The versatile open source genome analysis software
The GenomeTools genome analysis system is a free collection of bioinformatics tools (in the realm of genome informatics) combined into a single binary named gt. It is based on a C library named “libgenometools” which consists of several modules.
If you are interested in gene prediction, have a look at GenomeThreader.
GenomeTools: a comprehensive software library for efficient processing of structured genome annotations.
IEEE/ACM Transactions on Computational Biology and Bioinformatics 2013, 10(3):645–656
Published software in GenomeTools
The GenomeTools distribution includes several published software tools:
- LTRharvest, an efficient and flexible software tool for
de novo detection of LTR retrotransposons.
D. Ellinghaus, S. Kurtz, and U. Willhoeft.
LTRharvest, a efficient and flexible software for de novo detection of LTR retrotransposons.
BMC Bioinformatics 2008, 9:18
- Tallymer, a collection of flexible and memory-efficient
programs for k-mer counting and indexing of large sequence sets.
S. Kurtz, A. Narechania, J.C. Stein, and D. Ware.
A new method to compute K-mer frequencies and its application to annotate large repetitive plant genomes.
BMC Genomics 2008, 9:517
- uniquesub, a program for computing minimum unique substrings.
S. Gräf, F.G.G. Nielsen, S. Kurtz, M.A. Huynen, E. Birney, H. Stunnenberg, and P. Flicek.
Optimized design and assessment of whole genome tiling arrays.
Bioinformatics 2007, 23(13):i195–i204
- AnnotationSketch, a library for drawing genome annotations.
S. Steinbiss, G. Gremme, C. Schärfer, M. Mader and S. Kurtz.
AnnotationSketch: a genome annotation drawing library.
Bioinformatics 2009, 25(4):533–534
- LTRdigest, a software tool for automated annotation of internal features of LTR retrotransposons.
S. Steinbiss, U. Willhoeft, G. Gremme and S. Kurtz.
Fine-grained annotation and classification of de novo predicted LTR retrotransposons.
Nucleic Acids Research 2009, 37(21):7002–7013
- MetaGenomeThreader, a software to predict genes, such as PCS's (predicted coding sequences) in sequences of metagenome projects.
D.J. Schmitz-Hübsch and S. Kurtz.
MetaGenomeThreader: A software tool for predicting genes in DNA-sequences of metagenome projects.
In R. Daniel and W. Streit (Eds.), Metagenomics. Methods in Molecular Biology, 325–338, Humana Press, Totowa, NJ, ISBN 978-1-60761-822-5
- GtEncseq, a compressed biosequence representation with many features.
S. Steinbiss and S. Kurtz.
A New Efficient Data Structure for Storage and Retrieval of Multiple Biosequences.
IEEE/ACM Transactions on Computational Biology and Bioinformatics 2012, 9(2):345–357
- Readjoiner, a sequence assembler based on the assembly string graph framework.
G. Gonnella and S. Kurtz.
Readjoiner: a fast and memory efficient string graph-based sequence assembler.
BMC Bioinformatics 2012, 13:82
The following software tools are based on the GenomeTools library:
a software tool to compute eukaryote gene structure predictions.
G. Gremme, V. Brendel, M.E. Sparks, and S. Kurtz.
Engineering a software tool for gene structure prediction in higher organisms.
Information and Software Technology 2005, 47(15):965–978
- FISH Oracle,
a web server for visualizing cancer genomics data.
M. Mader, R. Simon, S. Steinbiss and S. Kurtz.
FISH Oracle: a web server for flexible visualization of DNA copy number data in a genomic context.
Journal of Clinical Bioinformatics 2011, 1:20
- ParsEval, a
tool for comparing genome annotations.
D.S. Standage and V.P. Brendel.
ParsEval: parallel comparison and analysis of gene structure annotations.
BMC Bioinformatics 2012, 13:187
a graphical interface for working with LTR retrotransposon predictions.
S. Steinbiss, S. Kastens and S. Kurtz.
LTRsift: a graphical user interface for semi-automatic classification and postprocessing of de novo detected LTR retrotransposons.
Mobile DNA 2012, 3:18
- FISH Oracle 2,
a web-based software tool for the integrative analysis of cancer genomics data.
M. Mader, R. Simon, and S. Kurtz.
FISH Oracle 2: a web server for integrative visualization of genomic data in cancer research.
Journal of Clinical Bioinformatics 2014, 4:5