Long noncoding RNAs (lncRNAs) play important biological roles and have been implicated in human diseases. To characterize lncRNAs, identifying and annotating lncRNAs is necessary. Here, we propose a novel lncRNA identification and functional annotation tool named LncADeep. First, LncADeep identifies lncRNAs by integrating sequence intrinsic and homology features based on deep belief networks. Second, LncADeep predicts lncRNA-protein interactions using sequence and structure features based on deep neural networks. Third, since accurate lncRNA-protein interactions can help to infer the functions of lncRNAs, LncADeep conducts KEGG and Reactome pathway enrichment analysis and functional module detection with the predicted interacting proteins of lncRNAs. Case studies show that LncADeep's annotations for lncRNAs comply with their known functions. As a tool for lncRNA identification and functional annotation based on deep learning, LncADeep has outperformed state-of-the-art tools on predicting lncRNAs and lncRNA-protein interactions, and can automatically provide informative functional annotations for lncRNAs.
LncADeep is freely available for non-commercial use at http://cqb.pku.edu.cn/ZhuLab/lncadeep or https://github.com/cyang235/LncADeep.
For lncRNA identification.
For predicting lncRNA-protein interactions and annotating lncRNA functions.
- Python, version >= 2.7.6
- numpy, version >= 1.8.2
- pandas, version >= 0.18.0
- theano, version >= 0.8.2
- Keras, version 1.2.2
- h5py, version >= 2.5.0
- R, version >= 3.3.2
- iGraph R package
- MCL package
- LncADeep 1.0 (Tested on Linux_64, including CentOS 6.5 and Ubuntu 16.04)
Please install numpy, theano, pandas, Keras, h5py, and iGraph according to their manuals. The following are examples for installing these prerequisites.
numpy, theano, pandas, and h5py are python packages, which can be installed with pip, for example:
# we use python v2.7.13
# our machine is implemented with the following versions
pip install numpy # numpy v1.13.1
pip install Theano # Theano v0.9.0
pip install pandas # pandas v0.20.3
pip install h5py # h5py v2.7.0
iGraph is an R package, which can be installed with:
# we use R v3.3.2
# Download and install the package
install.packages("igraph")
Keras is a Python Deep Learning library.
For Keras, please be noted that we use Keras v1.2.2, and we use theano as its backend. Please edit the file ~/.keras/keras.json and change the backend. For example,
# Download Keras v1.2.2
wget https://github.com/fchollet/keras/archive/1.2.2.tar.gz
# unpack the zipped file
tar xzvf 1.2.2.tar.gz
# install Keras v1.2.2
cd keras-1.2.2
python setup.py install
# edit ~/.keras/keras.json and change the backend
{
"image_dim_ordering": "th",
"epsilon": 1e-07,
"floatx": "float32",
"backend": "theano"
}
HMMER and MCL package have been included in LncADeep package. You don't need to install them yourself. After installing the above prerequisites, you can now install LncADeep.
download the zipped file
wget http://cqb.pku.edu.cn/ZhuLab/LncADeep/LncADeep_v1.0.tgz
unpack the zipped file
tar xzvf LncADeep_v1.0.tgz
change directory to LncADeep
cd LncADeep_v1.0
configure and add directory to the PATH, and you are done!
chmod +x configure
./configure
source $HOME/.bash_profile
clone LncADeep package
git clone https://github.com/cyang235/LncADeep.git
change directory to LncADeep
cd LncADeep
download Pfam 29.0 database
wget ftp://ftp.ebi.ac.uk/pub/databases/Pfam/releases/Pfam29.0/Pfam-A.hmm.gz
gzip -d Pfam-A.hmm.gz
mv Pfam-A.hmm ./LncADeep_lncRNA/src/
# the Pfam-A.hmm need be put in directory /path to LncADeep/LncADeep_lncRNA/src/
configure and add directory to the PATH, and you are done!
