01、写在前面
大家拿到自己的服务器(趁开学| 入手足够完成硕博生涯的生信环境)后可能需要安装很多的软件与包，Linux中许多包的安装依赖过多、安装复杂。而conda作为一个能够直接安装超过90%软件的"管家"，能够像Windows和手机中的应用商店那样一键为你解决安装软件的烦恼。conda 是一个开源的软件包管理和环境管理系统，用于简化在不同平台上安装、运行和管理软件包。该工具用于支持 Python 语言，但也能够管理非 Python 的软件。conda的特点介绍、安装使用可见教程：生信软件管家——conda的安装、使用、卸载。
本教程基于Linux环境演示，计算资源不足的同学可参考：
足够支持你完成硕博生涯的生信环境
忘记宣传了，独享用户连技术支持都是独享的
RTX5090、4080S、5070显卡上机
如果你对下面的教程比较迷茫，那么你可以先行学习编程教程：
十小时学会Linux
生信Linux及服务器使用技巧
5.5h入门R语言
02、利用conda分享/复刻生信环境
大家除了能按部就班的利用conda安装生信软件外，还能够通过yml文件打包/复刻生信环境(如下图所示)，其中能够收录完整的配置（包括 Python 版本、所有依赖库及其精确版本、甚至操作系统相关的依赖），通过这样一个几kb的文件就能直接收录成百上千个依赖信息。这样任何人都能在自己的电脑或服务器上快速复现与原环境完全一致的配置，彻底解决 “我这能跑，你那报错” 的依赖冲突问题。让环境从 “隐性经验” 变成 “显性文件”。
具体使用起来也很简单，例如我这里存在一些conda虚拟环境：
大家按照这个语法即可完成环境打包与再安装的流程：
# 激活环境：
conda activate your_env_name
# 打包环境为yml文件：
conda env export > my_env.yml
# 在新机器上复刻环境：
conda env create -f my_env.yml例如我现在需要用到一文学会scFAST-seq数据分析中用到的seeksoul环境，对应的代码就是：
# 激活环境：
conda activate seeksoultools
# 打包环境为yml文件：
conda env export > seeksoultools.yml
# 在新机器上复刻环境：
conda env create -f seeksoultools.yml本质上是一个文本文件，打开后内容如下：
name: /data2/analysis/xunyin/seeksoultools
channels:- http://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main- http://mirrors.ustc.edu.cn/anaconda/pkgs/free- bioconda- https://mirror.nju.edu.cn/anaconda/cloud/pytorch- https://mirror.nju.edu.cn/anaconda/cloud/conda-forge- https://mirror.nju.edu.cn/anaconda/pkgs/msys2- https://mirror.nju.edu.cn/anaconda/pkgs/r- https://mirror.nju.edu.cn/anaconda/pkgs/free- https://mirror.nju.edu.cn/anaconda/pkgs/main- conda-forge
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r-rann=2.6.1=r41h7525677_3- r-rappdirs=0.3.3=r41h06615bd_1- r-rcolorbrewer=1.1_3=r41h785f33e_1- r-rcpp=1.0.10=r41h38f115c_0- r-rcppannoy=0.0.20=r41h7525677_0- r-rcpparmadillo=0.12.2.0.0=r41h358215d_0- r-rcppeigen=0.3.3.9.3=r41h9f5de39_0- r-rcppparallel=5.1.6=r41h38f115c_0- r-rcppprogress=0.4.2=r41hc72bb7e_2- r-rcpptoml=0.1.7=r41h7525677_2- r-rcurl=1.98_1.12=r41h133d619_0- r-readr=2.1.4=r41h38f115c_0- r-readxl=1.4.2=r41h81ef4d7_0- r-rematch=1.0.1=r41hc72bb7e_1005- r-rematch2=2.1.2=r41hc72bb7e_2- r-reprex=2.0.2=r41hc72bb7e_1- r-reshape2=1.4.4=r41h7525677_2- r-restfulr=0.0.15=r41h73dbb54_0- r-reticulate=1.28=r41h38f115c_0- r-rgeos=0.6_2=r41h0d5128d_1- r-rjson=0.2.21=r41h7525677_2- r-rlang=1.1.0=r41h38f115c_0- r-rmarkdown=2.21=r41hc72bb7e_0- r-rocr=1.0_11=r41hc72bb7e_2- r-rpart=4.1.19=r41h06615bd_0- r-rprojroot=2.0.3=r41hc72bb7e_1- r-rspectra=0.16_1=r41h9f5de39_1- r-rstudioapi=0.14=r41hc72bb7e_1- r-rtsne=0.16=r41h37cf8d7_1- r-rvest=1.0.3=r41hc72bb7e_1- r-sass=0.4.5=r41h38f115c_0- r-scales=1.2.1=r41hc72bb7e_1- r-scattermore=0.8=r41h06615bd_1- r-sctransform=0.3.5=r41h9f