Bioinformatics with Python

引言

Prepare my software：

“Anaconda, as it has become the de-facto standard for data science and bioinformatics. Also, it is the distribution that will allow you to install software from Bioconda”

Package：

Package	Purpose	Package
pandas		DendroPY	phylogenetics
Numpy		PyMol	Molecular visualization
Scipy		scikit-learn	ML tools
Biopython		Cpython	High performance for Big data
seaborn		Numba	High performance for Big data
rpy2	R interface	Dask	Parallel processing for Big Data
PyVCF	NGS	jupytext/lab
Pysam	NGS	R
HTSeq	NGS processing

A table showing the various software packages that are useful in bioinformatics

#01 install base conda env 
conda create -n bioinformatics_base python=3.10
conda activate bioinformatics_base
# why use base env? 不同类型的包太多了。新的任务可以clone base env ,在此基础上install special packages.
#we can use 
# conda create -n scikit-learn --clone bioinformatics_base
# conda activate scikit-learn & conda install scikit-learn

#02 add the bioconda and conda-forge channels to our source list
conda config --add channels bioconda
conda config --add channels conda-forge

#03 install packages
# install from requirements 
#conda list -e > reqiurements.txt
conda install --yes --file requirements.txt

#04 install R from conda
conda install rpy2 r-essentials r-gridextra

Requirements.txt

biopython==1.79
jupyterlab==3.2.1
jupytext==1.13
matplotlib==3.4.3
numpy==1.21.3
pandas==1.3.4
scipy==1.7.1

# env for R
create -n bioinformatics_r --clone bioinformatics_base
conda activate bioinformatics_r
conda install r-ggplot2=3.3.5 r-lazyeval r-gridextra rpy2

Aligment

“pysam, a Python wrapper to the SAMtools C API”

conda install –c bioconda pysam

pandas