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%global _empty_manifest_terminate_build 0
Name: python-MODApy
Version: 0.7.8
Release: 1
Summary: Package to perform several analysis on Multi-Omics Data
License: GNU General Public License (GPL)
URL: https://github.com/juancgvazquez/MODApy
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/8c/19/befa942842a3bff717c5b5e356f0bd503c416fcb1d90caa4a6acc90eb63b/MODApy-0.7.8.tar.gz
BuildArch: noarch
Requires: python3-rq
Requires: python3-uvicorn
Requires: python3-pandas
Requires: python3-numpy
Requires: python3-configparser
Requires: python3-argparse
Requires: python3-Cython
Requires: python3-cyvcf2
Requires: python3-xlrd
Requires: python3-openpyxl
Requires: python3-XlsxWriter
Requires: python3-matplotlib
Requires: python3-matplotlib-venn
Requires: python3-xmltodict
Requires: python3-pyyaml
Requires: python3-requests
Requires: python3-tqdm
Requires: python3-fastapi
%description
# MODA
Multi-Omics Data Analysis (MODA) is an analitical platform for presicion medicine in Python and R, the MODApy and MODAr family. It allows easy and friendly analysis the Whole Exome Sequencing (WES) data or clinical applications such as SNP identification and priorization and SNP database generation from analyzed patients. In this sence it allows building a home made data base for local and private use.
## Current facilities
The platform can be deployed in a local or remote server acceced trhough a Shiny web interface trhough RStudio Server and Python. The platform allow retrieving raw sequencing data from sequencong providers such as Macrogen(r) and others, by providing approrpiate url to the data. The patients will be downloaded and orgnanized into a local data base.
* SNP hunting: It apply GATK best practices for mutation detection and annotation trhough an optimized pipeline.
* SNP population based database: It allows building a local data base of SNP, with Zigocity information, allele frequency and SNP population frequency. Such type of database is important when analyzing local variant frequencies that may allow filtering or population characterization.
* Single, duos and trios WES analysis and comparison by means of User defined Gene sets implementing state of the art processing pipelines applying good practices protocols. A gene set is a list of genes pathological or pehnotipical associated by the user. It can be used for the study of mendelian/hereditary diseases. The results are saved in an Excel(r) file with clinvar, dbGap, dbSNP annotation plus local allee frequency and population based frequency. The files also hold url links to genecards, snpDB and Varsome web platforms for each detected variant to simplify acces to uptodate information.
## Under development
* Gene Fusion RNAseq-based detection: It is based on [Arriba](https://arriba.readthedocs.io/en/latest/) software, the winner of the [DREAM SMC-RNA Challenge](https://www.synapse.org/SMC_RNA) in addition with specific visualization capabilities exclusive for MODA family.
* Gene expression analysis: Gene and exon quantification
* Immune tumor microenvironment deconvolution of RNAseq samples trhough [MIXTURE]
## Current services
* Local and remote Instalation of the MODA family in your local or remote server for institutional use
* We provided WES analysis and advice
* We provide Gene-Fusion, gene expression and immune deconvolution analysis
Please contact for further details efernandez at cidie . ucc . edu . ar
Authors:
Juan Carlos Vázquez, UTN FRC - UCC
Elmer Andrés Fernández (PhD) - CIDIE - UCC - CONICET
%package -n python3-MODApy
Summary: Package to perform several analysis on Multi-Omics Data
Provides: python-MODApy
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-MODApy
# MODA
Multi-Omics Data Analysis (MODA) is an analitical platform for presicion medicine in Python and R, the MODApy and MODAr family. It allows easy and friendly analysis the Whole Exome Sequencing (WES) data or clinical applications such as SNP identification and priorization and SNP database generation from analyzed patients. In this sence it allows building a home made data base for local and private use.
## Current facilities
The platform can be deployed in a local or remote server acceced trhough a Shiny web interface trhough RStudio Server and Python. The platform allow retrieving raw sequencing data from sequencong providers such as Macrogen(r) and others, by providing approrpiate url to the data. The patients will be downloaded and orgnanized into a local data base.
