diff --git a/biology/Makefile b/biology/Makefile
index bf5f4af2c23c..75eb9e66c060 100644
--- a/biology/Makefile
+++ b/biology/Makefile
@@ -101,6 +101,7 @@
     SUBDIR += phyml
     SUBDIR += plinkseq
     SUBDIR += primer3
+    SUBDIR += prodigal
     SUBDIR += protomol
     SUBDIR += psi88
     SUBDIR += py-Genesis-PyAPI
diff --git a/biology/prodigal/Makefile b/biology/prodigal/Makefile
new file mode 100644
index 000000000000..c227b6a80f17
--- /dev/null
+++ b/biology/prodigal/Makefile
@@ -0,0 +1,25 @@
+# $FreeBSD$
+
+PORTNAME=	prodigal
+DISTVERSIONPREFIX=	v
+DISTVERSION=	2.6.3-2
+DISTVERSIONSUFFIX=	-gfe80417
+CATEGORIES=	biology
+
+MAINTAINER=	yuri@FreeBSD.org
+COMMENT=	Protein-coding gene prediction for prokaryotic genomes
+
+LICENSE=	GPLv3
+LICENSE_FILE=	${WRKSRC}/LICENSE
+
+USES=		gmake
+USE_GITHUB=	yes
+GH_ACCOUNT=	hyattpd
+GH_PROJECT=	Prodigal
+
+BINARY_ALIAS=	gcc=${CC}
+MAKE_ARGS=	INSTALLDIR=${STAGEDIR}${PREFIX}/bin
+
+PLIST_FILES=	bin/${PORTNAME}
+
+.include <bsd.port.mk>
diff --git a/biology/prodigal/distinfo b/biology/prodigal/distinfo
new file mode 100644
index 000000000000..f2c70fef6b3f
--- /dev/null
+++ b/biology/prodigal/distinfo
@@ -0,0 +1,3 @@
+TIMESTAMP = 1549215427
+SHA256 (hyattpd-Prodigal-v2.6.3-2-gfe80417_GH0.tar.gz) = a8229a3ce38f7d6553730d5a5d3c46c619abd502de8527cb175e9af022c3b371
+SIZE (hyattpd-Prodigal-v2.6.3-2-gfe80417_GH0.tar.gz) = 611025
diff --git a/biology/prodigal/pkg-descr b/biology/prodigal/pkg-descr
new file mode 100644
index 000000000000..7d9eb1c4c855
--- /dev/null
+++ b/biology/prodigal/pkg-descr
@@ -0,0 +1,22 @@
+Fast, reliable protein-coding gene prediction for prokaryotic genomes.
+
+Features:
+* Predicts protein-coding genes: Prodigal provides fast, accurate protein-coding
+  gene predictions in GFF3, Genbank, or Sequin table format.
+* Handles draft genomes and metagenomes: Prodigal runs smoothly on finished
+  genomes, draft genomes, and metagenomes.
+* Runs quickly: Prodigal analyzes the E. coli K-12 genome in 10 seconds on a
+  modern MacBook Pro.
+* Runs unsupervised: Prodigal is an unsupervised machine learning algorithm. It
+  does not need to be provided with any training data, and instead automatically
+  learns the properties of the genome from the sequence itself, including RBS
+  motif usage, start codon usage, and coding statistics.
+* Handles gaps and partial genes: The user can specify if Prodigal should build
+  genes across runs of N's as well as how to handle genes at the edges of
+  contigs.
+* Identifies translation initiation sites: Prodigal predicts the correct
+  translation initiation site for most genes, and can output information about
+  every potential start site in the genome, including confidence score, RBS
+  motif, and much more.
+
+WWW: https://github.com/hyattpd/Prodigal