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)