@@ 4728,6 4728,45 @@ meso, or continuum scale.")
files.")
(license license:expat)))
+(define-public lsgkm
+ (package
+ (name "lsgkm")
+ (version "0.1.1")
+ (source
+ (origin
+ (method git-fetch)
+ (uri (git-reference
+ (url "https://github.com/Dongwon-Lee/lsgkm.git")
+ (commit (string-append "v" version))))
+ (file-name (git-file-name name version))
+ (sha256
+ (base32
+ "0b3m94kndvimdfjaf1q2yhmsn7lm5s9v81c5xgfjcp6ig7mh3sa5"))))
+ (build-system gnu-build-system)
+ (arguments
+ (list
+ #:make-flags '(list "-C" "src")
+ #:tests? #false ;there are no executable tests
+ #:phases
+ #~(modify-phases %standard-phases
+ (delete 'configure)
+ (replace 'install
+ (lambda _
+ (let ((bin (string-append #$output "/bin")))
+ (for-each (lambda (file)
+ (install-file file bin))
+ '("src/gkmtrain"
+ "src/gkmpredict"))))))))
+ (home-page "https://github.com/Dongwon-Lee/lsgkm")
+ (synopsis "Predict regulatory DNA elements in large-scale data")
+ (description "gkm-SVM, a sequence-based method for predicting regulatory
+DNA elements, is a useful tool for studying gene regulatory mechanisms.
+LS-GKM is an effort to improve the method. 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.")
+ (license license:gpl3+)))
+
(define-public python-pybigwig
(package
(name "python-pybigwig")