chmod +x configure
./configure
source $HOME/.bash_profile
An ab initio lncRNA identification and functional annotation tool based on deep learning
usage: LncADeep.py [options]
An ab initio lncRNA identification and functional annotation tool based on
deep learning
optional arguments:
-h, --help show this help message and exit
-v, --version show program's version number and exit
-MODE {lncRNA,anno}, --MODE {lncRNA,anno}
(Required) The mode used for lncRNA identification or
functional annotation. If "lncRNA" is chosen, LncADeep
will identify lncRNAs. If "anno" is chosen, LncADeep
will predict lncRNA-protein interactions and annotate
lncRNA functions. Default is "lncRNA"
-o OUT_PREFIX, --out OUT_PREFIX
(Required) The output prefix of results
-f FASTA_FILE, --fasta FASTA_FILE
(Required for lncRNA identification) Sequence file in
FASTA format to be predicted
-m {full,partial}, --model {full,partial}
(Optional for lncRNA identification) The model used
for lncRNA identification, default is "partial"
-s {human,mouse}, --species {human,mouse}
(Optional for lncRNA identification) The species used
for lncRNA identification, default is "human"
-th THREAD, --thread THREAD
(Optional for lncRNA identification) Use multi-thread
for predicting, default is 1
-HMM HMMTHREAD, --HMMthread HMMTHREAD
(Optional for lncRNA identification) The thread number
of using HMMER, default is 8
-l RNA_FILE, --lncRNA RNA_FILE
(Required for functional annotation) lncRNA sequence
file in FASTA format
-p PROTEIN_FILE, --protein PROTEIN_FILE
(Optional for functional annotation) protein sequence
file in FASTA format
-a {1,0}, --annotation {1,0}
(Optional for functional annotation) To annotate
lncRNA functions. If "1" is selected, LncADeep will
annotate the functions for lncRNAs, otherwise LncADeep
will only give the interacting proteins for lncRNAs.
The default is "1".
-r PAIR_FILE, --pair PAIR_FILE
(Optional for functional annotation) The lncRNA-
protein pairs to be predicted. If this option is
selected, LncADeep will only output interacting
proteins.
The files for example are stored at directory /path to LncADeep/data
-
Identify lncRNAs using model for transcripts including full- and partial-length
python LncADeep.py -MODE lncRNA -f ./data/LncADeep_lncRNA/lncRNA_mRNA_test.fa -o testThe output files will be generated at directory
test_LncADeep_lncRNA_results. -
To use multi-processes (e.g., 4) for lncRNA identification
python LncADeep.py -MODE lncRNA -f ./data/LncADeep_lncRNA/lncRNA_mRNA_test.fa -o test \ -th 4 -
To use the model for full-length transcripts, please use the following command
python LncADeep.py -MODE lncRNA -f ./data/LncADeep_lncRNA/lncRNA_mRNA_test.fa -o test \ -m full -
LncADeep has been trained on the datasets of two species, including "human" and "mouse", the default model is "human". To use the model trained on mouse full- and partial-length transcripts, please use the following command.
python LncADeep.py -MODE lncRNA -f ./data/LncADeep_lncRNA/lncRNA_mRNA_test.fa -o test \ -s mouse -
To use the model trained on mouse full-length transcripts, please use the following command.