* SNP hunting: It apply GATK best practices for mutation detection and annotation trhough an optimized pipeline.
* SNP population based database: It allows building a local data base of SNP, with Zigocity information, allele frequency and SNP population frequency. Such type of database is important when analyzing local variant frequencies that may allow filtering or population characterization.
* Single, duos and trios WES analysis and comparison by means of User defined Gene sets implementing state of the art processing pipelines applying good practices protocols. A gene set is a list of genes pathological or pehnotipical associated by the user. It can be used for the study of mendelian/hereditary diseases. The results are saved in an Excel(r) file with clinvar, dbGap, dbSNP annotation plus local allee frequency and population based frequency. The files also hold url links to genecards, snpDB and Varsome web platforms for each detected variant to simplify acces to uptodate information.
## Under development
* Gene Fusion RNAseq-based detection: It is based on [Arriba](https://arriba.readthedocs.io/en/latest/) software, the winner of the [DREAM SMC-RNA Challenge](https://www.synapse.org/SMC_RNA) in addition with specific visualization capabilities exclusive for MODA family.
* Gene expression analysis: Gene and exon quantification
* Immune tumor microenvironment deconvolution of RNAseq samples trhough [MIXTURE]
## Current services
* Local and remote Instalation of the MODA family in your local or remote server for institutional use
* We provided WES analysis and advice
* We provide Gene-Fusion, gene expression and immune deconvolution analysis
Please contact for further details efernandez at cidie . ucc . edu . ar
Authors:
Juan Carlos Vázquez, UTN FRC - UCC
Elmer Andrés Fernández (PhD) - CIDIE - UCC - CONICET
%package help
Summary: Development documents and examples for MODApy
Provides: python3-MODApy-doc
%description help
# MODA
Multi-Omics Data Analysis (MODA) is an analitical platform for presicion medicine in Python and R, the MODApy and MODAr family. It allows easy and friendly analysis the Whole Exome Sequencing (WES) data or clinical applications such as SNP identification and priorization and SNP database generation from analyzed patients. In this sence it allows building a home made data base for local and private use.
## Current facilities
The platform can be deployed in a local or remote server acceced trhough a Shiny web interface trhough RStudio Server and Python. The platform allow retrieving raw sequencing data from sequencong providers such as Macrogen(r) and others, by providing approrpiate url to the data. The patients will be downloaded and orgnanized into a local data base.
* SNP hunting: It apply GATK best practices for mutation detection and annotation trhough an optimized pipeline.
* SNP population based database: It allows building a local data base of SNP, with Zigocity information, allele frequency and SNP population frequency. Such type of database is important when analyzing local variant frequencies that may allow filtering or population characterization.
* Single, duos and trios WES analysis and comparison by means of User defined Gene sets implementing state of the art processing pipelines applying good practices protocols. A gene set is a list of genes pathological or pehnotipical associated by the user. It can be used for the study of mendelian/hereditary diseases. The results are saved in an Excel(r) file with clinvar, dbGap, dbSNP annotation plus local allee frequency and population based frequency. The files also hold url links to genecards, snpDB and Varsome web platforms for each detected variant to simplify acces to uptodate information.
## Under development
* Gene Fusion RNAseq-based detection: It is based on [Arriba](https://arriba.readthedocs.io/en/latest/) software, the winner of the [DREAM SMC-RNA Challenge](https://www.synapse.org/SMC_RNA) in addition with specific visualization capabilities exclusive for MODA family.
* Gene expression analysis: Gene and exon quantification
* Immune tumor microenvironment deconvolution of RNAseq samples trhough [MIXTURE]
## Current services
* Local and remote Instalation of the MODA family in your local or remote server for institutional use
* We provided WES analysis and advice
* We provide Gene-Fusion, gene expression and immune deconvolution analysis
Please contact for further details efernandez at cidie . ucc . edu . ar
Authors:
Juan Carlos Vázquez, UTN FRC - UCC
Elmer Andrés Fernández (PhD) - CIDIE - UCC - CONICET
%prep
%autosetup -n MODApy-0.7.8
%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-MODApy -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 0.7.8-1
- Package Spec generated
|
