TIGR-GLIMMER(1) 					      General Commands Manual						   TIGR-GLIMMER(1)

NAME

       tigr-glimmer -- Find/Score potential genes in genome-file using the probability model in icm-file

SYNOPSIS

       tigr-glimmer3 [genome-file]  [icm-file]	[[options]]

DESCRIPTION

       tigr-glimmer is a system for finding genes in microbial DNA, especially the genomes of bacteria and archaea. tigr-glimmer (Gene Locator and
       Interpolated Markov Modeler) uses interpolated Markov models (IMMs) to identify the coding regions and distinguish them from noncoding DNA.
       The IMM approach, described in our Nucleic Acids Research paper on tigr-glimmer 1.0 and in our subsequent paper on tigr-glimmer 2.0, uses a
       combination of Markov models from 1st through 8th-order, weighting each model according to its predictive power. tigr-glimmer 1.0  and  2.0
       use 3-periodic nonhomogenous Markov models in their IMMs.

       tigr-glimmer  is the primary microbial gene finder at TIGR, and has been used to annotate the complete genomes of B. burgdorferi (Fraser et
       al., Nature, Dec. 1997), T. pallidum (Fraser et al., Science, July 1998), T.  maritima,	D.  radiodurans,  M.  tuberculosis,  and  non-TIGR
       projects  including  C.	trachomatis,  C. pneumoniae, and others. Its analyses of some of these genomes and others is available at the TIGR
       microbial database site.

       A special version of tigr-glimmer designed for small eukaryotes, GlimmerM, was used to find the genes in chromosome 2 of the malaria  para-
       site,  P. falciparum.. GlimmerM is described in S.L. Salzberg, M. Pertea, A.L. Delcher, M.J. Gardner, and H. Tettelin, "Interpolated Markov
       models for eukaryotic gene finding," Genomics 59 (1999), 24-31.	Click here (http://www.tigr.org/software/glimmerm/) to visit the  GlimmerM
       site, which includes information on how to download the GlimmerM system.

       The  tigr-glimmer  system consists of two main programs. The first of these is the training program, build-imm. This program takes an input
       set of sequences and builds and outputs the IMM for them. These sequences can be complete genes or just partial orfs.  For  a  new  genome,
       this  training  data  can  consist of those genes with strong database hits as well as very long open reading frames that are statistically
       almost certain to be genes. The second program is glimmer, which uses this IMM to identify putative genes in an entire genome. tigr-glimmer
       automatically  resolves	conflicts  between  most overlapping genes by choosing one of them. It also identifies genes that are suspected to
       truly overlap, and flags these for closer inspection by the user. These ``suspect'' gene candidates have been a very  small  percentage	of
       the total for all the genomes analyzed thus far.  tigr-glimmer is a program that...

OPTIONS

       -C n	 Use n as GC percentage of independent model

		 Note:	n should be a percentage, e.g., -C 45.2

       -f	 Use ribosome-binding energy to choose start codon

       +f	 Use first codon in orf as start codon

       -g n	 Set minimum gene length to n

       -i filename
		 Use filename	to select regions of bases that are off limits, so that no bases within that area will be examined

       -l	 Assume linear rather than circular genome, i.e., no wraparound

       -L filename
		 Use filename to specify a list of orfs that should be scored separately, with no overlap rules

       -M	 Input is a multifasta file of separate genes to be scored separately, with no overlap rules

       -o n	 Set minimum overlap length to n.  Overlaps shorter than this are ignored.

       -p n	 Set minimum overlap percentage to n%.	Overlaps shorter than this percentage of *both* strings are ignored.

       -q n	 Set the maximum length orf that can be rejected because of the independent probability score column to (n - 1)

       -r	 Don't use independent probability score column

       +r	 Use independent probability score column

       -r	 Don't use independent probability score column

       -s s	 Use string s as the ribosome binding pattern to find start codons.

       +S	 Do  use stricter independent intergenic model that doesn't give probabilities to in-frame stop codons.  (Option is obsolete since
		 this is now the only behaviour

       -t n	 Set threshold score for calling as gene to n.	If the in-frame score >= n, then the region is given a	number	and  considered  a
		 potential gene.

       -w n	 Use "weak" scores on tentative genes n or longer.  Weak scores ignore the independent probability score.

SEE ALSO

       tigr-adjust  (1),  tigr-anomaly	 (1),  tigr-build-icm (1), tigr-check (1), tigr-codon-usage (1), tigr-compare-lists (1), tigr-extract (1),
       tigr-generate (1), tigr-get-len (1), tigr-get-putative (1), tigr-glimmer3 (1), tigr-long-orfs (1)

       http://www.tigr.org/software/glimmer/

       Please see the readme in /usr/share/doc/glimmer for a description on how to use Glimmer.

AUTHOR

       This manual page was quickly copied from the glimmer web site by Steffen Moeller moeller@debian.org for the Debian system.

																   TIGR-GLIMMER(1)