gkm-SVM, a sequence-based method for predicting regulatory DNA elements,
is a useful tool for studying gene regulatory mechanisms.
In continuous efforts to improve the method, new software, LS-GKM,
is introduced. It offers much better scalability and provides further
advanced gapped k-mer based kernel functions. As a result, LS-GKM
achieves considerably higher accuracy than the original gkm-SVM.
Please cite the following paper if you use LS-GKM in your research:
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Ghandi, M.†, Lee, D.†, Mohammad-Noori, M. & Beer, M. A. Enhanced Regulatory Sequence Prediction Using Gapped k-mer Features. PLoS Comput Biol 10, e1003711 (2014). doi:10.1371/journal.pcbi.1003711 † Co-first authors
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Lee, D. LS-GKM: A new gkm-SVM for large-scale Datasets. Bioinformatics btw142 (2016). doi:10.1093/bioinformatics/btw142
After downloading and extracting the source codes, type:
$ cd src
$ make
If successful, You should be able to find the following executables in the current (src) directory:
gkmtrain
gkmpredict
make install will simply copy these two executables to the ../bin direcory
We introduce the users to the basic workflow of LS-GKM. Please refer to help messages
for more detailed information of each program. You can access to it by running the programs
without any argument/parameter.
You train a SVM classifier using gkmtrain. It takes three arguments;
positive sequence file, negative sequence file, and prefix of output.
Usage: gkmtrain [options] <posfile> <negfile> <outprefix>
train gkm-SVM using libSVM
Arguments:
posfile: positive sequence file (FASTA format)
negfile: negative sequence file (FASTA format)
outprefix: prefix of output file(s) <outprefix>.model.txt or
<outprefix>.cvpred.txt
Options:
-t <0 ~ 5> set kernel function (default: 4 wgkm)
NOTE: RBF kernels (3 and 5) work best with -c 10 -g 2
0 -- gapped-kmer
1 -- estimated l-mer with full filter
2 -- estimated l-mer with truncated filter (gkm)
3 -- gkm + RBF (gkmrbf)
4 -- gkm + center weighted (wgkm)
[weight = max(M, floor(M*exp(-ln(2)*D/H)+1))]
5 -- gkm + center weighted + RBF (wgkmrbf)
-l <int> set word length, 3<=l<=12 (default: 11)
-k <int> set number of informative column, k<=l (default: 7)
-d <int> set maximum number of mismatches to consider, d<=4 (default: 3)
-g <float> set gamma for RBF kernel. -t 3 or 5 only (default: 1.0)
-M <int> set the initial value (M) of the exponential decay function
for wgkm-kernels. max=255, -t 4 or 5 only (default: 50)
-H <float> set the half-life parameter (H) that is the distance (D) required
to fall to half of its initial value in the exponential decay
function for wgkm-kernels. -t 4 or 5 only (default: 50)
-c <float> set the regularization parameter SVM-C (default: 1.0)
-e <float> set the precision parameter epsilon (default: 0.001)
-w <float> set the parameter SVM-C to w*C for the positive set (default: 1.0)
-m <float> set cache memory size in MB (default: 100.0)
NOTE: Large cache signifcantly reduces runtime. >4Gb is recommended
-s if set, use the shrinking heuristics
-x <int> set N-fold cross validation mode (default: no cross validation)
-i <int> run i-th cross validation only 1<=i<=ncv (default: all)
-r <int> set random seed for shuffling in cross validation mode (default: 1)
-v <0 ~ 4> set the level of verbosity (default: 2)
0 -- error msgs only (ERROR)
1 -- warning msgs (WARN)
2 -- progress msgs at coarse-grained level (INFO)
3 -- progress msgs at fine-grained level (DEBUG)
4 -- progress msgs at finer-grained level (TRACE)
-T <1|4|16> set the number of threads for parallel calculation, 1, 4, or 16
(default: 1)
First try to train a model using simple test files. Type the following command in tests/ directory:
$ ../bin/gkmtrain wgEncodeSydhTfbsGm12878Nfe2hStdAlnRep0.tr.fa wgEncodeSydhTfbsGm12878Nfe2hStdAlnRep0.neg.tr.fa test_gkmtrain
It will generate test_gkmtrain.model.txt, which will then be used for scoring of
any DNA sequences as described below. This result should be the same as wgEncodeSydhTfbsGm12878Nfe2hStdAlnRep0.model.txt
You can also perform cross-validation (CV) analysis with -x <N> option. For example,
the following command will perform 5-fold CV.
$ ../bin/gkmtrain -x 5 wgEncodeSydhTfbsGm12878Nfe2hStdAlnRep0.tr.fa wgEncodeSydhTfbsGm12878Nfe2hStdAlnRep0.neg.tr.fa test_gkmtrain
The result will be stored in test_gkmtrain.cvpred.txt, and this should be the same as
wgEncodeSydhTfbsGm12878Nfe2hStdAlnRep0.cvpred.txt
Please note that it will run SVM training N times, which can take time if training
sets are large. In this case, you can perform CV analysis on a specific set
by using -i <I> option for parallel runnings. The output will be <outprefix>.cvpred.<I>.txt
The format of the cvpred file is as follows:
[sequenceid] [SVM score] [label] [CV-set]
...
You use gkmpredict to score any set of sequences.
Usage: gkmpredict [options] <test_seqfile> <model_file> <output_file>
score test sequences using trained gkm-SVM
Arguments:
test_seqfile: sequence file for test (fasta format)
model_file: output of gkmtrain
output_file: name of output file
Options:
-v <0|1|2|3|4> set the level of verbosity (default: 2)
0 -- error msgs only (ERROR)
1 -- warning msgs (WARN)
2 -- progress msgs at coarse-grained level (INFO)
3 -- progress msgs at fine-grained level (DEBUG)
4 -- progress msgs at finer-grained level (TRACE)
-T <1|4|16> set the number of threads for parallel calculation, 1, 4, or 16
(default: 1)
Here, you will try to score the positive and the negative test sequences. Type:
$ ../bin/gkmpredict wgEncodeSydhTfbsGm12878Nfe2hStdAlnRep0.test.fa wgEncodeSydhTfbsGm12878Nfe2hStdAlnRep0.model.txt test_gkmpredict.txt
$ ../bin/gkmpredict wgEncodeSydhTfbsGm12878Nfe2hStdAlnRep0.neg.test.fa wgEncodeSydhTfbsGm12878Nfe2hStdAlnRep0.model.txt test_gkmpredict.neg.txt
You need to generate all possible non-redundant k-mers using the Python script
scripts/nrkmers.py. Then, you score them using gkmpredict as described above.
The output of lgkmpredict can be directly used by the deltaSVM script deltasvm.pl
available from our deltasvm website.
Please email Dongwon Lee (dwlee AT jhu DOT edu) if you have any questions.