Difference between pages "Recent News" and "Tutorial"

Revision as of 19:19, 5 August 2019

gkmSVM-R Tutorial notes

INSTALLATION for linux or mac (R 3.5 or later)

$ R
> if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")
> BiocManager::install()
> BiocManager::install(c('GenomicRanges','rtracklayer','BSgenome', 'BSgenome.Hsapiens.UCSC.hg19.masked', 'BSgenome.Hsapiens.UCSC.hg18.masked'))
> install.packages('ROCR','kernlab','seqinr')

$ git clone https://github.com/mghandi/gkmSVM.git
$ R CMD INSTALL gkmSVM

--or--

> install.packages('gkmSVM')

INSTALLATION for linux or mac (R 3.4 or earlier)

$ R
> source("https://bioconductor.org/biocLite.R")
> biocLite('GenomicRanges')
> biocLite('rtracklayer')
> biocLite('BSgenome')
> biocLite('BSgenome.Hsapiens.UCSC.hg19.masked') (or other genomes)
> biocLite('BSgenome.Hsapiens.UCSC.hg18.masked')
> install.packages('ROCR')
> install.packages('kernlab')
> install.packages('seqinr')
> quit()

$ git clone https://github.com/mghandi/gkmSVM.git
$ R CMD INSTALL gkmSVM

--or--

> install.packages('gkmSVM')

Now to run gkmSVM-R on the ctcf test set from Ghandi Lee, Mohammad-Noori, Beer, PLOS CompBio 2014:

Input files: ctcfpos.bed, nr10mers.fa, ref.fa, alt.fa from www.beerlab.org/gkmsvm

1. generate GC, length, and repeat matched negative set and extract fasta sequence files for ctcfpos.fa and ctcfneg_1x.fa: (Larger negative sets can be generated by increasing xfold, and running time can be decreased by reducing nMaxTrials, at the cost of not matching difficult sequences. In general training on larger sequence sets will produce more accurate and robust models.)

$ R
> library(gkmSVM)
> genNullSeqs('ctcfpos.bed',nMaxTrials=10,xfold=1,genomeVersion='hg18', outputPosFastaFN='ctcfpos.fa', outputBedFN='ctcfneg_1x.bed', outputNegFastaFN='ctcfneg_1x.fa')

2. calculate kernel matrix:

> gkmsvm_kernel('ctcfpos.fa','ctcfneg_1x.fa', 'ctcf_1x_kernel.out')

3. perform SVM training with cross-validation:

> gkmsvm_trainCV('ctcf_1x_kernel.out','ctcfpos.fa','ctcfneg_1x.fa',svmfnprfx='ctcf_1x', outputCVpredfn='ctcf_1x_cvpred.out', outputROCfn='ctcf_1x_roc.out')

4. generate 10-mer weights:

> gkmsvm_classify('nr10mers.fa',svmfnprfx='ctcf_1x', 'ctcf_1x_weights.out')

This should get AUROC=.955 and AUPRC=.954 with some small variation arising from the randomly sampled negative sets. You can then select the top weights with:

$ sort –grk 2 ctcf_1x_weights.out | head -12

which should give weights very similar to:

CACCTGGTGG 5.133463 CACCAGGTGG 5.090566 CACCAGGGGG 5.038873 CCACTAGGGG 4.833398 CCACCAGGGG 4.832404 CACCTAGTGG 4.782613 CACCAGAGGG 4.707206 CACTAGGGGG 4.663015 CACTAGAGGG 4.610800 CACTAGGTGG 4.580834 CCACTAGAGG 4.529869 CAGCAGAGGG 4.335304

5. To calculate the impact of a variant, in this case on CTCF binding, we use gkmsvm_classify to find the score difference between two alleles given in FASTA format in ‘ref.fa’ and ‘alt.fa’. This is only different by a scale factor from deltaSVM calculated directly from SVM weights, as described in (Lee, Gorkin, Baker, Strober, Aasoni, McCallion, Beer, Nature Genetics 2015).

> gkmsvm_delta('ref.fa','alt.fa',svmfnprfx='ctcf_1x', 'dsvm_ctcf_1x.out')

If you find this tool useful, please cite:

Ghandi, Mohammad-Noori, Ghareghani, Lee, Garraway, and Beer, Bioinformatics (2016); and
Ghandi, Lee, Mohammad-Noori, and Beer, PLOS Computational Biology (2014).

