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 parasite, 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)