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@@ -0,0 +1 @@ +/arg_ranker-3.3.tar.gz diff --git a/python-arg-ranker.spec b/python-arg-ranker.spec new file mode 100644 index 0000000..7127cad --- /dev/null +++ b/python-arg-ranker.spec @@ -0,0 +1,336 @@ +%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 @@ -0,0 +1 @@ +d4bf30ad4b1244a669521c304e5560bc arg_ranker-3.3.tar.gz |