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+%global _empty_manifest_terminate_build 0
+Name:		python-arg-ranker
+Version:	3.3
+Release:	1
+Summary:	Ranking the risk of antibiotic resistance for genomes/metagenomes
+License:	MIT
+URL:		https://github.com/caozhichongchong/ARG_Ranker
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/98/7d/cc0ec417cd56279c677763dd177b7dbb158014239048dcdb68f78177fb07/arg_ranker-3.3.tar.gz
+BuildArch:	noarch
+
+
+%description
+# arg_ranker
+arg_ranker evaluates the risk of ARGs in genomes and metagenomes
+
+## Install
+experimental version using SARGv3\
+`pip install arg_ranker`\
+Long term support version using SARGv1\
+`pip install arg-ranker==3.0.2`
+### Please make sure to install arg_ranker >= v3
+To all users,\
+We have noticed an error of arg_ranker.v2 when reporting the total ARG abundance in metagenomes.\
+If the total abundance is used in your research, please update arg_ranker to v3 and re-run your metagenomes (`arg_ranker -i $INPUT -kkdb $KRAKENDB`).\
+Alternatively, you can fix arg_ranker.v2 by replacing its original ARG_table.sum.py with [ARG_table.sum.py](https://github.com/caozhichongchong/arg_ranker/tree/v2.0/arg_ranker/bin_v2only/ARG_table.sum.py)\
+and re-run the last two commands in arg_ranker.sh `python $PATH_to_arg_ranker/bin/ARG_table.sum.py -i ...` and `arg_ranker -i ...`.\
+You can find the path to ARG_table.sum.py in arg_ranker.sh.\
+Note that this [ARG_table.sum.py](https://github.com/caozhichongchong/arg_ranker/tree/v2.0/arg_ranker/bin_v2only/ARG_table.sum.py) is only meant for fixing arg_ranker.v2 and the results of arg_ranker.v2.\
+Please do not replace ARG_table.sum.py in arg_ranker.v3 with this [ARG_table.sum.py](https://github.com/caozhichongchong/arg_ranker/tree/v2.0/arg_ranker/bin_v2only/ARG_table.sum.py).\
+We are really sorry about this inconvenience.\
+Please feel free to reach out to anniz44@mit.edu if you have any questions.
+
+To check installed version `pip show arg_ranker`\
+To upgrade `pip install arg_ranker --upgrade`
+
+## Requirement
+* python 3
+* diamond: `conda install -c bioconda diamond=0.9.36` (https://github.com/bbuchfink/diamond)
+* blast+: `conda install -c bioconda blast` (https://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/)
+* For metagenomes:
+    * kraken2: `conda install -c bioconda kraken2`(https://github.com/DerrickWood/kraken2/wiki)
+        * to compute the abundance of ARGs as copy number of ARGs per bacterial cell (recommended)
+            * download the kraken2 standard database (50 GB of disk space): `kraken2-build --standard --db $KRAKENDB` \
+            where $KRAKENDB is your preferred database name/location
+            * MicrobeCensu: `git clone https://github.com/snayfach/MicrobeCensus && cd MicrobeCensus && python setup.py install` to estimate the average genome size for metagenomes.
+            (https://github.com/snayfach/MicrobeCensus)
+        * to compute the abundance of ARGs as copy number of ARGs per 16S
+            * download the kraken2 16S database (73.2 MB of disk space): `kraken2-build --db $DBNAME --special greengenes`
+
+## How to use it
+* put all your genomes (.fa or .fasta) and metagenomes (.fq or .fastq) into one folder ($INPUT)
+* run `arg_ranker -i $INPUT` (genomes only)
+* run `arg_ranker -i $INPUT -kkdb $KRAKENDB` (genomes/metagenomes + kraken2 standard database)
+    * or run `arg_ranker -i $INPUT -kkdb $KRAKENDB -kkdbtype 16S` (kraken2 16S database)
+* run `sh arg_ranking/script_output/arg_ranker.sh`
+
+## Output
+* Sample_ranking_results.txt (Table 1) - LTS SARGv1 version
+    * arg_ranker = 3.0.2
+    * python >= 3.5
+    * diamond = 0.9.36
+    * blast = 2.13.0
+    * kraken2 = 2.1.2
+   
+    |Sample|Rank_I_per|Rank_II_per|Rank_III_per|Rank_IV_per|Unassessed_per|Total_abu|Rank_code|Rank_I_risk|Rank_II_risk|Rank_III_risk|Rank_IV_risk|ARGs_unassessed_risk|note1|
+    | :--------: | :--------: | :--------: | :--------: | :--------: | :--------: | :--------: | :--------: | :--------: | :--------: | :--------: | :--------: | :--------: | :--------: |
+    |WEE300_all-trimmed-decont_1.fastq|4.6E-02|0.0E+00|6.8E-02|7.5E-01|1.3E-01|1.9E+00|1.5-0.0-0.4-1.7-0.4|1.5|0.0|0.4|1.7|0.4|hospital_metagenome|
+    |EsCo_genome.fasta|0.0E+00|0.0E+00|2.4E-01|7.6E-01|0.0E+00|2.1E+01|0.0-0.0-1.6-1.7-0.0|0.0|0.0|1.6|1.7|0.0|E.coli_genome|
+
+1. Rank_I_per - Unassessed_per: percentage of ARGs of a risk Rank\
+Total_abu: total abundance of all ARGs
+2. For genomes, we output the copy number of ARGs detected in each genome.