5de39_1- r-selectr=0.4_2=r41hc72bb7e_2- r-seurat=4.1.1=r41h7525677_1- r-seuratobject=4.1.3=r41h38f115c_0- r-shiny=1.7.4=r41h785f33e_0- r-sitmo=2.0.2=r41h7525677_1- r-snow=0.4_4=r41hc72bb7e_1- r-sourcetools=0.1.7_1=r41h38f115c_0- r-sp=1.6_0=r41h133d619_0- r-spatstat.core=2.4_4=r41h7525677_1- r-spatstat.data=3.0_1=r41hc72bb7e_0- r-spatstat.geom=3.1_0=r41h133d619_0- r-spatstat.random=3.1_4=r41h38f115c_0- r-spatstat.sparse=3.0_1=r41h133d619_0- r-spatstat.utils=3.0_2=r41h133d619_0- r-stringi=1.7.12=r41h1ae9187_0- r-stringr=1.5.0=r41h785f33e_0- r-survival=3.5_5=r41h133d619_0- r-sys=3.4.1=r41h06615bd_0- r-tensor=1.5=r41hc72bb7e_1004- r-tibble=3.2.1=r41h133d619_1- r-tidyr=1.3.0=r41h38f115c_0- r-tidyselect=1.2.0=r41hc72bb7e_0- r-tidyverse=1.3.2=r41hc72bb7e_1- r-timechange=0.2.0=r41h38f115c_0- r-tinytex=0.44=r41hc72bb7e_0- r-tzdb=0.3.0=r41h7525677_1- r-utf8=1.2.3=r41h133d619_0- r-uuid=1.1_0=r41h06615bd_1- r-uwot=0.1.14=r41h7525677_1- r-vctrs=0.6.1=r41h38f115c_0- r-viridislite=0.4.1=r41hc72bb7e_1- r-vroom=1.6.1=r41h38f115c_0- r-withr=2.5.0=r41hc72bb7e_1- r-xfun=0.38=r41h38f115c_0- r-xml=3.99_0.14=r41hb43fdd4_0- r-xml2=1.3.3=r41h044e5c7_2- r-xtable=1.8_4=r41hc72bb7e_4- r-yaml=2.3.7=r41h133d619_0- r-zoo=1.8_11=r41h06615bd_1- readline=8.2=h8228510_1- rseqc=3.0.0=py_3- samtools=1.16.1=h00cdaf9_2- scipy=1.9.1=py39h8ba3f38_0- sed=4.8=he412f7d_0- setuptools=67.6.1=pyhd8ed1ab_0- six=1.16.0=pyh6c4a22f_0- sqlite=3.40.0=h4ff8645_0- star=2.7.10a=h9ee0642_0- subread=2.0.1=h7132678_2- sysroot_linux-64=2.12=he073ed8_15- tenacity=8.2.2=pyhd8ed1ab_0- tk=8.6.12=h27826a3_0- tktable=2.10=hb7b940f_3- tzdata=2023c=h71feb2d_0- varscan=2.4.4=hdfd78af_1- wheel=0.40.0=pyhd8ed1ab_0- xopen=1.7.0=py39hf3d152e_1- xorg-fixesproto=5.0=h7f98852_1002- xorg-inputproto=2.3.2=h7f98852_1002- xorg-kbproto=1.0.7=h7f98852_1002- xorg-libice=1.0.10=h7f98852_0- xorg-libsm=1.2.3=hd9c2040_1000- xorg-libx11=1.8.4=h0b41bf4_0- xorg-libxau=1.0.9=h7f98852_0- xorg-libxdmcp=1.1.3=h7f98852_0- xorg-libxext=1.3.4=h0b41bf4_2- xorg-libxfixes=5.0.3=h7f98852_1004- xorg-libxi=1.7.10=h7f98852_0- xorg-libxrender=0.9.10=h7f98852_1003- xorg-libxt=1.2.1=h7f98852_2- xorg-libxtst=1.2.3=h7f98852_1002- xorg-recordproto=1.14.2=h7f98852_1002- xorg-renderproto=0.11.1=h7f98852_1002- xorg-xextproto=7.3.0=h0b41bf4_1003- xorg-xproto=7.0.31=h7f98852_1007- xz=5.2.6=h166bdaf_0- zlib=1.2.13=h166bdaf_4- zstandard=0.19.0=py39h29414ee_1- zstd=1.5.2=h3eb15da_6- pip:- opencv-contrib-python==4.8.1.78- openslide-python==1.3.1- pillow==10.1.0- pycv2==0.0.1- seeksoultools==1.2.0
prefix: /data2/analysis/xunyin/seeksoultools好啦，大家尝试一下自己打包conda镜像并分发给小伙伴们吧~
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