Download the AnnoLnc standalone package. You can also find all source code and data at our ftp server.

The AnnoLnc standalone version requires a computer with a modern Linux operating system, with a minimal requirement of 4 CPU cores, 8GB memory, and 450GB free disk space.

Below is a step-by-step instruction of installing the AnnoLnc standalone version. The Linux distribution is assumed to be CentOS or Ubuntu(and root privilege is required):

1. Download the standalone version package and uncompress the standalone package by run:

tar -zxvf annolnc_v1.0.tar.gz

2. Run the installation script:

For CentOS:


For Ubuntu:


Then the installation of AnnoLnc will be finished automatically. You can run AnnoLnc after the script is done without error.


1. The expression module and co-expression and GO module wouldn't work properly unless you download the bam file from and uncompress them to the directory "$directory_of_AnnoLnc/data/hg19/expression".

2. The mirna interaction module wouldn't work properly unless you download the maf file from and uncompress them to the directory "$directory_of_AnnoLnc/data/hg19/miRNA_interaction"

3. If you need the customized samples for expression calling provided by expr module, please put your expression profile data(in bam format) into the directory $directory_of_AnnoLnc/data/hg19/expression/bams. Then input the following commands:


User can run the Annolnc standalone version by simply run run the follow command:

bash -i input_demo/test.fa -o output -s hg19 -m /sstr

Where -i specifies the input lncRNA fasta file to annotate, -o specifies the output directory to put annotations , -s specifies the genome build of species, and -m specifies the annotation modules to run.

Also, users can run sh -h to view the complete help page.

1. The default R version of CentOS is over 3.5, which is good. But the default R version of Ubuntu is 3.2.3, which is too old for running Annolnc.So, if you run "bash", but failed with message "R version is less than 3.5.0", you should update your R version to over 3.5 by run "bash". But you should be aware of that update R will delete all the old R packages, please be careful with this opreation.

2. If the automated installation script doesn't work for you or you want to install AnnoLnc at another Linux distribution. You can refer to the following tutorial:

2.1 Install basic dependencies by running the following:

For CentOS:

yum -y install python-devel
yum -y install python2-pip
yum -y install java
yum -y install R
yum -y install BEDTools
yum -y install libcurl libcurl-devel
yum -y install libxml2 libxml2-devel

For Ubuntu:

apt-get install -y python-dev
apt-get install -y python-pip
apt-get install -y default-jdk
apt-get install -y r-base r-base-dev
apt-get install -y bedtools
apt-get install -y libcurl4-openssl-dev
apt-get install -y libxml2 libxml2-dev

For other distribution:
just install python java R bedtools libcurl and libxml2 via related commands.

2.2 Install R packages by running the following (if the repository does not work, please use a different repo listed at

R -e "install.packages('stringr', repos='')"
R -e 'if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager", repos=""); BiocManager::install("GOstats")'
R -e "install.packages('snowfall', repos='')"
R -e "install.packages('plyr', repos='')"

2.3 Install python packages by running the following:

python -m pip install --upgrade pip==18.1
pip install biopython
pip install bx-python==0.7.3 mako paste sqlalchemy routes webob mercurial bz2file webhelpers kombu h5py pysam pycrypto

There are several differences between the AnnoLnc Web Server and the standalone version. Include:

  1. The standalone version doesn't support the summary function.
  2. The standalone version doesn't provide the UCSC visulization link.
  3. The standalone version doesn't support the different color visulization of secendery structure result.
  4. The standalone version doesn't only caculate the expression profile of each sample and do nomalization in all of your samples in expression module.
  5. The standalone version has removed the prediction part in protein interaction modules.