+3. For metagenomes, we compute the abundance of ARGs as the copy number of ARGs divided by the bacterial cell number or 16S copy number in the same metagenome.\
+If you downloaded the kraken2 standard database, we compute the copy number of ARGs divided by the bacterial cell number.\
+If you downloaded the kraken2 16S database, we compute the copy number of ARGs divided by the 16S copy number.\
+The copy number of ARGs, 16S, and bacterial cells were computed as the number of reads mapped to them divided by their gene/genome length.
+4. We compute the contribution of each ARG risk Rank as the average abundance of ARGs of a risk Rank divided by the average abundance of all ARGs\
+Rank_I_risk - Unassessed_risk: the contribution of ARGs of a risk Rank\
+Rank_code: a code of contribution from Rank I to Unassessed
+
+* Sample_ARGpresence.txt:\
+The abundance, the gene family, and the antibiotic of resistance of ARGs detected in the input samples
+
+## Test
+run `arg_ranker -i example -kkdb $KRAKENDB`\
+run `sh arg_ranking/script_output/arg_ranker.sh`\
+The arg_ranking/Sample_ranking_results.txt should look like Table 1 (using kraken2 standard database)
+
+## Metadata for your samples (optional)
+arg_ranker can merge your sample metadata into the results of ARG ranking (i.e. note1 in Table 1).\
+Simply put all information you would like to include into a tab-delimited table\
+Make sure that your sample names are listed as the first column (check example/metadata.txt).
+
+## Copyright
+Dr. An-Ni Zhang (MIT), Prof. Eric Alm (MIT), Prof. Tong Zhang* (University of Hong Kong)
+
+## Citation
+Zhang, AN., Gaston, J.M., Dai, C.L. et al. An omics-based framework for assessing the health risk of antimicrobial resistance genes. Nat Commun 12, 4765 (2021). https://doi.org/10.1038/s41467-021-25096-3\
+Correction: bacA is a bacitracin resistance gene, not a beta-lactamase (Fig 3).
+## Contact
+anniz44@mit.edu or caozhichongchong@gmail.com
+
+%package -n python3-arg-ranker
+Summary:	Ranking the risk of antibiotic resistance for genomes/metagenomes
+Provides:	python-arg-ranker
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-arg-ranker
+# arg_ranker
+arg_ranker evaluates the risk of ARGs in genomes and metagenomes
+
+## Install
+experimental version using SARGv3\
+`pip install arg_ranker`\
+Long term support version using SARGv1\
+`pip install arg-ranker==3.0.2`
+### Please make sure to install arg_ranker >= v3
+To all users,\
+We have noticed an error of arg_ranker.v2 when reporting the total ARG abundance in metagenomes.\
+If the total abundance is used in your research, please update arg_ranker to v3 and re-run your metagenomes (`arg_ranker -i $INPUT -kkdb $KRAKENDB`).\
+Alternatively, you can fix arg_ranker.v2 by replacing its original ARG_table.sum.py with [ARG_table.sum.py](https://github.com/caozhichongchong/arg_ranker/tree/v2.0/arg_ranker/bin_v2only/ARG_table.sum.py)\
+and re-run the last two commands in arg_ranker.sh `python $PATH_to_arg_ranker/bin/ARG_table.sum.py -i ...` and `arg_ranker -i ...`.\
+You can find the path to ARG_table.sum.py in arg_ranker.sh.\
+Note that this [ARG_table.sum.py](https://github.com/caozhichongchong/arg_ranker/tree/v2.0/arg_ranker/bin_v2only/ARG_table.sum.py) is only meant for fixing arg_ranker.v2 and the results of arg_ranker.v2.\
+Please do not replace ARG_table.sum.py in arg_ranker.v3 with this [ARG_table.sum.py](https://github.com/caozhichongchong/arg_ranker/tree/v2.0/arg_ranker/bin_v2only/ARG_table.sum.py).\
+We are really sorry about this inconvenience.\
+Please feel free to reach out to anniz44@mit.edu if you have any questions.
