%global _empty_manifest_terminate_build 0 Name: python-BFG-Y2H Version: 0.1.2 Release: 1 Summary: Analysis scripts for BFG-Y2H data License: MIT License URL: https://github.com/RyogaLi/BFG_Y2H Source0: https://mirrors.aliyun.com/pypi/web/packages/5c/7c/d61e8e2a4fa3a4bfb097b044609a1f3980860368e398d8c55fe9ede6bcea/BFG-Y2H-0.1.2.tar.gz BuildArch: noarch %description ### BFG Y2H Analysis Pipeline ### **Requirements** * Python 3.7 * Bowtie 2 and Bowtie2 build ### Files required ### The pipeline requires reference files before running. They can be found on GALEN: ``` all reference files contain all the barcodes in fasta format path: /home/rothlab/rli/02_dev/08_bfg_y2h/bfg_data/reference/ ``` Before running the pipeline, you need to copy everything in these two folders to your designated directory. #### Build new reference ### If you need to build a new reference for your analysis, please follow: 1. You can refer to the create_fasta.py script to build the new fasta file 2. Make sure the name for the sequences follows the format: `>*;ORF-BC-ID;*;up/dn`. In other words, the ORF-ID should always be the second item, and the up/dn identifier should always be the last item. (see examples below) 3. Example sequences in output fasta file: ``` >G1;YDL169C_BC-1;7;up CCCTTAGAACCGAGAGTGTGGGTTAAATGGGTGAATTCAGGGATTCACTCCGTTCGTCACTCAATAA >G1;YMR206W_BC-1;1.0;DB;up CCATACGAGCACATTACGGGGCTTGAGTTATATAGTCGATCCGGGCTAACTCGCATACCTCTGATAAC >G09;56346_BC-1;24126.0;DB;dn TCGATAGGTGCGTGTGAAGGATGTTCCCCCGGTCACCGGGCCAGTCCTCAGTCGCTCAGTCAAG ``` 4. After making the fasta file, build index with bowtie2-build `bowtie2-build filename.fasta filename` 5. Update main.py to use the summary files you generated * Edit parse_input_files() to add a case ### Running the pipeline ### * Install from pypi (recommend): `python -m pip install BFG-Y2H` * Install and build from github, the update.sh might need to be modified before you install ``` 1. download the package from github 2. inside the root folder, run ./update.sh ``` 1. Input arguments: ``` usage: bfg [-h] [--fastq FASTQ] [--output OUTPUT] --mode MODE [--alignment] [--ref REF] [--cutOff CUTOFF] BFG-Y2H optional arguments: -h, --help show this help message and exit --fastq FASTQ Path to all fastq files you want to analyze --output OUTPUT Output path for sam files --mode MODE pick yeast or human or virus or hedgy or LAgag --alignment turn on alignment --ref REF path to all reference files --cutOff CUTOFF assign cut off ``` 2. All the input fastq files should have names following the format: y|hAD*DB*_GFP_(pre|med|high) (for human and yeast) 3. Run the pipeline on GALEN ``` # this will run the pipeline using slurm # all the fastq files in the given folder will be processed # run with alignment bfg --fastq /path/to/fastq_files/ --output /path/to/output_dir/ --mode yeast/human/virus/hedgy --alignment --ref path/to/reference # if alignment was finished, you want to only do read counts bfg --fastq /path/to/fastq_files/ --output /path/to/output_dir/ --mode yeast/human/virus/hedgy --ref path/to/reference ``` ### Output files ### * After running the pipeline, one folder will be generated for each group pair (yAD*DB*) * The folder called `GALEN_jobs` saves all the bash scripts submited to GALEN * In the output folder for each group pair, we aligned R1 and R2 separately to the reference sequences for GFP_pre, GFP_med and GFP_high. * `*_sorted.sam`: Raw sam files generated from bowtie2 * `*_noh.csv`: shrinked sam files, used for scoring * `*_counts.csv`: barcode counts for uptags, dntags, and combined (up+dn) %package -n python3-BFG-Y2H Summary: Analysis scripts for BFG-Y2H data Provides: python-BFG-Y2H BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-BFG-Y2H ### BFG Y2H Analysis Pipeline ### **Requirements** * Python 3.7 * Bowtie 2 and Bowtie2 build ### Files required ### The pipeline requires reference files before running. They can be found on GALEN: ``` all reference files contain all the barcodes in fasta format path: /home/rothlab/rli/02_dev/08_bfg_y2h/bfg_data/reference/ ``` Before running the pipeline, you need to copy everything in these two folders to your designated directory. #### Build new reference ### If you need to build a new reference for your analysis, please follow: 1. You can refer to the create_fasta.py script to build the new fasta file 2. Make sure the name for the sequences follows the format: `>*;ORF-BC-ID;*;up/dn`. In other words, the ORF-ID should always be the second item, and the up/dn identifier should always be the last item. (see examples below) 3. Example sequences in output fasta file: ``` >G1;YDL169C_BC-1;7;up CCCTTAGAACCGAGAGTGTGGGTTAAATGGGTGAATTCAGGGATTCACTCCGTTCGTCACTCAATAA >G1;YMR206W_BC-1;1.0;DB;up CCATACGAGCACATTACGGGGCTTGAGTTATATAGTCGATCCGGGCTAACTCGCATACCTCTGATAAC >G09;56346_BC-1;24126.0;DB;dn TCGATAGGTGCGTGTGAAGGATGTTCCCCCGGTCACCGGGCCAGTCCTCAGTCGCTCAGTCAAG ``` 4. After making the fasta file, build index with bowtie2-build `bowtie2-build filename.fasta filename` 5. Update main.py to use the summary files you generated * Edit parse_input_files() to add a case ### Running the pipeline ### * Install from pypi (recommend): `python -m pip install BFG-Y2H` * Install and build from github, the