python LncADeep.py -MODE lncRNA -f ./data/LncADeep_lncRNA/lncRNA_mRNA_test.fa -o test \ -m full -s mouse -
LncADeep accepts nucleotide FASTA sequence as input, e.g.:
>RNA_id_1 GGAAACGGCCGTGGGCATTTTGGTGTATTTTTATTCAACTTTGAAAGACATATTTTATTTTTACACATTTTATTTTATACAGTA TAGACATACATATGCATACACGCCTCCTCTCATGACATTAAACTTTTGCACAACTTCACAATTGTAAATGATCACAGAAAAATG CCTCAAAATGAATGTATCATATCCTAGCCCCACCACTTAACCTCTCTGTGCCTCAGTTTTCTCCTCTGTAAAACGGGGATAATA ATAGTATCTACTTTATAAGTTGCTTGTAAGGGTTCAATGTGATTATGGTGTGAATGTGGGAAGCGCTCAGAAAGTATCATTTTC ATTATTATTAGAACTATTATTCCTTAATTGCAAACATTTAAATTCTAATTTTAT >RNA_id_2 CATCTCTTTCCTTCTCAGGAAATTTTATACATTGTCAATTATTCCTTCTCTCTAACTTCAACCTCGCCTTCTTTGCTGAGTCTG ACCCATCAACAGTTAAACATGATCAAGTCTTCCGATTTAAAAGTCCCTCTTTCTTGACACAGCTCATTTATAGCCAAACTTCTT TCTGAAGAGTAGTCTACATTCATTTTCTTTTTCTCCCTCACTTCTGATAATATTGAACCAACTCCATTTTAGTTTCTGTCCCTA TCATTCCTCTAAATTGATTAAGGTCTCCAGAATATTCCTCTGTATTTACGGGCATTATTCACTGCTCTTCTTATTTGACTACTC AGCAAGCATTTAACTTTTGATCAGTTTTTCCTTAAAATACTTTACTTGGCTTCCTTGACATCATGGTTTTTGTTCAGATCTCTG TGGTTATTTCTGTCTCCTTTGCTGCCTTCTCCTCTTGGTCCTTG # LncADeep will predict whether `RNA_id_1` and `RNA_id_2` are lncRNAs. # More example input can be found at directory `/path to LncADeep/data/LncADeep_lncRNA`
-
To predict lncRNA-protein interactions and annotate the functions of lncRNAs.
python LncADeep.py -MODE anno -l ./data/LncADeep_anno/ENST00000424518.5.fa -o test # Here, LncADeep will predict the interactions between given lncRNAs and 20,121 reviewed proteins # and then annotate the functions of lncRNAs with their predicted interacting proteins. # The output files will be generated at directory `test_LncADeep_anno_results` -
To predict lncRNA-protein interactions.
python LncADeep.py -MODE anno -l ./data/LncADeep_anno/ENST00000424518.5.fa -o test -a 0 # Here, LncADeep will predict the interactions between given lncRNAs and 20,121 reviewed proteins -
To predict lncRNA-protein interactions for given pairs.
python LncADeep.py -MODE anno -l ./data/LncADeep_anno/ENST00000424518.5.fa -o test \ -r ./data/LncADeep_anno/pair.dat -p ./data/LncADeep_anno/protein.fa # Here, LncADeep will predict the interactions between given lncRNAs and proteins for given pairs # Users are required to provide the lncRNA and protein sequences in FASTA format # and lncRNA-protein pairs in text format, see below -
lncRNA-protein pairs in text format as input, e.g.:
ENST00000424518.5|ENSG00000228630.5|OTTHUMG00000152934.1|OTTHUMT00000328662.1|HOTAIR-001|HOTAIR|2421| sp|P27361|MK03_HUMAN ENST00000424518.5|ENSG00000228630.5|OTTHUMG00000152934.1|OTTHUMT00000328662.1|HOTAIR-001|HOTAIR|2421| sp|P53779|MK10_HUMAN ENST00000424518.5|ENSG00000228630.5|OTTHUMG00000152934.1|OTTHUMT00000328662.1|HOTAIR-001|HOTAIR|2421| sp|Q15049|MLC1_HUMAN ENST00000424518.5|ENSG00000228630.5|OTTHUMG00000152934.1|OTTHUMT00000328662.1|HOTAIR-001|HOTAIR|2421| sp|Q9UHC1|MLH3_HUMAN ENST00000424518.5|ENSG00000228630.5|OTTHUMG00000152934.1|OTTHUMT00000328662.1|HOTAIR-001|HOTAIR|2421| sp|P0DMT0|MLN_HUMAN # LncADeep will predict the interactions for the above lncRNA-protein pairs. # Users are also required to provide the lncRNA and protein FASTA sequence files. # More example input can be found at directory `/path to LncADeep/data/LncADeep_anno`
Please direct your questions to: Dr. Huaiqiu Zhu, hqzhu@pku.edu.cn