+
+To check installed version `pip show arg_ranker`\
+To upgrade `pip install arg_ranker --upgrade`
+
+## Requirement
+* python 3
+* diamond: `conda install -c bioconda diamond=0.9.36` (https://github.com/bbuchfink/diamond)
+* blast+: `conda install -c bioconda blast` (https://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/)
+* For metagenomes:
+    * kraken2: `conda install -c bioconda kraken2`(https://github.com/DerrickWood/kraken2/wiki)
+        * to compute the abundance of ARGs as copy number of ARGs per bacterial cell (recommended)
+            * download the kraken2 standard database (50 GB of disk space): `kraken2-build --standard --db $KRAKENDB` \
+            where $KRAKENDB is your preferred database name/location
+            * MicrobeCensu: `git clone https://github.com/snayfach/MicrobeCensus && cd MicrobeCensus && python setup.py install` to estimate the average genome size for metagenomes.
+            (https://github.com/snayfach/MicrobeCensus)
+        * to compute the abundance of ARGs as copy number of ARGs per 16S
+            * download the kraken2 16S database (73.2 MB of disk space): `kraken2-build --db $DBNAME --special greengenes`
+
+## How to use it
+* put all your genomes (.fa or .fasta) and metagenomes (.fq or .fastq) into one folder ($INPUT)
+* run `arg_ranker -i $INPUT` (genomes only)
+* run `arg_ranker -i $INPUT -kkdb $KRAKENDB` (genomes/metagenomes + kraken2 standard database)
+    * or run `arg_ranker -i $INPUT -kkdb $KRAKENDB -kkdbtype 16S` (kraken2 16S database)
+* run `sh arg_ranking/script_output/arg_ranker.sh`
+
+## Output
+* Sample_ranking_results.txt (Table 1) - LTS SARGv1 version
+    * arg_ranker = 3.0.2
+    * python >= 3.5
+    * diamond = 0.9.36
+    * blast = 2.13.0
+    * kraken2 = 2.1.2
+   
+    |Sample|Rank_I_per|Rank_II_per|Rank_III_per|Rank_IV_per|Unassessed_per|Total_abu|Rank_code|Rank_I_risk|Rank_II_risk|Rank_III_risk|Rank_IV_risk|ARGs_unassessed_risk|note1|
+    | :--------: | :--------: | :--------: | :--------: | :--------: | :--------: | :--------: | :--------: | :--------: | :--------: | :--------: | :--------: | :--------: | :--------: |
+    |WEE300_all-trimmed-decont_1.fastq|4.6E-02|0.0E+00|6.8E-02|7.5E-01|1.3E-01|1.9E+00|1.5-0.0-0.4-1.7-0.4|1.5|0.0|0.4|1.7|0.4|hospital_metagenome|
+    |EsCo_genome.fasta|0.0E+00|0.0E+00|2.4E-01|7.6E-01|0.0E+00|2.1E+01|0.0-0.0-1.6-1.7-0.0|0.0|0.0|1.6|1.7|0.0|E.coli_genome|
+
+1. Rank_I_per - Unassessed_per: percentage of ARGs of a risk Rank\
+Total_abu: total abundance of all ARGs
+2. For genomes, we output the copy number of ARGs detected in each genome.
+3. For metagenomes, we compute the abundance of ARGs as the copy number of ARGs divided by the bacterial cell number or 16S copy number in the same metagenome.\
+If you downloaded the kraken2 standard database, we compute the copy number of ARGs divided by the bacterial cell number.\
+If you downloaded the kraken2 16S database, we compute the copy number of ARGs divided by the 16S copy number.\
+The copy number of ARGs, 16S, and bacterial cells were computed as the number of reads mapped to them divided by their gene/genome length.
