%global _empty_manifest_terminate_build 0 Name: python-ScaleHD Version: 1.1.1 Release: 1 Summary: Automated DNA micro-satellite genotyping. License: GPLv3 URL: https://github.com/helloabunai/ScaleHD Source0: https://mirrors.aliyun.com/pypi/web/packages/e7/8a/61a646e73b2bee543cb4040ddd30d10270e2021f2fdfb0966fb5aedb158c/ScaleHD-1.1.1.tar.gz BuildArch: noarch %description ScaleHD is a package for automating the process of genotyping microsatellite repeats in Huntington Disease data. We utilise machine learning approaches to take into account natural data 'artefacts', such as PCR slippage and somatic mosaicism, when processing data. This provides the end-user with a simple to use platform which can robustly predict genotypes from input data. By default, input is a pair of unaligned .fastq sequence data -- both forward and reverse reads, per sample. We utilise both forward and reverse reads in order to reduce the complex dimensionality issue posed by Huntington Disease's multiple repeat tract genetic structure. Reverse reads allow us to determine the current sample's CCG state -- this provides us with a mechanism by which to more easily call the entire genotype. Forward reads are utilised in a similar approach, to determine the CAG and intervening structure. The general overview of the application is as follows: 1) Input FastQ files are subsampled, if an overwhelming number of reads are present. This can be overruled with the -b flag. 2) Sequence quality control is carried out per the user's instructions. We reccomend trimming of any 5-prime spacer+primer combinations, for optimal alignment. 3) Alignment of these files, to a typical HD structure (CAG_1_1_CCG_2) reference, is carried out. 4) Assemblies are scanned with Digital Signal Processing to detect any possible atypical structures (e.g. CAG_2_1_CCG_3). 4.1) If no atypical alleles are detected, proceed as normal. 4.2) If atypical alleles are detected, a custom reference is generated, and re-alignment to this is carried out. 5) With the appropriate allele information and sequence assembly(ies) present, sampled are genotyped. 6) Output is written for the current sample; the procedure is repeated for the next sample in the queue (if present). Check the full documentation at http://scalehd.rtfd.io %package -n python3-ScaleHD Summary: Automated DNA micro-satellite genotyping. Provides: python-ScaleHD BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-ScaleHD ScaleHD is a package for automating the process of genotyping microsatellite repeats in Huntington Disease data. We utilise machine learning approaches to take into account natural data 'artefacts', such as PCR slippage and somatic mosaicism, when processing data. This provides the end-user with a simple to use platform which can robustly predict genotypes from input data. By default, input is a pair of unaligned .fastq sequence data -- both forward and reverse reads, per sample. We utilise both forward and reverse reads in order to reduce the complex dimensionality issue posed by Huntington Disease's multiple repeat tract genetic structure. Reverse reads allow us to determine the current sample's CCG state -- this provides us with a mechanism by which to more easily call the entire genotype. Forward reads are utilised in a similar approach, to determine the CAG and intervening structure. The general overview of the application is as follows: 1) Input FastQ files are subsampled, if an overwhelming number of reads are present. This can be overruled with the -b flag. 2) Sequence quality control is carried out per the user's instructions. We reccomend trimming of any 5-prime spacer+primer combinations, for optimal alignment. 3) Alignment of these files, to a typical HD structure (CAG_1_1_CCG_2) reference, is carried out. 4) Assemblies are scanned with Digital Signal Processing to detect any possible atypical structures (e.g. CAG_2_1_CCG_3). 4.1) If no atypical alleles are detected, proceed as normal. 4.2) If atypical alleles are detected, a custom reference is generated, and re-alignment to this is carried out. 5) With the appropriate allele information and sequence assembly(ies) present, sampled are genotyped. 6) Output is written for the current sample; the procedure is repeated for the next sample in the queue (if present). Check the full documentation at http://scalehd.rtfd.io %package help Summary: Development documents and examples for ScaleHD Provides: python3-ScaleHD-doc %description help ScaleHD is a package for automating the process of genotyping microsatellite repeats in Huntington Disease data. We utilise machine learning approaches to take into account natural data 'artefacts', such as PCR slippage and somatic mosaicism, when processing data. This provides the end-user with a simple to use platform which can robustly predict genotypes from input data. By default, input is a pair of unaligned .fastq sequence data -- both forward and reverse reads, per sample. We utilise both forward and reverse reads in order to reduce the complex dimensionality issue posed by Huntington Disease's multiple repeat tract genetic structure. Reverse reads allow us to determine the current sample's CCG state -- this provides us with a mechanism by which to more easily call the entire genotype. Forward reads are utilised in a similar approach, to determine the CAG and intervening structure. The general overview of the application is as follows: 1) Input FastQ files are subsampled, if an overwhelming number of reads are present. This can be overruled with the -b flag. 2) Sequence quality control is carried out per the user's instructions. We reccomend trimming of any 5-prime spacer+primer combinations, for optimal alignment. 3) Alignment of these files, to a typical HD structure (CAG_1_1_CCG_2) reference, is carried out. 4) Assemblies are scanned with Digital Signal Processing to detect any possible atypical structures (e.g. CAG_2_1_CCG_3). 4.1) If no atypical alleles are detected, proceed as normal. 4.2) If atypical alleles are detected, a custom reference is generated, and re-alignment to this is carried out. 5) With the appropriate allele information and sequence assembly(ies) present, sampled are genotyped. 6) Output is written for the current sample; the procedure is repeated for the next sample in the queue (if present). Check the full documentation at http://scalehd.rtfd.io %prep %autosetup -n ScaleHD-1.1.1 %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-ScaleHD -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Tue Jun 20 2023 Python_Bot - 1.1.1-1 - Package Spec generated