update.sh might need to be modified before you install ``` 1. download the package from github 2. inside the root folder, run ./update.sh ``` 1. Input arguments: ``` usage: bfg [-h] [--fastq FASTQ] [--output OUTPUT] --mode MODE [--alignment] [--ref REF] [--cutOff CUTOFF] BFG-Y2H optional arguments: -h, --help show this help message and exit --fastq FASTQ Path to all fastq files you want to analyze --output OUTPUT Output path for sam files --mode MODE pick yeast or human or virus or hedgy or LAgag --alignment turn on alignment --ref REF path to all reference files --cutOff CUTOFF assign cut off ``` 2. All the input fastq files should have names following the format: y|hAD*DB*_GFP_(pre|med|high) (for human and yeast) 3. Run the pipeline on GALEN ``` # this will run the pipeline using slurm # all the fastq files in the given folder will be processed # run with alignment bfg --fastq /path/to/fastq_files/ --output /path/to/output_dir/ --mode yeast/human/virus/hedgy --alignment --ref path/to/reference # if alignment was finished, you want to only do read counts bfg --fastq /path/to/fastq_files/ --output /path/to/output_dir/ --mode yeast/human/virus/hedgy --ref path/to/reference ``` ### Output files ### * After running the pipeline, one folder will be generated for each group pair (yAD*DB*) * The folder called `GALEN_jobs` saves all the bash scripts submited to GALEN * In the output folder for each group pair, we aligned R1 and R2 separately to the reference sequences for GFP_pre, GFP_med and GFP_high. * `*_sorted.sam`: Raw sam files generated from bowtie2 * `*_noh.csv`: shrinked sam files, used for scoring * `*_counts.csv`: barcode counts for uptags, dntags, and combined (up+dn) %package help Summary: Development documents and examples for BFG-Y2H Provides: python3-BFG-Y2H-doc %description help ### BFG Y2H Analysis Pipeline ### **Requirements** * Python 3.7 * Bowtie 2 and Bowtie2 build ### Files required ### The pipeline requires reference files before running. They can be found on GALEN: ``` all reference files contain all the barcodes in fasta format path: /home/rothlab/rli/02_dev/08_bfg_y2h/bfg_data/reference/ ``` Before running the pipeline, you need to copy everything in these two folders to your designated directory. #### Build new reference ### If you need to build a new reference for your analysis, please follow: 1. You can refer to the create_fasta.py script to build the new fasta file 2. Make sure the name for the sequences follows the format: `>*;ORF-BC-ID;*;up/dn`. In other words, the ORF-ID should always be the second item, and the up/dn identifier should always be the last item. (see examples below) 3. Example sequences in output fasta file: ``` >G1;YDL169C_BC-1;7;up CCCTTAGAACCGAGAGTGTGGGTTAAATGGGTGAATTCAGGGATTCACTCCGTTCGTCACTCAATAA >G1;YMR206W_BC-1;1.0;DB;up CCATACGAGCACATTACGGGGCTTGAGTTATATAGTCGATCCGGGCTAACTCGCATACCTCTGATAAC >G09;56346_BC-1;24126.0;DB;dn TCGATAGGTGCGTGTGAAGGATGTTCCCCCGGTCACCGGGCCAGTCCTCAGTCGCTCAGTCAAG ``` 4. After making the fasta file, build index with bowtie2-build `bowtie2-build filename.fasta filename` 5. Update main.py to use the summary files you generated * Edit parse_input_files() to add a case ### Running the pipeline ### * Install from pypi (recommend): `python -m pip install BFG-Y2H` * Install and build from github, the update.sh might need to be modified before you install ``` 1. download the package from github 2. inside the root folder, run ./update.sh ``` 1. Input arguments: ``` usage: bfg [-h] [--fastq FASTQ] [--output OUTPUT] --mode MODE [--alignment] [--ref REF] [--cutOff CUTOFF] BFG-Y2H optional arguments: -h, --help show this help message and exit --fastq FASTQ Path to all fastq files you want to analyze --output OUTPUT Output path for sam files --mode MODE pick yeast or human or virus or hedgy or LAgag --alignment turn on alignment --ref REF path to all reference files --cutOff CUTOFF assign cut off ``` 2. All the input fastq files should have names following the format: y|hAD*DB*_GFP_(pre|med|high) (for human and yeast) 3. Run the pipeline on GALEN ``` # this will run the pipeline using slurm # all the fastq files in the given folder will be processed # run with alignment bfg --fastq /path/to/fastq_files/ --output /path/to/output_dir/ --mode yeast/human/virus/hedgy --alignment --ref path/to/reference # if alignment was finished, you want to only do read counts bfg --fastq /path/to/fastq_files/ --output /path/to/output_dir/ --mode yeast/human/virus/hedgy --ref path/to/reference ``` ### Output files ### * After running the pipeline, one folder will be generated for each group pair (yAD*DB*) * The folder called `GALEN_jobs` saves all the bash scripts submited to GALEN * In the output folder for each group pair, we aligned R1 and R2 separately to the reference sequences for GFP_pre, GFP_med and GFP_high. * `*_sorted.sam`: Raw sam files generated from bowtie2 * `*_noh.csv`: shrinked sam files, used for scoring * `*_counts.csv`: barcode counts for uptags, dntags, and combined (up+dn) %prep %autosetup -n BFG-Y2H-0.1.2 %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-BFG-Y2H -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Tue Jun 20 2023 Python_Bot - 0.1.2-1 - Package Spec generated