### Contact
Kishore Jaganathan: kishorejaganathan@gmail.com
%package help
Summary: Development documents and examples for spliceai
Provides: python3-spliceai-doc
%description help
## SpliceAI: A deep learning-based tool to identify splice variants
[](https://img.shields.io/badge/release-v1.3-orange.svg)
[](https://img.shields.io/badge/license-GPLv3-green.svg)
[](https://pepy.tech/badge/spliceai)
This package annotates genetic variants with their predicted effect on splicing, as described in [Jaganathan *et al*, Cell 2019 in press](https://doi.org/10.1016/j.cell.2018.12.015).
**Update**: The annotations for all possible substitutions, 1 base insertions, and 1-4 base deletions within genes are available [here](https://basespace.illumina.com/s/5u6ThOblecrh) for download.
### Installation
The simplest way to install SpliceAI is through pip or conda:
```sh
pip install spliceai
# or
conda install -c bioconda spliceai
```
Alternately, SpliceAI can be installed from the [github repository](https://github.com/Illumina/SpliceAI.git):
```sh
git clone https://github.com/Illumina/SpliceAI.git
cd SpliceAI
python setup.py install
```
SpliceAI requires ```tensorflow>=1.2.0```, which is best installed separately via pip or conda (see the [TensorFlow](https://www.tensorflow.org/) website for other installation options):
```sh
pip install tensorflow
# or
conda install tensorflow
```
### Usage
SpliceAI can be run from the command line:
```sh
spliceai -I input.vcf -O output.vcf -R genome.fa -A grch37
# or you can pipe the input and output VCFs
cat input.vcf | spliceai -R genome.fa -A grch37 > output.vcf
```
Required parameters:
- ```-I```: Input VCF with variants of interest.
- ```-O```: Output VCF with SpliceAI predictions `ALLELE|SYMBOL|DS_AG|DS_AL|DS_DG|DS_DL|DP_AG|DP_AL|DP_DG|DP_DL` included in the INFO column (see table below for details). Only SNVs and simple INDELs (REF or ALT is a single base) within genes are annotated. Variants in multiple genes have separate predictions for each gene.
- ```-R```: Reference genome fasta file. Can be downloaded from [GRCh37/hg19](http://hgdownload.cse.ucsc.edu/goldenPath/hg19/bigZips/hg19.fa.gz) or [GRCh38/hg38](http://hgdownload.cse.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz).
- ```-A```: Gene annotation file. Can instead provide `grch37` or `grch38` to use GENCODE V24 canonical annotation files included with the package. To create custom gene annotation files, use `spliceai/annotations/grch37.txt` in repository as template.
Optional parameters:
- ```-D```: Maximum distance between the variant and gained/lost splice site (default: 50).
- ```-M```: Mask scores representing annotated acceptor/donor gain and unannotated acceptor/donor loss (default: 0).
Details of SpliceAI INFO field:
| ID | Description |
| -------- | ----------- |
| ALLELE | Alternate allele |
| SYMBOL | Gene symbol |
| DS_AG | Delta score (acceptor gain) |
| DS_AL | Delta score (acceptor loss) |
| DS_DG | Delta score (donor gain) |
| DS_DL | Delta score (donor loss) |
| DP_AG | Delta position (acceptor gain) |
| DP_AL | Delta position (acceptor loss) |
| DP_DG | Delta position (donor gain) |
| DP_DL | Delta position (donor loss) |
Delta score of a variant, defined as the maximum of (DS_AG, DS_AL, DS_DG, DS_DL), ranges from 0 to 1 and can be interpreted as the probability of the variant being splice-altering. In the paper, a detailed characterization is provided for 0.2 (high recall), 0.5 (recommended), and 0.8 (high precision) cutoffs. Delta position conveys information about the location where splicing changes relative to the variant position (positive values are downstream of the variant, negative values are upstream).
### Examples
A sample input file and the corresponding output file can be found at `examples/input.vcf` and `examples/output.vcf` respectively. The output `T|RYR1|0.00|0.00|0.91|0.08|-28|-46|-2|-31` for the variant `19:38958362 C>T` can be interpreted as follows:
* The probability that the position 19:38958360 (=38958362-2) is used as a splice donor increases by 0.91.
* The probability that the position 19:38958331 (=38958362-31) is used as a splice donor decreases by 0.08.
Similarly, the output `CA|TTN|0.07|1.00|0.00|0.00|-7|-1|35|-29` for the variant `2:179415988 C>CA` has the following interpretation:
* The probability that the position 2:179415981 (=179415988-7) is used as a splice acceptor increases by 0.07.
* The probability that the position 2:179415987 (=179415988-1) is used as a splice acceptor decreases by 1.00.
### Contact
Kishore Jaganathan: kishorejaganathan@gmail.com
%prep
%autosetup -n spliceai-1.3.1
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-spliceai -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue Apr 25 2023 Python_Bot <Python_Bot@openeuler.org> - 1.3.1-1
- Package Spec generated