diff --git a/Dockerfile b/Dockerfile
index ad8eb21..2ddfa88 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -9,7 +9,8 @@ RUN apt-get update --fix-missing && \
         wget \
         bzip2 \
         make \
-        gcc && \
+        gcc \
+        vcftools && \
     rm -rf /bar/lib/apt/lists/*
 
 WORKDIR /opt/clair
diff --git a/README.md b/README.md
index 09cd4ec..835e966 100644
--- a/README.md
+++ b/README.md
@@ -36,7 +36,10 @@ pypy3 -m pip install blosc intervaltree
 pip install numpy blosc intervaltree tensorflow==1.13.2 pysam matplotlib
 conda install -c anaconda pigz
 conda install -c conda-forge parallel zstd
-conda install -c bioconda samtools vcflib bcftools
+conda install -c bioconda samtools vcflib
+
+# install vcftools
+sudo apt-get install vcftools
 
 # clone Clair
 git clone --depth=1 https://github.com/HKU-BAL/Clair.git
@@ -73,6 +76,9 @@ conda config --add channels conda-forge
 conda create -n clair-env -c bioconda clair
 conda activate clair-env
 
+# install vcftools
+sudo apt-get install vcftools
+
 # store clair.py PATH into $CLAIR variable
 CLAIR=`which clair.py`
 
@@ -239,7 +245,7 @@ export CUDA_VISIBLE_DEVICES=""
 cat command.sh | parallel -j4
 
 # concatenate vcf files and sort the variants called
-`vcfcat ${OUTPUT_PREFIX}*.vcf | bcftools sort -m 4G - | bgziptabix snp_and_indel.vcf.gz`
+vcfcat ${OUTPUT_PREFIX}*.vcf | vcf-sort -c | bgziptabix snp_and_indel.vcf.gz
 ```
 
 #### Note
@@ -249,7 +255,8 @@ cat command.sh | parallel -j4
 * callVarBamParallel will generate commonds for chr{1..22},X,Y, to call variants on all chromosomes, please use option `--includingAllContigs`.
 * If you are going to call on non-human BAM file (e.g. bacteria), please use `--includingAllContigs` option to include all contigs
 * `CUDA_VISIBLE_DEVICES=""` makes GPUs invisible to Clair so it will use CPU for variant calling. Please notice that unless you want to run `commands.sh` in serial, you cannot use GPU because one running copy of Clair will occupy all available memory of a GPU. While the bottleneck of `callVarBam` is at the `CreateTensor` script, which runs on CPU, the effect of GPU accelerate is insignificant (roughly about 15% faster). But if you have multiple GPU cards in your system, and you want to utilize them in variant calling, you may want split the `commands.sh` in to parts, and run the parts by firstly `export CUDA_VISIBLE_DEVICES="$i"`, where `$i` is an integer from 0 identifying the ID of the GPU to be used.
-* `vcfcat` and `bgziptabix` commands are from [vcflib](https://github.com/vcflib/vcflib), and are installed by default using option 2 (conda).
+* `vcfcat` and `bgziptabix` commands are from [vcflib](https://github.com/vcflib/vcflib), and are installed by default using option 2 (conda) or option 3 (docker).
+* `vcf-sort` command is from [vcftools](https://github.com/vcftools/vcftools)
 * Please also check the notes in the above sections for other considerations.
 
 ---