All images should use Alpine as the base image.

For building bioinformatics-related images, use Mamba instead of Conda.

Do not set a mirror source, and the image can be built in stages to aim for the smallest Dockerfile.

1.The commonly used Docker analysis commands are as follows.

Stop all Docker containers

docker ps -a | grep "Exited" | awk '{print $1 }'|xargs docker stop

delete all containers

docker ps -a | grep "Exited" | awk '{print $1 }'|xargs docker rm

disable Cache with --no-cache

docker build --no-cache -t my_image .

clean Up Build Cache

docker builder prune -f

save and load images

docker save -o my_ubuntu_v3.tar runoob/ubuntu:v3
docker load --input my_ubuntu_v3.tar

2.Several important practical use cases of the images are as follows.

minimal bioinformatics environment

FROM alpine:latest
RUN apk update && \
    apk add --no-cache bash openjdk21 git && mkdir -p /lib64/ /ref/ /script/ /raw_data/ /outdir/ && \
    wget -q -O /etc/apk/keys/sgerrand.rsa.pub https://alpine-pkgs.sgerrand.com/sgerrand.rsa.pub && \
    wget https://github.com/sgerrand/alpine-pkg-glibc/releases/download/2.35-r1/glibc-2.35-r1.apk && \
    apk add --no-cache --force-overwrite glibc-2.35-r1.apk && \
    rm glibc-2.35-r1.apk && ln -s /usr/glibc-compat/lib/* /lib64/ && \
    wget -q -O /opt/Miniforge3-Linux-x86_64.sh https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh && \
    bash /opt/Miniforge3-Linux-x86_64.sh -f -b -p /opt/conda/ && rm -rf /opt/Miniforge3-Linux-x86_64.sh /var/cache/apk/*

metagenomic data analysis

FROM alpine:latest
RUN apk add --no-cache bash && mkdir -p /lib64/ /ref/ /script/ /raw_data/ /outdir/ && \
    wget -q -O /etc/apk/keys/sgerrand.rsa.pub https://alpine-pkgs.sgerrand.com/sgerrand.rsa.pub && \
    wget https://github.com/sgerrand/alpine-pkg-glibc/releases/download/2.35-r1/glibc-2.35-r1.apk &&  \
    apk add --no-cache --force-overwrite glibc-2.35-r1.apk && rm glibc-2.35-r1.apk && ln -s /usr/glibc-compat/lib/* /lib64/ && \
    wget -q -O /opt/Miniforge3-Linux-x86_64.sh https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh && \
    bash /opt/Miniforge3-Linux-x86_64.sh -f -b -p /opt/conda/ && rm -rf /opt/Miniforge3-Linux-x86_64.sh /var/cache/apk/* && \
    /opt/conda/bin/mamba create --name rgi --channel conda-forge --channel bioconda --channel defaults rgi &&  \
    /opt/conda/bin/conda config --add channels ursky && \
    /opt/conda/bin/mamba create -y --name metawrap --channel ursky  \
    --channel conda-forge --channel bioconda --channel defaults blas=2.5=mkl biopython metawrap-mg=1.3.2 checkm-genome && \
    /opt/conda/bin/mamba create --name gtdbtk --channel conda-forge --channel bioconda --channel defaults gtdbtk && \
    /opt/conda/bin/mamba create --name metaphlan --channel conda-forge --channel bioconda --channel defaults metaphlan && \
    /opt/conda/bin/mamba run -n rgi mamba install --channel conda-forge --channel bioconda --channel defaults staramr  \
    quast minimap2 megahit cd-hit spades freebayes fastqtk seqtk bbmap fastp fastqc prinseq cutadapt multiqc trimmomatic  \
    bcftools prokka covtobed fastani megan kraken2 krakentools krona bracken drep coverm &&  \
    /opt/conda/bin/conda clean -a -y && /opt/conda/bin/mamba clean -a -y
ENV LD_LIBRARY_PATH=/lib/:/lib64/:$LD_LIBRARY_PATH

COVID-19 and other microbiome detection based on amplicon methods is as follows. This is a good example of building the image in stages.

FROM alpine AS nextclade
# glibc+conda+nextclade
RUN apk update && \
    apk add --no-cache bash openjdk21 git && mkdir -p /lib64/ /ref/ /script/ /raw_data/ /outdir/ && \
    wget -q -O /etc/apk/keys/sgerrand.rsa.pub https://alpine-pkgs.sgerrand.com/sgerrand.rsa.pub && \
    wget https://github.com/sgerrand/alpine-pkg-glibc/releases/download/2.35-r1/glibc-2.35-r1.apk && \
    apk add --no-cache --force-overwrite glibc-2.35-r1.apk && \
    rm glibc-2.35-r1.apk && ln -s /usr/glibc-compat/lib/* /lib64/ && \
    wget -q -O /opt/Miniforge3-Linux-x86_64.sh https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh && \
    bash /opt/Miniforge3-Linux-x86_64.sh -f -b -p /opt/conda/ && rm -rf /opt/Miniforge3-Linux-x86_64.sh /var/cache/apk/* && \
    cd /bin/ && wget -O nextclade https://github.com/nextstrain/nextclade/releases/latest/download/nextclade-x86_64-unknown-linux-musl && chmod u+x ./nextclade

FROM nextclade AS pangolin
RUN git clone https://github.com/cov-lineages/pangolin.git && cd pangolin/ &&  \
    /opt/conda/bin/mamba env create -f environment.yml --name pangolin &&  \
    /opt/conda/envs/pangolin/bin/pip install . &&  rm -rf /opt/pangolin/ && \
    /opt/conda/bin/conda clean -a -y

FROM pangolin AS micro2amplicon
COPY jvarkit.jar /software/
RUN /opt/conda/bin/mamba install --channel conda-forge --channel bioconda --channel defaults ivar=1.3 trimmomatic  \
    bowtie2 bbmap fastp seqtk samtools bedtools bcftools bwa prinseq cutadapt drep
RUN /opt/conda/bin/pip3 install seaborn matplotlib numpy pysam pandas