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GlimmerM |
Overview
A gene finder derived from Glimmer, but developed specifically for eukaryotes. It is based on a dynamic programing algorithm that considers all combinations of possible exons for inclusion in a gene model and chooses the best of these combinations. The decision about what gene model is best is a combination of the strength of the splice sites and the score of the exons generated by an interpolated Markov model (IMM). The system has been trained for Arabidopsis thaliana, Oryza sativa (rice), and Plasmodium falciparum (the malaria parasite), and should work well on closely related organisms. Use the GlimmerM Web Interface to run GlimmerM directly, or see below for instructions on downloading the complete system including source code.
System requirements
GlimmerM is released as source code and was tested on Linux RedHat 6.x+, Sun Solaris, and Alpha OSF1, but should work on any Unix system.
Obtaining GlimmerM
This software is OSI
Certified Open Source Software .
To download the complete GlimmerM system, just click here .
After downloading, uncompress the distribution file by typing:% tar -xzf GlimmerM.tar.gz
A directory named 'GlimmerM/' will be created which contains the executable, training data sets, and other supporting files.
Training data sets are available here.
Documentation
A basic user manual for GlimmerM is available here.
References
1. A.L. Delcher, D. Harmon, S. Kasif, O. White,
and S.L. Salzberg.
Improved
microbial gene identification with GLIMMER (306K, PDF format)
Nucleic
Acids Research, 27:23, 4636-4641.
2. Gardner MJ, Tettelin
H, Carucci DJ, Cummings LM, Aravind L, Koonin EV, Shallom S, Mason T, Yu
K, Fujii C, Peterson J, Shen K, Jing J, Aston C, Lai Z, Schwartz DC, Pertea
M, Salzberg S, Zhou L, Sutton GG, Clayton R, White O, Smith HO, Fraser
CM, Hoffman SL, et al.
Chromosome 2 sequence of the human malaria parasite
Plasmodium falciparum. Science. 1998 Nov 6;282(5391):1126-32.
3. Salzberg, S., Delcher,
A., Fasman, K., and Henderson, J. (1998a). A decision tree system for
finding genes in DNA. J. Computat. Biol. 5(4), 667-680.
4. S. Salzberg, A. Delcher,
S. Kasif, and O. White. Microbial
gene identification using interpolated Markov models (73K, PDF
format)
Nucleic Acids Research 26:2 (1998b), 544-548. Reproduced
with permission from NAR Online at http://www.oup.co.uk/nar.
5. Salzberg SL, Pertea M, Delcher AL, Gardner
MJ, Tettelin H. Interpolated Markov models for eukaryotic gene finding.
Genomics.
1999 Jul 1;59(1):24-31.
6. Pertea M, Salzberg SL, Gardner MJ. Finding
genes in Plasmodium falciparum. Nature, 2000 Mar 2;404(6773):34.
7. Yuan Q, Quackenbush J, Sultana R, Pertea M,
Salzberg SL, Buell CR. Rice bioinformatics. analysis of rice sequence
data and leveraging the data to other plant species. Plant Physiol.
2001 Mar;125(3):1166-74.
8. Pertea, M. and Salzberg, S.L. Computational
gene finding in plants. Plant Mol Biol 2002; 48(1-2): 39-48.
9. Pertea, M. and Salzberg, S.L. Using GlimmerM to
find genes in eukaryotic genomes. Current Protocols in Bioinformatics,
2002.