+4. We compute the contribution of each ARG risk Rank as the average abundance of ARGs of a risk Rank divided by the average abundance of all ARGs\
+Rank_I_risk - Unassessed_risk: the contribution of ARGs of a risk Rank\
+Rank_code: a code of contribution from Rank I to Unassessed
+
+* Sample_ARGpresence.txt:\
+The abundance, the gene family, and the antibiotic of resistance of ARGs detected in the input samples
+
+## Test
+run `arg_ranker -i example -kkdb $KRAKENDB`\
+run `sh arg_ranking/script_output/arg_ranker.sh`\
+The arg_ranking/Sample_ranking_results.txt should look like Table 1 (using kraken2 standard database)
+
+## Metadata for your samples (optional)
+arg_ranker can merge your sample metadata into the results of ARG ranking (i.e. note1 in Table 1).\
+Simply put all information you would like to include into a tab-delimited table\
+Make sure that your sample names are listed as the first column (check example/metadata.txt).
+
+## Copyright
+Dr. An-Ni Zhang (MIT), Prof. Eric Alm (MIT), Prof. Tong Zhang* (University of Hong Kong)
+
+## Citation
+Zhang, AN., Gaston, J.M., Dai, C.L. et al. An omics-based framework for assessing the health risk of antimicrobial resistance genes. Nat Commun 12, 4765 (2021). https://doi.org/10.1038/s41467-021-25096-3\
+Correction: bacA is a bacitracin resistance gene, not a beta-lactamase (Fig 3).
+## Contact
+anniz44@mit.edu or caozhichongchong@gmail.com
+
+%package help
+Summary:	Development documents and examples for arg-ranker
+Provides:	python3-arg-ranker-doc
+%description help
+# arg_ranker
+arg_ranker evaluates the risk of ARGs in genomes and metagenomes
+
+## Install
+experimental version using SARGv3\
+`pip install arg_ranker`\
+Long term support version using SARGv1\
+`pip install arg-ranker==3.0.2`
+### Please make sure to install arg_ranker >= v3
+To all users,\
+We have noticed an error of arg_ranker.v2 when reporting the total ARG abundance in metagenomes.\
+If the total abundance is used in your research, please update arg_ranker to v3 and re-run your metagenomes (`arg_ranker -i $INPUT -kkdb $KRAKENDB`).\
+Alternatively, you can fix arg_ranker.v2 by replacing its original ARG_table.sum.py with [ARG_table.sum.py](https://github.com/caozhichongchong/arg_ranker/tree/v2.0/arg_ranker/bin_v2only/ARG_table.sum.py)\
+and re-run the last two commands in arg_ranker.sh `python $PATH_to_arg_ranker/bin/ARG_table.sum.py -i ...` and `arg_ranker -i ...`.\
+You can find the path to ARG_table.sum.py in arg_ranker.sh.\
+Note that this [ARG_table.sum.py](https://github.com/caozhichongchong/arg_ranker/tree/v2.0/arg_ranker/bin_v2only/ARG_table.sum.py) is only meant for fixing arg_ranker.v2 and the results of arg_ranker.v2.\
+Please do not replace ARG_table.sum.py in arg_ranker.v3 with this [ARG_table.sum.py](https://github.com/caozhichongchong/arg_ranker/tree/v2.0/arg_ranker/bin_v2only/ARG_table.sum.py).\
+We are really sorry about this inconvenience.\
+Please feel free to reach out to anniz44@mit.edu if you have any questions.
+
+To check installed version `pip show arg_ranker`\
+To upgrade `pip install arg_ranker --upgrade`
+
+## Requirement
+* python 3
+* diamond: `conda install -c bioconda diamond=0.9.36` (https://github.com/bbuchfink/diamond)
+* blast+: `conda install -c bioconda blast` (https://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/)
+* For metagenomes:
+    * kraken2: `conda install -c bioconda kraken2`(https://github.com/DerrickWood/kraken2/wiki)
+        * to compute the abundance of ARGs as copy number of ARGs per bacterial cell (recommended)
+            * download the kraken2 standard database (50 GB of disk space): `kraken2-build --standard --db $KRAKENDB` \
+            where $KRAKENDB is your preferred database name/location
+            * MicrobeCensu: `git clone https://github.com/snayfach/MicrobeCensus && cd MicrobeCensus && python setup.py install` to estimate the average genome size for metagenomes.
