autohic使用

cd ~/yt/soft/AutoHiC
git clone https://github.com/Jwindler/AutoHiC.git
cd AutoHiC

##激活conda
source /home/lx_sky6/yt/soft/miniconda3/bin/activate
##create AutoHiC env
conda env create -f autohic.yaml
# activate AutoHiC
conda activate autohic
# configuration environment
cd ./src/models/swin

# install dependencies
pip install -e . -i https://pypi.tuna.tsinghua.edu.cn/simple/


##有些依赖安不上，自己conda安装
conda install -c jrhawley hic-straw
conda install -c conda-forge mmcv-full=1.2.4  ##(>=1.2.4<=1.4.0)
##1.0-cuda

##ImportError: /home/lx_sky6/yt/soft/miniconda3/envs/autohic/lib/python3.9/site-packages/torch/lib/../../../../libgomp.so.1: version `GOMP_4.0' not found (required by /home/lx_sky6/yt/soft/miniconda3/envs/autohic/lib/python3.9/site-packages/torch/lib/libtorch_cpu.so)

##gcc版本太低，
cp /home/lx_sky6/software/miniconda3/envs/py_36/lib/libgomp.so.1.0.0  /home/lx_sky6/yt/soft/miniconda3/envs/autohic/lib
conda install gcc

##ImportError: /home/lx_sky6/yt/soft/miniconda3/envs/autohic/lib/python3.9/site-packages/torch/lib/../../../../libstdc++.so.6: version `GLIBCXX_3.4.20' not found (required by /home/lx_sky6/yt/soft/miniconda3/envs/autohic/lib/python3.9/site-packages/hicstraw.cpython-39-x86_64-linux-gnu.so)
##同上
ln -s libstdc++.so.6.0.30 libstdc++.so.6
ln -s libstdc++.so.6.0.30 libstdc++.so

gcc: fatal error: cannot execute 'cc1plus': execvp: No such file or directory
##gcc和g++版本不对呀

##安装的gcc到7.3.0和g++到7.3.0
conda install -c moussi gcc_impl_linux-64
conda install -c moussi gxx_impl_linux-64

cd /home/lx_sky6/yt/soft/miniconda3/envs/autohic/bin
ln -s x86_64-conda_cos6-linux-gnu-gcc gcc
ln -s x86_64-conda_cos6-linux-gnu-g++ g++

##安装mmdet
pip install mmdet

python -c 'import torch;print(torch.__version__);print(torch.version.cuda)'
##查看版本
##安装mmcv（查看https://mmcv.readthedocs.io/en/latest/get_started/installation.html）
pip install mmcv==2.0.0rc4 -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.10/index.html

##下载Pre-trained model
https://pan.baidu.com/s/1CturvBMowVMwpeKYKjsa9w?pwd=v4et

##运行（已经运行juicer和3ddna得到初步结果，利用autohic纠错）

cd /home/ubuntu/AutoHic  
# run onehic
python3.9 onehic.py -hic test.hic -asy test.assembly -autohic /home/centos/AutoHic -p pretrained.pth -out ./

# run 3d-dna to get fasta
bash run-asm-pipeline-post-review.sh -r adjusted.assembly genome.fasta merged_nodups.txt 

# Please specify the absolute path of each file
# adjusted.assembly is output from onehic.py
# merged_nodups.txt is output from Juicer

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