@@ Line 1: / Line 1: @@
-'''[http://www.bme.jhu.edu/news-events/news-highlights.php?id=412  Beer Lab awarded NIH grant for regulatory contributions to disease. ]'''
+gkmSVM-R Tutorial notes
-'''[http://www.bme.jhu.edu/news-events/news-highlights.php?id=360   kmer-SVM Genome Research paper voted Top 10 in Regulatory Genomics.] '''
+INSTALLATION for linux or mac (R 3.5 or later)
-'''[http://www.hopkinsmedicine.org/institute_basic_biomedical_sciences/news_events/Announcements/2013_04_YID.html  Dongwon Lee awarded Young Investigator Day Award.] '''
+$ R <br/>
+> if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") <br/>
+> BiocManager::install() <br/>
+> BiocManager::install(c('GenomicRanges','rtracklayer','BSgenome', 'BSgenome.Hsapiens.UCSC.hg19.masked', 'BSgenome.Hsapiens.UCSC.hg18.masked')) <br/>
+> install.packages('ROCR','kernlab','seqinr') <br/>
+$ git clone https://github.com/mghandi/gkmSVM.git <br/>
+$ R CMD INSTALL gkmSVM <br/>
+--or--
+> install.packages('gkmSVM') <br/>
+INSTALLATION for linux or mac (R 3.4 or earlier)
+$ R <br/>
+> source("https://bioconductor.org/biocLite.R") <br/>
+> biocLite('GenomicRanges') <br/>
+> biocLite('rtracklayer') <br/>
+> biocLite('BSgenome') <br/>
+> biocLite('BSgenome.Hsapiens.UCSC.hg19.masked')     (or other genomes) <br/>
+> biocLite('BSgenome.Hsapiens.UCSC.hg18.masked') <br/>
+> install.packages('ROCR') <br/>
+> install.packages('kernlab') <br/>
+> install.packages('seqinr') <br/>
+> quit() <br/>
+$ git clone https://github.com/mghandi/gkmSVM.git <br/>
+$ R CMD INSTALL gkmSVM <br/>
+--or--
+> install.packages('gkmSVM') <br/>
+Now to run gkmSVM-R on the ctcf test set from Ghandi Lee, Mohammad-Noori, Beer, PLOS CompBio 2014:
+Input files: [http://www.beerlab.org/gkmsvm/ctcfpos.bed ctcfpos.bed], [http://www.beerlab.org/gkmsvm/nr10mers.fa nr10mers.fa], [http://www.beerlab.org/gkmsvm/ref.fa ref.fa], [http://www.beerlab.org/gkmsvm/alt.fa alt.fa] from [http://www.beerlab.org/gkmsvm www.beerlab.org/gkmsvm]
+. generate GC, length, and repeat matched negative set and extract fasta sequence files for ctcfpos.fa and ctcfneg_1x.fa:  (Larger negative sets can be generated by increasing xfold, and running time can be decreased by reducing nMaxTrials, at the cost of not matching difficult sequences.  In general training on larger sequence sets will produce more accurate and robust models.)
+$ R <br/>
+> library(gkmSVM) <br/>
+> genNullSeqs('ctcfpos.bed',nMaxTrials=10,xfold=1,genomeVersion='hg18',   outputPosFastaFN='ctcfpos.fa', outputBedFN='ctcfneg_1x.bed', outputNegFastaFN='ctcfneg_1x.fa') <br/>
+. calculate kernel matrix:
+> gkmsvm_kernel('ctcfpos.fa','ctcfneg_1x.fa', 'ctcf_1x_kernel.out')
+. perform SVM training with cross-validation:
+> gkmsvm_trainCV('ctcf_1x_kernel.out','ctcfpos.fa','ctcfneg_1x.fa',svmfnprfx='ctcf_1x', outputCVpredfn='ctcf_1x_cvpred.out', outputROCfn='ctcf_1x_roc.out')
+. generate 10-mer weights:
+> gkmsvm_classify('nr10mers.fa',svmfnprfx='ctcf_1x', 'ctcf_1x_weights.out')
+This should get AUROC=.955 and AUPRC=.954 with some small variation arising from the randomly sampled negative sets.  You can then select the top weights with:
+$ sort –grk  2 ctcf_1x_weights.out | head -12
+which should give weights very similar to:
+<code>
+CACCTGGTGG      5.133463 <br/>
+CACCAGGTGG      5.090566 <br/>
+CACCAGGGGG      5.038873 <br/>
+CCACTAGGGG      4.833398 <br/>
+CCACCAGGGG      4.832404 <br/>
+CACCTAGTGG      4.782613 <br/>
+CACCAGAGGG      4.707206 <br/>
+CACTAGGGGG      4.663015 <br/>
+CACTAGAGGG      4.610800 <br/>
+CACTAGGTGG      4.580834 <br/>
+CCACTAGAGG      4.529869 <br/>
+CAGCAGAGGG      4.335304 <br/>
+</code>
+. To calculate the impact of a variant, in this case on CTCF binding, we use gkmsvm_classify to find the
+score difference between two alleles given in FASTA format in ‘ref.fa’ and ‘alt.fa’. This is only different
+by a scale factor from deltaSVM calculated directly from SVM weights, as described in (Lee, Gorkin,
+Baker, Strober, Aasoni, McCallion, Beer, Nature Genetics 2015).
+> gkmsvm_delta('ref.fa','alt.fa',svmfnprfx='ctcf_1x', 'dsvm_ctcf_1x.out')
+If you find this tool useful, please cite:
+Ghandi, Mohammad-Noori, Ghareghani, Lee, Garraway, and Beer, Bioinformatics (2016); and <br/>
+Ghandi, Lee, Mohammad-Noori, and Beer, PLOS Computational Biology (2014).

Difference between pages "Recent News" and "Tutorial"

Revision as of 19:19, 5 August 2019

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