+            (https://github.com/snayfach/MicrobeCensus)
+        * to compute the abundance of ARGs as copy number of ARGs per 16S
+            * download the kraken2 16S database (73.2 MB of disk space): `kraken2-build --db $DBNAME --special greengenes`
+
+## How to use it
+* put all your genomes (.fa or .fasta) and metagenomes (.fq or .fastq) into one folder ($INPUT)
+* run `arg_ranker -i $INPUT` (genomes only)
+* run `arg_ranker -i $INPUT -kkdb $KRAKENDB` (genomes/metagenomes + kraken2 standard database)
+    * or run `arg_ranker -i $INPUT -kkdb $KRAKENDB -kkdbtype 16S` (kraken2 16S database)
+* run `sh arg_ranking/script_output/arg_ranker.sh`
+
+## Output
+* Sample_ranking_results.txt (Table 1) - LTS SARGv1 version
+    * arg_ranker = 3.0.2
+    * python >= 3.5
+    * diamond = 0.9.36
+    * blast = 2.13.0
+    * kraken2 = 2.1.2
+   
+    |Sample|Rank_I_per|Rank_II_per|Rank_III_per|Rank_IV_per|Unassessed_per|Total_abu|Rank_code|Rank_I_risk|Rank_II_risk|Rank_III_risk|Rank_IV_risk|ARGs_unassessed_risk|note1|
+    | :--------: | :--------: | :--------: | :--------: | :--------: | :--------: | :--------: | :--------: | :--------: | :--------: | :--------: | :--------: | :--------: | :--------: |
+    |WEE300_all-trimmed-decont_1.fastq|4.6E-02|0.0E+00|6.8E-02|7.5E-01|1.3E-01|1.9E+00|1.5-0.0-0.4-1.7-0.4|1.5|0.0|0.4|1.7|0.4|hospital_metagenome|
+    |EsCo_genome.fasta|0.0E+00|0.0E+00|2.4E-01|7.6E-01|0.0E+00|2.1E+01|0.0-0.0-1.6-1.7-0.0|0.0|0.0|1.6|1.7|0.0|E.coli_genome|
+
+1. Rank_I_per - Unassessed_per: percentage of ARGs of a risk Rank\
+Total_abu: total abundance of all ARGs
+2. For genomes, we output the copy number of ARGs detected in each genome.
+3. For metagenomes, we compute the abundance of ARGs as the copy number of ARGs divided by the bacterial cell number or 16S copy number in the same metagenome.\
+If you downloaded the kraken2 standard database, we compute the copy number of ARGs divided by the bacterial cell number.\
+If you downloaded the kraken2 16S database, we compute the copy number of ARGs divided by the 16S copy number.\
+The copy number of ARGs, 16S, and bacterial cells were computed as the number of reads mapped to them divided by their gene/genome length.
+4. We compute the contribution of each ARG risk Rank as the average abundance of ARGs of a risk Rank divided by the average abundance of all ARGs\
+Rank_I_risk - Unassessed_risk: the contribution of ARGs of a risk Rank\
+Rank_code: a code of contribution from Rank I to Unassessed
+
+* Sample_ARGpresence.txt:\
+The abundance, the gene family, and the antibiotic of resistance of ARGs detected in the input samples
+
+## Test
+run `arg_ranker -i example -kkdb $KRAKENDB`\
+run `sh arg_ranking/script_output/arg_ranker.sh`\
+The arg_ranking/Sample_ranking_results.txt should look like Table 1 (using kraken2 standard database)
+
+## Metadata for your samples (optional)
+arg_ranker can merge your sample metadata into the results of ARG ranking (i.e. note1 in Table 1).\
+Simply put all information you would like to include into a tab-delimited table\
+Make sure that your sample names are listed as the first column (check example/metadata.txt).
+
+## Copyright
+Dr. An-Ni Zhang (MIT), Prof. Eric Alm (MIT), Prof. Tong Zhang* (University of Hong Kong)
+
+## Citation
+Zhang, AN., Gaston, J.M., Dai, C.L. et al. An omics-based framework for assessing the health risk of antimicrobial resistance genes. Nat Commun 12, 4765 (2021). https://doi.org/10.1038/s41467-021-25096-3\
+Correction: bacA is a bacitracin resistance gene, not a beta-lactamase (Fig 3).
+## Contact
+anniz44@mit.edu or caozhichongchong@gmail.com
+
+%prep
+%autosetup -n arg-ranker-3.3
+
+%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-arg-ranker -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Thu May 18 2023 Python_Bot <Python_Bot@openeuler.org> - 3.3-1
+- Package Spec generated